3 ms·

> If CASP is calling methods that use any sequence similarity (the grey area) 'ab initio', that's disingenuous and intellectually dishonest. The category is gi

by timr 5y ago

> If CASP is calling methods that use any sequence similarity (the grey area) 'ab initio', that's disingenuous and intellectually dishonest. The category is given the name, not the methods. People can use any method they like to solve the structures. The organizers are not zealots. The ab initio portion of CASP consists of proteins that the organizers know have low sequence identity to anything in the existing databases. They represent proteins that are "difficult" to solve using what any practitioner might call homology modeling. That doesn't mean that you can't use a method that takes into account the biological databases -- and essentially all of the good methods do! For example, the Rosetta method has competed in both the homology modeling and the ab initio categories for many years. They mix a bit of both -- using homology models to get the fold, and fragment insertion to model the floppy bits. I haven't paid close attention to CASP in a long time, but I assume the competitor list still has tons of entries from people who cling tightly to the purist vision of ab initio modeling. They don't tend to do very well.

dekhn 5y ago
OK, be aware the person you're correcting has: competed in CASP (on a competitor team with Sali), and published papers with Baker on Rosetta methods (my paper is cited in the most RoseTTA paper). "They mix a bit of both -- using homology models to get the fold, and fragment insertion to model the floppy bits." that's the best description of what I believe AF2 is doing, but that AF2 is being marketed as not depending on any sequence similarity. If the CASP folks really are saying "if you have 20% sequence identity and use the structure from that alignment it's ab initio"... that's really just totally misleading. Of course, even ab initio methods are parameterized on biological information; for example, I used AMBER to do MD simulations and many of the force field terms were determined using spectroscopic data from fragments of biological models. That, however is ab initio, because nothing even as large as a single amino acid is parameterized. I'm not saying there's anything wrong with homology modelling, or that the purist vision of ab initio is right. For practical purposes, exploiting subtle structural information through sequence alignment is a very nice way to save enormous amounts of computer time.
- timr 5y ago
  > OK, be aware the person you're correcting has: competed in CASP (on a competitor team with Sali), and published papers with Baker on Rosetta methods (my paper is cited in the most RoseTTA paper). OK, great. Me too. I'm not saying anything controversial here. Right from the top of the "ab initio" tab on predictioncenter.org: "Modeling proteins with no or marginal similarity to existing structures (ab initio, new fold, non-template or free modeling) is the most challenging task in tertiary structure prediction."
  - dekhn 5y ago
    I think the more important question to resolve here is: did AlphaFold change anything with respect to structure prediction that enabled them to make accurate predictions in the complete absence of sequence similarity to proteins with known structure? My understanding is no, they did the equivalent of template modelling, which uses sequence/structure relationships (that are more subtle than the ones you get from homology modelling). I'm less interested in reconciling my internal mental model of psp wiht CASPs, than I am in understanding if AF2 is somehow able to get all the necessarily structural constraints through coevolution of amino acid pairs entirely without some (direct or indirect) learned relationship between the sequence similarity to known structures (be it even short fragments like helices). If they really did do that, and nobody did it before, that's great and I will happily promote the DM work, as it supports what I said when I did CASP: ML and MD will eventually win, although in a way that exploits the rich sequence evolutionary information we have, rather than predominantly by having an accurate force field and good smapling methods.

by timr 5y ago

dekhn 5y ago

OK, be aware the person you're correcting has: competed in CASP (on a competitor team with Sali), and published papers with Baker on Rosetta methods (my paper is cited in the most RoseTTA paper). "They mix a bit of both -- using homology models to get the fold, and fragment insertion to model the floppy bits." that's the best description of what I believe AF2 is doing, but that AF2 is being marketed as not depending on any sequence similarity. If the CASP folks really are saying "if you have 20% sequence identity and use the structure from that alignment it's ab initio"... that's really just totally misleading. Of course, even ab initio methods are parameterized on biological information; for example, I used AMBER to do MD simulations and many of the force field terms were determined using spectroscopic data from fragments of biological models. That, however is ab initio, because nothing even as large as a single amino acid is parameterized. I'm not saying there's anything wrong with homology modelling, or that the purist vision of ab initio is right. For practical purposes, exploiting subtle structural information through sequence alignment is a very nice way to save enormous amounts of computer time.

timr 5y ago
> OK, be aware the person you're correcting has: competed in CASP (on a competitor team with Sali), and published papers with Baker on Rosetta methods (my paper is cited in the most RoseTTA paper). OK, great. Me too. I'm not saying anything controversial here. Right from the top of the "ab initio" tab on predictioncenter.org: "Modeling proteins with no or marginal similarity to existing structures (ab initio, new fold, non-template or free modeling) is the most challenging task in tertiary structure prediction."
- dekhn 5y ago
  I think the more important question to resolve here is: did AlphaFold change anything with respect to structure prediction that enabled them to make accurate predictions in the complete absence of sequence similarity to proteins with known structure? My understanding is no, they did the equivalent of template modelling, which uses sequence/structure relationships (that are more subtle than the ones you get from homology modelling). I'm less interested in reconciling my internal mental model of psp wiht CASPs, than I am in understanding if AF2 is somehow able to get all the necessarily structural constraints through coevolution of amino acid pairs entirely without some (direct or indirect) learned relationship between the sequence similarity to known structures (be it even short fragments like helices). If they really did do that, and nobody did it before, that's great and I will happily promote the DM work, as it supports what I said when I did CASP: ML and MD will eventually win, although in a way that exploits the rich sequence evolutionary information we have, rather than predominantly by having an accurate force field and good smapling methods.