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Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

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  • Marcin J Skwark
  • Daniele Raimondi
  • Mirco Michel
  • Arne Elofsson

Abstract

Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for β-sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction.Author Summary: Here, we introduce a novel protein contact prediction method PconsC2 that, to the best of our knowledge, outperforms earlier methods. PconsC2 is based on our earlier method, PconsC, as it utilizes the same set of contact predictions from plmDCA and PSICOV. However, in contrast to PconsC, where each residue pair is analysed independently, the initial predictions are analysed in context of neighbouring residue pairs using a deep learning approach, inspired by earlier work. We find that for each layer the deep learning procedure improves the predictions. At the end, after five layers of deep learning and inclusion of a few extra features provides the best performance. An improvement can be seen for all types of proteins, independent on length, number of homologous sequences and structural class. However, the improvement is largest for β-sheet containing proteins. Most importantly the improvement brings for the first time sufficiently accurate predictions to some protein families with less than 1000 homologous sequences. PconsC2 outperforms as well state of the art machine learning based predictors for protein families larger than 100 effective sequences. PconsC2 is licensed under the GNU General Public License v3 and freely available from http://c2.pcons.net/.

Suggested Citation

  • Marcin J Skwark & Daniele Raimondi & Mirco Michel & Arne Elofsson, 2014. "Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-14, November.
  • Handle: RePEc:plo:pcbi00:1003889
    DOI: 10.1371/journal.pcbi.1003889
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    References listed on IDEAS

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    1. Lukas Burger & Erik van Nimwegen, 2010. "Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments," PLOS Computational Biology, Public Library of Science, vol. 6(1), pages 1-18, January.
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    1. Pedro L Teixeira & Jeff L Mendenhall & Sten Heinze & Brian Weiner & Marcin J Skwark & Jens Meiler, 2017. "Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-24, May.
    2. Sheng Wang & Siqi Sun & Zhen Li & Renyu Zhang & Jinbo Xu, 2017. "Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-34, January.
    3. Erik Aurell, 2016. "The Maximum Entropy Fallacy Redux?," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-7, May.
    4. Tatjana Braun & Julia Koehler Leman & Oliver F Lange, 2015. "Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-20, December.

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