Many high-throughput experimental technologies have been developed to assess the effects of large numbers of mutations (variation) on phenotypes. However, designing functional assays for these methods is challenging, and systematic testing of all combinations is impossible, so robust methods to predict the effects of genetic variation are needed. Most prediction methods exploit evolutionary sequence conservation but do not consider the interdependencies of residues or bases. We present EVmutation, an unsupervised statistical method for predicting the effects of mutations that explicitly captures residue dependencies between positions. We validate EVmutation by comparing its predictions with outcomes of high-throughput mutagenesis experiments and measurements of human disease mutations and show that it outperforms methods that do not account for epistasis. EVmutation can be used to assess the quantitative effects of mutations in genes of any organism. We provide pre-computed predictions for ∼7,000 human proteins at http://evmutation.org/.

/pdf/mutation-effects-predicted-from-sequence-co-variation-59oew9srdw.pdf

Mutation effects predicted from sequence co-variation.

The functions of proteins and RNAs are defined by the collective interactions of many residues, and yet most statistical models of biological sequences consider sites nearly independently Recent approaches have demonstrated benefits of including interactions to capture pairwise covariation, but leave higher-order dependencies out of reach Here we show how it is possible to capture higher-order, context-dependent constraints in biological sequences via latent variable models with nonlinear dependencies We found that DeepSequence ( https://githubcom/debbiemarkslab/DeepSequence ), a probabilistic model for sequence families, predicted the effects of mutations across a variety of deep mutational scanning experiments substantially better than existing methods based on the same evolutionary data The model, learned in an unsupervised manner solely on the basis of sequence information, is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space

/pdf/deep-generative-models-of-genetic-variation-capture-the-4kcuh22i81.pdf

Deep generative models of genetic variation capture the effects of mutations.

A Comprehensive Biophysical Description of Pairwise Epistasis throughout an Entire Protein Domain

The emergence of drug resistance is a major challenge for controlling diverse infectious diseases and cancer In this Review, the authors discuss the mechanisms and evolutionary consequences of drug resistance They highlight commonalities and distinctions across diverse pathogens and systems, and the implications for optimizing the current use and future development of drug therapies

Evolutionary consequences of drug resistance: shared principles across diverse targets and organisms

The structure of fitness landscapes is critical for understanding adaptive protein evolution. Previous empirical studies on fitness landscapes were confined to either the neighborhood around the wild type sequence, involving mostly single and double mutants, or a combinatorially complete subgraph involving only two amino acids at each site. In reality, the dimensionality of protein sequence space is higher (20(L)) and there may be higher-order interactions among more than two sites. Here we experimentally characterized the fitness landscape of four sites in protein GB1, containing 20(4) = 160,000 variants. We 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. These indirect paths alleviate the constraint on adaptive protein evolution, suggesting that the heretofore neglected dimensions of sequence space may change our views on how proteins evolve.

/pdf/adaptation-in-protein-fitness-landscapes-is-facilitated-by-1v2t6wtjhj.pdf

Adaptation in protein fitness landscapes is facilitated by indirect paths

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.

/pdf/a-quantitative-high-resolution-genetic-profile-rapidly-4ww0s2bebe.pdf

A quantitative high-resolution genetic profile rapidly identifies sequence determinants of hepatitis C viral fitness and drug sensitivity.

Hepatitis C virus (HCV) encodes mechanisms to evade the multilayered antiviral actions of the host immune system. Great progress has been made in elucidating the strategies HCV employs to down-regulate interferon (IFN) production, impede IFN signaling transduction, and impair IFN-stimulated gene (ISG) expression. However, there is a limited understanding of the mechanisms governing how viral proteins counteract the antiviral functions of downstream IFN effectors due to the lack of an efficient approach to identify such interactions systematically. To study the mechanisms by which HCV antagonizes the IFN responses, we have developed a high-throughput profiling platform that enables mapping of HCV sequences critical for anti-IFN function at high resolution. Genome-wide profiling performed with a 15-nt insertion mutant library of HCV showed that mutations in the p7 region conferred high levels of IFN sensitivity, which could be alleviated by the expression of WT p7 protein. This finding suggests that p7 protein of HCV has an immune evasion function. By screening a liver-specific ISG library, we identified that IFI6-16 significantly inhibits the replication of p7 mutant viruses without affecting WT virus replication. In contrast, knockout of IFI6-16 reversed the IFN hypersensitivity of p7 mutant virus. In addition, p7 was found to be coimmunoprecipitated with IFI6-16 and to counteract the function of IFI6-16 by depolarizing the mitochondria potential. Our data suggest that p7 is a critical immune evasion protein that suppresses the antiviral IFN function by counteracting the function of IFI6-16.

https://www.pnas.org/content/pnas/114/8/2018.full.pdf

Systematic identification of anti-interferon function on hepatitis C virus genome reveals p7 as an immune evasion protein.

Pairing high-throughput sequencing technologies with high-throughput mutagenesis enables genome-wide investigations of pathogenic organisms. Knowledge of the specific functions of protein domains encoded by the genome of the hepatitis C virus (HCV), a major human pathogen that contributes to liver disease worldwide, remains limited to insight from small-scale studies. To enhance the capabilities of HCV researchers, we have obtained a high-resolution functional map of the entire viral genome by combining transposon-based insertional mutagenesis with next-generation sequencing. We generated a library of 8,398 mutagenized HCV clones, each containing one 15-nucleotide sequence inserted at a unique genomic position. We passaged this library in hepatic cells, recovered virus pools, and simultaneously assayed the abundance of mutant viruses in each pool by next-generation sequencing. To illustrate the validity of the functional profile, we compared the genetic footprints of viral proteins with previously solved protein structures. Moreover, we show the utility of these genetic footprints in the identification of candidate regions for epitope tag insertion. In a second application, we screened the genetic footprints for phenotypes that reflected defects in later steps of the viral life cycle. We confirmed that viruses with insertions in a region of the nonstructural protein NS4B had a defect in infectivity while maintaining genome replication. Overall, our genome-wide HCV mutant library and the genetic footprints obtained by high-resolution profiling represent valuable new resources for the research community that can direct the attention of investigators toward unidentified roles of individual protein domains. IMPORTANCE Our insertional mutagenesis library provides a resource that illustrates the effects of relatively small insertions on local protein structure and HCV viability. We have also generated complementary resources, including a website (http://hangfei.bol.ucla.edu) and a panel of epitope-tagged mutant viruses that should enhance the research capabilities of investigators studying HCV. Researchers can now detect epitope-tagged viral proteins by established antibodies, which will allow biochemical studies of HCV proteins for which antibodies are not readily available. Furthermore, researchers can now quickly look up genotype-phenotype relationships and base further mechanistic studies on the residue-by-residue information from the functional profile. More broadly, this approach offers a general strategy for the systematic functional characterization of viruses on the genome scale.

/pdf/a-comprehensive-functional-map-of-the-hepatitis-c-virus-3pira4x3i4.pdf

Shawna Truong

Papers

A quantitative high-resolution genetic profile rapidly identifies sequence determinants of hepatitis C viral fitness and drug sensitivity.

Systematic identification of anti-interferon function on hepatitis C virus genome reveals p7 as an immune evasion protein.

A Comprehensive Functional Map of the Hepatitis C Virus Genome Provides a Resource for Probing Viral Proteins