Mutation effects predicted from sequence co-variation

Abstract

Increasing interest in determining the effects of genetic variation for bioengineering, human health and basic biological research has propelled the development of technologies for high-throughput mutagenesis and selection. However, since designing functional assays is challenging and systematic testing of combinations of mutations is intractable, there is a parallel need to develop more accurate computational predictions.. Most computational methods have relied significantly on the signal of evolutionary conservation, but do not account for dependencies between positions in a sequence. We present an unsupervised method for predicting the effects of mutations (EVmutation) that explicitly captures residue dependencies between positions. We find that it improves the prediction accuracies of a comprehensive collection of recent high-throughput experimental fitness landscapes, biochemical measurements and human disease mutations. We suggest EVmutation may be useful to assess the quantitative effects of mutations in genes of any organism and provide precomputed predictions for ~ 7000 human proteins.

Inference of epistatic effects leading to entrenchment and drug resistance in HIV-1 protease

“…As with the stability measurements, we find that the relative Potts energy correlates well with infectivity (r ¼ À0:64; P < 10 À5 ), shown in figure 3B. In the same comparison using the inde- The results presented here are reinforced by other recent studies of protein evolutionary landscapes Mann et al 2014;Figliuzzi et al 2015;Hopf et al 2017) where varying measures of experimental fitness are compared with statistical energies derived from correlated Potts models constructed from MSAs. The range of statistical energies and the correlation with fitness are qualitatively similar to those presented by Ferguson et al (2013) and Mann et al (2014) where statistical energies of engineered HIV-1 Gag variants generated using a similar inference technique are compared with replicative fitness assays.…”

“…Given a multiple sequence alignment (MSA) of related protein sequences, a probabilistic model of the network of interacting protein residues can be inferred from the pair correlations encoded in the MSA. Recently, probabilistic models, called Potts models, have been used to assign scores to individual protein sequences which correlate with experimental measures of fitness (Haq et al 2012;Ferguson et al 2013;Mann et al 2014;Figliuzzi et al 2015;Hopf et al 2017). These advances build upon previous and ongoing work in which Potts models have been used to extract information from sequence data regarding tertiary and quaternary structure of protein families (Weigt et al 2009;Morcos et al 2011Morcos et al , 2014Marks et al 2012;Sulkowska et al 2012;Sutto et al 2015;Barton et al 2016a;Haldane et al 2016;Jacquin et al 2016) and sequencespecific quantitative predictions of viral protein stability and fitness (Haq et al 2012;Shekhar et al 2013;Barton et al 2016b;Butler et al 2016).…”

“…Protein contacts derived from Potts models have been used for several innovative purposes; for example, for ab-initio structure predictions (Tang et al 2015), to bias molecular dynamics simulations to reveal metastable conformations (Morcos et al 2013), and to distinguish sequence-specific interactions which contribute to the stability of alternate functional conformations (Haldane et al 2016). More recently, these models have been used to probe protein fitness landscapes as the Potts Hamiltonian provides a mapping from protein sequences to statistical energy scores in which sequences with lower scores are more probable (Shekhar et al 2013;Figliuzzi et al 2015;Hopf et al 2017). We make use of this property of Potts statistical models in this work.…”

Variant Interpretation: Functional Assays to the Rescue

“…[67][68][69] MAVE data can also be useful in evaluating new predictive tools, as was done for EVmutation, which predicts variant effects in proteins from co-variation in multiple-sequence alignments. 70 Predictive models can also be trained on MAVE results from fully random libraries, as opposed to libraries of SNVs. Functional data from random libraries can be extremely informative, revealing general patterns.…”

Structure of the peptidoglycan polymerase RodA resolved by evolutionary coupling analysis

“…Other residues near the central cavity were similarly essential for RodA function, including Asp255 as reported previously 1 , as well as Asp152 (Figure 3b, Extended Data Figure 7). Analysis of evolutionary co-variation data with EVmutation 20 likewise predicts these residues and the salt bridge to be immutable. Taken together, the high degree of sequence conservation, intolerance to mutation, and catalytic essentiality of residues surrounding the central cavity confirm that this portion of the protein plays critical role in peptidoglycan polymerization, making it a prime target for antibiotic discovery.…”