Abstract
Based on the development of new algorithms and growth of sequence databases, it has recently become possible to build robust higher-order sequence models based on sets of aligned protein sequences. Such models have proven useful in de novo structure prediction, where the sequence models are used to find pairs of residues that co-vary during evolution, and hence are likely to be in spatial proximity in the native protein. The accuracy of these algorithms, however, drop dramatically when the number of sequences in the alignment is small. We have developed a method that we termed CE-YAPP (CoEvolution-YAPP), that is based on YAPP (Yet Another Peak Processor), which has been shown to solve a similar problem in NMR spectroscopy. By simultaneously performing structure prediction and contact assignment, CE-YAPP uses structural self-consistency as a filter to remove false positive contacts. Furthermore, CE-YAPP solves another problem, namely how many contacts to choose from the ordered list of covarying amino acid pairs. We show that CE-YAPP consistently improves contact prediction from multiple sequence alignments, in particular for proteins that are difficult targets. We further show that the structures determined from CE-YAPP are also in better agreement with those determined using traditional methods in structural biology.
Highlights
A large and recent increase in known protein sequences has sparked an interest in using the multiple sequence alignments (MSAs) of protein families to predict native contacts in globular proteins[1], membrane proteins[2,3], as well as predicting contacts in protein-protein interfaces[4,5]
Our results show that CE-YAPP provides an effective solution to the problem of both finding a useful number of contacts and filtering false positive (FP) in a noisy prediction
We developed CE-YAPP which achieves this goal by taking an automatically chosen set of long-range predicted co-evolution contacts (PCCs) and identifying the FP contacts within these
Summary
A large and recent increase in known protein sequences has sparked an interest in using the multiple sequence alignments (MSAs) of protein families to predict native contacts in globular proteins[1], membrane proteins[2,3], as well as predicting contacts in protein-protein interfaces[4,5]. Even with many sequences and conservative choices for how many contacts to use, one generally ends up with a number of false positive (FP) predictions, i.e. pairs of residues that show some level of coevolution, but are not in close proximity in the three-dimensional structure In practical applications, these two problems are tightly related: One would like to include as many contacts as possible to restrain the three dimensional structure, but at the same time risk including many FPs. For example, one would on average expect ~5 of the top 20 (i.e. 25%) coevolving pairs of residues to be FPs for a 100-residue long protein with an MSA with 500 sequences, increasing to ~20 of the top 50 (40%) coevolving pairs to be FPs. For the same protein, provided only 100 sequences, one would on average expect ~8 of to top 20 For the same protein, provided only 100 sequences, one would on average expect ~8 of to top 20 (i. e. 40%) increasing to ~28 of the top 50 (55%) coevolving pairs to be FPs18
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