ArticleProceedings of the National Academy of Sciences of the United States of America2019
Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 43 citations in OpenAlex.
- Deep learning guided design of protease substrates.Nature communications · 2026Article
- Article
- Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.Biomolecules · 2024Review
- Leveraging yeast sequestration to study and engineer posttranslational modification enzymes.Biotechnology and bioengineering · 2024Review
- Modular and integrative activity reporters enhance biochemical studies in the yeast ER.Protein engineering, design & selection : PEDS · 2024Article
- Advances in ligand-specific biosensing for structurally similar molecules.Cell systems · 2023Review
- Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network.bioRxiv : the preprint server for biology · 2023Article
- Profiling Substrate Specificity of the SUMO Protease Ulp1 by the YESS-PSSC System to Advance the Conserved Mechanism for Substrate Cleavage.International journal of molecular sciences · 2022Article
- Article
- Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning.ACS central science · 2022Article
- LONGL-Net: temporal correlation structure guided deep learning model to predict longitudinal age-related macular degeneration severity.PNAS nexus · 2022Article
- Making the cut with protease engineering.Cell chemical biology · 2022Review
- Structure-based prediction of HDAC6 substrates validated by enzymatic assay reveals determinants of promiscuity and detects new potential substrates.Scientific reports · 2022Article
- Protein-Based Systems for Translational Regulation of Synthetic mRNAs in Mammalian Cells.Life (Basel, Switzerland) · 2021Review
- Data-driven computational protein design.Current opinion in structural biology · 2021Review
- Protein nanomechanics in biological context.Biophysical reviews · 2021Review
- Structural basis for peptide substrate specificities of glycosyltransferase GalNAc-T2.ACS catalysis · 2021Article
- An automated protocol for modelling peptide substrates to proteases.BMC bioinformatics · 2020Article
- Learning peptide recognition rules for a low-specificity protein.Protein science : a publication of the Protein Society · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.