[HN Gopher] Improved protein structure prediction using potentia...
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Improved protein structure prediction using potentials from deep
learning
Author : lawrenceyan
Score : 27 points
Date : 2020-02-21 20:31 UTC (2 hours ago)
(HTM) web link (www.nature.com)
(TXT) w3m dump (www.nature.com)
| allovernow wrote:
| Paywalled, can anyone with access to the text expand upon the ML
| algorithm? Specifically, is AlphaFold based on AlphaGo MCTS? Or
| is the name similarity incidental?
| asparagui wrote:
| not paywalled, try this link: https://rdcu.be/b0mtx
|
| just a naming similarity, the common part is a pun that is a
| popular google naming scheme, eg. alpha-bet.
| deepnotderp wrote:
| Literally no similarity, it's an IBM Watson like branding move
| lawrenceyan wrote:
| Predicting protein structure based on a given DNA/RNA sequence
| has been a field of study that has existed for quite a while now.
| There have been two primary methodologies that have been
| explored, one of which has been to try and simulate the actual
| physical dynamics of a given system at a molecular/atomic level.
| At places like D.E. Shaw or with Folding@Home, you'll see
| approaches like these being taken and with relative success.
| Though generally with purely physics based solutions, you quickly
| run into exponentially growing simulation time scales as well as
| a lack of accuracy due to a currently insufficient understanding
| of molecular mechanics.
|
| The other approach has been to take the problem and look at it
| purely as a translation problem, ignoring simulation of
| intermediary steps, to go directly from sequence to folded
| protein target.
|
| With the advent of deep learning and a massive repository of data
| from an existing Protein Data Bank (PDB) et al., this approach
| has become increasingly popular, and for protein structure
| competitions like CASP, has quickly become state of the art
| within the field. DeepMind's recent breakthrough with AlphaFold
| in the paper above is just another solid step in the right
| direction.
| dekhn wrote:
| PDB is not a massive repository. It's a very biased, tiny
| dataset (~20K structures) and an enormous amount of data
| cleaning has to be done to do anything related to big-data
| machine learning on it.
|
| What's far more important is evolutionary data- for example,
| making alignments of many similar proteins, and computing
| correlated variations across them. Those variations are often
| the best structural clues-- better and cheaper to obtain than
| protein structures.
|
| I wouldn't really call DM's work a "breakthrough", other groups
| were exploring similar ideas. DM executed well (they're a games
| company and understand the rules of the competition) and had a
| huge amount of compute resources which handles a lot of the
| challenges of optimizing a process like this.
| lawrenceyan wrote:
| My summary is pretty generalized, aimed more towards a layman
| audience, and so I definitely am missing pieces. Co-
| evolutionary couplings between different protein sequences
| provide a very rich source of information, and are definitely
| very important!
|
| For those of you that are curious as to what these couplings
| represent, the basic idea, is that intuitively you can sort
| of see how given proteins are a product of evolution, that
| they're might be a large amount of conserved structure
| between one protein to another. Co-evolutionary coupling is
| just an attempt at quantifying this relationship in a
| rigorous statistical manner.
| dekhn wrote:
| I mainly don't want ML folks to suddenly think protein
| folding is easy because the PDB is a good training set.
| It's not.
| [deleted]
| lawrenceyan wrote:
| Why frame things in such a pessimistic manner? It seems
| like it would only be a net benefit to have more people
| become interested in protein folding as a field of study.
| Is there really a need for this type of gate keeping
| here?
| dekhn wrote:
| yes, personally I think there is. I've spent a tremendous
| amount of my career watching computer scientists
| misunderstand how to work on protein folding and waste a
| lot of people's time. Because the concept of protein
| folding is so unbelievably complex, most CS and ML folks
| get the basic talk: "nearly all proteins fold reversibly
| to a global minimum energy structure which is completely
| defined by the sequence of the protein", which isn't
| remotely true (basically a weak form of Anfinsen's dogma
| and Levinthal's paradox). It's easy to explain, and CS
| and ML people get excited and go off to work on the
| problem. This led to a lot of publications that focused
| on rapidly finding heuristics that could sample enough
| space to find an approximation of the lowest energy
| structure. these methods typically failed to make good
| predictions although eventually methods like Rosetta did
| start making good predictions around 15-20 years ago
| (amusingly, the author of Rosetta told me: "the larger
| the PDB (training data set) gets, the worse the
| predictions we make".
|
| But people who spend a long time getting a biological
| education know why this is true: most proteins don't fold
| to their energetic minimum, they fold to a collection of
| kinetically accessible states, rarely finding their true
| minimum (some small proteins do fold quickly, and we
| typically can predict their structure). And, many of the
| physical approximations that are used lead to
| inaccuracies (for example, some variables are constrained
| to specific values to save time, but making good
| predictions requires them to be unconstrained).
|
| Some of my work made significant contributions to
| changing these beliefs, and I've very thankful for the CS
| and ML folks who contributed to that, but all of them
| spent a lot of time learning about proteins before they
| were useful contributors.
|
| Myself I've had to "unlearn" a lot of the early things
| that were explained to me when I was a layperson, because
| when you're first learning something, if somebody gives
| you a simplified view, it can be really hard to move on
| to the more subtle and nuanced details in the field (for
| example, many people learn Mendelian genetics and then
| spend years struggling to understand why most traits
| don't follow mendelian statistics).
|
| My goal here is to prevent wasted time on behalf of the
| experienced contributors in the field. I do appreciate
| good attempts at explaining the field to laypeople, but
| want to set the expectations on contributing accurately.
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