A Top-Down Approach to a Complex Natural System: Protein Folding

Abstract We develop a general method for applying functional models to natural systems and cite recent progress in protein modeling that demonstrates the power of this approach. Functional modeling constrains the range of acceptable structural models of a system, reduces the difficulty of finding them, and improves their fidelity.  However, functional models are distinctly different from the structural models that are more commonly applied in science.  In particular, structural and functional models ask different questions and provide different kinds of answers.  As we clarify these differences and articulate how to use these models jointly, we extend our ability to do science and gain insight into the proper use of the terms organization, order, and emergence when describing systems in nature.

Published September 30 online at Springerlink

I have a self archived version of the paper Here

Protein folding as computer game?

Yes.  Also, it’s a lot of fun.  Yay science!  Check out the amazing article in Wired and download the game for free from Foldit’s website.

Besides just being a really cool concept (players work on real models of protein and their solutions to the puzzles can hold real contributions to scientists), I was surprised at how well done the game seems.  A lot of the rookie game design mistakes (poor tutorials, confusing or nonexistent feedback, non-intuitive UI) were not there, and I even got flashy fireworks across my screen whenever I finished one of the tutorial level puzzles.  Overall, a wonderful example of the power of games.

Protein folding is among the most complex problems for scientists to figure out.  Calculating the countless atomic interactions that determine a given protein’s 3-D shape is a huge task.  It’s also arguably among the most important: proteins of thousands of different types allow living things to complete all sorts of functions, and each of these functions depends not only upon the chemistry of the chain but also upon its precise shape.  In order to do their job, proteins must be made of the right stuff and organized in the right way.  That means that completely understanding processes from digestion to cell formation to immune responses and more hinges on understanding protein folding.  And if scientists can learn more about the patterns of protein folding, it may help them to design new proteins that can help combat a number of diseases.  So who should we turn to to solve these difficult and fundamental puzzles?  If you ask biochemist David Baker of the University of Washington, you may get an answer you wouldn’t expect: Gamers.

The idea began with a contest, the Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP), in which teams compete to predict the shapes proteins will take based on the sequence of their parts. The contest encourages scientists to come up with new approaches for figuring out how proteins take the shapes they do.  Baker’s team has dominated the competition since 1998 using a computer program which tried different possible shapes and selected the best.  His secret was that he added to his computing power by designing a program called Rosetta@home, which could run as a screen saver on the computers of volunteers when they weren’t being used, doing scientific tests all the while.  And starting last summer, Baker has added brainpower to computer power, challenging gamers to bend proteins into the best possible shapes in a puzzle game called Foldit.

Biochemists, computer scientists, and game designers came together to create the game, in which users can move parts of the protein according to scientific rules and compete to find the best shapes for each sequence.  Players can view their rank and chat online while they play (check out the video below to see how it’s done).  As a recent article in Wired magazine argues, this competition can be intense, and the experiment of letting gamers take the reins is working: at the 2008 CASP competition, the gamers held their own against world-class biochemists.  Looking ahead, Baker is adapting Foldit to allow players to design new proteins, which could bring serious medical advances.  A high score may be something to be proud of for a whole new set of reasons!

I’ll be the first to admit that a lot of the science behind this is beyond me, but then again, that’s one of the great things about citizen science.  Not everyone has to be an expert in everything to contribute to valuable advancements in scientific knowledge.  The creativity behind this idea and the gamers who make it run is an amazing and vital part of science.  Sound cool to you?  Then get in on the citizen-sciene action, and download Rosetta@home or Foldit!

-Tim

I don’t really like playing computer games, but I must say I have a soft spot for puzzles. When I discovered sudoku, about two years ago, I played it nonstop for almost three weeks, after which I got tired of wasting so much time without getting anything done…

And then, after reading BoingBoing a couple of days ago, I discovered Foldit!

This free downloadable game is a product of collaboration between departments of Computer Science and Engineering and Biochemistry of University of Washington.

Scientists today have a pretty good idea about the amino acid sequence in proteins, but understanding how amino acid sequences fold into a three-dimensional structure require a lot of computational power since there can be close to an infinite number of solutions. And shape of proteins is important in determining the kinds of interactions that a protein can have. Understanding and, eventually synthesizing proteins requires understanding of both amino acid sequences and of protein shape in space. The idea of the game is to use human brain, and especially people’s love for 3-dimentional puzzles to determine an optimal way in which a given protein would fold. Since there is no true solution, the score is attributed in relation to how much energy is needed to sustain the protein in the shape that you created. The challenge is not to ’solve’ the puzzle, but to beat other players’ scores. The ‘best’ solutions are then analyzed by scientists and, hopefully, human players can figure things out faster than computers.

Players create groups, share solutions and compete agains each other. The principles behind each protein puzzle are simple enough yet challenging: proteins have to be compact, hydrophobic moleculs should stay inside the structure, and all elements have to be apart enough not to clash and to permit biochemical processes to take place.

In the current stage, players solve puzzles concerning proteins shapes of which we already know. The scientists are collecting data hoping that human participants are more effective than computer in determining the optimal shape of protein folding. If they are right, then later new puzzles will tackle proteins that are currently not well understood, and the game will also be potentially useful in designing new proteins, which has implications for virtually all areas where biochemistry is involved: from finding a cure for cancer to lessening the effect of global warming.

Here you can download Foldit (Windows, Mac and Linux).

1. Ashellia, the Female Human Paladin, explains why she hates cinematics in games. I think she’s right on all counts, but stupid melodramatic bits of fluff with rubbish scripts still have the power to move me … dammit.
2. The Escapist wonders why top ten lists are so popular
3. rerollz gives a lesson on PvP Etiquette: how to QQ with class. (QQ means cry more noob, if you haven’t seen that acronym before.)
4. Gamebizblog.com asks whether hardcore gaming websites are drifting too far from the market these days. It turns out that the amount of boxes a game will sell is not necessarily related to the internet buzz.
5. Big Bear Butt Blogger has some great tips for the new tank.
6. Over in Sexy Videogameland (best blog name ever?), Leigh Alexander discusses user-generated content and why she prefers games with a strong direction from the designers, rather than from the other players.
7. Zork gives a step by step guide to making Blizzard icons look cool. People never cease to amaze me. Someone played WoW and thought , “Man, those icons are just uncool!” Isn’t humanity wonderful?
8. Kalon at ThinkTank ponders why on some raid nights, things just fall apart. And he’s not afraid to point fingers.
9. This is a great story that I got from the GameCulture blog about how gamers were recruited to help scientists fold proteins, and beat some of the biochemists at their own game.
10. Game Design Concepts is running a free online course on game design from June til September this year. He notes: By the end of this course, you will be familiar with the (relatively small) body of work that is accepted in the game industry as the theoretical foundation of game design. You will also be comfortable enough in processes to start designing your own games, as well as critically analyzing other people’s games.
11. Unwize asks whether players should be punished for not reading through quest text carefully enough.
12. This is more of a metalink but Tesh has a great collection of game design related links in this post.
13. Remember Zork and all the other text based interactive fiction games? Ever wonder what happened to that genre? They kept going and have a huge indie following. Emily Short is one of the most innovative of the new authors, check out her website for an introduction to the genre. And particularly check out her work.
14. Storybird is a new collaborative storytelling tool. It hasn’t gone live yet but I was impressed with what I saw.

In my presentation at the 2008 IA Summit, I discussed how many human activities can be understood as games, and benefit from adopting their characteristics. When we think of games as being specifically unproductive, we’re missing the opportunity to engage users at a level beyond what can be achieved in more conventional interfaces.

In fact games can serve as catalysts of production. Take fold.it, which is a puzzle game that challenges players to find the best ways to fold proteins. This is in fact among the most difficult problems in modern biology, as a protein can take on very different characteristics depending upon its shape. For example, mad cow disease is caused by proteins that already exist in the body, but which have been folded into irregular shapes that make them agents of the disease.

A screenshot from fold.it

People who play fold.it are actually contributing to science, because the game uses the real physical properties of the proteins as its rules. Players are awarded points for things like reducing the size of the protein efficiently, or turning certain types of molecules so they all face inward. The New York Times notes that it’s plausible that by playing this game, you could actually win a Nobel prize (even if you know nothing of biochemistry).

The real pioneer in the productive use of games, though, is Luis Von Ahn of Carnegie Mellon University. I’ll discuss his work in depth in an upcoming posting.

Everything – I mean everything! has FOUR spacial DIMENSIONS! That’s why proteins like to fold the way they do, why it’s hard to figure out the shortest path from one point to another. We need a new “top down” way of looking at the universe, even the very small like quarks. [that's why quarks seem to disappear - they don't]. That’s where “dark matter” and “dark energy” is hiding — in fact, that’s where MASS IS. That’s where gravity lies. a “higgs field” or “higgs boson particle” is simply the 4th spacial dimension. You won’t find a particle that you can see that you can interact with in our dimension by smashing the atoms, unless you’re lucky, because it’s like being a two dimension being on a piece of paper not knowing you’re on a piece of paper. You scratch the paper’s surface and all this ’stuff’ suddenly magically appears that you didn’t see before. It’s higher dimensional stuff that fell onto your paper (ie – into your dimension). Read on:

The DIGG
http://digg.com/general_sciences/Everything_has_four_spacial_dimensions_plus_time

the ORIGINAL ARTICLE:
http://free.naplesplus.us/articles/view.php/37648/everything-is-four-dimensional-plus-time

This is the latest installment of Science For The Kids.  Every few weeks I try to bring some interesting science entertainment for parents to share with their children.    I was never interested in science as a child because of the way I was taught – too formal and simplified, without context to the world around me.  Perhaps this series will inspire many of you to supplement your child’s classwork with these alternatives.

This weekend is the first annual WORLD SCIENCE FESTIVAL in NYC!  Take your kid!

Squid Labs has a fantastic science site called Instructables, which is kind of like an internet show & Tell science fair.  Basically, the site allows you to upload your project, product modification, science craft, or invention, so that others can learn how to do it themselves.  You can come up with all kinds of fun ideas that are practical, silly, or, just crazy.

Phil Plait, The Bad Astronomer, was asked by his friend, an elementary school teacher, to answer some basic astronomy questions from her students.  Sometimes it’s best to learn from the experts who are most passionate about the subject, and Phil is the perfect astronomer to ask because he has such a wide scope of knowledge on the subject and he cares deeply about science integrity.  Check out the videos here:

Part 1, Part 2, Part 3, Part 4, and Part 5

Put your children (or yourself) on the fast track to winning the nobel prize by introducing them to the new science game Fold It!  This puzzle uses real-world protein molecules and asks you to fold the chain of amino acids and connect them in interesting ways.  There are nearly an infinite number of ways to fold these molecules, and computers have systematically tried many of them, but folding a protein in just the right way may solve some of the world’s most debilitating diseases, such as HIV, cancer, and alzheimers.

Foldit is a protein folding puzzle game based on real life proteins.

Wenn man ein sehr sehr kompliziertes und Aufwändiges Wissenschaftsprojekt per Computer berechnen will – was braucht man dann dafür?

Entweder nimmt baut man dann eine riesige Serverfarm auf, oder man teilt das Projekt in unzählige kleine Stücke und verteilt sie auf viele andere PCs.

Nach der zuletzt genannten Methode geht das BOINC-Programm vor. Nach der Installation von BOINC kann man sich eins oder mehrere der vielen Projekte aussuchen, welche man unterstützen möchte. BOINC verbindet sich dann selbstständig mit den ausgewählten Projekten und lädt sich die zugewiesenen Einheiten (Units) runter. Dabei braucht man nicht mal Angst um einen lahmenden PC zu haben, denn BOINC nutzt nur die ungenutzten PC-Ressourcen. Das heißt, sobald man irgend etwas startet, was viele Ressourcen verbraucht, gibt BOINC diese auch augenblicklich wieder frei. Eine Übersicht aller Projekte findet man unter folgendem Link.

Wenn ihr euch nun entschieden habt auch Crunsher bei BOINC zu werden könnt ihr hier eine Installationsanleitung finden. (Ab Punkt 2.)

Ich persönlich lasse BOINC gerade für das World Community Grid laufen. Auch empfehlenswert ist Rosetta@Home, bei welchem ich auch schon gerechnet habe. Bei dem Projekt geht es um Protein Folding, die Erforschung der 3D-Struktur der Proteine. (Dadurch könnten Krankheiten wie etwa Krebs verhindert werden).

Viel Spaß

~Wegi

又賣廣告！

請支持Team HongKong (Team ID 32535)。按這裡看看參加辨法。PS3參加辨法在這裡。無論你的是Windows, Linux, Mac or PS3，都要支持我地呀！

「Folding@home」是由美國史丹佛大學所主導的分散式運算研究計畫，透過電腦的模擬運算來研究蛋白質折疊，誤折，聚合，藉此解開包括阿茲海默症、狂牛病、帕金森氏症…等疾病的成因與療法。Folding@home 透過網路將模擬所需的計算分散到參與計畫的電腦上執行，原先支援的平台包括 PC Windows、Mac OS、Linux 等。後續史丹佛大學與 SCE 合作，於 2007年3 月 22 日推出 PS3 版終端軟體，藉由 PS3 Cell 微處理器的獨特架構與高浮點運算效能特性，可提供較一般 PC 高出 10 倍以上的運算資源。

根據 SCE 方面的資料，截至 4 月 26 日止，已有超過 25 萬 PS3 使用者參加此一計畫，提供超過每秒 400 兆次浮點數運算（TeraFLOPS）的運算資源，使得該計畫瞬間總運算能力超越每秒 700 兆次浮點數運算，是 PS3 加入前的 2 倍以上。因 PS3 加入所產生的連鎖效果，也讓參與的 PC 數量增加了 20 ％。

A friend of mine is getting ready to do some experiments involving purified human proteins expressed in E. coli, and she asked me if I knew anything about protein misfolding – apparently, proteins sometimes misfold when expressed in foreign vectors such as E. coli. Unfortunately, I didn’t, but a Google search hit brought up an explanation that’s really not that surprising when you think about it, and has to do with the fact that many proteins fold correctly only with the help of chaperone proteins or cofactors. Obviously, this can be a big problem for an experimentalist who wants to get usable amounts of a specific, correctly folded protein.

Does anyone know where to find good information about this problem or have suggestions for how to get around it (with or without changing vectors – I’m not sure if E.coli is a crucial part of the study or not)? The document I linked has some solutions but I’m wondering if there are any resources or “easy” tips out there I can forward along.

Les consoles de videojocs poden tenir una altra utilitat diferent de la lúdica i segurament impensada per molts dels seus propietaris. Es poden convertir en petites parts d’un super ordinador que permeti dur a terme una recerca científica amb uns objectius molt ambiciosos i assolir-los en un temps molt més curt.
Això és el que fa el projecte Folding@home, de la Universitat de Stanford, amb les consoles de videojocs PlayStation 3, encara que també utilitzen ordinadors personals. Simplement cal baixar i instal·lar un petit programa que aprofita els recursos d’aquests dispositius que cada usuari no utilitza i els fa servir per fer les simulacions d’investigació del projecte.
La recerca d’aquest projecte està dedicada a la conformació de proteïnes i les conseqüències que es produeixen quan aquest procés no es realitza correctament. Aquestes conseqüències prenen forma de malalties molt greus com ara l’Alzheimer, el Parkinson, la malaltia de Creutzfeldt-Jakob o l’Esclerosi Lateral Amiotròfica, així com alguns tipus de càncer.
Des que Sony, el fabricant d’aquestes consoles, va entrar a formar part d’aquest projecte, el mes de març de l’any passat, més d’un milió d’usuaris s’han enregistrat i s’han baixat el programa per poder contribuir-hi.
Un exemple més de col·laboració, socialització o distribució de recursos per assolir una fita comuna, encara que no és el primer del què us parlem en aquest blog: recordeu PatientsLikeMe.

I got to listen to a talk today given by Dave Baker, one of the big names in the protein folding field, and I was not disappointed. There’s a lot of potential material from today’s talk that would be good subject matter for this blog, and I’ll no doubt post more about folding in the future, but the highlight of the talk was FoldIt!

Before I get into too much detail about FoldIt specifically there are a couple things about protein folding I glossed over in the last post. In computer modeling of protein folding, there are usually two major problem areas – sampling and scoring.

For a given protein, we know the fundamental building blocks (amino acids) that compose it and the order in which they are connected – the whole problem of protein folding is how that linear chain “curls up” (i.e. folds) in 3D space. If we were to take every potential position and assign it a score (sometimes called the energy score) we could build a gigantic scoring landscape. Imagine the Grand Canyon: every potential spot you could stand represents a particular conformation (i.e amino acid A is a certain distance and certain angle relative to B) and your elevation represents how good that conformation’s score is (let’s say the lower you are the better). Any step you took in one direction would represent slightly changing the conformation (maybe you pull a certain bond a little further apart) and if you had to walk uphill to that new position your changes would be bad, while walking downhill would be good. A good folding algorithm will try and walk down the canyon as far as possible until it finds a position where every possible next step would take it uphill (once again I’m oversimplyfing for clarity, but this is the general idea).

What are sampling and scoring in this analogy? Scoring is basically your ability to recapitulate the Grand Canyon. That is, if you built a computer model of the canyon, your map is going to be of limited resolution (depending on the manner in which it was built) and will not quite recapitulate the real Grand Canyon. I’m not really going to talk much about scoring this post so even if it doesn’t make sense, read on. Sampling is easier to think about – it’s basically how much of the Grand Canyon you are able to visit. If you walk across the whole canyon you can be sure that your lowest point is the actual lowest point in the entire canyon since you walked across the whole damn canyon. Had you walked only 50% of it, you may have found a place in the canyon that’s pretty low , but that unwalked portion contains an even lower point. Sampling is a typically computational intense process – as fast as computers are today, it takes a long time calculate the score for every position. Furthermore you can always take smaller steps between points (think of someone with a large stride as compared to someone with a small stride…the large strider may “stride” past and miss a pathway the small strider will see), so there’s really an infinite amount of points to sample.

Now one way to approach the problem of sampling is by increasing computing power. The most famous example of this is SETI@home, or Folding@home mentioned yesterday. There’s a similar program for called rosetta@home, which is developed by the Baker lab. “rosetta” refers to the program which makes the calculation to assign a score for a given conformation. Simply put, rosetta@home is a screen saver which uses sophisticated algorithms to move around amino acid side chains and try to pack the 3D protein structure into its lowest energy (i.e. most stable) state. By installing rosetta@home you donate your computer’s idle time to performing these calculations which are then communicated back to the Baker lab.

This all well and good, but the Baker lab went even one step further and developed FoldIt! – a computer game similar to rosetta@home. I like to think of FoldIt as the Web 2.0 approach to protein folding (Web 2.0 is a poorly defined keyword first introduced by Tim O’Reilly but which generally refers to harnessing the power of the collective to accomplish tasks – think of wikipedia, digg, or youtube). FoldIt! is a computer game that allows you to change the 3d structure of the protein by moving parts of it around, while rosetta scores your conformation on the fly. Basically instead of having the computer decide which path to take down the canyon, you’re able to run haphazardly around and try to find the lowest point on your own. The great thing is you don’t need to understand anything about protein folding, all you need to do is understand that you need to move thing around and watch your score go up. You’ll quickly realize certain obvious things – having parts of the protein overlap is bad (steric clash in scientific terms) and fitting things into empty space is good, but all you honestly need to do is look at the score and try to make it go up by whatever means necessary. Your scores are submitted back to the Baker lab website and compared against all other players’ scores. Whereas rosetta@home uses the idle time of thousands of computers, FoldIt! uses the idle time of thousands of people and their computers, making it potentially even more powerful.

In an email to students and faculty before his talk, Dave described FoldIt like this:

“We are developing a multiplayer interactive protein folding and design game for both education and research-our hope is that large groups of people interacting with computers and with each other through the multiplayer game may be able to solve hard optimization problems that neither computers nor people can solve alone. Please help us to test and improve the game! “

If we go back to the canyon analogy, rosetta@home is like having thousands of people walking the canyon so you can accomplish the task faster, but the manner in which they walk is still pretty systematic and similar. That is if there are valleys that look like a lot like the bottom of the canyon but aren’t actually (local minima), the similar nature of everyone’s walking manner will cause people to still end up there. In FoldIt! people are running all over the canyon – some are flapping their arms, some are walking backwards – a lot of energy and effort is wasted, but the naivety of the approach frees it from harmful biases and allows hidden paths that were never known before to be discovered. It’s entirely possible that analysis of human generated structures will reveal a few key rules that algorithms were missing entirely – these rules could be folded into new algorithms and would hopefully allow rosetta to generate a better model in the same amount of sampling.

I think FoldIt! is a great idea, and it’ll be interesting to see if it leads to any new ways to thinking about the folding problem. I think this sort of approach is good for areas where we’re still fundamentally unsure of the best ways to approach the problem – the utility might be more limited in fields where we have a pretty good idea of what we’re doing.

I should note that Dave did not present this as a 2.0 approach and was much more bullish on its education prospects (although he was hopeful for its research utility), and that this “naive 2.0 approach” is my own interpretation of his project, but I think the idea itself is something that might have real application in other fields.

To download FoldIt! go here, the game is not yet live (they’re aiming for early next year), but there are playable puzzles and a real time leaderboard.

As this blog reflects an intersection of my interests in technology and general science, I figured an ideal topic to start off with would be the Protein Folding Problem.

You’ve probably heard about the protein folding problem, although you may not have realized it. The most common references online to protein folding usually involve Folding@home (a distributed computing approach to the problem similar to SETI@home). Usually these references are entitled “Use your PS3 to cure cancer” or some other such overstatement. While there are no doubt real implications to understanding protein folding (including possible cancer therapeutics), such overstatements aren’t helpful in understanding exactly what you’re making your PS3 do.

So what is the protein folding problem? In the simplest terms it’s the manner in which a protein adopts its 3D conformation. If you can remember back to basic high school biology you might recall that the central dogma of biology is this:

DNA->RNA->protein

DNA is the set of instructions for making everything in a cell. RNA is an intermediate, essentially a specific subset of the instructions in your DNA that is then assembled into a protein (the biologists amongst you may take exception from this oversimplification, but for now we’ll stick with it) . You may know of proteins as they pertain to your diet, but in actuality proteins are much more- they are the molecular workhorses of the cell. Most of the chemistry and processes carried out in a cell are done by proteins. These include things like breaking down your food into energy, or recognizing viral particles. In the cell, proteins get stuff done.

Ok so what’s this whole folding business? Well proteins are composed of 20 basic building blocks called amino acids. String these amino acids together in a linear chain and you get a protein. In biology, these amino acids are represented by single letters; for instance DYKDDDDK represents an 8 amino acid protein starting with D and ending with K. Because amino acids are (essentially) the only components of proteins every protein can be represented by sequence of letters. Some proteins are very large (hundreds of letters) while others are small (like the 8 letter protein above) – given that there are 20 naturally occurring amino acid possibilities at each point and the unbounded size of the sequence there is an infinte number of potential protein sequences.

Now of course the human genome is finite, and you might recall we’ve gone ahead and sequenced the whole thing - and we’ve gotten pretty good at determining what parts of the DNA are actually turned into protein. So essentially we know what almost all the proteins in your cells are – at least on the level of these string-like representations. However your proteins do stuff based on their actual 3D structure. That is, when I write DYKDDDDK that doesn’t mean there’s a happy trail of letters in your body wandering about the cell and carrying out their business. Rather each letter represents a specific chemical structure. D for instance is Aspartic acid which looks like this:

Y is Tyrosine which looks like this:

You can string them together and see what DY looks like in 2D but of course the world is actually in 3D, and each of those oxygens, carbons, etc occupy a position in space relative to one another.

And therein lies the protein folding problem. How do you translate an amino acid sequence (i.e. DYDDDDK) into a three dimensional structure? Well it’s not an easy problem, as there aren’t a clear set of rules – the position of each part of each amino acid can be influenced by a whole host of factors including the amino acids neighboring it, the presence of water, and many other things. You can start to see that the problem begins to get very complex, much too complex for a mere human to think about.

Enter the computers. Looking at 3D structures of proteins that people have determined experimentally, there’s no clear set of rules (although some trends become evident) – however this is science and there are theoretically some set of axioms we started with (yes, even in biology). Things like the electric charge of these amino acids (things that are positive will be attracted to things that are negative, neutral things will want to pack in the interior and hide from charged water molecules) are known and can be used to calculate energy maps for specific conformations. You can do multiple iterations and find the most stable (i.e. lowest energy) conformation and use it as your predicted protein structure. This is essentially what Folding@home is doing (at least this is one approach to the problem, and the one I believe that Folding@home is taking). You can even do this for known structures and see how good your algorithm is at returning the known structure.

So why does anyone care what a protein looks like in 3D? Well the 3D structure is important for understanding the function of the protein and that’s a very important thing to understand. If you protein is a drug target (say an HIV protein) knowing its 3D structure could help you design a drug that binds a specific structure on the protein and thereby inhibit its function (i.e. viral replication). A lot of diseases are the results of misfolded proteins (cystic fibrosis, Mad Cow Disease) so understanding the folding process itself is an interesting thing.

So there you have it a basic introduction to the protein folding problem and one computational approach to it. There are other approaches as well, and I’ve definitely oversimplified things. If you’re curious I suggest the Folding@home website, wikipedia, or a simple google search to explore further.