A quantitative high-resolution genetic profile rapidly identifies sequence determinants of hepatitis C viral fitness and drug sensitivity.
Hangfei Qi,C. Anders Olson,Nicholas C. Wu,Ruian Ke,Claude Loverdo,Virginia Chu,Shawna Truong,Roland Remenyi,Zugen Chen,Yushen Du,Sheng Yao Su,Laith Q. Al-Mawsawi,Ting-Ting Wu,Shu Hua Chen,Chung Yen Lin,Weidong Zhong,James O. Lloyd-Smith,Ren Sun +17 more
TLDR
This high-resolution profiling methodology will be useful for next-generation drug development to select drugs with higher fitness costs to resistance, and also for informing the rational use of drugs based on viral variant spectra from patients.Abstract:
Widely used chemical genetic screens have greatly facilitated the identification of many antiviral agents. However, the regions of interaction and inhibitory mechanisms of many therapeutic candidates have yet to be elucidated. Previous chemical screens identified Daclatasvir (BMS-790052) as a potent nonstructural protein 5A (NS5A) inhibitor for Hepatitis C virus (HCV) infection with an unclear inhibitory mechanism. Here we have developed a quantitative high-resolution genetic (qHRG) approach to systematically map the drug-protein interactions between Daclatasvir and NS5A and profile genetic barriers to Daclatasvir resistance. We implemented saturation mutagenesis in combination with next-generation sequencing technology to systematically quantify the effect of every possible amino acid substitution in the drug-targeted region (domain IA of NS5A) on replication fitness and sensitivity to Daclatasvir. This enabled determination of the residues governing drug-protein interactions. The relative fitness and drug sensitivity profiles also provide a comprehensive reference of the genetic barriers for all possible single amino acid changes during viral evolution, which we utilized to predict clinical outcomes using mathematical models. We envision that this high-resolution profiling methodology will be useful for next-generation drug development to select drugs with higher fitness costs to resistance, and also for informing the rational use of drugs based on viral variant spectra from patients.read more
Citations
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Mutation effects predicted from sequence co-variation.
Thomas A. Hopf,John Ingraham,Frank J. Poelwijk,Charlotta P I Schärfe,Charlotta P I Schärfe,Michael Springer,Chris Sander,Debora S. Marks +7 more
TL;DR: This work presents EVmutation, an unsupervised statistical method for predicting the effects of mutations that explicitly captures residue dependencies between positions and shows that it outperforms methods that do not account for epistasis.
Journal ArticleDOI
Deep generative models of genetic variation capture the effects of mutations.
TL;DR: DeepSequence is an unsupervised deep latent-variable model that predicts the effects of mutations on the basis of evolutionary sequence information that is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space.
Journal ArticleDOI
A Comprehensive Biophysical Description of Pairwise Epistasis throughout an Entire Protein Domain
TL;DR: The stability analysis shows that although significant positive epistasis is rare, many deleterious mutations are beneficial in at least one alternative mutational background, and the distribution of conditionally beneficial mutations throughout the domain demonstrates that the functional portion of sequence space can be significantly expanded by epistasis.
Journal ArticleDOI
Evolutionary consequences of drug resistance: shared principles across diverse targets and organisms
Diarmaid Hughes,Dan I. Andersson +1 more
TL;DR: Commonalities and differences related to resistance development that could guide strategies to improve therapeutic effectiveness and the development of a new generation of drugs are described.
Journal ArticleDOI
Adaptation in protein fitness landscapes is facilitated by indirect paths
TL;DR: It is found that while reciprocal sign epistasis blocked many direct paths of adaptation, such evolutionary traps could be circumvented by indirect paths through genotype space involving gain and subsequent loss of mutations, suggesting that the heretofore neglected dimensions of sequence space may change the authors' views on how proteins evolve.
References
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TL;DR: This document has been approved by the AASLD, the Infectious Diseases Society of America, and the American College of Gastroenterology.
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Complete Replication of Hepatitis C Virus in Cell Culture
Brett D. Lindenbach,Brett D. Lindenbach,Brett D. Lindenbach,Matthew J. Evans,Matthew J. Evans,Matthew J. Evans,Andrew J. Syder,Andrew J. Syder,Andrew J. Syder,Benno Wölk,Benno Wölk,Benno Wölk,Timothy L. Tellinghuisen,Timothy L. Tellinghuisen,Timothy L. Tellinghuisen,Christopher C. Liu,Christopher C. Liu,Christopher C. Liu,Toshiaki Maruyama,Toshiaki Maruyama,Toshiaki Maruyama,Richard O. Hynes,Richard O. Hynes,Richard O. Hynes,Dennis R. Burton,Dennis R. Burton,Dennis R. Burton,Jane A. McKeating,Jane A. McKeating,Jane A. McKeating,Charles M. Rice,Charles M. Rice,Charles M. Rice +32 more
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