Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan

The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects, and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype–fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise and exhibits high generalization power in terms of broader sequence space exploration and higher fitness variant predictions. We investigate the role of epistasis and show that the inferred model provides structural information about the 3D contacts in the molecular fold.     

The mutational structure of metabolism in Caenorhabditis elegans

A properly functioning organism must maintain metabolic homeostasis. Deleterious mutations degrade organismal function, presumably at least in part via effects on metabolic function. Here we present an initial investigation into the mutational structure of the Caenorhabditis elegans metabolome by means of a mutation accumulation experiment. We find that pool sizes of 29 metabolites vary greatly in their vulnerability to mutation, both in terms of the rate of accumulation of genetic variance (the mutational variance, VM) and the rate of change of the trait mean (the mutational bias, ΔM). Strikingly, some metabolites are much more vulnerable to mutation than any other trait previously studied in the same way. Although we cannot statistically assess the strength of mutational correlations between individual metabolites, principal component analysis provides strong evidence that some metabolite pools are genetically correlated, but also that there is substantial scope for independent evolution of different groups of metabolites. Averaged over mutation accumulation lines, PC3 is positively correlated with relative fitness, but a model in which metabolites are uncorrelated with fitness is nearly as good by Akaike's Information Criterion.     

Exploring amino acid functions in a deep mutational landscape

Amino acids fulfil a diverse range of roles in proteins, each utilising its chemical properties in different ways in different contexts to create required functions. For example, cysteines form disulphide or hydrogen bonds in different circumstances and charged amino acids do not always make use of their charge. The repertoire of amino acid functions and the frequency at which they occur in proteins remains understudied. Measuring large numbers of mutational consequences, which can elucidate the role an amino acid plays, was prohibitively time‐consuming until recent developments in deep mutational scanning. In this study, we gathered data from 28 deep mutational scanning studies, covering 6,291 positions in 30 proteins, and used the consequences of mutation at each position to define a mutational landscape. We demonstrated rich relationships between this landscape and biophysical or evolutionary properties. Finally, we identified 100 functional amino acid subtypes with a data‐driven clustering analysis and studied their features, including their frequencies and chemical properties such as tolerating polarity, hydrophobicity or being intolerant of charge or specific amino acids. The mutational landscape and amino acid subtypes provide a foundational catalogue of amino acid functional diversity, which will be refined as the number of studied protein positions increases.     

Fitness change in relation to mutation number in spontaneous mutation accumulation lines of Chlamydomonas reinhardtii

Although all genetic variation ultimately stems from mutations, their properties are difficult to study directly. Here, we used multiple mutation accumulation (MA) lines derived from five genetic backgrounds of the green algae Chlamydomonas reinhardtii that have been previously subjected to whole genome sequencing to investigate the relationship between the number of spontaneous mutations and change in fitness from a nonevolved ancestor. MA lines were on average less fit than their ancestors and we detected a significantly negative correlation between the change in fitness and the total number of accumulated mutations in the genome. Likewise, the number of mutations located within coding regions significantly and negatively impacted MA line fitness. We used the fitness data to parameterize a maximum likelihood model to estimate discrete categories of mutational effects, and found that models containing one to two mutational effect categories (one neutral and one deleterious category) fitted the data best. However, the best‐fitting mutational effects models were highly dependent on the genetic background of the ancestral strain.     

The population genetics of adaptation on correlated fitness landscapes: the block model

Several recent theoretical studies of the genetics of adaptation have focused on the mutational landscape model, which considers evolution on rugged fitness landscapes (i.e., ones having many local optima). Adaptation in this model is characterized by several simple results. Here I ask whether these results also hold on correlated fitness landscapes, which are smoother than those considered in the mutational landscape model. In particular, I study the genetics of adaptation in the block model, a tunably rugged model of fitness landscapes. Considering the scenario in which adaptation begins from a high fitness wild-type DNA sequence, I use extreme value theory and computer simulations to study both single adaptive steps and entire adaptive walks. I show that all previous results characterizing single steps in adaptation in the mutational landscape model hold at least approximately on correlated landscapes in the block model; many entire-walk results, however, do not.     

Effective epitope identification employing phylogenetic,mutational variability,sequence entropy,and correlated mutation analysis targeting NS5B protein of hepatitis C virus: from bioinformatics to therapeutics

Hepatitis C virus (HCV) is considered as a foremost cause affecting numerous human liver‐related disorders. An effective immuno‐prophylactic measure (like stable vaccine) is still unavailable for HCV. We perform an in silico analysis of nonstructural protein 5B (NS5B) based CD4 and CD8 epitopes that might be implicated in improvement of treatment strategies for efficient vaccine development programs against HCV. Here, we report on effective utilization of knowledge obtained from multiple sequence alignment and phylogenetic analysis for investigation and evaluation of candidate epitopes that have enormous potential to be used in formulating proficient vaccine, embracing multiple strains prevalent among major geographical locations. Mutational variability data discussed herein focus on discriminating the region under active evolutionary pressure from those having lower mutational potential in existing experimentally verified epitopes, thus, providing a concrete framework for designing an effective peptide‐based vaccine against HCV. Additionally, we measured entropy distribution in NS5B residues and pinpoint the positions in epitopes that are more susceptible to mutations and, thus, account for virus strategy to evade the host immune system. Findings from this study are expected to add more details on the sequence and structural aspects of NS5B protein, ultimately facilitating our understanding about the pathophysiology of HCV and assisting advance studies on the function of NS5B antigen on the epitope level. We also report on the mutational crosstalk between functionally important coevolving residues, using correlated mutation analysis, and identify networks of coupled mutations that represent pathways of allosteric communication inside and among NS5B thumb, finger, and palm domains. Copyright © 2015 John Wiley & Sons, Ltd.     

SPONTANEOUS MUTATIONAL GENOTYPE-ENVIRONMENT INTERACTION FOR FITNESS-RELATED TRAITS IN DROSOPHILA MELANOGASTER

Spontaneous mutations were allowed to accumulate for 104–161 generations in 113–176 inbred lines, independently maintained by a single brother-sister mating per generation, all of them derived from a completely homozygous population of Drosophila melanogaster. In each of two to three consecutive generations, all lines were scored for fecundity, egg-to-pupa and pupa-to-adult viabilities, both in the standard laboratory culture medium (ST) and in three harsh media differing from the former by a single factor: higher temperature (HT), higher NaCl concentration (HSC), or a much reduced concentration of nutrients (D). Relative to the standard medium, productivity (fecundity × viability) decreased by 25% (HT), 66% (HSC), and 80% (D). In each medium, mutational variances of those traits and mutational covariances between all possible pairs were calculated from the between-line divergence (codivergence). Mutational correlations between character states in different media were also obtained. Because we used inbred lines, those estimates were mainly due to the accumulation of mildly detrimental mutations, deleterious mutations of large effect being underrepresented. For all traits, mutational heritabilities ranged from 1.41 × 10^–4 to 11.24 × 10^–4, and did not increase with intensified environmental harshness. Mutational correlations between character states in different media were usually not large (average absolute value 0.31), reflecting a high degree of environmental specificity of the mutations involved. In our results, mutations quasi-neutral in ST conditions and mildly detrimental in more stressful media were not, as a class, important. Mutational correlations between fecundity and egg-to-pupa viability were small and positive in all media. Those involving pupa-to-adult viability were positive in HT, nonsignificant in HSC, and negative in ST and D, showing how the genetic covariance structure of quantitative traits in populations may change in variable environments.     

加工假基因演化过程中的两种信息流

定义描述DNA序列组分差异性和碱基关联的两个参数,分析了人类加工假基因演化过程中其组分信息和碱基关联信息的变化特征,发现随时间的推移,加工假基因的组分逐步向其侧翼序列漂移,紧邻碱基关联逐步增强。这表明本研究所得参数可很好地用来表征加工假基因的突变信息。     

A framework for exhaustively mapping functional missense variants

Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here, we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin‐like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.     

Predicting mutant outcome by combining deep mutational scanning and machine learning

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens. Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted “targets”: (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b-d) three stability related properties of IGBPG (immunoglobulin G-binding β1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment—the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations.     

SPONTANEOUS MUTATION ACCUMULATION IN MULTIPLE STRAINS OF THE GREEN ALGA,CHLAMYDOMONAS REINHARDTII

Estimates of mutational parameters, such as the average fitness effect of a new mutation and the rate at which new genetic variation for fitness is created by mutation, are important for the understanding of many biological processes. However, the causes of interspecific variation in mutational parameters and the extent to which they vary within species remain largely unknown. We maintained multiple strains of the unicellular eukaryote Chlamydomonas reinhardtii, for approximately 1000 generations under relaxed selection by transferring a single cell every ～10 generations. Mean fitness of the lines tended to decline with generations of mutation accumulation whereas mutational variance increased. We did not find any evidence for differences among strains in any of the mutational parameters estimated. The overall change in mean fitness per cell division and rate of input of mutational variance per cell division were more similar to values observed in multicellular organisms than to those in other single‐celled microbes. However, after taking into account differences in genome size among species, estimates from multicellular organisms and microbes, including our new estimates from C. reinhardtii, become substantially more similar. Thus, we suggest that variation in genome size is an important determinant of interspecific variation in mutational parameters.     

Mutational neighbourhood and mutation supply rate constrain adaptation in Pseudomonas aeruginosa

Understanding adaptation by natural selection requires understanding the genetic factors that determine which beneficial mutations are available for selection. Here, using experimental evolution of rifampicin-resistant Pseudomonas aeruginosa, we show that different genotypes vary in their capacity for adaptation to the cost of antibiotic resistance. We then use sequence data to show that the beneficial mutations associated with fitness recovery were specific to particular genetic backgrounds, suggesting that genotypes had access to different sets of beneficial mutations. When we manipulated the supply rate of beneficial mutations, by altering effective population size during evolution, we found that it constrained adaptation in some selection lines by restricting access to rare beneficial mutations, but that the effect varied among the genotypes in our experiment. These results suggest that mutational neighbourhood varies even among genotypes that differ by a single amino acid change, and this determines their capacity for adaptation as well as the influence of population biology processes that alter mutation supply rate.     

MUTATIONAL MELTDOWN IN LABORATORY YEAST POPULATIONS

Abstract.— In small or repeatedly bottlenecked populations, mutations are expected to accumulate by genetic drift, causing fitness declines. In mutational meltdown models, such fitness declines further reduce population size, thus accelerating additional mutation accumulation and leading to extinction. Because the rate of mutation accumulation is determined partly by the mutation rate, the risk and rate of meltdown are predicted to increase with increasing mutation rate. We established 12 replicate populations of Saccharomyces cerevisiae from each of two isogenic strains whose genomewide mutation rates differ by approximately two orders of magnitude. Each population was transferred daily by a fixed dilution that resulted in an effective population size near 250. Fitness declines that reduce growth rates were expected to reduce the numbers of cells transferred after dilution, thus reducing population size and leading to mutational meltdown. Through 175 daily transfers and approximately 2900 generations, two extinctions occurred, both in populations with elevated mutation rates. For one of these populations there is direct evidence that extinction resulted from mutational meltdown: Extinction immediately followed a major fitness decline, and it recurred consistently in replicate populations reestablished from a sample frozen after this fitness decline, but not in populations founded from a predecline sample. Wild‐type populations showed no trend to decrease in size and, on average, they increased in fitness.     

Large‐scale analysis of Drosophila core promoter function using synthetic promoters

The core promoter plays a central role in setting metazoan gene expression levels, but how exactly it “computes” expression remains poorly understood. To dissect its function, we carried out a comprehensive structure–function analysis in Drosophila. First, we performed a genome‐wide bioinformatic analysis, providing an improved picture of the sequence motifs architecture. We then measured synthetic promoters’ activities of ~3,000 mutational variants with and without an external stimulus (hormonal activation), at large scale and with high accuracy using robotics and a dual luciferase reporter assay. We observed a strong impact on activity of the different types of mutations, including knockout of individual sequence motifs and motif combinations, variations of motif strength, nucleosome positioning, and flanking sequences. A linear combination of the individual motif features largely accounts for the combinatorial effects on core promoter activity. These findings shed new light on the quantitative assessment of gene expression in metazoans.     

Stabilizing Selection,Purifying Selection,and Mutational Bias in Finite Populations

Genomic traits such as codon usage and the lengths of noncoding sequences may be subject to stabilizing selection rather than purifying selection. Mutations affecting these traits are often biased in one direction. To investigate the potential role of stabilizing selection on genomic traits, the effects of mutational bias on the equilibrium value of a trait under stabilizing selection in a finite population were investigated, using two different mutational models. Numerical results were generated using a matrix method for calculating the probability distribution of variant frequencies at sites affecting the trait, as well as by Monte Carlo simulations. Analytical approximations were also derived, which provided useful insights into the numerical results. A novel conclusion is that the scaled intensity of selection acting on individual variants is nearly independent of the effective population size over a wide range of parameter space and is strongly determined by the logarithm of the mutational bias parameter. This is true even when there is a very small departure of the mean from the optimum, as is usually the case. This implies that studies of the frequency spectra of DNA sequence variants may be unable to distinguish between stabilizing and purifying selection. A similar investigation of purifying selection against deleterious mutations was also carried out. Contrary to previous suggestions, the scaled intensity of purifying selection with synergistic fitness effects is sensitive to population size, which is inconsistent with the general lack of sensitivity of codon usage to effective population size.     

Selection for distinct gene expression properties favours the evolution of mutational robustness in gene regulatory networks

Mutational robustness is a genotype's tendency to keep a phenotypic trait with little and few changes in the face of mutations. Mutational robustness is both ubiquitous and evolutionarily important as it affects in different ways the probability that new phenotypic variation arises. Understanding the origins of robustness is specially relevant for systems of development that are phylogenetically widespread and that construct phenotypic traits with a strong impact on fitness. Gene regulatory networks are examples of this class of systems. They comprise sets of genes that, through cross‐regulation, build the gene activity patterns that define cellular responses, different tissues or distinct cell types. Several empirical observations, such as a greater robustness of wild‐type phenotypes, suggest that stabilizing selection underlies the evolution of mutational robustness. However, the role of selection in the evolution of robustness is still under debate. Computer simulations of the dynamics and evolution of gene regulatory networks have shown that selection for any gene activity pattern that is steady and self‐sustaining is sufficient to promote the evolution of mutational robustness. Here, I generalize this scenario using a computational model to show that selection for different aspects of a gene activity phenotype increases mutational robustness. Mutational robustness evolves even when selection favours properties that conflict with the stationarity of a gene activity pattern. The results that I present support an important role for stabilizing selection in the evolution of robustness in gene regulatory networks.     

CARd: Carbon distribution analysis program for protein sequences

Carbon distribution is responsible for stability and structure of proteins. Arrangement of carbon along the protein sequence is depends on how the amino acids are organized and is guided by mRNAs. An atomic level revision is important for understanding these codes. This will ultimately help in identification of disorders and suggest mutations. For this purpose a carbon distribution analysis program has been developed. This program captures the hydrophobic / hydrophilic / disordered regions in a protein. The program gives accurate results. The calculations are precise and sensitive to single amino acid resolution. This program is to help in mutational studies leading to protein stabilisation.     

The Adaptive Potential of the Middle Domain of Yeast Hsp90

The distribution of fitness effects (DFEs) of new mutations across different environments quantifies the potential for adaptation in a given environment and its cost in others. So far, results regarding the cost of adaptation across environments have been mixed, and most studies have sampled random mutations across different genes. Here, we quantify systematically how costs of adaptation vary along a large stretch of protein sequence by studying the distribution of fitness effects of the same ≈2,300 amino-acid changing mutations obtained from deep mutational scanning of 119 amino acids in the middle domain of the heat shock protein Hsp90 in five environments. This region is known to be important for client binding, stabilization of the Hsp90 dimer, stabilization of the N-terminal-Middle and Middle-C-terminal interdomains, and regulation of ATPase–chaperone activity. Interestingly, we find that fitness correlates well across diverse stressful environments, with the exception of one environment, diamide. Consistent with this result, we find little cost of adaptation; on average only one in seven beneficial mutations is deleterious in another environment. We identify a hotspot of beneficial mutations in a region of the protein that is located within an allosteric center. The identified protein regions that are enriched in beneficial, deleterious, and costly mutations coincide with residues that are involved in the stabilization of Hsp90 interdomains and stabilization of client-binding interfaces, or residues that are involved in ATPase–chaperone activity of Hsp90. Thus, our study yields information regarding the role and adaptive potential of a protein sequence that complements and extends known structural information.     

The mutational burden of acral melanoma revealed by whole‐genome sequencing and comparative analysis

Acral melanoma is a subtype of melanoma with distinct epidemiological, clinical and mutational profiles. To define the genomic alterations in acral melanoma, we conducted whole‐genome sequencing and SNP array analysis of five metastatic tumours and their matched normal genomes. We identified the somatic mutations, copy number alterations and structural variants in these tumours and combined our data with published studies to identify recurrently mutated genes likely to be the drivers of acral melanomagenesis. We compared and contrasted the genomic landscapes of acral, mucosal, uveal and common cutaneous melanoma to reveal the distinctive mutational characteristics of each subtype.