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Category : Protein Folding

Geroscience and it’s Impact on the Human Healthspan: A podcast with John Newman – GeriPal – A Geriatrics and Palliative Care Blog

Ok, I'll admit it. When I hear the phrase "the biology of aging" I'm mentally preparing myself to only understand about 5% of what the presenter is going to talk about (that's on a good day). While I have words like telomeres, sirtuins, or senolytics memorized for the boards, I've never been able to see how this applies to my clinical practice as it always feels so theoretical. Well, today that changed for me thanks to our podcast interview with John Newman, a "geroscientist" and geriatrician here at UCSF and at the Buck Institute for Research on Aging.

In this podcast, John breaks down what geroscience is and how it impacts how we think about many age-related conditions and diseases. For example, rather than thinking about multimorbidity as the random collection of multiple different clinical problems, we can see it as an expression of the fundamental mechanisms of aging. This means, that rather than treating individuals diseases, targeting aging pathways may be a better way to prevent or ameliorate multimorbidity. We talk with John about this, and current trials underway to test this hypothesis, along with so much more!

If you're interested in taking a deeper dive in the subject, take a look at these papers that John co-authored:

You can also find us onYoutube!

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Alex: This is Alex Smith.

Eric: Alex, we have someone in our studio audience ... our office studio? Our studio-

Alex: Our office studio? We have John Newman, who's a geriatrician and geroscientist-

Eric: A gero-what?

Alex: A geroscientist-

Eric: A gero-what?

Alex: A geroscientist who has held a joint appointment between UCSF and the Buck Institute for Aging Research. Welcome to the GeriPal PodCast, John.

John: Thanks, guys. Thanks, Alex. Thanks, Eric.

Eric: I'm really interested in figuring out what a geroscientist is. But before we do that, can we have a little song request for Alex?

John: Yeah, what should we sing about? Do you know a little song called Who Wants to Live Forever?

Alex: Ah, more Queen.

Eric: Boy, you can never get enough Queen.

Alex: Maybe our audience can. (singing).

Eric: John Newman does, right? John Newman wants all of us to live forever. At least that's why I am currently, those who are watching this on YouTube, can see I am getting fresh stem cells from my baby farm that I get infused every day, so I can live forever.

John: We're transfusing you as we speak.

Eric: As we speak.

John: As we speak.

Eric: Yeah, I give John hefty amounts of money for those baby transfusions.

John: Hey, that deal's just for you, Eric. Don't go advertising me.

Eric: So John, geroscience. What is this field, and is it about living forever?

John: It is not about living forever. It's about living healthier, longer, and staying independent. So what is geroscience?

Eric: That, I have no idea.

Alex: That's, I don't know.

John: I thought I was here, so you guys would tell me.

Alex: Gero ... Gero-

Eric: Gero- so, older.

Alex: Ger, Jerry, Ben and Jerry's.

John: Aging ... aging science.

Alex: Aging science.

John: Aging science with a flavor of people.

Eric: So what is the difference between you and Alex? You're both researchers. Is Alex a geroscientist?

John: Well, geroscience is a, it's a made-up word which was coined for a new field, and a whole new idea, which is now reality. Which is going to sound a little crazy. Taking what we know about the biological mechanisms that drive aging. The biology of aging.

John: And not only understanding that, which itself sounds a little crazy. But actually turning that into therapies, to help to treat or prevent disease, or help to improve the lives, especially of older adults.

John: Geroscience is the idea of translational geriatrics, taking what we know about the basic science of the processes that drive aging. And turning it into therapies and helping to improve people's lives.

Eric: The processes that ... Okay. As I age, I start developing some chronic medical conditions. They start building up. I have more and more medical conditions. If only I could just focus on making sure I don't develop those, or I treat these well; like diabetes, COPD. Would I prevent aging? Is that the goal here?

John: Well, one place this comes from is, what's that common underlying factor that's putting you at risk for COPD and for diabetes and for Alzheimer's disease and for cardiovascular disease and for strokes? And for osteoarthritis and osteoporosis? And for almost everything that we treat.

Eric: Nacho cheese Doritos?

John: That's a big one! That's a big one.

Eric: But there's more.

John: But what's the common variable for all of those? We call these age-related chronic conditions or age-related diseases.

Eric: Yeah.

John: Because they're all driven by aging. The key thing is that's not an accident or it's not just like a probability thing or it's not just time passing. But it's actually the biological mechanisms that change in our bodies as we get older that make us what we perceive as older. There's a biology there. And that biology puts you at risk for all these different chronic diseases.

John: You could try to treat or prevent all of these individually. But, if you're not changing, if you're not affecting the aging that's driving all of them, there's a limit to how far you can go with that, or how effective it's going to be.

John: You prevent diabetes, but you get cancer. You cure Alzheimer's disease, which would be amazing, but then you have a stroke. But if you intervene on the aging that's behind all of this, then maybe you can slow or delay or prevent all of these together. That's the great hope of geroscience.

Alex: Is delay or preventing aging ... Earlier, you said that it's not so much about living longer, as it is improving healthy years of life. And yet, but what you just said was, "delay or prevent aging." I'm a little lost there. Can you help me out?

John: Well, delay or prevent all of the diseases and conditions that are driven by aging. So, targeting aging as the underlying biology that causes or contributes to diabetes, dementia, cancer, heart disease, and all of that.

John: All of that might wind up helping you live longer; who knows. But that's not the goal.

Alex: Ah.

John: The goal is to be healthier for longer.

Alex: Oh.

John: To spend, so we all can spend more years independent and being able to do the things we want to do in a state of better health.

Alex: It's like the principle of the double effect. For those palliative care listeners, right, the primary ... right? We're relating the subject to you.

Alex: The primary intended target in the principle of double effect in palliative care is often opioids for pain relief. And yet, as a secondary effect, if the patient dies sooner, that's acceptable.

Alex: Your primary intended effect is to treat the disease that are associated with aging; the conditions that are associated with aging. As a secondary effect, if people end up living longer, then that's fine too. But it's not the primary target of geroscience?

John: I love that analogy. Living longer may wind up being a side effect of being healthier longer. But when you talk about ... Aging is a weird subject, right? Because it's this, it's not a disease. It's not a bad thing. There are many many positive elements of aging. I am happier now than I was 20 years. Hopefully I'll be even happier 20 years from now, even if I can't jump as high.

John: Aging is not a disease. It's not a bad thing. And yet we're trying to target, almost to treat it. The goal is if we can slow or reduce the bad aspects of aging, the parts of aging that give rise to chronic disease. And you're living healthier longer, you'll probably live longer, too, in good health.

John: When I go to a room and I ask people, "We're talking about aging as a target for therapies. Who wants to live to be 200?"

John: Not a whole lot of people raise their hands, because most of them are thinking, "I'm not sure how I'm going to feel when I'm 95 or 85 or 75. You extrapolate that out, and what am I going to feel like when I'm 200?"

Alex: Right.

John: That doesn't seem like a great choice. But if you ask people, "What if you could have the health that you have now, or the health that you had when you're 60, or the health you had when you're 50? And just keep that for longer?"

Alex: Uh-huh.

John: Most people would volunteer for that.

Alex: Interesting.

Eric: I just want to make sure that when you ... When I hear "aging," I think probably the common definition is, I'm getting older.

Eric: When you hear the word "aging," what do you mean by aging? Especially as we're targeting aging? I can't target the clock; I guess I could target my clock and just turn it around. What do you mean when you say "aging"?

John: Well, here's the geriatrician's perspective. How do we know what "old" is, what an older adult is, for making a clinical decision, for example?

Eric: Yeah.

John: For thinking about prognosis, for example. There's someone I know who's done a lot of work around prognosis and how to estimate someone's life expectancy.

Alex: I don't know who you're talking about [laughter].

John: Yeah, the name, it's right on the tip of my tongue. So how do we know? Of course, we know that someone's birthday doesn't really help a whole lot with that stuff. There's 85-year-olds who are very fit and active and healthy and young. And there are 85-year-olds who are not very young. What makes that difference?

John: In geriatrics, we think of things like functional assessments. Mobility and ADL function, idea function, frailty, trying to get that certain aspect of what does it mean to be older, to have an advanced stage of aging? Geroscience and aging biology is sort of the molecular reflection of that.

John: If we know that you have two 85-year-olds, and one of them needs help with ADLs, they're going to be at risk for complications from surgery. But not necessarily because they need help with ADLs, but because that reflects their biology. The stage of their aging.

Alex: Right.

John: We're learning more and more about what that biology really is.

Alex: Right.

John: Is it their telomeres, for example? Like Eric said earlier. Or how many senescent cells they have in their body. Or what is their NAD reserve? What is the state of their chronic inflammation? How are their proteins folding? What is their proteostatic resilience?

John: We're getting closer to be able to understand what all of these biological aspects are, so we can look at someone who we think, "Is this person an old 85-year-old or a young 85-year-old?" And know what their biology tells us.

Alex: I don't want to ... I mean, I don't want to belabor this point. But I know some of our listeners are probably skeptics. We have one skeptic, a nurse practitioner on our hospice and palliative care service. Patrice Villars. We mentioned we were doing this podcast.

Alex: And she said, "Is it," something along the lines of, "is it morally responsible to focus on helping people to live longer, given the current burden of climate change that humans are placing on the planet currently, much less if we were to live longer lives?"

John: I love that question. I love ... Aging is a really unusual field to study, because it's, again, it's not a disease. It's something universal that happens to all of us. That does make it a little bit different, and opens up these really interesting questions about not only how to study it, and what our goals should be; but also how to ...

John: If we have therapies that effectively target aging, target mechanisms of aging, who gets them? How do we decide how to use them? How do we decide who should have them and who doesn't get them? Is that different from the way that we decide who gets other treatments? These are really interesting questions.

Eric: In the work that you do, I'm guessing ... because I think this is a really fascinating thing to do, like if you extend the life, even 20 years, the ethical issues that come up. Even from a population density standpoint, can our earth even handle that? Then who gets all these treatments? In the field of geroscience, are there ethicists in that field, too, that are looking at these questions?

John: Yeah. This is a brand-new field, an emerging field. There are ethicists who are particularly thinking about these questions. Not very many, in the same way there's not very many clinician scientists who are helping to develop and study these therapies.

John: But there are people who are just starting to really think about, because these questions; even five years ago, this would have felt like a really academic philosophical kind of discussion. Therapies that target aging. Sure, let's maybe 10 years, 20 years down the line.

John: But the crazy thing is the first clinical trial that takes a drug in this case, that targets a cellular mechanism of aging, was given to older adults to treat a chronic syndrome of aging. That clinical trial, that first clinical trial has already been done.

Eric: What are they doing?

John: This was a drug that targets protein quality control. It's a drug called Rapamycin and its related drugs. We can talk about how this fits into the bigger scheme of mechanisms of aging. But it helps to activate pathways in your cells that clean up misfolded proteins and help your cells to make proteins that are more functional.

John: This drug and a related drug were first given to older adults before flu vaccine, to see if it would improve response to the flu vaccine. And it did. Then the next step, they did a clinical trial where they treated people with these drugs for just a month, and then gave them a flu vaccine, and saw that it improved their response to a flu vaccine.

John: But then over the next six months, they saw how often they got respiratory or other infections. What they actually found was that the people who received this treatment just for a month had about a third fewer infections over the next six months. So it had this really interesting long-lasting effect on their immune function in these older adults. It was helping to ameliorate what we call immunosenescence, the decline in function of our immune systems as we get older.

John: It's a really small, limited thing. One drug, some older people, flu vaccine, it was just looking at infections. Doesn't seem like a big deal, and it's not. Except it was the first randomized controlled trial of a drug like this that targets mechanisms of aging in older adults, to improve syndromes of older adults.

Alex: And it's already happened.

John: And it's already happened.

Eric: I guess that's probably the hard part with this is that I would imagine you can't do a randomized controlled trial with this drug for 20-year-olds and wait 90 years to see what happens to them. And then market that drug after a hundred years.

John: Exactly. I mean, hey, the average R01 lasts for five years.

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Geroscience and it's Impact on the Human Healthspan: A podcast with John Newman - GeriPal - A Geriatrics and Palliative Care Blog

Recommendation and review posted by Alexandra Lee Anderson

DeepMind timeline: The history of the UK’s pioneering AI firm –

DeepMind timeline: The history of the UK's pioneering AI firm | Startups | TechworldThe London startup has made headlines for both breakthroughs and controversies since it was founded in 2010


DeepMind's efforts to achieve artificial general intelligence have won the firm both plaudits and critics since it was founded in 2010. The firm's research into deep learning techniquesconvinced the search engine giant Googleto spend 400 million on the company in 2014, but it has since incurred heavy losses and while its scientific discoveries have earned acclaim, DeepMind has also been rebuked for itslaissez faire approach to data privacy and security practices.

Read next: Google DeepMind: the story behind the world's leading AI startup

Here's our timeline of DeepMind's short but eventful history.

DeepMind was founded in London by machine learning researcher Shane Legg and childhood friends Demis Hassabis and former consultant Mustafa Suleyman. The cofounders all metat University College London, where Legg was a research associate and Hassibis was studying for a PhD in cognitive neuroscience.

The trio declared a grand ambition for their new company: "To solve intelligence and then to use that to solve everything else."

They initially pursued this lofty goal through video games. A 16-year-old Hasabis had co-developed the hit simulation game Theme Park, and at 22 was running his own games studio. He combined this experience with his neuroscience PhD to create AIprogrammesthat could master video games.One ofthese systemstaught itself how to play 49 different Atari games, including Pong and Space Invaders, just byviewing the score and pixels on the screen.

These experiments with video games led DeepMind to focus on an AI technique called deep reinforcement learning, which combines the pattern recognition of deep learning with the reward signals for completing tasks achieved through reinforcement learning.

DeepMind announced the technique in a research paper about its Atari trials, which it called "the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning."

The technique was responsible for DeepMind'smost impressive achievements, but the company's relentless focus on the technique has been questioned by some AI experts. In August 2019, Gary Marcus, the founder of Robust.AI and a professor of psychology and neural science at NYU, noted inWiredthat the company was still yet to find a large-scale commercial application of deep reinforcement learning.

"Ten years from now we will conclude that deep reinforcement learning was overrated in the late 2010s, and that many other important research avenues were neglected," he wrote. "Every dollar invested in reinforcement learning is a dollar not invested somewhere else, at a time when, for example, insights from the human cognitive sciences might yield valuable clues."

Google made DeepMind one of its biggest-ever European acquisitions when it splashed out 400 million on the London-based startup.Googleagreed to establish an AIethics boardas part of the deal, but the members and workings of the board have never been made clear.

A DeepMind-created system became the first AI to beat a professional Go player when AlphaGo routed European champion Fan Hu by a score of five to zero.Later that year, the system defeatedKe Jie, the world's number one player of the ancient and highly complex board game.

DeepMind began its controversial relationship with the Royal Free hospital in London when the two organisationssigned a deal that gave the Google subsidiary access to healthcare dataon 1.6 million patients. DeepMind later announced that the partnership hadyieldedan app called Streams that would help clinicians monitor patients for early signs of kidney disease.

DeepMind turned its ambition to use AI to improve healthcare into a separate division of the company:DeepMindHealth.Suleyman, whose mother was an NHS nurse, who chosen to lead the unit.

Suleymanwent on to sign further NHS dealswithTaunton & Somerset Foundation Trust,Yeovil District Hospital,University College London Hospital,Imperial College Healthcare and Moorfields Eye Hospital to apply AI to various medical challenges.

The Information Commissioner's Office (ICO), theUK's data regulator, ruled that the Royal Free"failed" to comply with data protection rules when it provided DeepMind with patient data as it didn't properly inform patients about how their details would be used.

Read next: DeepMind report fails to justify NHS use, claim privacy campaigners

The Royal Free accepted the findings and was not fined. The Trust announced that it hadstarted to address the concerns.

DeepMind revealedit had attracted a major client in the US when it announced that itwas teaming up with the US Department of Veterans Affairs to predict patient deterioration by analysing patterns in medical records.

Read next: DeepMind researcher says AI agents should cooperate for social good

The project also involves researching ways to improve the algorithmsDeepMind uses to detect acute kidney injury.

Privacy campaigners raised alarm whenDeepMind announcedthat its healthcare subsidiary was being absorbed into Google. The arrangement meant that the group would no longer operate as an independent unit but instead merge with the newly-formed Google Health team, led by former Geisinger CEO David Feinberg.

Critics argued that the shift betrayed DeepMind's promise never to share data with its parent company. DeepMind claimed that all patient data would remain separate from Google services and projects.

DeepMind made its biggest scientific breakthrough yet when its AlphaFold system won a competitionto predict the 3D shapes of proteins based on their genetic codes.The victory suggested that AI could help understand the protein-folding puzzle that plays a key role in the development of new drugs.

This is a lighthouse project, our first major investment in terms of people and resources into a fundamental, very important, real-world scientific problem, DeepMind CEO Demis Hassabistold the Guardian.

DeepMind continued its long history of applying AI tovideo games by introducing AlphaStar, a programme that can play strategy game StarCraft II. The system went on to defeat some of the world's best StarCraft II players.

DeepMind announced that Mustafa Suleyman, the company'scofounder and head of applied artificial intelligence,was leaving the company for an indefinite period that the company said would likelyend later the same year.DeepMindclaimedthat the decision was mutual andnotrelated to his performance,but rumours spread that his departure was related to the company's various healthcare controversies.

Read next:Google DeepMind loses its cofounder Mustafa Suleyman indefinitely

On September 18, Dr Dominic King, the UK site lead at Google Health, announced in a blogpost that Google had completed its takeover of DeepMind's health division.

"It's clear that a transition like this takes time," he wrote."Health data is sensitive, and we gave proper time and care to make sure that we had the full consent and cooperation of our partners. This included giving them the time to ask questions and fully understand our plans and to choose whether to continue our partnerships. As has always been the case, our partners are in full control of all patient data and we will only use patient data to help improve care, under their oversight and instructions.

The Royal Free,University College London Hospitals,Imperial College Healthcare, Moorfields Eye Hospital, Taunton & Somerset, and University College London Hospitals NHS Foundation Trust all went on to release statementsconfirming that theircontractual arrangements had been moved to Google.



DeepMind timeline: The history of the UK's pioneering AI firm -

Recommendation and review posted by Alexandra Lee Anderson

Diseases Folding@home

The Folding@home project (FAH) is dedicated to understanding protein folding, the diseases that result from protein misfolding and aggregation, and novel computational ways to develop new drugs in general. Here, we briefly describe our goals, what we are doing, and some highlights so far.

A distributed computing project must not only run calculations on millions of PCs, but such projects must produce results, especially in the form of peer-reviewed publications, public lectures, and other ways that disseminate the results from FAH to the greater scientific community. In the sidebar, you will find links to our progress in different areas.

You will also find updates about our work, advancements and new projects in the main Folding@home blog.

Proteins are necklaces of amino acids, long chain molecules. They are the basis of how biology gets things done. As enzymes, they are the driving force behind all of the biochemical reactions that make biology work. As structural elements, they are the main constituent of our bones, muscles, hair, skin and blood vessels. As antibodies, they recognize invading elements and allow the immune system to get rid of the unwanted invaders. For these reasons, scientists have sequenced the human genome the blueprint for all of the proteins in biology but how can we understand what these proteins do and how they work?

However, only knowing this sequence tells us little about what the protein does and how it does it. In order to carry out their function (e.g. as enzymes or antibodies), they must take on a particular shape, also known as a fold. Thus, proteins are truly amazing machines: before they do their work, they assemble themselves! This self-assembly is called folding.

Diseases such as Alzheimers disease, Huntingtons disease, cystic fibrosis, BSE (Mad Cow disease), an inherited form of emphysema, and even many cancers are believed to result from protein misfolding. When proteins misfold, they can clump together (aggregate). These clumps can often gather in the brain, where they are believed to cause the symptoms of Mad Cow or Alzheimers disease.


Diseases Folding@home

Recommendation and review posted by Alexandra Lee Anderson

Molecular Biology 02: ‘Thermodynamics of protein folding’

These are my notes from lecture 02 of Harvards BCMP 200: Molecular Biology course, delivered by Joe Loparo on September 5, 2014.

Continued from lecture 01. is always 0 or +180. If you plot and you find only a few clusters are well-represented: a range of -helix combinations, a -sheet area, and a third rarer area (called L and populated by left-handed -helices). is ususally found in the trans conformation due to steric hindrance of the consecutive side chains, however, proline because it is anchored to the backbone has a unique twist that enables a cis conformation.

-helices and -sheets are two ways of allowing the NH and C=O groups on the backbone to form hydrogen bonds. -helices contain 3.6 residues per rotation, or in other words, each residue spans 100 of rotation. Consecutive rungs of an -helix turns are separated by 5.4. -helices are almost exclusively right-handed. In a right-handed -helix, you turn counter-clockwise as you go up. In a left-handed -helix you turn clockwise as you go up. Side chains point outward from the helix. If you plot out where each residue falls on the helix based on the 3.6 residues/turn rule, you find that amphipathic, half-buried helices have all the hydrophobic residues on one side and the hydrophilic ones on the other side. A fully buried helix will be all hydrophobic residues and a fully exposed helix will be all hydrophilic residues.

In -sheets, all potential H-bonds are satisfied except for the flanking strands at either end of the sheet. About 20% of -sheets found in nature are mixed parallel and anti-parallel, the other 80% are pure one or the other. -sheets are not flat, but pleated.

A single sheet or helix is not stable in water. Tertiary structure is the packing of these elements, and loops connecting them, onto each other.

There are two fundamental problems in protein folding:

As an example, consider the metalloprotease cleaveage of Notch to create the Notch intracellular domain (NICD), which then translocates to the nucleus and affects transcription. The proteolytic site of Notch is protected by Lin12/Notch repeats which are connected to the EGF repeats that interact with Notchs ligand. The ligand is believed to apply a force that unfolds this region, allowing cleavage. Mutations which destabilize this fold and result in constitutive activation cause tumors.

Thermodynamics can only describe whether a chemical reaction will occur spontaneously or not, not how fast it will occur (see Biochemistry 01).

The energy of a system is its capacity to do work.

U = q + w

Where U is internal energy, q is heat and w is work.

q := heat = C(Tf-Ti)

Where C is the heat capacity and f and i mean final and initial.

w := work = Fxx

Where F is force and x is displacement along the x axis.

If you dissolve urea in water at a 4M solution, it will dissolve spontaneously and the solution will become cold (just like guanidine, as I learned here).

Gibbs free energy is defined as:

G = H - TS

Where G, H, T and S are Gibbs free energy, enthalpy, temperature and entropy respectively.

G = H - TS

If G < 0 the reaction will proceed spontaneously.

In the urea example, H > 0 because energy is required to pull apart the interacting urea molecules, using heat from the water. Yet the reaction still occurs spontaneously because S > 0 by a lot - the urea solution is much more entropic than urea and water separately.

For the reaction A + B C + D, we define:

Keq = ([C]eq[D]eq)/([A]eq[B]eq)

Keq = e-G/RT

ATP is a special molecule: its hydrolysis into ADP is spontaneous at physiological concentrations of the reactants and products, i.e. G < 0 for this reaction:

ATP + H2O ADP + Pi

Le Chateliers principle says you could drive the reaction in reverse, making ATP spontaneously, simply by increasing the concentrations of the procuts. However [Pi] never gets high enough in the cell for ATP to be spontaneously generated from ADP. The unfavorable production of ATP is instead created via a coupled reaction with favorable reactions such as the release of protons across the mitochondrial membrane (see Biochemistry 08).

H := Enthalpy = U + PV

Where U, P and V are internal energy, pressure and volume.

In physiological conditions, changes in pressure and volume are almost always negligible, so H and U are closely coupled. In other words, in most biological systems, the enthalpy is equal to the internal energy.

People have developed molecular dynamics simulations of the fundamental atomic forces that determine a proteins enthalpy (dihedral angles, Van der Waals interactions, electrostatic interactions, etc) and attempt to minimize the energy to determine a proteins fold. But there are so many degrees of freedom that computational expense prohibits running the simulation long enough to find the lowest energy state. Still there are attempts, such as Folding@Home, Foldit, and D.E. Shaws Anton. Anton holds the record for the longest molecular dynamics simulation - it ran for some untold amount of time, calculating the energy a protein would have at every femtosecond or something, in order to simulate 1 millisecond of the proteins movement. Obviously, the time that Anton took to simulate that millisecond was more than a millisecond.

S := Entropy = kbln(W)

Where kb is Boltzmanns constant and W is the number of microstates that give rise to the macrostate of interest.

My favorite explanation of this is that given by Richard Feynman. When I read it, I understood for the first time how physical entropy and information entropy are the same concept:

So we now have to talk about what we mean by disorder and what we mean by order. Suppose we divide the space into little volume elements. If we have black and white molecules, how many ways could we distribute them among the volume elements so that white is on one side and black is on the other? On the other hand, how many ways could we distribute them with no restriction on which goes where? Clearly, there are many more ways to arrange them in the latter case. We measure disorder by the number of ways that the insides can be arranged, so that from the outside it looks the same. The logarithm of that number of ways is the entropy. The number of ways in the separated case is less, so the entropy is less, or the disorder is less.

Richard Feynman, quoted here

In biology, entropy is very often the driving force, for instance for the burial of hydrophobic protein domains. Imagine a water molecule in a tetrahedron. The tetrahedron has four corners, and the water has two hydrogens, so you can place the molecule in 4 choose 2 = 6 orientations. If you add a nonpolar group of a neighboring molecule at one corner of the tetrahedron, only three of the six states remain favorable (by still allowing hydrogen bonding). So Shydrophobic = kbln(3) - kbln(6) < 0, meaning that entropy has decreased.

Consider the mixing of epoxy and hardener into cured epoxy. This reaction has S < 0 because the solid has fewer microstates than the liquids did. Yet the reaction occurs spontaneously at room temperature, so it must be true that H < 0. Heat is therefore released - in fact, the reaction is extremely exothermic. Joe measured the temperature of 5-minute epoxy and it rose from 21C to >40C at the 5 minute mark.

An incorrect and simplistic view of protein folding is as follows. An unfolded protein has high configurational entropy but also high enthalpy because it has few stabilizing interactions. A folded protein has far less entropy, but also far less enthalpy. There is a tradeoff between H and S here. Note that because G = H - TS, increased temperature weights the S term more heavily, meaning that higher temperature favors unfolding.

That entire explanation only considers the energy of the protein and not that of the solvent. In fact, hydrophobic domains of a protein constrain the possible configurations of surrounding water (see explanation above), and so their burial upon folding increases the waters entropy. Moreover, it turns out that the hydrogen bonding of polar residues and the backbone is satisfied both in an unfolded state (by water) and in a folded state (by each other). Therefore enthalpy is zero sum, and protein folding is driven almost entirely by entropy.

Here is a description of a technique called differential scanning calorimetry. You apply equal amounts of heat to two solutions, one with only buffer and the other with buffer and protein, and you measure the temperature in each solution. Eventually the protein reaches its melting temperature Tm, where the protein is 50% folded and 50% unfolded and G = 0. At Tm, the melting of the protein aborbs lots of the applied heat, and so the temperature does not rise as much as it does in the buffer-only solution.

Another technique for measuring protein stability is the force required to unfold it using single molecule atomic force microscopy.

Common denaturants are urea and guanidine hydrochloride. Amazingly, we still do not know how they work. It is thought that they stabilize all constituent parts of the unfolded protein. Guanidine may surround those unfavorable hydrophobic domains of the protein but then expose its own hydrophilic side to water, so that the movement of the water is not constrained.

Continued here:

Molecular Biology 02: 'Thermodynamics of protein folding'

Recommendation and review posted by Alexandra Lee Anderson

Thermodynamics of spontaneous protein folding: role of …


Free energy change in individual transformations

It is standard practice in biochemistry to consider the Gibbs Free Energy of transformation of the sort A B in isolation in determining whether it will proceed spontaneously. A chemical reaction for which G is negative may generate heat (i.e. have a negative enthalpy change (H) ) which affects its aqueous surroundings, but it seems justified to consider the reaction in isolation as there is no sense that the change in the vibration of the water molecules is driving or coupled to the reaction.

This approach has been applied to the structural change of protein folding with the conclusion (consistent with the first explanation) that the change in enthalpy (H) is sufficient to produce a negative G and hence drive protein folding (Citation 1, below).

Free energy change in coupled transformationsMany biochemical changes involve transformations which individually have a positive free energy change, but are made possible by coupling to another reaction with negative free energy change, of greater magnitude:

A B , G1 = +x

C D , G2 = y

If y>x and these two reactions are coupled (generally through a complex reaction path on an enzyme) , then we have:

A + C B + D , Goverall = ve

See also Berg et al.

Although one can reject the second explanation in the question as it stands because it ignores the free energy change in the protein folding, perhaps it was intended to mean that the folding of the protein (A B) should be considered as coupled to the change in the environment of the water (C D), and that the negative G for the aqueous environment made a greater contribution to the overall G than that for the protein folding.

Is it valid to consider these two systems as coupled? In the original version of my answer I argued against this point of view, but am no longer convinced by my own arguments. The water environment is clearly essential for the hydrophobic effect the burying of the hydrophobic residues in the centre of the protein away from the water. This is evident if one considers the same protein in a hydrophobic environment such as a cell membrane it would not fold. In membrane proteins it is hydrophobic residues that are exposed to the lipid bilayer and it is their interiors that sometimes have hydrophilic channels.

So in this coupled system, what is the determinant of the negative free energy change? Minikel (Citation 2, below) asserts that there is no net enthalpy change for the protein folding, and it is the entropy effect on the G for the aqueous environment that drives the folding. He indicates that this view is supported by differential scanning colorimetry and, although he doesnt cite references, there is a recent (if rather complex) review of this topic by Christopher M. Johnson.

Citation 1: Assertion of role of H of protein

The following explanation, taken from Essential Biochemistry, treats the protein folding in isolation and asserts that change in enthalpy is sufficient to produce a negative free energy change:

The folding of a protein also provides an example of the "H" and "TS" terms competing with one another to determine the G of the folding process. As described above, the change in entropy of the protein as it folds is negative, so the "TS" term is positive. However, in addition to entropic effects there are enthalpic contributions to protein folding. These include hydrogen bonding, ionic salt bridges, and Van der Waals forces. An input of thermal (heat) energy is required to disrupt these forces, and conversely when these interactions form during protein folding they release heat (the H is negative). When all of these entropic and enthalpic contributions are weighed, the enthalpy term wins out over the entropy term. Therefore the free energy of protein folding is negative, and protein folding is a spontaneous process.

Citation 2: Rebuttal of role of H of protein and assertion of role of water

The following explanation, taken from on-line lecture notes of of Eric V. Minikel of Harvard University, rebutting the point of view above:

An incorrect and simplistic view of protein folding is as follows. An unfolded protein has high configurational entropy but also high enthalpy because it has few stabilizing interactions. A folded protein has far less entropy, but also far less enthalpy. There is a tradeoff between H and S here. Note that because G = H - TS, increased temperature weights the S term more heavily, meaning that higher temperature favors unfolding.

That entire explanation only considers the energy of the protein and not that of the solvent. In fact, hydrophobic domains of a protein constrain the possible configurations of surrounding water (see explanation above), and so their burial upon folding increases the waters entropy. Moreover, it turns out that the hydrogen bonding of polar residues and the backbone is satisfied both in an unfolded state (by water) and in a folded state (by each other). Therefore enthalpy is zero sum, and protein folding is driven almost entirely by entropy.

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The Science Behind Foldit | Foldit

Foldit is a revolutionary crowdsourcing computer game enabling you to contribute to important scientific research. This page describes the science behind Foldit and how your playing can help.

What is a protein? Proteins are the workhorses in every cell of every living thing. Your body is made up of trillions of cells, of all different kinds: muscle cells, brain cells, blood cells, and more. Inside those cells, proteins are allowing your body to do what it does: break down food to power your muscles, send signals through your brain that control the body, and transport nutrients through your blood. Proteins come in thousands of different varieties, but they all have a lot in common. For instance, they're made of the same stuff: every protein consists of a long chain of joined-together amino acids.

What are amino acids? Amino acids are small molecules made up of atoms of carbon, oxygen, nitrogen, sulfur, and hydrogen. To make a protein, the amino acids are joined in an unbranched chain, like a line of people holding hands. Just as the line of people has their legs and feet "hanging" off the chain, each amino acid has a small group of atoms (called a sidechain) sticking off the main chain (backbone) that connects them all together. There are 20 different kinds of amino acids, which differ from one another based on what atoms are in their sidechains. These 20 amino acids fall into different groups based on their chemical properties: acidic or alkaline, hydrophilic (water-loving) or hydrophobic (greasy).

What shape will a protein fold into? Even though proteins are just a long chain of amino acids, they don't like to stay stretched out in a straight line. The protein folds up to make a compact blob, but as it does, it keeps some amino acids near the center of the blob, and others outside; and it keeps some pairs of amino acids close together and others far apart. Every kind of protein folds up into a very specific shape -- the same shape every time. Most proteins do this all by themselves, although some need extra help to fold into the right shape. The unique shape of a particular protein is the most stable state it can adopt. Picture a ball at the top of a hill -- the ball will always roll down to the bottom. If you try to put the ball back on top it will still roll down to the bottom of the hill because that is where it is most stable.

Why is shape important? This structure specifies the function of the protein. For example, a protein that breaks down glucose so the cell can use the energy stored in the sugar will have a shape that recognizes the glucose and binds to it (like a lock and key) and chemically reactive amino acids that will react with the glucose and break it down to release the energy.

What do proteins do? Proteins are involved in almost all of the processes going on inside your body: they break down food to power your muscles, send signals through your brain that control the body, and transport nutrients through your blood. Many proteins act as enzymes, meaning they catalyze (speed up) chemical reactions that wouldn't take place otherwise. But other proteins power muscle contractions, or act as chemical messages inside the body, or hundreds of other things. Here's a small sample of what proteins do:

Proteins are present in all living things, even plants, bacteria, and viruses. Some organisms have proteins that give them their special characteristics:

You can find more information on the rules of protein folding in our FAQ.

What big problems is this game tackling?

How does my game playing contribute to curing diseases?

With all the things proteins do to keep our bodies functioning and healthy, they can be involved in disease in many different ways. The more we know about how certain proteins fold, the better new proteins we can design to combat the disease-related proteins and cure the diseases. Below, we list three diseases that represent different ways that proteins can be involved in disease.

What other good stuff am I contributing to by playing?

Proteins are found in all living things, including plants. Certain types of plants are grown and converted to biofuel, but the conversion process is not as fast and efficient as it could be. A critical step in turning plants into fuel is breaking down the plant material, which is currently done by microbial enzymes (proteins) called "cellulases". Perhaps we can find new proteins to do it better.

Can humans really help computers fold proteins?

Were collecting data to find out if humans' pattern-recognition and puzzle-solving abilities make them more efficient than existing computer programs at pattern-folding tasks. If this turns out to be true, we can then teach human strategies to computers and fold proteins faster than ever!

You can find more information about the goals of the project in our FAQ.

Brian Koepnick, Jeff Flatten, Tamir Husain, Alex Ford, Daniel-Adriano Silva, Matthew J. Bick, Aaron Bauer, Gaohua Liu, Yojiro Ishida, Alexander Boykov, Roger D. Estep, Susan Kleinfelter, Toke Nrgrd-Solano, Linda Wei, Foldit Players, Gaetano T. Montelione, Frank DiMaio, Zoran Popovi, Firas Khatib, Seth Cooper and David Baker. De novo protein design by citizen scientists Nature (2019). [link]

Thomas Muender, Sadaab Ali Gulani, Lauren Westendorf, Clarissa Verish, Rainer Malaka, Orit Shaer and Seth Cooper.Comparison of mouse and multi-touch for protein structure manipulation in a citizen science game interface.Journal of Science Communication (2019). [link]

Lorna Dsilva, Shubhi Mittal, Brian Koepnick, Jeff Flatten, Seth Cooper and Scott Horowitz.Creating custom Foldit puzzles for teaching biochemistry.Biochemistry and Molecular Biology Education (2019). [link]

Seth Cooper, Amy L. R. Sterling, Robert Kleffner, William M. Silversmith and Justin B. Siegel.Repurposing citizen science games as software tools for professional scientists.Proceedings of the 13th International Conference on the Foundations of Digital Games (2018). [link]

Robert Kleffner, Jeff Flatten, Andrew Leaver-Fay, David Baker, Justin B. Siegel, Firas Khatib and Seth Cooper. Foldit Standalone: a video game-derived protein structure manipulation interface using Rosetta. Bioinformatics (2017). [link]

Jacqueline Gaston and Seth Cooper. To three or not to three: improving human computation game onboarding with a three-star system. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2017). [link]

Scott Horowitz, Brian Koepnick, Raoul Martin, Agnes Tymieniecki, Amanda A. Winburn, Seth Cooper, Jeff Flatten, David S. Rogawski, Nicole M. Koropatkin, Tsinatkeab T. Hailu, Neha Jain, Philipp Koldewey, Logan S. Ahlstrom, Matthew R. Chapman, Andrew P. Sikkema, Meredith A. Skiba, Finn P. Maloney, Felix R. M. Beinlich, Foldit Players, University of Michigan students, Zoran Popovi, David Baker, Firas Khatib and James C. A. Bardwell. Determining crystal structures through crowdsourcing and coursework. Nature Communications 7, Article number: 12549 (2016). [link]

Dun-Yu Hsiao, Min Sun, Christy Ballweber, Seth Cooper and Zoran Popovi. Proactive sensing for improving hand pose estimation. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2016). [link]

Dun-Yu Hsiao, Seth Cooper, Christy Ballweber and Zoran Popovi. User behavior transformation through dynamic input mappings. Proceedings of the 9th International Conference on the Foundations of Digital Games (2014). [link]

George A. Khoury, Adam Liwo, Firas Khatib, Hongyi Zhou, Gaurav Chopra, Jaume Bacardit, Leandro O. Bortot, Rodrigo A. Faccioli, Xin Deng, Yi He, Pawel Krupa, Jilong Li, Magdalena A. Mozolewska, Adam K. Sieradzan, James Smadbeck, Tomasz Wirecki, Seth Cooper, Jeff Flatten, Kefan Xu, David Baker, Jianlin Cheng, Alexandre C. B. Delbem, Christodoulos A. Floudas, Chen Keasar, Michael Levitt, Zoran Popovi, Harold A. Scheraga, Jeffrey Skolnick, Silvia N. Crivelli and Foldit Players. WeFold: a coopetition for protein structure prediction. Proteins (2014). [link]

Christopher B. Eiben, Justin B. Siegel, Jacob B. Bale, Seth Cooper, Firas Khatib, Betty W. Shen, Foldit Players, Barry L. Stoddard, Zoran Popovi and David Baker. Increased Diels-Alderase activity through backbone remodeling guided by Foldit players. Nature Biotechnology (2012). [link]

Erik Andersen, Eleanor O'Rourke, Yun-En Liu, Richard Snider, Jeff Lowdermilk, David Truong, Seth Cooper and Zoran Popovi. The impact of tutorials on games of varying complexity. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2012). [link]

Firas Khatib, Seth Cooper, Michael D. Tyka, Kefan Xu, Ilya Makedon, Zoran Popovi, David Baker and Foldit Players. Algorithm discovery by protein folding game players. Proceedings of the National Academy of Sciences of the United States of America (2011). [link]

Miroslaw Gilski, Maciej Kazmierczyk, Szymon Krzywda, Helena Zbransk, Seth Cooper, Zoran Popovi, Firas Khatib, Frank DiMaio, James Thompson, David Baker, Iva Pichov and Mariusz Jaskolskia. High-resolution structure of a retroviral protease folded as a monomer. Acta Crystallographica (2011). [link]

Firas Khatib, Frank DiMaio, Foldit Contenders Group, Foldit Void Crushers Group, Seth Cooper, Maciej Kazmierczyk, Miroslaw Gilski, Szymon Krzywda, Helena Zbransk, Iva Pichov, James Thompson, Zoran Popovi, Mariusz Jaskolski and David Baker. Crystal structure of a monomeric retroviral protease solved by protein folding game players. Nature Structural and Molecular Biology (2011). [link]

Seth Cooper, Firas Khatib, Ilya Makedon, Hao Lu, Janos Barbero, David Baker, James Fogarty, Zoran Popovi and Foldit Players. Analysis of social gameplay macros in the Foldit cookbook. Proceedings of the 6th International Conference on the Foundations of Digital Games (2011). [link]

Seth Cooper, Firas Khatib, Adrien Treuille, Janos Barbero, Jeehyung Lee, Michael Beenen, Andrew Leaver-Fay, David Baker, Zoran Popovi and Foldit Players. Predicting protein structures with a multiplayer online game. Nature (2010). [link]

Seth Cooper, Adrien Treuille, Janos Barbero, Andrew Leaver-Fay, Kathleen Tuite, Firas Khatib, Alex Cho Snyder, Michael Beenen, David Salesin, David Baker, Zoran Popovi and Foldit players. The challenge of designing scientific discovery games. Proceedings of the 5th International Conference on the Foundations of Digital Games (2010). [link]

Foldit has been in dozens of publications over the years - to list them all would take a page of their own. For a sampling, please see our Center for Game Science page.

Check out the Rosetta@Home Screensaver to see how computers fold proteins using distributed computing.

Thank you for using Foldit in your classroom! We have put together a set of instructions to assist you in setting up your students to play Foldit.

You can find the researchers and supporters associated with this study on the game's credits page.

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The Science Behind Foldit | Foldit

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Protein Folding – an overview | ScienceDirect Topics


Protein folding is catalysed in vivo by isomerases and chaperone proteins. Molecular chaperones are ubiquitous proteins that assist folding, assembly, transport, and degradation of proteins within the cell. The first identified chaperones were heat-shock proteins (HSPs), whose names is derived from the elevated levels produced when cells are grown at higher-than-normal temperatures. HSPs stabilize other proteins during their synthesis and assist in protein folding by binding and releasing unfolded or misfolded proteins using an ATP-independent mechanism. Proteins unable to maintain their proper shape are broken down by the proteasome (see Section 1 of Chapter 10) and eliminated, as shown in Fig. 9.33. These events may be favourable if the proteins are previously mutated and hence dangerous for the survival of the cell, but they become a problem if the proteins are necessary for its normal functioning.

Figure 9.33. Function of heat-shock proteins.

HSP 90 is the best known of HSPs and its activity is coupled to an ATPase cycle that is controlled by several cofactors. It has three major domains, namely a highly conserved N-terminal ATPase domain, a middle domain, and a C-terminal dimerization domain. The crystal structure of HSP 90 bound to ATP has shown how this nucleotide is hydrolysed,135 but the detailed mechanism of protein folding remains unknown.

HSP 90 has emerged as an attractive cancer target because its inhibition blocks a large number of cancer-related signalling pathways since a large number of intra-cellular signalling molecules require association with HSP 90 to achieve their active conformation, correct cellular location, and stability.136 These include steroid hormone receptors, transcription factors like the tumor suppressor protein p53 and kinases like Src-kinase.

The conformational changes that take place in HSP 90 after binding and hydrolysis of ATP regulate the stabilization and maturation of client proteins, including hypoxia-inducible factor-1 (HIF-1), a relevant anticancer target.137 This ATP site is known by X-ray crystallography to be very different from that of kinases, allowing the design of inhibitors with high selectivity with regard to other ATP-binding proteins.

The design and study of selective inhibitors of HSP 90 was initially controversial because this protein is critical for the survival of both normal and sick cells. However, HSP does not have much activity under normal conditions. When the cell is under stress by genetic mutations or environmental changes such as heat or infection HSP 90 activity is increased as an emergency response that stabilizes partially unfolded proteins and helps them to achieve their correct shape. This activity also assists the survival of cancer cells despite an abundance of misfolded and unstable proteins, and this is one of the reasons to study HSP 90 as an anticancer target.

The main strategy employed in the design of HSP 90 inhibitors is based in the synthesis of analogues of the natural antitumor geldanamycin, a benzoquinone derivative belonging to the ansamycin class, although some companies working in this field are designing entirely synthetic molecules not related to this compound.

Geldanamycin was originally believed to be a TK inhibitor, but it was later identified as an ATP-competitive inhibitor of HSP 90. It could not be advanced to the clinical stage because it showed unacceptable hepatotoxicity, probably associated with the presence of the electrophilic methoxybenzoquinone moiety. For this reason, displacement of the 17-methoxy group by nucleophiles led to less toxic analogues such as tanespimycin (17-allylaminogeldanamycin, 17-AAG).138 Another problem associated with geldanamycin is its very low solubility, which was solved with the development of the water-soluble analogue alvespimycin (17-dimethylaminoethylaminogeldanamycin, 17-DMAG).139 Both analogues were better tolerated than the parent natural product and are under clinical trials. In another approach, the problematic quinone moiety of 17-AAG was reduced to the hydroquinone stage. The resulting compound, IPI-504, can be formulated as a soluble salt that is suitable for intravenous or oral formulations. It has shown encouraging results in Phase I trials in patients with gastrointestinal stromal tumors that were resistant to imatinib, although further clinical development is necessary.

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Protein Structures: Primary, Secondary, Tertiary, Quaternary …

Proteins are the largest and most varied class of biological molecules, and they show the greatest variety of structures. Many have intricate three-dimensional folding patterns that result in a compact form, but others do not fold up at all (natively unstructured proteins) and exist in random conformations. The function of proteins depends on their structure, and defining the structure of individual proteins is a large part of modern Biochemistry and Molecular Biology.

To understand how proteins fold, we will start with the basics of structure, and progress through to structures of increasing complexity.

Peptide Bonds

To make a protein, amino acids are connected together by a type of amide bond called a peptide bond. This bond is formed between the alpha amino group of one amino acid and the carboxyl group of another in a condensation reaction. When two amino acids join, the result is called a dipeptide, three gives a tripeptide, etc. Multiple amino acids result in a polypeptide (often shortened to peptide). Because water is lost in the course of creating the peptide bond, individual amino acids are referred to as amino acid residues once they are incorporated. Another property of peptides is polarity: the two ends are different. One end has a free amino group (called the N-terminal) and the other has a free carboxyl group (C-terminal).

In the natural course of making a protein, polypeptides are elongated by the addition of amino acids to the C-terminal end of the growing chain. Conventionally, peptides are written N-terminal first; therefore gly-ser is not the same as ser-gly or GS is not the same as SG. The connection gives rise to a repeating pattern of NCC-NCC-NCC atoms along the length of the molecule. This is referred to as the backbone of the peptide. If stretched out, the side chains of the individual residues project outwards from this backbone.

The peptide bond is written as a single bond, but it actually has some characteristics of a double bond because of the resonance between the C-O and C-N bonds:

This means that the six atoms involved are coplanar, and that there is not free rotation around the CN axis. This constrains the flexibility of the chain and prevents some folding patterns.

Primary Structure of Proteins

It is convenient to discuss protein structure in terms of four levels (primary to quaternary) of increasing complexity. Primary structure is simply the sequence of residues making up the protein. Thus primary structure involves only the covalent bonds linking residues together.

The minimum size of a protein is defined as about 50 residues; smaller chains are referred to simply as peptides. So the primary structure of a small protein would consist of a sequence of 50 or so residues. Even such small proteins contain hundreds of atoms and have molecular weights of over 5000 Daltons (Da). There is no theoretical maximum size, but the largest protein so far discovered has about 30,000 residues. Since the average molecular weight of a residue is about 110 Da, that single chain has a molecular weight of over 3 million Daltons.

Secondary Structure

This level of structure describes the local folding pattern of the polypeptide backbone and is stabilized by hydrogen bonds between N-H and C=O groups. Various types of secondary structure have been discovered, but by far the most common are the orderly repeating forms known as the a helix and the b sheet.

An a helix, as the name implies, is a helical arrangement of a single polypeptide chain, like a coiled spring. In this conformation, the carbonyl and N-H groups are oriented parallel to the axis. Each carbonyl is linked by a hydrogen bond to the N-H of a residue located 4 residues further on in the sequence within the same chain. All C=O and N-H groups are involved in hydrogen bonds, making a fairly rigid cylinder. The alpha helix has precise dimensions: 3.6 residues per turn, 0.54 nm per turn. The side chains project outward and contact any solvent, producing a structure something like a bottle brush or a round hair brush. An example of a protein with many a helical structures is the keratin that makes up human hair.

The structure of a b sheet is very different from the structure of an a helix. In a b sheet, the polypeptide chain folds back on itself so that polypeptide strands like side by side, and are held together by hydrogen bonds, forming a very rigid structure. Again, the polypeptide N-H and C=O groups form hydrogen bonds to stabilize the structure, but unlike the a helix, these bonds are formed between neighbouring polypeptide (b) strands. Generally the primary structure folds back on itself in either a parallel or antiparallel arrangement, producing a parallel or antiparallel b sheet. In this arrangement, side chains project alternately upward and downward from the sheet. The major constituent of silk (silk fibroin) consists mainly of layers of b sheet stacked on top of each another.

Other types of secondary structure. While the a helix and b sheet are by far the most common types of structure, many others are possible. These include various loops, helices and irregular conformations. A single polypeptide chain may have different regions that take on different secondary structures. In fact, many proteins have a mixture of a helices, b sheets, and other types of folding patterns to form various overall shapes.

What determines whether a particular part of a sequence will fold into one or the other of these structures? A major determinant is the interactions between side chains of the residues in the polypeptide. Several factors come into play: steric hindrance between nearby large side chains, charge repulsion between nearby similarly-charged side chains, and the presence of proline. Proline contains a ring that constrains bond angles so that it will not fit exactly into an a helix or b sheet. Further, there is no H on one peptide bond when proline is present, so a hydrogen bond cannot form. Another major factor is the presence of other chemical groups that interact with each other. This contributes to the next level of protein structure, the tertiary structure.

Tertiary Structure

This level of structure describes how regions of secondary structure fold together that is, the 3D arrangement of a polypeptide chain, including a helices, b sheets, and any other loops and folds. Tertiary structure results from interactions between side chains, or between side chains and the polypeptide backbone, which are often distant in sequence. Every protein has a particular pattern of folding and these can be quite complex.

Whereas secondary structure is stabilized by H-bonding, all four weak forces contribute to tertiary structure. Usually, the most important force is hydrophobic interaction (or hydrophobic bonds). Polypeptide chains generally contain both hydrophobic and hydrophilic residues. Much like detergent micelles, proteins are most stable when their hydrophobic parts are buried, while hydrophilic parts are on the surface, exposed to water. Thus, more hydrophobic residues such as trp are often surrounded by other parts of the protein, excluding water, while charged residues such as asp are more often on the surface.

Other forces that contribute to tertiary structure are ionic bonds between side chains, hydrogen bonds, and van der Waals forces. These bonds are far weaker than covalent bonds, and it takes multiple interactions to stabilize a structure.

There is one covalent bond that is also involved in tertiary structure, and that is the disulfide bond that can form between cysteine residues. This bond is important only in non-cytoplasmic proteins since there are enzyme systems present in the cytoplasm to remove disulfide bonds.

Visualization of protein structures Because the 3D structures of proteins involve thousands of atoms in complex arrangements, various ways of depicting them so they are understood visually have been developed, each emphasizing a different property of the protein. Software tools have been written to depict proteins in many different ways, and have become essential to understanding protein structure and function.

Structural Domains of Proteins

Protein structure can also be described by a level of organization that is distinct from the ones we have just discussed. This organizational unit is the protein domain, and the concept of domains is extremely important for understanding tertiary structure. A domain is a distinct region (sequence of amino acids) of a protein, while a structural domain is an independently-folded part of a protein that folds into a stable structure. A protein may have many domains, or consist only of a single domain. Larger proteins generally consist of connected structural domains. Domains are often separated by a loosely folded region and may create clefts between them..

Quaternary Structure

Some proteins are composed of more than one polypeptide chain. In such proteins, quaternary structure refers to the number and arrangement of the individual polypeptide chains. Each polypeptide is referred to as a subunit of the protein. The same forces and bonds that create tertiary structure also hold subunits together in a stable complex to form the complete protein.

Individual chains may be identical, somewhat similar, or totally different. As examples, CAP protein is a dimer with two identical subunits, whereas hemoglobin is a tetramer containing two pairs of non-identical (but similar) subunits. It has 2 a subunits and 2 b subunits. Secreted proteins often have subunits that are held together by disulfide bonds. Examples include tetrameric antibody molecules that commonly have two larger subunits and two smaller subunits (heavy chains and light chains) connected by disulfide bonds and noncovalent forces.

In some proteins, intertwined a helices hold subunits together; these are called coiled-coils. This structure is stabilized by a hydrophobic surface on each a helix that is created by a heptameric repeat pattern of hydrophilic/hydrophobic residues. The sequence of the protein can be represented as abcdefgabcdefgabcdefg with positions a and d filled with hydrophobic residues such as A, V, L etc. Each a helix has a hydrophobic surface that therefore matches the other. When the two helices coil around each other, those surfaces come together, burying the hydrophobic side chains and forming a stable structure. An example of such a protein is myosin, the motor protein found in muscle that allows contraction.

Protein Folding

How and why do proteins naturally form secondary, tertiary and quaternary structures? This question is a very active area of research and is certainly not completely understood. A folded, biologically-active protein is considered to be in its native state, which is generally thought to be the conformation with least free energy.

Proteins can be unfolded or denatured by treatment with solvents that disrupt weak bonds. Thus organic solvents that disrupt hydrophobic interactions, high concentrations of urea or guanidine that interfere with H-bonding, extreme pH or even high temperatures, will all cause proteins to unfold. Denatured proteins have a random, flexible conformation and usually lack biological activity. Because of exposed hydrophobic groups, they often aggregate and precipitate. This is what happens when you fry an egg.

If the denaturing condition is removed, some proteins will re-fold and regain activity. This process is called renaturation. Therefore, all the information necessary for folding is present in the primary structure (sequence) of the protein. During renaturation, the polypeptide chain is thought to fold up into a loose globule by hydrophobic effects, after which small regions of secondary structure form into especially favorable sequences. These sequences then interact with each other to stabilize intermediate structures before the final conformation is attained.

Many proteins have great difficulty renaturing, and proteins that assist other proteins to fold are called molecular chaperones. They are thought to act by reversibly masking exposed hydrophobic regions to prevent aggregation during the multi-step folding process. Proteins that must cross membranes (eg. mitochondrial proteins) must stay unfolded until they reach their destination, and molecular chaperones may protect and assist during this process.

Protein families/Types of proteins

Proteins are classified in a number of ways, according to structure, function, location and/or properties. For example, many proteins combine tightly with other substances such as carbohydrates (glycoproteins), lipids (lipoproteins), or metal ions (metalloproteins). The diversity of proteins that form from the 20 amino acids is greatly increased by associations such as these. Proteins that are tightly bound to membranes are called membrane proteins. Proteins with similar activities are given functional classifications. For example, proteins that break down other proteins are called proteases.

Because almost all proteins arise by an evolutionary process, ie. new ones are derived from old ones, they can be classified into families by their relatedness. Proteins that derive from the same ancestor are called homologous proteins. Studying the sequences of homologous proteins can give clues to the structure and function of the protein. Residues that are critical for function do not change on an evolutionary timescale; they are referred to as conserved residues. Identifying such residues by comparing amino acid sequences often helps clarify what a protein is doing or how it is folded. For example the proteases trypsin and chymotrypsin are members of the serine protease family; so-named because of a conserved serine residue that is essential to catalyze the reaction. Trypsin and chymotrypsin contain very similar folding patterns and reaction mechanisms. Recognizing a pattern of conserved residues in protein sequences often allows scientists to deduce the function of a protein.

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Protein Folding – Anfinsen’s Experiment ~ Biology Exams 4 U

How Protein folds? During translation, the linear chain of amino acids formed will be gradually released from the ribosome, and these amino acids should fold properly to make a functional protein, the ultimate nano machines in the cells. Protein folding is undoubtedly the most critical events that determine the ability of that given protein to work properly.

How protein folds? Is it a random process? It shouldnt be, as folding determines the function.


Protein folding refers to the set of ordered pathways by which protein folds into their native functional confirmation.

Protein folding is primarily driven by hydrophobic forces.

Anfinsen's Experiment

First step..

The first insight to this question was provided by Christian Anfinsen at the NIH. He was working on the properties of ribonuclease A (a single chain protein of 124 amino acids with 4 di-sulphide bonds). He unfolded (denatured) ribonuclease A using urea and mercaptoethanol (denaturants). The protein lost its function. Then he allowed to renature ribonuclease A by removing denaturants, and found out that ribonuclease A folded spontaneously and become functional. He concluded that Ribonuclease A can self assemble into its 3D functional structure.

Protein Folding inside the Cell

Inside the cell, protein folding is assisted by different proteins collectively called as accessory proteins.

The importance of studying protein folding?

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Denaturation and Protein Folding | Introduction to Chemistry

Each protein has its own unique sequence of amino acids and the interactions between these amino acids create a specify shape. This shape determines the proteins function, from digesting protein in the stomach to carrying oxygen in the blood.

If the protein is subject to changes in temperature, pH, or exposure to chemicals, the internal interactions between the proteins amino acids can be altered, which in turn may alter the shape of the protein. Although the amino acid sequence (also known as the proteins primary structure) does not change, the proteins shape may change so much that it becomes dysfunctional, in which case the protein is considered denatured. Pepsin, the enzyme that breaks down protein in the stomach, only operates at a very low pH. At higher pHs pepsins conformation, the way its polypeptide chain is folded up in three dimensions, begins to change. The stomach maintains a very low pH to ensure that pepsin continues to digest protein and does not denature.

Because almost all biochemical reactions require enzymes, and because almost all enzymes only work optimally within relatively narrow temperature and pH ranges, many homeostatic mechanisms regulate appropriate temperatures and pH so that the enzymes can maintain the shape of their active site.

It is often possible to reverse denaturation because the primary structure of the polypeptide, the covalent bonds holding the amino acids in their correct sequence, is intact. Once the denaturing agent is removed, the original interactions between amino acids return the protein to its original conformation and it can resume its function.

However, denaturation can be irreversible in extreme situations, like frying an egg. The heat from a pan denatures the albumin protein in the liquid egg white and it becomes insoluble. The protein in meat also denatures and becomes firm when cooked.

Chaperone proteins (or chaperonins) are helper proteins that provide favorable conditions for protein folding to take place. The chaperonins clump around the forming protein and prevent other polypeptide chains from aggregating. Once the target protein folds, the chaperonins disassociate.

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