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Proc Natl Acad Sci U S A. 2019 Jan 2;116(1):168-176. doi: 10.1073/pnas.1805256116. Epub 2018 Dec 26.

Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations.

Author information

1
Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854.
2
Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ 08854.
3
Computational Biology & Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ 08854.
4
Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854.
5
Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854; sagar.khare@rutgers.edu.

Abstract

Biophysical interactions between proteins and peptides are key determinants of molecular recognition specificity landscapes. However, an understanding of how molecular structure and residue-level energetics at protein-peptide interfaces shape these landscapes remains elusive. We combine information from yeast-based library screening, next-generation sequencing, and structure-based modeling in a supervised machine learning approach to report the comprehensive sequence-energetics-function mapping of the specificity landscape of the hepatitis C virus (HCV) NS3/4A protease, whose function-site-specific cleavages of the viral polyprotein-is a key determinant of viral fitness. We screened a library of substrates in which five residue positions were randomized and measured cleavability of ∼30,000 substrates (∼1% of the library) using yeast display and fluorescence-activated cell sorting followed by deep sequencing. Structure-based models of a subset of experimentally derived sequences were used in a supervised learning procedure to train a support vector machine to predict the cleavability of 3.2 million substrate variants by the HCV protease. The resulting landscape allows identification of previously unidentified HCV protease substrates, and graph-theoretic analyses reveal extensive clustering of cleavable and uncleavable motifs in sequence space. Specificity landscapes of known drug-resistant variants are similarly clustered. The described approach should enable the elucidation and redesign of specificity landscapes of a wide variety of proteases, including human-origin enzymes. Our results also suggest a possible role for residue-level energetics in shaping plateau-like functional landscapes predicted from viral quasispecies theory.

KEYWORDS:

machine learning; molecular modeling; protease; sequence−function mapping; substrate specificity

PMID:
30587591
PMCID:
PMC6320525
[Available on 2019-07-02]
DOI:
10.1073/pnas.1805256116

Conflict of interest statement

The authors declare no conflict of interest.

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