Epistasis and intramolecular networks in protein evolution

Evolutionary rewiring of the dynamic network underpinning allosteric epistasis in NS1 of influenza A virus

Viral proteins frequently mutate to evade or antagonize host innate immune responses, yet the impact of these mutations on the molecular energy landscape remains unclear. Epistasis, the intramolecular communications between mutations, often renders the combined mutational effects unpredictable. Nonstructural protein 1 (NS1) is a major virulence factor of the influenza A virus (IAV) that activates host PI3K by binding to its p85β subunit. Here, we present the deep analysis for the impact of evolutionary mutations in NS1 that emerged between the 1918 pandemic IAV strain and its descendant PR8 strain. Our analysis reveal how the mutations rewired inter-residue communications which underlies long-range allosteric and epistatic networks in NS1. Our findings show that PR8 NS1 binds to p85β with approximately 10-fold greater affinity than 1918 NS1 due to allosteric mutational effects. Notably, these mutations also exhibited long-range epistatic effects. NMR chemical shift perturbation and methyl-axis order parameter analyses revealed that the mutations induced long-range structural and dynamic changes in PR8 NS1, enhancing its affinity to p85β. Complementary MD simulations and graph-based network analysis uncover how these mutations rewire dynamic residue interaction networks, which underlies the long-range epistasis and allosteric effects on p85β-binding affinity. Significantly, we find that conformational dynamics of residues with high betweenness centrality play a crucial role in communications between network communities and are highly conserved across influenza A virus evolution. These findings advance our mechanistic understanding of the allosteric and epistatic communications between distant residues and provides insight into their role in the molecular evolution of NS1.

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.

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

Epistasis arises from shifting the rate-limiting step during enzyme evolution of a β-lactamase

Epistasis, the non-additive effect of mutations, can provide combinatorial improvements to enzyme activity that substantially exceed the gains from individual mutations. Yet the molecular mechanisms of epistasis remain elusive, undermining our ability to predict pathogen evolution and engineer biocatalysts. Here we reveal how directed evolution of a β-lactamase yielded highly epistatic activity enhancements. Evolution selected four mutations that increase antibiotic resistance 40-fold, despite their marginal individual effects (≤2-fold). Synergistic improvements coincided with the introduction of super-stochiometric burst kinetics, indicating that epistasis is rooted in the enzyme's conformational dynamics. Our analysis reveals that epistasis stemmed from distinct effects of each mutation on the catalytic cycle. The initial mutation increased protein flexibility and accelerated substrate binding, which is rate-limiting in the wild-type enzyme. Subsequent mutations predominantly boosted the chemical steps by fine-tuning substrate interactions. Our work identifies an overlooked cause for epistasis: changing the rate-limiting step can result in substantial synergy that boosts enzyme activity.

The Mutational Road not Taken: Using Ancestral Sequence Resurrection to Evaluate the Evolution of Plant Enzyme Substrate Preferences

We investigated the flowering plant salicylic acid methyl transferase (SAMT) enzyme lineage to understand the evolution of substrate preference change. Previous studies indicated that a single amino acid replacement to the SAMT active site (H150M) was sufficient to change ancestral enzyme substrate preference from benzoic acid to the structurally similar substrate, salicylic acid (SA). Yet, subsequent studies have shown that the H150M function-changing replacement did not likely occur during the historical episode of enzymatic divergence studied. Therefore, we reinvestigated the origin of SA methylation preference here and additionally assessed the extent to which epistasis may act to limit mutational paths. We found that the SAMT lineage of enzymes acquired preference to methylate SA from an ancestor that preferred to methylate benzoic acid as previously reported. In contrast, we found that a different amino acid replacement, Y267Q, was sufficient to change substrate preference with others providing small positive-magnitude epistatic improvements. We show that the kinetic basis for the ancestral enzymatic change in substate preference by Y267Q appears to be due to both a reduced specificity constant, kcat/KM, for benzoic acid and an improvement in KM for SA. Therefore, this lineage of enzymes appears to have had multiple mutational paths available to achieve the same evolutionary divergence. While the reasons remain unclear for why one path was taken, and the other was not, the mutational distance between ancestral and descendant codons may be a factor.

Evolution shapes interaction patterns for epistasis and specific protein binding in a two-component signaling system

The elegant design of protein sequence/structure/function relationships arises from the interaction patterns between amino acid positions. A central question is how evolutionary forces shape the interaction patterns that encode long-range epistasis and binding specificity. Here, we combined family-wide evolutionary analysis of natural homologous sequences and structure-oriented evolution simulation for two-component signaling (TCS) system. The magnitude-frequency relationship of coupling conservation between positions manifests a power-law-like distribution and the positions with highly coupling conservation are sparse but distributed intensely on the binding surfaces and hydrophobic core. The structure-specific interaction pattern involves further optimization of local frustrations at or near the binding surface to adapt the binding partner. The construction of family-wide conserved interaction patterns and structure-specific ones demonstrates that binding specificity is modulated by both direct intermolecular interactions and long-range epistasis across the binding complex. Evolution sculpts the interaction patterns via sequence variations at both family-wide and structure-specific levels for TCS system.

Enantioselective transformation of phytoplankton-derived dihydroxypropanesulfonate by marine bacteria

Chirality, a fundamental property of matter, is often overlooked in the studies of marine organic matter cycles. Dihydroxypropanesulfonate (DHPS), a globally abundant organosulfur compound, serves as an ecologically important currency for nutrient and energy transfer from phytoplankton to bacteria in the ocean. However, the chirality of DHPS in nature and its transformation remain unclear. Here, we developed a novel approach using chiral phosphorus-reagent labeling to separate DHPS enantiomers. Our findings demonstrated that at least one enantiomer of DHPS is present in marine diatoms and coccolithophores, and that both enantiomers are widespread in marine environments. A novel chiral-selective DHPS catabolic pathway was identified in marine Roseobacteraceae strains, where HpsO and HpsP dehydrogenases at the gateway to DHPS catabolism act specifically on R-DHPS and S-DHPS, respectively. R-DHPS is also a substrate for the dehydrogenase HpsN. All three dehydrogenases generate stable hydrogen bonds between the chirality-center hydroxyls of DHPS and highly conserved residues, and HpsP also form coordinate-covalent bonds between the chirality-center hydroxyls and Zn2+, which determines the mechanistic basis of strict stereoselectivity. We further illustrated the role of enzymatic promiscuity in the evolution of DHPS metabolism in Roseobacteraceae and SAR11. This study provides the first evidence of chirality's involvement in phytoplankton-bacteria metabolic currencies, opening a new avenue for understanding the ocean organosulfur cycle.

Pervasive epistasis exposes intramolecular networks in adaptive enzyme evolution

Enzyme evolution is characterized by constant alterations of the intramolecular residue networks supporting their functions. The rewiring of these network interactions can give rise to epistasis. As mutations accumulate, the epistasis observed across diverse genotypes may appear idiosyncratic, that is, exhibit unique effects in different genetic backgrounds. Here, we unveil a quantitative picture of the prevalence and patterns of epistasis in enzyme evolution by analyzing 41 fitness landscapes generated from seven enzymes. We show that >94% of all mutational and epistatic effects appear highly idiosyncratic, which greatly distorted the functional prediction of the evolved enzymes. By examining seemingly idiosyncratic changes in epistasis along adaptive trajectories, we expose several instances of higher-order, intramolecular rewiring. Using complementary structural data, we outline putative molecular mechanisms explaining higher-order epistasis along two enzyme trajectories. Our work emphasizes the prevalence of epistasis and provides an approach to exploring this phenomenon through a molecular lens.

Discovery and Engineering of a Novel Bacterial L-Aspartate α-Decarboxylase for Efficient Bioconversion

L-aspartate α-decarboxylase (ADC) is a pyruvoyl-dependent decarboxylase that catalyzes the conversion of L-aspartate to β-alanine in the pantothenate pathway. The enzyme has been extensively used in the biosynthesis of β-alanine and D-pantothenic acid. However, the broad application of ADCs is hindered by low specific activity. To address this issue, we explored 412 sequences and discovered a novel ADC from Corynebacterium jeikeium (CjADC). CjADC exhibited specific activity of 10.7 U/mg and Km of 3.6 mM, which were better than the commonly used ADC from Bacillus subtilis. CjADC was then engineered leveraging structure-guided evolution and generated a mutant, C26V/I88M/Y90F/R3V. The specific activity of the mutant is 28.8 U/mg, which is the highest among the unknown ADCs. Furthermore, the mutant displayed lower Km than the wild-type enzyme. Moreover, we revealed that the introduced mutations increased the structural stability of the mutant by promoting the frequency of hydrogen-bond formation and creating a more hydrophobic region around the active center, thereby facilitating the binding of L-aspartate to the active center and stabilizing the substrate orientation. Finally, the whole-cell bioconversion showed that C26V/I88M/Y90F/R3V completely transformed 1-molar L-aspartate in 12 h and produced 88.6 g/L β-alanine. Our study not only identified a high-performance ADC but also established a research framework for rapidly screening novel enzymes using a protein database.

Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases

In this work, we investigated the lactate dehydrogenase (LDH) from Selenomonas ruminantium (S. rum), an enzyme that differs at key amino acid positions from canonical allosteric LDHs. The wild type (Wt) of this enzyme recognises pyuvate as all LDHs. However, introducing a single point mutation in the active site loop (I85R) allows S. Rum LDH to recognize the oxaloacetate substrate as a typical malate dehydrogenase (MalDH), whilst maintaining homotropic activation as an LDH. We report the tertiary structure of the Wt and I85RLDH mutant. The Wt S. rum enzyme structure binds NADH and malonate, whilst also resembling the typical compact R-active state of canonical LDHs. The structure of the mutant with I85R was solved in the Apo State (without ligand), and shows no large conformational reorganization such as that observed with canonical allosteric LDHs in Apo state. This is due to a local structural feature typical of S. rum LDH that prevents large-scale conformational reorganization. The S. rum LDH was also studied using Molecular Dynamics simulations, probing specific local deformations of the active site that allow the S. rum LDH to sample the T-inactive state. We propose that, with respect to the LDH/MalDH superfamily, the S. rum enzyme possesses a specificstructural and dynamical way to ensure homotropic activation.

Slc11 Synapomorphy: A Conserved 3D Framework Articulating Carrier Conformation Switch

Transmembrane carriers of the Slc11 family catalyze proton (H+)-dependent uptake of divalent metal ions (Me2+) such as manganese and iron-vital elements coveted during infection. The Slc11 mechanism of high-affinity Me2+ cell import is selective and conserved between prokaryotic (MntH) and eukaryotic (Nramp) homologs, though processes coupling the use of the proton motive force to Me2+ uptake evolved repeatedly. Adding bacterial piracy of Nramp genes spread in distinct environmental niches suggests selective gain of function that may benefit opportunistic pathogens. To better understand Slc11 evolution, Alphafold (AF2)/Colabfold (CF) 3D predictions for bacterial sequences from sister clades of eukaryotic descent (MCb and MCg) were compared using both native and mutant templates. AF2/CF model an array of native MCb intermediates spanning the transition from outwardly open (OO) to inwardly open (IO) carriers. In silico mutagenesis targeting (i) a set of (evolutionarily coupled) sites that may define Slc11 function (putative synapomorphy) and (ii) residues from networked communities evolving during MCb transition indicates that Slc11 synapomorphy primarily instructs a Me2+-selective conformation switch which unlocks carrier inner gate and contributes to Me2+ binding site occlusion and outer gate locking. Inner gate opening apparently proceeds from interaction between transmembrane helix (h) h5, h8 and h1a. MCg1 xenologs revealed marked differences in carrier shape and plasticity, owing partly to an altered intramolecular H+ network. Yet, targeting Slc11 synapomorphy also converted MCg1 IO models to an OO state, apparently mobilizing the same residues to control gates. But MCg1 response to mutagenesis differed, with extensive divergence within this clade correlating with MCb-like modeling properties. Notably, MCg1 divergent epistasis marks the emergence of the genus Bordetella-Achromobacter. Slc11 synapomorphy localizes to the 3D areas that deviate least among MCb and MCg1 models (either IO or OO) implying that it constitutes a 3D network of residues articulating a Me2+-selective carrier conformation switch which is maintained in fast-evolving clades at the cost of divergent epistatic interactions impacting carrier shape and dynamics.

Genetic Control of GCF Exudation: Innate Immunity Genes and Periodontitis Susceptibility

Chronic periodontitis is a bacterial infection associated with dentally adherent biofilm (plaque) accumulation and age-related comorbidities. The disease begins as an inflammatory exudate from gingival margins, gingival crevicular fluid (GCF) in response to biofilm lysine. After a week of experimental gingivitis (no oral hygiene), biofilm lysine concentration was linearly related to biofilm accumulation (plaque index) but to GCF as an arch-shaped double curve which separated 9 strong from 6 weak GCF responders (hosts). Host DNA was examined for single nucleotide polymorphisms (SNPs) of alleles reported in 7 periodontitis-associated genes. Across all 15 hosts, an adenine SNP (A) at IL1B-511 (rs16944), was significant for strong GCF (Fisher's exact test, p < 0.05), and a thymidine SNP (T) at IL1B+3954 (rs1143634) for weak GCF provided 2 hosts possessing IL6-1363(T), rs2069827, were included. The phenotype of IL1B+3954(T) was converted from weak to strong in one host, and of the non-T allele from strong to weak in the other (specific epistasis, Fisher's exact test, p < 0.01). Together with homozygous alternate or reference SNPs at IL10-1082 or CD14-260 in 4 hosts, all hosts were identified as strong or weak GCF responders. The GCF response is therefore a strong or weak genetic trait that indicates strong or weak innate immunity in EG and controllable or uncontrollable periodontal disease, dental implant survival and late-life comorbidities.

Molecular determinants of protein evolvability

The plethora of biological functions that sustain life is rooted in the remarkable evolvability of proteins. An emerging view highlights the importance of a protein's initial state in dictating evolutionary success. A deeper comprehension of the mechanisms that govern the evolvability of these initial states can provide invaluable insights into protein evolution. In this review, we describe several molecular determinants of protein evolvability, unveiled by experimental evolution and ancestral sequence reconstruction studies. We further discuss how genetic variation and epistasis can promote or constrain functional innovation and suggest putative underlying mechanisms. By establishing a clear framework for these determinants, we provide potential indicators enabling the forecast of suitable evolutionary starting points and delineate molecular mechanisms in need of deeper exploration.

Paired associated SARS-CoV-2 spike variable positions: a network analysis approach to emerging variants

Amino acids in variable positions of proteins may be correlated, with potential structural and functional implications. Here, we apply exact tests of independence in R × C contingency tables to examine noise-free associations between variable positions of the SARS-CoV-2 spike protein, using as a paradigm sequences from Greece deposited in GISAID (N = 6,683/1,078 full length) for the period 29 February 2020 to 26 April 2021 that essentially covers the first three pandemic waves. We examine the fate and complexity of these associations by network analysis, using associated positions (exact P ≤ 0.001 and Average Product Correction ≥ 2) as links and the corresponding positions as nodes. We found a temporal linear increase of positional differences and a gradual expansion of the number of position associations over time, represented by a temporally evolving intricate web, resulting in a non-random complex network of 69 nodes and 252 links. Overconnected nodes corresponded to the most adapted variant positions in the population, suggesting a direct relation between network degree and position functional importance. Modular analysis revealed 25 k-cliques comprising 3 to 11 nodes. At different k-clique resolutions, one to four communities were formed, capturing epistatic associations of circulating variants (Alpha, Beta, B.1.1.318), but also Delta, which dominated the evolutionary landscape later in the pandemic. Cliques of aminoacidic positional associations tended to occur in single sequences, enabling the recognition of epistatic positions in real-world virus populations. Our findings provide a novel way of understanding epistatic relationships in viral proteins with potential applications in the design of virus control procedures. IMPORTANCE Paired positional associations of adapted amino acids in virus proteins may provide new insights for understanding virus evolution and variant formation. We investigated potential intramolecular relationships between variable SARS-CoV-2 spike positions by exact tests of independence in R × C contingency tables, having applied Average Product Correction (APC) to eliminate background noise. Associated positions (exact P ≤ 0.001 and APC ≥ 2) formed a non-random, epistatic network of 25 cliques and 1-4 communities at different clique resolutions, revealing evolutionary ties between variable positions of circulating variants and a predictive potential of previously unknown network positions. Cliques of different sizes represented theoretical combinations of changing residues in sequence space, allowing the identification of significant aminoacidic combinations in single sequences of real-world populations. Our analytic approach that links network structural aspects to mutational aminoacidic combinations in the spike sequence population offers a novel way to understand virus epidemiology and evolution.

Rethinking the protein folding problem from a new perspective

One of the main concerns of Anfinsen was to reveal the connection between the amino-acid sequence and their biologically active conformation. This search gave rise to two crucial questions in structural biology, namely, why the proteins fold and how a sequence encodes its folding. As to the why, he proposes a plausible answer, namely, the thermodynamic hypothesis. As to the how, this remains an unsolved challenge. Consequently, the protein folding problem is examined here from a new perspective, namely, as an 'analytic whole'. Conceiving the protein folding in this way enabled us to (i) examine in detail why the force-field-based approaches have failed, among other purposes, in their ability to predict the three-dimensional structure of a protein accurately; (ii) propose how to redefine them to prevent these shortcomings, and (iii) conjecture on the origin of the state-of-the-art numerical-methods success to predict the tridimensional structure of proteins accurately.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

Inferring protein fitness landscapes from laboratory evolution experiments

Directed laboratory evolution applies iterative rounds of mutation and selection to explore the protein fitness landscape and provides rich information regarding the underlying relationships between protein sequence, structure, and function. Laboratory evolution data consist of protein sequences sampled from evolving populations over multiple generations and this data type does not fit into established supervised and unsupervised machine learning approaches. We develop a statistical learning framework that models the evolutionary process and can infer the protein fitness landscape from multiple snapshots along an evolutionary trajectory. We apply our modeling approach to dihydrofolate reductase (DHFR) laboratory evolution data and the resulting landscape parameters capture important aspects of DHFR structure and function. We use the resulting model to understand the structure of the fitness landscape and find numerous examples of epistasis but an overall global peak that is evolutionarily accessible from most starting sequences. Finally, we use the model to perform an in silico extrapolation of the DHFR laboratory evolution trajectory and computationally design proteins from future evolutionary rounds.

Evolution of enzyme functionality in the flavin-containing monooxygenases

Among the molecular mechanisms of adaptation in biology, enzyme functional diversification is indispensable. By allowing organisms to expand their catalytic repertoires and adopt fundamentally different chemistries, animals can harness or eliminate new-found substances and xenobiotics that they are exposed to in new environments. Here, we explore the flavin-containing monooxygenases (FMOs) that are essential for xenobiotic detoxification. Employing a paleobiochemistry approach in combination with enzymology techniques we disclose the set of historical substitutions responsible for the family's functional diversification in tetrapods. Remarkably, a few amino acid replacements differentiate an ancestral multi-tasking FMO into a more specialized monooxygenase by modulating the oxygenating flavin intermediate. Our findings substantiate an ongoing premise that enzymatic function hinges on a subset of residues that is not limited to the active site core.

Deep metagenomic mining reveals bacteriophage sequence motifs driving host specificity

Bacteriophages can adapt to new hosts by altering sequence motifs through recombination or convergent evolution. Where such motifs exist and what fitness advantage they confer remains largely unknown. We report a new method, Bacteriophage Library Informed Sequence Scoring (BLISS), to discover sequence motifs in metagenomic datasets governing phage activity. BLISS uses experimental deep mutational scanning data to create sequence profiles to enable deep mining of metagenomes for functional motifs which are otherwise invisible to searches. We experimentally tested 10,073 BLISS-derived sequence motifs for the receptor-binding protein of the T7 phage. The screen revealed hundreds of T7 variants with novel host specificity with functional motifs sourced from distant families besides other major phyla. Position, substitution and location preferences on T7 dictated different specificities. To demonstrate therapeutic utility, we engineered highly active T7 variants against urinary tract pathogens. BLISS is a powerful tool to unlock the functional potential encoded in phage metagenomes.

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.

Energy landscape reshaped by strain-specific mutations underlies epistasis in NS1 evolution of influenza A virus

Elucidating how individual mutations affect the protein energy landscape is crucial for understanding how proteins evolve. However, predicting mutational effects remains challenging because of epistasis—the nonadditive interactions between mutations. Here, we investigate the biophysical mechanism of strain-specific epistasis in the nonstructural protein 1 (NS1) of influenza A viruses (IAVs). We integrate structural, kinetic, thermodynamic, and conformational dynamics analyses of four NS1s of influenza strains that emerged between 1918 and 2004. Although functionally near-neutral, strain-specific NS1 mutations exhibit long-range epistatic interactions with residues at the p85β-binding interface. We reveal that strain-specific mutations reshaped the NS1 energy landscape during evolution. Using NMR spin dynamics, we find that the strain-specific mutations altered the conformational dynamics of the hidden network of tightly packed residues, underlying the evolution of long-range epistasis. This work shows how near-neutral mutations silently alter the biophysical energy landscapes, resulting in diverse background effects during molecular evolution.

Influenza A virus (IAV) nonstructural protein 1 (NS1) is a multifunctional virulence factor that interacts with several host factors such as phosphatidylinositol-3-kinase (PI3K). NS1 binds specifically to the p85β regulatory subunit of PI3K and subsequently activates PI3K signaling. Here, Kim et al. show that functionally near-neutral, strain-specific NS1 mutations lead to variations in binding kinetics to p85β exhibit long-range epistatic interactions. Applying NMR they provide evidence that the structural dynamics of the NS1 hydrophobic core have evolved over time and contributed to epistasis.

Higher-order epistasis and phenotypic prediction

One core goal of genetics is to systematically understand the mapping between the DNA sequence of an organism (genotype) and its measurable characteristics (phenotype). Understanding this mapping is often challenging because of interactions between mutations, where the result of combining several different mutations can be very different than the sum of their individual effects. Here we provide a statistical framework for modeling complex genetic interactions of this type. The key idea is to ask how fast the effects of mutations change when introducing the same mutation in increasingly distant genetic backgrounds. We then propose a model for phenotypic prediction that takes into account this tendency for the effects of mutations to be more similar in nearby genetic backgrounds.

Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype–phenotype relationship typically reflects not only genetic interactions between pairs of sites but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis and reconstruct the genotype–phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA 5′ splice sites, for which we also validate our model predictions via additional low-throughput experiments.

Distal Mutations Shape Substrate-Binding Sites during Evolution of a Metallo-Oxidase into a Laccase

Laccases are in increasing demand as innovative solutions in the biorefinery fields. Here, we combine mutagenesis with structural, kinetic, and in silico analyses to characterize the molecular features that cause the evolution of a hyperthermostable metallo-oxidase from the multicopper oxidase family into a laccase (k _cat 273 s^-1 for a bulky aromatic substrate). We show that six mutations scattered across the enzyme collectively modulate dynamics to improve the binding and catalysis of a bulky aromatic substrate. The replacement of residues during the early stages of evolution is a stepping stone for altering the shape and size of substrate-binding sites. Binding sites are then fine-tuned through high-order epistasis interactions by inserting distal mutations during later stages of evolution. Allosterically coupled, long-range dynamic networks favor catalytically competent conformational states that are more suitable for recognizing and stabilizing the aromatic substrate. This work provides mechanistic insight into enzymatic and evolutionary molecular mechanisms and spots the importance of iterative experimental and computational analyses to understand local-to-global changes.

Proteins' Evolution upon Point Mutations

As the reader must be already aware, state-of-the-art protein folding prediction methods have reached a smashing success in their goal of accurately determining the three-dimensional structures of proteins. Yet, a solution to simple problems such as the effects of protein point mutations on their (i) native conformation; (ii) marginal stability; (iii) ensemble of high-energy nativelike conformations; and (iv) metamorphism propensity and, hence, their evolvability, remains as an unsolved problem. As a plausible solution to the latter, some properties of the amide hydrogen-deuterium exchange, a highly sensitive probe of the structure, stability, and folding of proteins, are assessed from a new perspective. The preliminary results indicate that the protein marginal stability change upon point mutations provides the necessary and sufficient information to estimate, through a Boltzmann factor, the evolution of the amide hydrogen exchange protection factors and, consequently, that of the ensemble of folded conformations coexisting with the native state. This work contributes to our general understanding of the effects of point mutations on proteins and may spur significant progress in our efforts to develop methods to determine the appearance of new folds and functions accurately.

Mitochondrial Genetics Reinforces Multiple Layers of Interaction in Alzheimer’s Disease

Simple Summary

Nuclear DNA remains the main source of genome-wide loci association in neurodegenerative diseases, only partially accounting for the heritability of Alzheimer’s Disease (AD). In this context, mitochondrial DNA (mtDNA) is gaining more attention. Here, we investigated mitochondrial genes and genetic variants that may influence mild cognitive impairment and AD, through an integrative analysis including both differential gene expression and mitochondrial genome-wide epistasis analysis. Our results highlight important layers of interactions involving mitochondrial genetics and suggest specific molecular alterations as potential biomarkers for AD.

Abstract

Nuclear DNA has been the main source of genome-wide loci association in neurodegenerative diseases, only partially accounting for the heritability of Alzheimer’s Disease (AD). In this context, mitochondrial DNA (mtDNA) is gaining more attention. Here, we investigated mitochondrial genes and genetic variants that may influence mild cognitive impairment and AD, through an integrative analysis including differential gene expression and mitochondrial genome-wide epistasis. We assessed the expression of mitochondrial genes in different brain tissues from two public RNA-Seq databases (GEO and GTEx). Then, we analyzed mtDNA from the ADNI Cohort and investigated epistasis regarding mitochondrial variants and levels of Aβ1−42, TAU, and Phosphorylated TAU (PTAU) from cognitively healthy controls, and both mild cognitive impairment (MCI) and AD cases. We identified multiple differentially expressed mitochondrial genes in the comparisons between cognitively healthy individuals and AD patients. We also found increased protein levels in MCI and AD patients when compared to healthy controls, as well as novel candidate networks of mtDNA epistasis, which included variants in all mitochondrially-encoded oxidative phosphorylation complexes, 12S rRNA and MT-DLOOP. Our results highlight layers of potential interactions involving mitochondrial genetics and suggest specific molecular alterations as potential biomarkers for AD.

Molecular Dynamics and MM-PBSA Analysis of the SARS-CoV-2 Gamma Variant in Complex with the hACE-2 Receptor

The SARS-CoV-2 virus, since its appearance in 2019, has caused millions of cases and deaths. To date, there is no effective treatment or a vaccine that is fully protective. Despite the efforts made by governments and health institutions around the globe to control its propagation, the evolution of the virus has accelerated, diverging into hundreds of variants. However, not all of them are variants of concern (VoC’s). VoC’s have appeared in different regions and throughout the two years of the pandemic they have spread around the world. Specifically, in South America, the gamma variant (previously known as P.1) appeared in early 2021, bringing with it a second wave of infections. This variant contains the N501Y, E484K and K417T mutations in the receptor binding domain (RBD) of the spike protein. Although these mutations have been described experimentally, there is still no clarity regarding their role in the stabilization of the complex with the human angiotensin converting enzyme 2 (hACE-2) receptor. In this article we dissect the influence of mutations on the interaction with the hACE-2 receptor using molecular dynamics and estimations of binding affinity through a screened version of the molecular mechanics Poisson Boltzmann surface area (MM-PBSA) and interaction entropy. Our results indicate that mutations E484K and K417T compensate each other in terms of binding affinity, while the mutation N501Y promotes a more convoluted effect. This effect consists in the adoption of a cis configuration in the backbone of residue Y495 within the RBD, which in turn promotes polar interactions with the hACE-2 receptor. These results not only correlate with experimental observations and complement previous knowledge, but also expose new features associated with the specific contribution of concerned mutations. Additionally, we propose a recipe to assess the residue-specific contribution to the interaction entropy.

The "Golden Age" of Behavior Genetics?

The search for genetic risk factors underlying the presumed heritability of all human behavior has unfolded in two phases. The first phase, characterized by candidate-gene-association (CGA) studies, has fallen out of favor in the behavior-genetics community, so much so that it has been referred to as a "cautionary tale." The second and current iteration is characterized by genome-wide association studies (GWASs), single-nucleotide polymorphism (SNP) heritability estimates, and polygenic risk scores. This research is guided by the resurrection of, or reemphasis on, Fisher's "infinite infinitesimal allele" model of the heritability of complex phenotypes, first proposed over 100 years ago. Despite seemingly significant differences between the two iterations, they are united in viewing the discovery of risk alleles underlying heritability as a matter of finding differences in allele frequencies. Many of the infirmities that beset CGA studies persist in the era of GWASs, accompanied by a host of new difficulties due to the human genome's underlying complexities and the limitations of Fisher's model in the postgenomics era.

A Fungal Defensin Inhibiting Bacterial Cell-Wall Biosynthesis with Non-Hemolysis and Serum Stability

Defensins are a class of cationic disulfide-bridged antimicrobial peptides (AMPs) present in many eukaryotic organisms and even in bacteria. They primarily include two distinct but evolutionarily related superfamilies (cis and trans). Defensins in fungi belong to the members of the cis-superfamily with the cysteine-stabilized α-helical and β-sheet fold. To date, many fungal defensin-like peptides (fDLPs) have been found through gene mining of the genome resource, but only a few have been experimentally characterized. Here, we report the structural and functional characterization of Pyronesin4 (abbreviated as Py4), a fDLP previously identified by genomic sequencing of the basal filamentous ascomycete Pyronema confluens. Chemically, synthetic Py4 adopts a native-like structure and exhibits activity on an array of Gram-positive bacteria including some clinical isolates of Staphylococcus and Staphylococcus warneri, a conditioned pathogen inhabiting in human skin. Py4 markedly altered the bacterial morphology and caused cytoplasmic accumulation of the cell-wall synthesis precursor through binding to the membrane-bound Lipid II, indicating that it works as an inhibitor of cell-wall biosynthesis. Py4 showed no hemolysis and high mammalian serum stability. This work identified a new fungal defensin with properties relevant to drug exploration. Intramolecular epistasis between mutational sites of fDLPs is also discussed.

Extremophile enzyme optimization for low temperature and high salinity are fundamentally incompatible

The evolutionary mechanisms behind cold and high-saline co-adaptation of proteins are not thoroughly understood. To explore how enzymes evolve in response to multiple environmental pressures we developed a novel in silico method to model the directed evolution of proteins, the Protein Evolution Parameter Calculator (PEPC). PEPC carries out single amino acid substitutions that lead to improvements in the selected user-defined parameters. To investigate the evolutionary relationship between increased flexibility and decreased isoelectric point, which are presumed indicators of cold and saline adaptation in proteins, we applied PEPC to a subset of core haloarchaea orthologous group (cHOG) proteins from the mesophilic Halobacterium salinarum NRC-1 and cold-tolerant Halorubrum lacusprofundi strain ATCC 49239. The results suggest that mutations that increase flexibility will also generally increase isoelectric point. These findings suggest that enzyme adaptation to low temperature and high salinity might be evolutionarily counterposed based on the structural characteristics of probable amino acid mutations. This may help to explain the apparent lack of truly psychrophilic halophiles in nature, and why microbes adapted to polar hypersaline environments typically have mesophilic temperature optima. A better understanding of protein evolution to extremely cold and salty conditions will aid in our understanding of where and how life is distributed on Earth and in our solar system.

Epistasis shapes the fitness landscape of an allosteric specificity switch

Epistasis is a major determinant in the emergence of novel protein function. In allosteric proteins, direct interactions between inducer-binding mutations propagate through the allosteric network, manifesting as epistasis at the level of biological function. Elucidating this relationship between local interactions and their global effects is essential to understanding evolution of allosteric proteins. We integrate computational design, structural and biophysical analysis to characterize the emergence of novel inducer specificity in an allosteric transcription factor. Adaptive landscapes of different inducers of the designed mutant show that a few strong epistatic interactions constrain the number of viable sequence pathways, revealing ridges in the fitness landscape leading to new specificity. The structure of the designed mutant shows that a striking change in inducer orientation still retains allosteric function. Comparing biophysical and functional properties suggests a nonlinear relationship between inducer binding affinity and allostery. Our results highlight the functional and evolutionary complexity of allosteric proteins.

Epistasis plays an important role in the evolution of novel protein functions because it determines the mutational path a protein takes. Here, the authors combine functional, structural and biophysical analyses to characterize epistasis in a computationally redesigned ligand-inducible allosteric transcription factor and found that epistasis creates distinct biophysical and biological functional landscapes.