Mutational effects on stability are largely conserved during protein evolution

Robustness of Ancestral Sequence Reconstruction to Among-site and Among-lineage Evolutionary Heterogeneity

Ancestral sequence reconstruction is typically performed using homogeneous evolutionary models, which assume that the same substitution propensities affect all sites and lineages. These assumptions are routinely violated: heterogeneous structural and functional constraints favor different amino acids at different sites, and these constraints often change among lineages as epistatic substitutions accrue at other sites. To evaluate how violations of the homogeneity assumption affect ancestral sequence reconstruction under realistic conditions, we developed site-specific substitution models and parameterized them using data from deep mutational scanning experiments on three protein families; we then used these models to perform ancestral sequence reconstruction on the empirical alignments and on alignments simulated under heterogeneous conditions derived from the experiments. Extensive among-site and -lineage heterogeneity is present in these datasets, but the sequences reconstructed from empirical alignments are almost identical when heterogeneous or homogeneous models are used for ancestral sequence reconstruction. Using models fit to deep mutational scanning data from distantly related proteins in which mutational effects are very different also has a minimal impact on ancestral sequence reconstruction. The rare differences occur primarily where phylogenetic signal is weak-at fast-evolving sites and nodes connected by long branches. When ancestral sequence reconstruction is performed on simulated data, errors in the reconstructed sequences become more likely as branch lengths increase, but incorporating heterogeneity into the model does not improve accuracy. These data establish that ancestral sequence reconstruction is robust to unincorporated realistic forms of evolutionary heterogeneity, because the primary determinant of ancestral sequence reconstruction is phylogenetic signal, not the substitution model. The best way to improve accuracy is therefore not to develop more elaborate models but to apply ancestral sequence reconstruction to densely sampled alignments that maximize phylogenetic signal at the nodes of interest.

Integrative In Silico Analysis to Identify Functional and Structural Impacts of nsSNPs on Programmed Cell Death Protein 1 (PD-1) Protein and UTRs: Potential Biomarkers for Cancer Susceptibility

Background: Programmed cell death protein 1 (PD-1), encoded by the PDCD1 gene, is critical in immune checkpoint regulation and cancer immune evasion. Variants in PDCD1 may alter its function, impacting cancer susceptibility and disease progression. Objectives: This study evaluates the structural, functional, and regulatory impacts of non-synonymous single-nucleotide polymorphisms (nsSNPs) in the PDCD1 gene, focusing on their pathogenic and oncogenic roles. Methods: Computational tools, including PredictSNP1.0, I-Mutant2.0, MUpro, HOPE, MutPred2, Cscape, Cscape-Somatic, GEPIA2, cBioPortal, and STRING, were used to analyze 695 nsSNPs in the PD1 protein. The analysis covered structural impacts, stability changes, regulatory effects, and oncogenic potential, focusing on conserved domains and protein-ligand interactions. Results: The analysis identified 84 deleterious variants, with 45 mapped to conserved regions like the Ig V-set domain essential for ligand-binding interactions. Stability analyses identified 78 destabilizing variants with significant protein instability (ΔΔG values). Ten nsSNPs were identified as potential cancer drivers. Expression profiling showed differential PDCD1 expression in tumor versus normal tissues, correlating with improved survival in skin melanoma but limited value in ovarian cancer. Regulatory SNPs disrupted miRNA-binding sites and transcriptional regulation, affecting PDCD1 expression. STRING analysis revealed key PD-1 protein partners within immune pathways, including PD-L1 and PD-L2. Conclusions: This study highlights the significance of PDCD1 nsSNPs as potential biomarkers for cancer susceptibility, advancing the understanding of PD-1 regulation. Experimental validation and multi-omics integration are crucial to refine these findings and enhance theraputic strategies.

Protein Quality Control is a Master Modulator of Molecular Evolution in Bacteria

The bacterial protein quality control (PQC) network comprises a set of genes that promote proteostasis (proteome homeostasis) through proper protein folding and function via chaperones, proteases, and protein translational machinery. It participates in vital cellular processes and influences organismal development and evolution. In this review, we examine the mechanistic bases for how the bacterial PQC network influences molecular evolution. We discuss the relevance of PQC components to contemporary issues in evolutionary biology including epistasis, evolvability, and the navigability of protein space. We examine other areas where proteostasis affects aspects of evolution and physiology, including host-parasite interactions. More generally, we demonstrate that the study of bacterial systems can aid in broader efforts to understand the relationship between genotype and phenotype across the biosphere.

Exploring Evolution to Uncover Insights Into Protein Mutational Stability

Determining the impact of mutations on the thermodynamic stability of proteins is essential for a wide range of applications such as rational protein design and genetic variant interpretation. Since protein stability is a major driver of evolution, evolutionary data are often used to guide stability predictions. Many state-of-the-art stability predictors extract evolutionary information from multiple sequence alignments of proteins homologous to a query protein, and leverage it to predict the effects of mutations on protein stability. To evaluate the power and the limitations of such methods, we used the massive amount of stability data recently obtained by deep mutational scanning to study how best to construct multiple sequence alignments and optimally extract evolutionary information from them. We tested different evolutionary models and found that, unexpectedly, independent-site models achieve similar accuracy to more complex epistatic models. A detailed analysis of the latter models suggests that their inference often results in noisy couplings, which do not appear to add predictive power over the independent-site contribution, at least in the context of stability prediction. Interestingly, by combining any of the evolutionary features with a simple structural feature, the relative solvent accessibility of the mutated residue, we achieved similar prediction accuracy to supervised, machine learning-based, protein stability change predictors. Our results provide new insights into the relationship between protein evolution and stability, and show how evolutionary information can be exploited to improve the performance of mutational stability prediction.

Robustness of ancestral sequence reconstruction to among-site evolutionary heterogeneity and epistasis

Ancestral sequence reconstruction (ASR) is typically performed using homogeneous evolutionary models, which assume that the same substitution propensities affect all sites and lineages. These assumptions are routinely violated: heterogeneous structural and functional constraints favor different amino acid states at different sites, and these constraints often change among lineages as epistatic substitutions accrue at other sites. To evaluate how realistic violations of the homogeneity assumption affect ASR, we developed site-specific substitution models and parameterized them using data from deep mutational scanning experiments on three protein families; we then used these models to perform ASR on the empirical alignments and on alignments simulated under heterogeneous conditions derived from the experiments. Extensive among-site and -lineage heterogeneity is present in these datasets, but the sequences reconstructed from empirical alignments are almost identical, irrespective of whether heterogeneous or homogeneous models are used for ASR. The rare differences occur primarily when phylogenetic signal is weak - at fast-evolving sites and nodes connected by long branches. When ASR is performed on simulated data, errors in the reconstructed sequences become more likely as branch lengths increase, but incorporating heterogeneity into the model does not improve accuracy. These data establish that ASR is robust to unincorporated realistic forms of evolutionary heterogeneity, because the primary determinant of ASR is phylogenetic signal, not the substitution model. The best way to improve accuracy is therefore not to develop more elaborate models but to apply ASR to densely sampled alignments that maximize phylogenetic signal at the nodes of interest.

Estimating the proportion of beneficial mutations that are not adaptive in mammals

Mutations can be beneficial by bringing innovation to their bearer, allowing them to adapt to environmental change. These mutations are typically unpredictable since they respond to an unforeseen change in the environment. However, mutations can also be beneficial because they are simply restoring a state of higher fitness that was lost due to genetic drift in a stable environment. In contrast to adaptive mutations, these beneficial non-adaptive mutations can be predicted if the underlying fitness landscape is stable and known. The contribution of such non-adaptive mutations to molecular evolution has been widely neglected mainly because their detection is very challenging. We have here reconstructed protein-coding gene fitness landscapes shared between mammals, using mutation-selection models and a multi-species alignments across 87 mammals. These fitness landscapes have allowed us to predict the fitness effect of polymorphisms found in 28 mammalian populations. Using methods that quantify selection at the population level, we have confirmed that beneficial non-adaptive mutations are indeed positively selected in extant populations. Our work confirms that deleterious substitutions are accumulating in mammals and are being reverted, generating a balance in which genomes are damaged and restored simultaneously at different loci. We observe that beneficial non-adaptive mutations represent between 15% and 45% of all beneficial mutations in 24 of 28 populations analyzed, suggesting that a substantial part of ongoing positive selection is not driven solely by adaptation to environmental change in mammals.

Investigating the role of non-synonymous variant D67N of ADGRE2 in chronic myeloid leukemia

BACKGROUND

Chronic myeloid leukaemia (CML) is a type of blood cancer that begins in the hematopoietic stem cells. It is primarily characterized by a specific chromosomal aberration, the Philadelphia chromosome. While the fusion gene is a major contributor to CML, several other genes including ADGRE2, that are reported as highly expressed in hematopoietic stem cells and could be utilized as a therapeutic marker in leukemic patients are implicated in the disease's progression. Until recently, little research had been conducted to identify single nucleotide polymorphisms (SNPs) associated with CML. Therefore, this study aims to investigate the influence of non-synonymous variants on the structure and function of the gene encoding adhesion G protein-coupled receptor E2, ADGRE2, and to evaluate their association with CML and its clinical and pathological characteristics.

METHODS

Non-synonymous SNPs of ADGRE2 were retrieved from the ENSEMBL, COSMIC, and gnomAD genome browsers, and the pathogenicity of deleterious variants was assessed using several established computational tools, including SIFT, CADD, REVEL, PolyPhen, and MetaLR.

RESULTS

Various in silico analyses explored the impact of damaging SNP on the function, stability, and structure of EGF-like modules containing mucin-like hormone receptor-like2 (EMR2) protein encoded by the ADGRE2 gene. Genotype analysis was performed on collected blood samples, revealing that altered genotype TT of variant rs765071211 (C/T) was associated significantly with CML patients compared to the control. Further in vitro and in vivo analyses suggest that this SNP holds potential for clinical translation.

The origin of mutational epistasis

The interconnected processes of protein folding, mutations, epistasis, and evolution have all been the subject of extensive analysis throughout the years due to their significance for structural and evolutionary biology. The origin (molecular basis) of epistasis-the non-additive interactions between mutations-is still, nonetheless, unknown. The existence of a new perspective on protein folding, a problem that needs to be conceived as an 'analytic whole', will enable us to shed light on the origin of mutational epistasis at the simplest level-within proteins-while also uncovering the reasons why the genetic background in which they occur, a key component of molecular evolution, could foster changes in epistasis effects. Additionally, because mutations are the source of epistasis, more research is needed to determine the impact of post-translational modifications, which can potentially increase the proteome's diversity by several orders of magnitude, on mutational epistasis and protein evolvability. Finally, a protein evolution thermodynamic-based analysis that does not consider specific mutational steps or epistasis effects will be briefly discussed. Our study explores the complex processes behind the evolution of proteins upon mutations, clearing up some previously unresolved issues, and providing direction for further research.

Linking Protein Stability to Pathogenicity: Predicting Clinical Significance of Single-Missense Mutations in Ocular Proteins Using Machine Learning

Understanding the effect of single-missense mutations on protein stability is crucial for clinical decision-making and therapeutic development. The impact of these mutations on protein stability and 3D structure remains underexplored. Here, we developed a program to investigate the relationship between pathogenic mutations with protein unfolding and compared seven machine learning (ML) models to predict the clinical significance of single-missense mutations with unknown impacts, based on protein stability parameters. We analyzed seven proteins associated with ocular disease-causing genes. The program revealed an R-squared value of 0.846 using Decision Tree Regression between pathogenic mutations and decreased protein stability, with 96.20% of pathogenic mutations in RPE65 leading to protein instability. Among the ML models, Random Forest achieved the highest AUC (0.922) and PR AUC (0.879) in predicting the clinical significance of mutations with unknown effects. Our findings indicate that most pathogenic mutations affecting protein stability occur in alpha-helices, beta-pleated sheets, and active sites. This study suggests that protein stability can serve as a valuable parameter for interpreting the clinical significance of single-missense mutations in ocular proteins.

Increased Positive Selection in Highly Recombining Genes Does not Necessarily Reflect an Evolutionary Advantage of Recombination

It is commonly thought that the long-term advantage of meiotic recombination is to dissipate genetic linkage, allowing natural selection to act independently on different loci. It is thus theoretically expected that genes with higher recombination rates evolve under more effective selection. On the other hand, recombination is often associated with GC-biased gene conversion (gBGC), which theoretically interferes with selection by promoting the fixation of deleterious GC alleles. To test these predictions, several studies assessed whether selection was more effective in highly recombining genes (due to dissipation of genetic linkage) or less effective (due to gBGC), assuming a fixed distribution of fitness effects (DFE) for all genes. In this study, I directly derive the DFE from a gene's evolutionary history (shaped by mutation, selection, drift, and gBGC) under empirical fitness landscapes. I show that genes that have experienced high levels of gBGC are less fit and thus have more opportunities for beneficial mutations. Only a small decrease in the genome-wide intensity of gBGC leads to the fixation of these beneficial mutations, particularly in highly recombining genes. This results in increased positive selection in highly recombining genes that is not caused by more effective selection. Additionally, I show that the death of a recombination hotspot can lead to a higher dN/dS than its birth, but with substitution patterns biased towards AT, and only at selected positions. This shows that controlling for a substitution bias towards GC is therefore not sufficient to rule out the contribution of gBGC to signatures of accelerated evolution. Finally, although gBGC does not affect the fixation probability of GC-conservative mutations, I show that by altering the DFE, gBGC can also significantly affect nonsynonymous GC-conservative substitution patterns.

Epistasis facilitates functional evolution in an ancient transcription factor

A protein's genetic architecture - the set of causal rules by which its sequence produces its functions - also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest - excluding the vast majority of possible genotypes and evolutionary trajectories - and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor's specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor's capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.

Analysis of a Novel Peptide That Is Capable of Inhibiting the Enzymatic Activity of the Protein Kinase A Catalytic Subunit-Like Protein from Trypanosoma equiperdum

A 26-residue peptide possessing the αN-helix motif of the protein kinase A (PKA) regulatory subunit-like proteins from the Trypanozoom subgenera (VAP26, sequence = VAPYFEKSEDETALILKLLTYNVLFS), was shown to inhibit the enzymatic activity of the Trypanosoma equiperdum PKA catalytic subunit-like protein, in a similar manner that the mammalian heat-stable soluble PKA inhibitor known as PKI. However, VAP26 does not contain the PKI inhibitory sequence. Bioinformatics analyzes of the αN-helix motif from various Trypanozoon PKA regulatory subunit-like proteins suggested that the sequence could form favorable peptide-protein interactions of hydrophobic nature with the PKA catalytic subunit-like protein, which possibly may represent an alternative PKA inhibitory mechanism. The sequence of the αN-helix motif of the Trypanozoon proteins was shown to be highly homologous but significantly divergent from the corresponding αN-helix motifs of their Leishmania and mammalian counterparts. This sequence divergence contrasted with the proposed secondary structure of the αN-helix motif, which appeared conserved in every analyzed regulatory subunit-like protein. In silico mutation experiments at positions I234, L238 and F244 of the αN-helix motif from the Trypanozoon proteins destabilized both the specific motif and the protein. On the contrary, mutations at positions T239 and Y240 stabilized the motif and the protein. These results suggested that the αN-helix motif from the Trypanozoon proteins probably possessed a different evolutionary path than their Leishmania and mammalian counterparts. Moreover, finding stabilizing mutations indicated that new inhibitory peptides may be designed based on the αN-helix motif from the Trypanozoon PKA regulatory subunit-like proteins.

Jointly modeling deep mutational scans identifies shifted mutational effects among SARS-CoV-2 spike homologs

Deep mutational scanning (DMS) is a high-throughput experimental technique that measures the effects of thousands of mutations to a protein. These experiments can be performed on multiple homologs of a protein or on the same protein selected under multiple conditions. It is often of biological interest to identify mutations with shifted effects across homologs or conditions. However, it is challenging to determine if observed shifts arise from biological signal or experimental noise. Here, we describe a method for jointly inferring mutational effects across multiple DMS experiments while also identifying mutations that have shifted in their effects among experiments. A key aspect of our method is to regularize the inferred shifts, so that they are nonzero only when strongly supported by the data. We apply this method to DMS experiments that measure how mutations to spike proteins from SARS-CoV-2 variants (Delta, Omicron BA.1, and Omicron BA.2) affect cell entry. Most mutational effects are conserved between these spike homologs, but a fraction have markedly shifted. We experimentally validate a subset of the mutations inferred to have shifted effects, and confirm differences of > 1,000-fold in the impact of the same mutation on spike-mediated viral infection across spikes from different SARS-CoV-2 variants. Overall, our work establishes a general approach for comparing sets of DMS experiments to identify biologically important shifts in mutational effects.

Occurrence and mechanism of target-site resistance to bensulfuron-methyl in Monochoria korsakowii from China

Monochoria korsakowii is an increasingly significant threat to rice production across China, particularly in Liaoning province. Few studies have reported herbicide resistance in M. korsakowii, and resistance status and mechanisms are poorly understood. Here, thirty field populations of M. korsakowii were collected from 11 rice-growing regions of Liaoning, and 97% of populations had evolved resistance to bensulfuron-methyl (BM), with majority (24 of 28) showing high resistance levels (RI > 10). The first in-depth analysis of molecular features of AHAS1 and AHAS2 in BM-resistant populations showed that four Pro197 mutations (Pro197 to His, Ala, Leu or Ser) in AHAS1 and one mutation (Pro197Ser) in AHAS2 were identified. Notably, novel double Pro197Ser mutations co-occurred in both AHAS1 and AHAS2 in the most resistant line LN-20. Furthermore, resistant mutants were used to investigate the effect of Pro197 mutations on AHAS functionality, binding modes, gene expression and cross-resistance in M. korsakowii. All the detected Pro197 mutations considerably reduced in vitro AHAS sensitivity to BM by weakening hydrogen bonds and hydrophobic interactions in the predicted BM-AHAS complexes, especially the double Pro197Ser mutations. This novel resistance mutation combination slightly impacted the extractable AHAS activity, and increased the affinity and catalytic rate of pyruvate. Also, the AHAS expression level was significantly up-regulated. Moreover, all mutations provided resistance only to other sulfonylureas herbicides but not triazolopyrimidine or pyrimidinyl-benzoates herbicides. In conclusion, bensulfuron-methyl resistance in M. korsakowii was grim in Liaoning, China, and amino acid mutations on AHAS isozymes were the primary resistance mechanism. Double Pro197Ser mutations in both AHAS1 and AHAS2 confer higher herbicide resistance than single mutations in AHAS1. Thus, this work deepens our understanding of resistance status and mechanisms of M. korsakowii.

Analysis of the structural dynamics of the mutations in the kinase domain of PINK1 protein associated with Parkinson's disease

Parkinson's disease (PD) is a very common neurodegenerative disorder and is considered to be one of the most severe disorders worldwide. Mutations in some PD causing genes are responsible for the early onset of the disease. Pathogenic variants in parkin, PINK1 and DJ1 genes can cause early-onset of PD. Many PINK1 gene mutations have been reported, but not all variants are pathogenic. The gene product of PINK1, also known as PINK1 protein, has 581 amino acid residues in it. Several different mutations are present throughout the kinase domain of PINK1 protein. In this work, we used in silico approaches to analyze the different types of mutations that are distributed in the kinase domain of the PINK1 protein. Based on our results, we categorized the mutations as high, moderate and low pathogenic variants. Furthermore, we performed molecular dynamics simulations of the pathogenic PINK1 variants to decipher their possible impacts on the structure and made a comparison with the wild type PINK1. In conclusion, we suggested the possible mechanistic roles of the pathogenic variants of PINK1 kinase domain that can affect its function. These pathogenic variants are the causative agents of early onset of PD called autosomal recessive Parkinson disease.

Global Analysis of Multi-Mutants to Improve Protein Function

The identification of amino acid substitutions that both enhance the stability and function of a protein is a key challenge in protein engineering. Technological advances have enabled assaying thousands of protein variants in a single high-throughput experiment, and more recent studies use such data in protein engineering. We present a Global Multi-Mutant Analysis (GMMA) that exploits the presence of multiply-substituted variants to identify individual amino acid substitutions that are beneficial for the stability and function across a large library of protein variants. We have applied GMMA to a previously published experiment reporting on >54,000 variants of green fluorescent protein (GFP), each with known fluorescence output, and each carrying 1-15 amino acid substitutions (Sarkisyan et al., 2016). The GMMA method achieves a good fit to this dataset while being analytically transparent. We show experimentally that the six top-ranking substitutions progressively enhance GFP. More broadly, using only a single experiment as input our analysis recovers nearly all the substitutions previously reported to be beneficial for GFP folding and function. In conclusion, we suggest that large libraries of multiply-substituted variants may provide a unique source of information for protein engineering.

Genetic ablation of diabetes-associated gene Ccdc92 reduces obesity and insulin resistance in mice

Multiple genome-wide association studies (GWAS) have identified specific genetic variants in the coiled-coil domain containing 92 (CCDC92) locus that is associated with obesity and type 2 diabetes in humans. However, the biological function of CCDC92 in obesity and insulin resistance remains to be explored. Utilizing wild-type (WT) and Ccdc92 whole-body knockout (KO) mice, we found that Ccdc92 KO reduced obesity and increased insulin sensitivity under high-fat diet (HFD) conditions. Ccdc92 KO inhibited macrophage infiltration and fibrosis in white adipose tissue (WAT), suggesting Ccdc92 ablation protects against adipose tissue dysfunction. Ccdc92 deletion also increased energy expenditure and further attenuated hepatic steatosis in mice on an HFD. Ccdc92 KO significantly inhibited the inflammatory response and suppressed the NLR Family Pyrin Domain Containing 3 (NLRP3) inflammasome in WAT. Altogether, we demonstrated the critical role of CCDC92 in metabolism, constituting a potential target for treating obesity and insulin resistance.

Consequences of Genetic Recombination on Protein Folding Stability

Genetic recombination is a common evolutionary mechanism that produces molecular diversity. However, its consequences on protein folding stability have not attracted the same attention as in the case of point mutations. Here, we studied the effects of homologous recombination on the computationally predicted protein folding stability for several protein families, finding less detrimental effects than we previously expected. Although recombination can affect multiple protein sites, we found that the fraction of recombined proteins that are eliminated by negative selection because of insufficient stability is not significantly larger than the corresponding fraction of proteins produced by mutation events. Indeed, although recombination disrupts epistatic interactions, the mean stability of recombinant proteins is not lower than that of their parents. On the other hand, the difference of stability between recombined proteins is amplified with respect to the parents, promoting phenotypic diversity. As a result, at least one third of recombined proteins present stability between those of their parents, and a substantial fraction have higher or lower stability than those of both parents. As expected, we found that parents with similar sequences tend to produce recombined proteins with stability close to that of the parents. Finally, the simulation of protein evolution along the ancestral recombination graph with empirical substitution models commonly used in phylogenetics, which ignore constraints on protein folding stability, showed that recombination favors the decrease of folding stability, supporting the convenience of adopting structurally constrained models when possible for inferences of protein evolutionary histories with recombination.

Accurate protein stability predictions from homology models

Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.

Intragenic compensation through the lens of deep mutational scanning

A significant fraction of mutations in proteins are deleterious and result in adverse consequences for protein function, stability, or interaction with other molecules. Intragenic compensation is a specific case of positive epistasis when a neutral missense mutation cancels effect of a deleterious mutation in the same protein. Permissive compensatory mutations facilitate protein evolution, since without them all sequences would be extremely conserved. Understanding compensatory mechanisms is an important scientific challenge at the intersection of protein biophysics and evolution. In human genetics, intragenic compensatory interactions are important since they may result in variable penetrance of pathogenic mutations or fixation of pathogenic human alleles in orthologous proteins from related species. The latter phenomenon complicates computational and clinical inference of an allele's pathogenicity. Deep mutational scanning is a relatively new technique that enables experimental studies of functional effects of thousands of mutations in proteins. We review the important aspects of the field and discuss existing limitations of current datasets. We reviewed ten published DMS datasets with quantified functional effects of single and double mutations and described rates and patterns of intragenic compensation in eight of them.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01005-w.

Sequence dependencies and biophysical features both govern cleavage of diverse cut-sites by HIV protease

The infectivity of HIV-1 requires its protease (PR) cleave multiple cut-sites with low sequence similarity. The diversity of cleavage sites has made it challenging to investigate the underlying sequence properties that determine binding and turnover of substrates by PR. We engineered a mutational scanning approach utilizing yeast display, flow cytometry, and deep sequencing to systematically measure the impacts of all individual amino acid changes at 12 positions in three different cut-sites (MA/CA, NC/p1, and p1/p6). The resulting fitness landscapes revealed common physical features that underlie cutting of all three cut-sites at the amino acid positions closest to the scissile bond. In contrast, positions more than two amino acids away from the scissile bond exhibited a strong dependence on the sequence background of the rest of the cut-site. We observed multiple amino acid changes in cut-sites that led to faster cleavage rates, including a preference for negative charge five and six amino acids away from the scissile bond at locations where the surface of protease is positively charged. Analysis of individual cut sites using full-length matrix-capsid proteins indicate that long-distance sequence context can contribute to cutting efficiency such that analyses of peptides or shorter engineered constructs including those in this work should be considered carefully. This work provides a framework for understanding how diverse substrates interact with HIV-1 PR and can be extended to investigate other viral PRs with similar properties.

Evolution of amino acid propensities under stability-mediated epistasis

Site-specific amino acid preferences are influenced by the genetic background of the protein. The preferences for resident amino acids are expected to, on average, increase over time because of replacements at other sites - a nonadaptive phenomenon referred to as the 'evolutionary Stokes shift'. Alternatively, decreases in resident amino acid propensity have recently been viewed as evidence of adaptations to external environmental changes. Using population genetics theory and thermodynamic stability-constraints, we show that nonadaptive evolution can lead to both positive and negative shifts in propensities following the fixation of an amino acid, emphasizing that the detection of negative shifts is not conclusive evidence of adaptation. Considering shifts in propensities over windows between substitutions at a focal site, we find that following ≈ 50% of substitutions the propensity for the new resident amino acid decreases over time, and both positive and negative shifts were comparable in magnitude. Preferences were often conserved via a significant negative autocorrelation in propensity changes-increases in propensities often followed by decreases, and vice versa. Lastly, we explore the underlying mechanisms that lead propensities to fluctuate. We observe that stabilizing replacements increase the mutational tolerance at a site and in doing so decrease the propensity for the resident amino acid. In contrast, destabilizing substitutions result in more rugged fitness landscapes that tend to favor the resident amino acid. In summary, our results characterize propensity trajectories under nonadaptive stability-constrained evolution against which evidence of adaptations should be calibrated.

Shifts in amino acid preferences as proteins evolve: A synthesis of experimental and theoretical work

Amino acid preferences vary across sites and time. While variation across sites is widely accepted, the extent and frequency of temporal shifts are contentious. Our understanding of the drivers of amino acid preference change is incomplete: To what extent are temporal shifts driven by adaptive versus nonadaptive evolutionary processes? We review phenomena that cause preferences to vary (e.g., evolutionary Stokes shift, contingency, and entrenchment) and clarify how they differ. To determine the extent and prevalence of shifted preferences, we review experimental and theoretical studies. Analyses of natural sequence alignments often detect decreases in homoplasy (convergence and reversions) rates, and variation in replacement rates with time-signals that are consistent with temporally changing preferences. While approaches inferring shifts in preferences from patterns in natural alignments are valuable, they are indirect since multiple mechanisms (both adaptive and nonadaptive) could lead to the observed signal. Alternatively, site-directed mutagenesis experiments allow for a more direct assessment of shifted preferences. They corroborate evidence from multiple sequence alignments, revealing that the preference for an amino acid at a site varies depending on the background sequence. However, shifts in preferences are usually minor in magnitude and sites with significantly shifted preferences are low in frequency. The small yet consistent perturbations in preferences could, nevertheless, jeopardize the accuracy of inference procedures, which assume constant preferences. We conclude by discussing if and how such shifts in preferences might influence widely used time-homogenous inference procedures and potential ways to mitigate such effects.

Within-host evolutionary dynamics of seasonal and pandemic human influenza A viruses in young children

The evolution of influenza viruses is fundamentally shaped by within-host processes. However, the within-host evolutionary dynamics of influenza viruses remain incompletely understood, in part because most studies have focused on infections in healthy adults based on single timepoint data. Here, we analyzed the within-host evolution of 82 longitudinally sampled individuals, mostly young children, infected with A/H1N1pdm09 or A/H3N2 viruses between 2007 and 2009. For A/H1N1pdm09 infections during the 2009 pandemic, nonsynonymous minority variants were more prevalent than synonymous ones. For A/H3N2 viruses in young children, early infection was dominated by purifying selection. As these infections progressed, nonsynonymous variants typically increased in frequency even when within-host virus titers decreased. Unlike the short-lived infections of adults where de novo within-host variants are rare, longer infections in young children allow for the maintenance of virus diversity via mutation-selection balance creating potentially important opportunities for within-host virus evolution.

The Stability Landscape of de novo TIM Barrels Explored by a Modular Design Approach

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The TIM barrel is a versatile fold to understand structure-stability relationships.

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A collection of de novo TIM barrels with improved hydrophobic cores was designed.

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DeNovoTIMs are reversible in chemical and thermal unfolding, which is uncommon in TIM barrels.

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Epistatic effects play a central role in DeNovoTIMs stabilization.

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DeNovoTIMs navigate a previously uncharted region of the stability landscape.

The ability to design stable proteins with custom-made functions is a major goal in biochemistry with practical relevance for our environment and society. Understanding and manipulating protein stability provide crucial information on the molecular determinants that modulate structure and stability, and expand the applications of de novo proteins. Since the (β/⍺)₈-barrel or TIM-barrel fold is one of the most common functional scaffolds, in this work we designed a collection of stable de novo TIM barrels (DeNovoTIMs), using a computational fixed-backbone and modular approach based on improved hydrophobic packing of sTIM11, the first validated de novo TIM barrel, and subjected them to a thorough folding analysis. DeNovoTIMs navigate a region of the stability landscape previously uncharted by natural TIM barrels, with variations spanning 60 degrees in melting temperature and 22 kcal per mol in conformational stability throughout the designs. Significant non-additive or epistatic effects were observed when stabilizing mutations from different regions of the barrel were combined. The molecular basis of epistasis in DeNovoTIMs appears to be related to the extension of the hydrophobic cores. This study is an important step towards the fine-tuned modulation of protein stability by design.

A High-Throughput Screening System Based on Fluorescence-Activated Cell Sorting for the Directed Evolution of Chitinase A

Chitinases catalyze the degradation of chitin, a polymer of N-acetylglucosamine found in crustacean shells, insect cuticles, and fungal cell walls. There is great interest in the development of improved chitinases to address the environmental burden of chitin waste from the food processing industry as well as the potential medical, agricultural, and industrial uses of partially deacetylated chitin (chitosan) and its products (chito-oligosaccharides). The depolymerization of chitin can be achieved using chemical and physical treatments, but an enzymatic process would be more environmentally friendly and more sustainable. However, chitinases are slow-acting enzymes, limiting their biotechnological exploitation, although this can be overcome by molecular evolution approaches to enhance the features required for specific applications. The two main goals of this study were the development of a high-throughput screening system for chitinase activity (which could be extrapolated to other hydrolytic enzymes), and the deployment of this new method to select improved chitinase variants. We therefore cloned and expressed the Bacillus licheniformis DSM8785 chitinase A (chiA) gene in Escherichia coli BL21 (DE3) cells and generated a mutant library by error-prone PCR. We then developed a screening method based on fluorescence-activated cell sorting (FACS) using the model substrate 4-methylumbelliferyl β-d-N,N′,N″-triacetyl chitotrioside to identify improved enzymes. We prevented cross-talk between emulsion compartments caused by the hydrophobicity of 4-methylumbelliferone, the fluorescent product of the enzymatic reaction, by incorporating cyclodextrins into the aqueous phases. We also addressed the toxicity of long-term chiA expression in E. coli by limiting the reaction time. We identified 12 mutants containing 2–8 mutations per gene resulting in up to twofold higher activity than wild-type ChiA.

De novo sequence redesign of a functional Ras-binding domain globally inverted the surface charge distribution and led to extreme thermostability

To acquire extremely thermostable proteins of given functions is challenging for conventional protein engineering. Here we applied ABACUS, a statistical energy function we developed for de novo amino acid sequence design, to globally redesign a Ras-binding domain (RBD), and obtained an extremely thermostable RBD that unfolds reversibly at above 110°C, the redesigned RBD experimentally confirmed to have expected structure and Ras-binding interface. Directed evolution of the redesigned RBD improved its Ras-binding affinity to the native protein level without excessive loss of thermostability. The designed amino acid substitutions were mostly at the protein surface. For many substitutions, strong epistasis or significantly differentiated effects on thermostability in the native sequence context relative to the redesigned sequence context were observed, suggesting the globally redesigned sequence to be unreachable through combining beneficial mutations of the native sequence. Further analyses revealed that by replacing 38 of a total of 48 non-interfacial surface residues at once, ABACUS redesign was able to globally "invert" the protein's charge distribution pattern in an optimized way. Our study demonstrates that computational protein design provides powerful new tools to solve challenging protein engineering problems.

γ-Glutamyl carboxylase mutations differentially affect the biological function of vitamin K-dependent proteins

γ-Glutamyl carboxylase (GGCX) is an integral membrane protein that catalyzes posttranslational carboxylation of a number of vitamin K-dependent (VKD) proteins involved in a wide variety of physiologic processes, including blood coagulation, vascular calcification, and bone metabolism. Naturally occurring GGCX mutations are associated with multiple distinct clinical phenotypes. However, the genotype-phenotype correlation of GGCX remains elusive. Here, we systematically examined the effect of all naturally occurring GGCX mutations on the carboxylation of 3 structure-function distinct VKD proteins in a cellular environment. GGCX mutations were transiently introduced into GGCX-deficient human embryonic kidney 293 cells stably expressing chimeric coagulation factor, matrix Gla protein (MGP), or osteocalcin as VKD reporter proteins, and then the carboxylation efficiency of these reporter proteins was evaluated. Our results show that GGCX mutations differentially affect the carboxylation of these reporter proteins and the efficiency of using vitamin K as a cofactor. Carboxylation of these reporter proteins by a C-terminal truncation mutation (R704X) implies that GGCX's C terminus plays a critical role in the binding of osteocalcin but not in the binding of coagulation factors and MGP. This has been confirmed by probing the protein-protein interaction between GGCX and its protein substrates in live cells using bimolecular fluorescence complementation and chemical cross-linking assays. Additionally, using a minigene splicing assay, we demonstrated that several GGCX missense mutations affect GGCX's pre-messenger RNA splicing rather than altering the corresponding amino acid residues. Results from this study interpreted the correlation of GGCX's genotype and its clinical phenotypes and clarified why vitamin K administration rectified bleeding disorders but not nonbleeding disorders.

Evolution of insulin at the edge of foldability and its medical implications

Proteins have evolved to be foldable, and yet determinants of foldability may be inapparent once the native state is reached. Insight has emerged from studies of diseases of protein misfolding, exemplified by monogenic diabetes mellitus due to mutations in proinsulin leading to endoplasmic reticulum stress and β-cell death. Cellular foldability of human proinsulin requires an invariant Phe within a conserved crevice at the receptor-binding surface (position B24). Any substitution, even related aromatic residue TyrB24, impairs insulin biosynthesis and secretion. As a seeming paradox, a monomeric TyrB24 insulin analog exhibits a native-like structure in solution with only a modest decrement in stability. Packing of TyrB24 is similar to that of PheB24, adjoining core cystine B19-A20 to seal the core; the analog also exhibits native self-assembly. Although affinity for the insulin receptor is decreased ∼20-fold, biological activities in cells and rats were within the range of natural variation. Together, our findings suggest that the invariance of PheB24 among vertebrate insulins and insulin-like growth factors reflects an essential role in enabling efficient protein folding, trafficking, and secretion, a function that is inapparent in native structures. In particular, we envision that the para-hydroxyl group of TyrB24 hinders pairing of cystine B19-A20 in an obligatory on-pathway folding intermediate. The absence of genetic variation at B24 and other conserved sites near this disulfide bridge-excluded due to β-cell dysfunction-suggests that insulin has evolved to the edge of foldability. Nonrobustness of a protein's fitness landscape underlies both a rare monogenic syndrome and "diabesity" as a pandemic disease of civilization.

Consequences of Stability-Induced Epistasis for Substitution Rates

Do interactions between residues in a protein (i.e., epistasis) significantly alter evolutionary dynamics? If so, what consequences might they have on inference from traditional codon substitution models which assume site-independence for the sake of computational tractability? To investigate the effects of epistasis on substitution rates, we employed a mechanistic mutation-selection model in conjunction with a fitness framework derived from protein stability. We refer to this as the stability-informed site-dependent (S-SD) model and developed a new stability-informed site-independent (S-SI) model that captures the average effect of stability constraints on individual sites of a protein. Comparison of S-SI and S-SD offers a novel and direct method for investigating the consequences of stability-induced epistasis on protein evolution. We developed S-SI and S-SD models for three natural proteins and showed that they generate sequences consistent with real alignments. Our analyses revealed that epistasis tends to increase substitution rates compared with the rates under site-independent evolution. We then assessed the epistatic sensitivity of individual site and discovered a counterintuitive effect: Highly connected sites were less influenced by epistasis relative to exposed sites. Lastly, we show that, despite the unrealistic assumptions, traditional models perform comparably well in the presence and absence of epistasis and provide reasonable summaries of average selection intensities. We conclude that epistatic models are critical to understanding protein evolutionary dynamics, but epistasis might not be required for reasonable inference of selection pressure when averaging over time and sites.

Computational analysis of hereditary spastic paraplegia mutations in the kinesin motor domains of KIF1A and KIF5A

Hereditary spastic paraplegias (HSPs) are a genetically heterogeneous collection of neurodegenerative disorders categorized by progressive lower-limb spasticity and frailty. The complex HSP forms are characterized by various neurological features including progressive spastic weakness, urinary sphincter dysfunction, extra pyramidal signs and intellectual disability (ID). The kinesin superfamily proteins (KIFs) are microtubule-dependent molecular motors involved in intracellular transport. Kinesins directionally transport membrane vesicles, protein complexes, and mRNAs along neurites, thus playing important roles in neuronal development and function. Recent genetic studies have identified kinesin mutations in patients with HSPs. In this study, we used the computational approaches to investigate the 40 missense mutations associated with HSP and ID in KIF1A and KIF5A. We performed homology modeling to construct the structures of kinesin–microtubule binding domain and kinesin–tubulin complex. We applied structure-based energy calculation methods to determine the effects of missense mutations on protein stability and protein–protein interaction. The results revealed that the most of disease-causing mutations could change the folding free energy of kinesin motor domain and the binding free energy of kinesin–tubulin complex. We found that E253K associated with ID in KIF1A decrease the protein stability of kinesin motor domains. We showed that the HSP mutations located in kinesin–tubulin complex interface, such as K253N and R280C in KIF5A, can destabilize the kinesin–tubulin complex. The computational analysis provides useful information for understanding the roles of kinesin mutations in the development of ID and HSPs.

Optimization of Protein Thermostability and Exploitation of Recognition Behavior to Engineer Altered Protein-DNA Recognition

The redesign of a macromolecular binding interface and corresponding alteration of recognition specificity is a challenging endeavor that remains recalcitrant to computational approaches. This is particularly true for the redesign of DNA binding specificity, which is highly dependent upon bending, hydrogen bonds, electrostatic contacts, and the presence of solvent and counterions throughout the molecular interface. Thus, redesign of protein-DNA binding specificity generally requires iterative rounds of amino acid randomization coupled to selections. Here, we describe the importance of scaffold thermostability for protein engineering, coupled with a strategy that exploits the protein's specificity profile, to redesign the specificity of a pair of meganucleases toward three separate genomic targets. We determine and describe a series of changes in protein sequence, stability, structure, and activity that accumulate during the engineering process, culminating in fully retargeted endonucleases.

Evolution Rapidly Optimizes Stability and Aggregation in Lattice Proteins Despite Pervasive Landscape Valleys and Mazes

The "fitness" landscapes of genetic sequences are characterized by high dimensionality and "ruggedness" due to sign epistasis. Ascending from low to high fitness on such landscapes can be difficult because adaptive trajectories get stuck at low-fitness local peaks. Compounding matters, recent theoretical arguments have proposed that extremely long, winding adaptive paths may be required to reach even local peaks: a "maze-like" landscape topography. The extent to which peaks and mazes shape the mode and tempo of evolution is poorly understood, due to empirical limitations and the abstractness of many landscape models. We explore the prevalence, scale, and evolutionary consequences of landscape mazes in a biophysically grounded computational model of protein evolution that captures the "frustration" between "stability" and aggregation propensity. Our stability-aggregation landscape exhibits extensive sign epistasis and local peaks galore. Although this frequently obstructs adaptive ascent to high fitness and virtually eliminates reproducibility of evolutionary outcomes, many adaptive paths do successfully complete the ascent from low to high fitness, with hydrophobicity a critical mediator of success. These successful paths exhibit maze-like properties on a global landscape scale, in which taking an indirect path helps to avoid low-fitness local peaks. This delicate balance of "hard but possible" adaptation could occur more broadly in other biological settings where competing interactions and frustration are important.

Impact of In Vivo Protein Folding Probability on Local Fitness Landscapes

It is incompletely understood how biophysical properties like protein stability impact molecular evolution and epistasis. Epistasis is defined as specific when a mutation exclusively influences the phenotypic effect of another mutation, often at physically interacting residues. In contrast, nonspecific epistasis results when a mutation is influenced by a large number of nonlocal mutations. As most mutations are pleiotropic, the in vivo folding probability-governed by basal protein stability-is thought to determine activity-enhancing mutational tolerance, implying that nonspecific epistasis is dominant. However, evidence exists for both specific and nonspecific epistasis as the prevalent factor, with limited comprehensive data sets to support either claim. Here, we use deep mutational scanning to probe how in vivo enzyme folding probability impacts local fitness landscapes. We computationally designed two different variants of the amidase AmiE with statistically indistinguishable catalytic efficiencies but lower probabilities of folding in vivo compared with wild-type. Local fitness landscapes show slight alterations among variants, with essentially the same global distribution of fitness effects. However, specific epistasis was predominant for the subset of mutations exhibiting positive sign epistasis. These mutations mapped to spatially distinct locations on AmiE near the initial mutation or proximal to the active site. Intriguingly, the majority of specific epistatic mutations were codon dependent, with different synonymous codons resulting in fitness sign reversals. Together, these results offer a nuanced view of how protein folding probability impacts local fitness landscapes and suggest that transcriptional-translational effects are as important as stability in determining evolutionary outcomes.

Systematically Scrutinizing the Impact of Substitution Sites on Thermostability and Detergent Tolerance for Bacillus subtilis Lipase A

Improving an enzyme's (thermo-)stability or tolerance against solvents and detergents is highly relevant in protein engineering and biotechnology. Recent developments have tended toward data-driven approaches, where available knowledge about the protein is used to identify substitution sites with high potential to yield protein variants with improved stability, and subsequently, substitutions are engineered by site-directed or site-saturation (SSM) mutagenesis. However, the development and validation of algorithms for data-driven approaches have been hampered by the lack of availability of large-scale data measured in a uniform way and being unbiased with respect to substitution types and locations. Here, we extend our knowledge on guidelines for protein engineering following a data-driven approach by scrutinizing the impact of substitution sites on thermostability or/and detergent tolerance for Bacillus subtilis lipase A (BsLipA) at very large scale. We systematically analyze a complete experimental SSM library of BsLipA containing all 3439 possible single variants, which was evaluated as to thermostability and tolerances against four detergents under respectively uniform conditions. Our results provide systematic and unbiased reference data at unprecedented scale for a biotechnologically important protein, identify consistently defined hot spot types for evaluating the performance of data-driven protein-engineering approaches, and show that the rigidity theory and ensemble-based approach Constraint Network Analysis yields hot spot predictions with an up to ninefold gain in precision over random classification.

T54R mutation destabilizes the dimer of superoxide dismutase 1T54R by inducing steric clashes at the dimer interface

Mutations cause abnormalities in protein structure, function and oligomerization. Different mutations in the superoxide dismutase 1 (SOD1) protein cause its misfolding, loss of dimerization and aggravate its aggregation in the amyotrophic lateral sclerosis disease. In this study, we report the mechanistic details of how a threonine-to-arginine mutation at the 54^th position (T54R) of SOD1 results in destabilization of the dimer interface of SOD1^T54R. Using computational and experimental methods, we show that the T54R mutation increases fluctuation of the mutation-harboring loop (R54-loop) of SOD1^T54R. Fluctuation of this loop causes steric clashes that involve arginine-54 (R54) and other residues of SOD1^T54R, resulting in loss of inter-subunit contacts at the dimer interface. Since the T54 residue-containing loop is necessary for the dimerization of wild-type SOD1, fluctuation of the R54-loop, steric clashes involving R54 and loss of inter-subunit contacts give rise to the loss of SOD1^T54R dimer stability. This correlates to energetically unfavorable tethering of the monomers of SOD1^T54R. The outcome is gradual splitting of SOD1^T54R dimers into monomers, thereby exposing the previously buried hydrophobic interface residues to the aqueous environment. This event finally leads to aggregation of SOD1^T54R. T54R mutation has no effect in altering the relative positions of copper and zinc ion binding residues of SOD1^T54R. The native SOD1 structure is stable, and there is no destabilizing effect at its dimer interface. Overall, our study reveals the intricate mechanism of T54R mutation-associated destabilization of the dimer of the SOD1^T54R protein.

T54R mutation destabilizes the dimer of SOD1^T54R.

ADAPTABLE: a comprehensive web platform of antimicrobial peptides tailored to the user's research

Antimicrobial peptides (AMPs) are part of the innate immune response to pathogens in all of the kingdoms of life. They have received significant attention because of their extraordinary variety of activities, in particular, as candidate drugs against the threat of super-bacteria. A systematic study of the relation between the sequence and the mechanism of action is urgently needed, given the thousands of sequences already in multiple web resources. ADAPTABLE web platform (http://gec.u-picardie.fr/adaptable) introduces the concept of "property alignment" to create families of property and sequence-related peptides (SR families). This feature provides the researcher with a tool to select those AMPs meaningful to their research from among more than 40,000 nonredundant sequences. Selectable properties include the target organism and experimental activity concentration, allowing selection of peptides with multiple simultaneous actions. This is made possible by ADAPTABLE because it not only merges sequences of AMP databases but also merges their data, thereby standardizing values and handling non-proteinogenic amino acids. In this unified platform, SR families allow the creation of peptide scaffolds based on common traits in peptides with similar activity, independently of their source.

Limits to Compensatory Mutations: Insights from Temperature-Sensitive Alleles

Previous experiments with temperature-sensitive mutants of the yeast enzyme orotidine 5'-phosphate decarboxylase (encoded in gene URA3) yielded the unexpected result that reversion occurs only through exact reversal of the original mutation (Jakubowska A, Korona R. 2009. Lack of evolutionary conservation at positions important for thermal stability in the yeast ODCase protein. Mol Biol Evol. 26(7):1431-1434.). We recreated a set of these mutations in which the codon had two nucleotide substitutions, making exact reversion much less likely. We screened these double mutants for reversion and obtained a number of compensatory mutations occurring at alternative sites in the molecule. None of these compensatory mutations fully restored protein performance. The mechanism of partial compensation is consistent with a model in which protein stabilization is additive, as the same secondary mutations can compensate different primary alternations. The distance between primary and compensatory residues precludes direct interaction between the sites. Instead, most of the compensatory mutants were clustered in proximity to the catalytic center. All of the second-site compensatory substitutions occurred at relatively conserved sites, and the amino acid replacements were to residues found at these sites in a multispecies alignment of the protein. Based on the estimated distribution of changes in Gibbs free energy among a large number of amino acid replacements, we estimate that, for most proteins, the probability that a second-site mutation would have a sufficiently large stabilizing effect to offset a temperature-sensitive mutation in the order of 10-4 or less. Hence compensation is likely to take place only for slightly destabilizing mutations because highly stabilizing mutations are exceeding rare.

Gene Families, Epistasis and the Amino Acid Preferences of Protein Homologs

In order to preserve structure and function, proteins tend to preferentially conserve amino acids at particular sites along the sequence. Because mutations can affect structure and function, the question arises whether the preference of a protein site for a particular amino acid varies between protein homologs, and to what extent that variation depends on sequence divergence. Answering these questions can help in the development of models of sequence evolution, as well as provide insights on the dependence of the fitness effects of mutations on the genetic background of sequences, a phenomenon known as epistasis. Here, I comment on recent computational work providing a systematic analysis of the extent to which the amino acid preferences of proteins depend on the background mutations of protein homologs.

MetaDome: Pathogenicity analysis of genetic variants through aggregation of homologous human protein domains

The growing availability of human genetic variation has given rise to novel methods of measuring genetic tolerance that better interpret variants of unknown significance. We recently developed a concept based on protein domain homology in the human genome to improve variant interpretation. For this purpose, we mapped population variation from the Exome Aggregation Consortium (ExAC) and pathogenic mutations from the Human Gene Mutation Database (HGMD) onto Pfam protein domains. The aggregation of these variation data across homologous domains into meta-domains allowed us to generate amino acid resolution of genetic intolerance profiles for human protein domains. Here, we developed MetaDome, a fast and easy-to-use web server that visualizes meta-domain information and gene-wide profiles of genetic tolerance. We updated the underlying data of MetaDome to contain information from 56,319 human transcripts, 71,419 protein domains, 12,164,292 genetic variants from gnomAD, and 34,076 pathogenic mutations from ClinVar. MetaDome allows researchers to easily investigate their variants of interest for the presence or absence of variation at corresponding positions within homologous domains. We illustrate the added value of MetaDome by an example that highlights how it may help in the interpretation of variants of unknown significance. The MetaDome web server is freely accessible at https://stuart.radboudumc.nl/metadome.

Massively Parallel Assays and Quantitative Sequence-Function Relationships

Over the last decade, a rich variety of massively parallel assays have revolutionized our understanding of how biological sequences encode quantitative molecular phenotypes. These assays include deep mutational scanning, high-throughput SELEX, and massively parallel reporter assays. Here, we review these experimental methods and how the data they produce can be used to quantitatively model sequence-function relationships. In doing so, we touch on a diverse range of topics, including the identification of clinically relevant genomic variants, the modeling of transcription factor binding to DNA, the functional and evolutionary landscapes of proteins, and cis-regulatory mechanisms in both transcription and mRNA splicing. We further describe a unified conceptual framework and a core set of mathematical modeling strategies that studies in these diverse areas can make use of. Finally, we highlight key aspects of experimental design and mathematical modeling that are important for the results of such studies to be interpretable and reproducible.

The Site-Specific Amino Acid Preferences of Homologous Proteins Depend on Sequence Divergence

The propensity of protein sites to be occupied by any of the 20 amino acids is known as site-specific amino acid preferences (SSAP). Under the assumption that SSAP are conserved among homologs, they can be used to parameterize evolutionary models for the reconstruction of accurate phylogenetic trees. However, simulations and experimental studies have not been able to fully assess the relative conservation of SSAP as a function of sequence divergence between protein homologs. Here, we implement a computational procedure to predict the SSAP of proteins based on the effect of changes in thermodynamic stability upon mutation. An advantage of this computational approach is that it allows us to interrogate a large and unbiased sample of homologous proteins, over the entire spectrum of sequence divergence, and under selection for the same molecular trait. We show that computational predictions have reproducibilities that resemble those obtained in experimental replicates, and can largely recapitulate the SSAP observed in a large-scale mutagenesis experiment. Our results support recent experimental reports on the conservation of SSAP of related homologs, with a slowly increasing fraction of up to 15% of different sites at sequence distances lower than 40%. However, even under the sole contribution of thermodynamic stability, our conservative approach identifies up to 30% of significant different sites between divergent homologs. We show that this relation holds for homologs of diverse sizes and structural classes. Analyses of residue contact networks suggest that an important determinant of these differences is the increasing accumulation of structural deviations that results from sequence divergence.

Exploiting correlated molecular-dynamics networks to counteract enzyme activity-stability trade-off

Rigidifying flexible sites is a powerful method to improve enzyme stability. However, if the highly flexible regions form the active site, modifying them risks losing activity due to the activity–stability trade-off. We hypothesized here that regions outside the active site whose dynamics were highly correlated to flexible active sites, would provide good targets for stabilizing mutations. To test this hypothesis, six variants were constructed in the 3M variant of Escherichia coli transketolase. The best variant had a 10.8-fold improved half-life at 55 °C, and increased the T_m and T_agg by 3 °C and 4.3 °C, respectively. The variants even increased the activity, by up to threefold. This study highlights how protein engineering strategies could be potentially improved by considering long-range dynamics.

The directed evolution of enzymes for improved activity or substrate specificity commonly leads to a trade-off in stability. We have identified an activity–stability trade-off and a loss in unfolding cooperativity for a variant (3M) of Escherichia coli transketolase (TK) engineered to accept aromatic substrates. Molecular dynamics simulations of 3M revealed increased flexibility in several interconnected active-site regions that also form part of the dimer interface. Mutating the newly flexible active-site residues to regain stability risked losing the new activity. We hypothesized that stabilizing mutations could be targeted to residues outside of the active site, whose dynamics were correlated with the newly flexible active-site residues. We previously stabilized WT TK by targeting mutations to highly flexible regions. These regions were much less flexible in 3M and would not have been selected a priori as targets using the same strategy based on flexibility alone. However, their dynamics were highly correlated with the newly flexible active-site regions of 3M. Introducing the previous mutations into 3M reestablished the WT level of stability and unfolding cooperativity, giving a 10.8-fold improved half-life at 55 °C, and increased midpoint and aggregation onset temperatures by 3 °C and 4.3 °C, respectively. Even the activity toward aromatic aldehydes increased up to threefold. Molecular dynamics simulations confirmed that the mutations rigidified the active-site via the correlated network. This work provides insights into the impact of rigidifying mutations within highly correlated dynamic networks that could also be useful for developing improved computational protein engineering strategies.

In silico thermodynamic stability of mammalian adaptation and virulence determinants in polymerase complex proteins of H9N2 virus

The polymerase complex proteins (PB2, PB1, and PA) are responsible primarily for the replication of avian influenza virus and play an important role in virus virulence, mammalian adaptation, and interspecies transmission. In this study; eight Egyptian LPAI-H9N2 viruses isolated from apparent healthy chickens and quails from 2014 to 2016. Characterization of complete nucleotide sequences, phylogenetic and mutation analysis were carried out. The measurement of thermodynamic stability of the H9N2 polymerase protein in comparison to human H3N2 and H1N1 proteins was carried out using in silico method. Phylogenetic analysis of these viruses revealed a close relationship to viruses isolated from neighboring Middle Eastern countries with an average of 96-99% homology. They are sharing the common ancestor A/quail/Hong Kong/G1/1997 (G1-Like) without any evidence for genetic reassortment. In addition, eight markers related to virulence were identified, including the combination of 627V and 391E in the PB2 gene with full-length PB1-F2 and PA-X proteins were observed in all viruses and the substitution N66S in PB1-F2 which suggest increasing virus virulence. Moreover, six markers that may affect the virus replication and transmission in mammalian hosts were identified. Five mutations related to mammalian adaptation show a structural stabilizing effect on LPAI-H9N2 polymerase complex protein according to the free-energy change (ΔΔG). Three out of those six adaptive mutations shown to increase polymerase complex protein stability were found in Egyptian LPAI-H9N2 viruses similar to Human H3N2 and H1N1 (661 in PB2, 225 and 409 in PA genes). Our results suggested that the stabilizing mutations in the polymerase complex protein have likely affected the protein structure and induced favorable conditions for avian virus replication and transmission in mammalian hosts. Indeed, the study reports the mutational analysis of the circulating LPAI-H9N2 strains in Egypt.