Missense meanderings in sequence space: a biophysical view of protein evolution

Identification of high-risk non-synonymous SNPs (nsSNPs) in DNAH1 and DNAH17 genes associated with male infertility: a bioinformatics analysis

Male infertility is a significant reproductive issue affecting a considerable number of couples worldwide. While there are various causes of male infertility, genetic factors play a crucial role in its development. We focused on identifying and analyzing the high-risk nsSNPs in DNAH1 and DNAH17 genes, which encode proteins involved in sperm motility. A total of 20 nsSNPs for DNAH1 and 10 nsSNPs for DNAH17 were analyzed using various bioinformatics tools including SIFT, PolyPhen-2, CADD, PhD-SNPg, VEST-4, and MutPred2. As a result, V1287G, L2071R, R2356W, R3169C, R3229C, E3284K, R4096L, R4133C, and A4174T in DNAH1 gene and C1803Y, C1829Y, R1903C, and L3595P in DNAH17 gene were identified as high-risk nsSNPs. These nsSNPs were predicted to decrease protein stability, and almost all were found in highly conserved amino acid positions. Additionally, 4 nsSNPs were observed to alter post-translational modification status. Furthermore, the interaction network analysis revealed that DNAH1 and DNAH17 interact with DNAH2, DNAH3, DNAH5, DNAH7, DNAH8, DNAI2, DNAL1, CFAP70, DNAI3, DNAI4, ODAD1, and DNAI7, demonstrating the importance of DNAH1 and DNAH17 proteins in the overall functioning of the sperm motility machinery. Taken together, these findings revealed the detrimental effects of identified high-risk nsSNPs on protein structure and function and highlighted their potential relevance to male infertility. Further studies are warranted to validate these findings and to elucidate the underlying mechanisms.

A gene-based model of fitness and its implications for genetic variation: linkage disequilibrium

A widely used model of the effects of mutations on fitness (the "sites" model) assumes that heterozygous recessive or partially recessive deleterious mutations at different sites in a gene complement each other, similarly to mutations in different genes. However, the general lack of complementation between major effect allelic mutations suggests an alternative possibility, which we term the "gene" model. This assumes that a pair of heterozygous deleterious mutations in trans behave effectively as homozygotes, so that the fitnesses of trans heterozygotes are lower than those of cis heterozygotes. We examine the properties of the two different models, using both analytical and simulation methods. We show that the gene model predicts positive linkage disequilibrium (LD) between deleterious variants within the coding sequence, under conditions when the sites model predicts zero or slightly negative LD. We also show that focussing on rare variants when examining patterns of LD, especially with Lewontin's D ' measure, is likely to produce misleading results with respect to inferences concerning the causes of the sign of LD. Synergistic epistasis between pairs of mutations was also modeled; it is less likely to produce negative LD under the gene model than the sites model. The theoretical results are discussed in relation to patterns of LD in natural populations of several species.

Thermal Adaptation of Cytosolic Malate Dehydrogenase Revealed by Deep Learning and Coevolutionary Analysis

Protein evolution has shaped enzymes that maintain stability and function across diverse thermal environments. While sequence variation, thermal stability and conformational dynamics are known to influence an enzyme's thermal adaptation, how these factors collectively govern stability and function across diverse temperatures remains unresolved. Cytosolic malate dehydrogenase (cMDH), a citric acid cycle enzyme, is an ideal model for studying these mechanisms due to its temperature-sensitive flexibility and broad presence in species from diverse thermal environments. In this study, we employ techniques inspired by deep learning and statistical mechanics to uncover how sequence variation and conformational dynamics shape patterns of cMDH's thermal adaptation. By integrating coevolutionary models with variational autoencoders (VAE), we generate a latent generative landscape (LGL) of the cMDH sequence space, enabling us to explore mutational pathways and predict fitness using direct coupling analysis (DCA). Structure predictions via AlphaFold and molecular dynamics simulations further illuminate how variations in hydrophobic interactions and conformational flexibility contribute to the thermal stability of warm- and cold-adapted cMDH orthologs. Notably, we identify the ratio of hydrophobic contacts between two regions as a predictive order parameter for thermal stability features, providing a quantitative metric for understanding cMDH dynamics across temperatures. The integrative computational framework employed in this study provides mechanistic insights into protein adaptation at both sequence and structural levels, offering unique perspectives on the evolution of thermal stability and creating avenues for the rational design of proteins with optimized thermal properties.

Functionally constrained human proteins are less prone to mutational instability from single amino acid substitutions

Missense mutations that disrupt protein structural stability are a common pathogenic mechanism in human genetic disease. Here, we quantify potential disruption of protein stability due to amino acid substitution and show that functionally constrained proteins are less susceptible to large mutational changes in stability. Mechanistically, this relates to greater intrinsic disorder among constrained proteins and to increased B-factors in the ordered regions of constrained proteins. This phenomenon means that constrained proteins exhibit smaller stability effects due to missense mutations, and partly explains why overtransmission of pathogenic missense variation is less prevalent in genetic disorders characterised by protein truncations. We show that the most functionally constrained proteins are depleted of both destabilising and overly-stabilising amino acid variation in disease-free populations. Despite this, amino acid substitutions with large stability effects in functionally constrained proteins are still highly prevalent among pathogenic human genetic variation. Importantly, we observe that there are approximately five times more missense variants with large stability effects than there are unambiguous loss-of-function mutations. Missense variants with disruption of stability effects recapitulate the per-gene patterns of functional constraint observed with protein truncating loss-of-function variation, yet their relative abundance abrogates difficulties encountered when estimating functional constraint for the shortest human genes.

Polymorphic Variants of Peptidylarginine Deiminase Gene from P. gingivalis-Searching for Targets for Supportive Therapy of Periodontitis

Periodontitis (PD), an oral inflammatory disease, is primarily caused by P. gingivalis. Peptidylarginine deiminase (PPAD) is considered an attractive virulence factor because, due to protein citrullination, it may have deleterious effects on host tissues. In this study, the ppad gene sequences from P. gingivalis were analyzed in the context of its impact on bacterial virulence and potential targets for PD therapy. Analyses of ppad sequences from 58 patients with various clinical stages of PD, 20 controls, and 60 sequences from public databases were conducted. Overall, 55 substitutions assigned as polymorphic variants (4), missense mutations (10), or synonymous variants (35) were identified in PD, and 22 synonymous variants were identified in controls. Among them, the G231N, E232T, N235D variant was found in ~25% of P. gingivalis strains from PD samples. It was located close to the catalytic triad and had two-fold higher activity in comparison with reference P. gingivalis, upregulated expression of key inflammatory mediators, and contributed to worsening periodontium conditions in advanced PD, suggesting their unambiguous impact on P. gingivalis virulence. Our results indicate the G231N, E232T, N235D variant of the ppad gene as a potential candidate, opening a path to searching for novel targets for supportive therapy of PD. Further validation of the identified mutations is needed in future studies.

Cryptic genetic variation shapes the fate of gene duplicates in a protein interaction network

Paralogous genes are often functionally redundant for long periods of time. While their functions are preserved, paralogs accumulate cryptic changes in sequence and expression, which could modulate the impact of future mutations through epistasis. We examine the impact of mutations on redundant myosin proteins that have maintained the same binding preference despite having accumulated differences in expression levels and amino acid substitutions in the last 100 million years. By quantifying the impact of all single-amino acid substitutions in their SH3 domains on the physical interaction with their interaction partners, we show that the same mutations in the paralogous SH3s change binding in a paralog-specific and interaction partner-specific manner. This contingency is explained by the difference in promoter strength of the two paralogous myosin genes and epistatic interactions between the mutations introduced and cryptic divergent sites within the SH3s. One significant consequence of this contingency is that while some mutations would be sufficient to nonfunctionalize one paralog, they would have minimal impact on the other. Our results reveal how cryptic divergence, which accumulates while maintaining functional redundancy in cellular networks, could bias gene duplicates to specific fates.

Genetic Code Expansion: Recent Developments and Emerging Applications

The concept of genetic code expansion (GCE) has revolutionized the field of chemical and synthetic biology, enabling the site-specific incorporation of noncanonical amino acids (ncAAs) into proteins, thus opening new avenues in research and applications across biology and medicine. In this review, we cover the principles of GCE, including the optimization of the aminoacyl-tRNA synthetase (aaRS)/tRNA system and the advancements in translation system engineering. Notable developments include the refinement of aaRS/tRNA pairs, enhancements in screening methods, and the biosynthesis of noncanonical amino acids. The applications of GCE technology span from synthetic biology, where it facilitates gene expression regulation and protein engineering, to medicine, with promising approaches in drug development, vaccine production, and gene editing. The review concludes with a perspective on the future of GCE, underscoring its potential to further expand the toolkit of biology and medicine. Through this comprehensive review, we aim to provide a detailed overview of the current state of GCE technology, its challenges, opportunities, and the frontier it represents in the expansion of the genetic code for novel biological research and therapeutic applications.

The proteome is a terminal electron acceptor

Microbial metabolism is impressively flexible, enabling growth even when available nutrients differ greatly from biomass in redox state. Escherichia coli, for example, rearranges its physiology to grow on reduced and oxidized carbon sources through several forms of fermentation and respiration. To understand the limits on and evolutionary consequences of this metabolic flexibility, we developed a coarse-grained mathematical framework coupling redox chemistry with principles of cellular resource allocation. Our models inherit key qualities from both of their antecedents: i) describing diverse metabolic chemistries and ii) enforcing the simultaneous balancing of atom (e.g., carbon), electron, and energy (adenosine triphosphate) flows, as in redox models, while iii) treating biomass as both the product and catalyst of the growth process, as in resource allocation models. Assembling integrated models of respiration, fermentation, and photosynthesis clarified key microbiological phenomena, including demonstrating that autotrophs grow more slowly than heterotrophs because of constraints imposed by the intracellular production of reduced carbon. Our model further predicted that heterotrophic growth is improved by matching the redox state of biomass to the nutrient environment. Through analysis of [Formula: see text]60,000 genomes and diverse proteomic datasets, we found evidence that proteins indeed accumulate amino acid substitutions promoting redox matching. We therefore propose an unexpected mode of genome evolution where substitutions neutral or even deleterious to the individual biochemical or structural functions of proteins can nonetheless be selected due to a redox-chemical benefit to the population.

Structure-based self-supervised learning enables ultrafast protein stability prediction upon mutation

Predicting free energy changes (ΔΔG) is essential for enhancing our understanding of protein evolution and plays a pivotal role in protein engineering and pharmaceutical development. While traditional methods offer valuable insights, they are often constrained by computational speed and reliance on biased training datasets. These constraints become particularly evident when aiming for accurate ΔΔG predictions across a diverse array of protein sequences. Herein, we introduce Pythia, a self-supervised graph neural network specifically designed for zero-shot ΔΔG predictions. Our comparative benchmarks demonstrate that Pythia outperforms other self-supervised pretraining models and force field-based approaches while also exhibiting competitive performance with fully supervised models. Notably, Pythia shows strong correlations and achieves a remarkable increase in computational speed of up to 105-fold. We further validated Pythia's performance in predicting the thermostabilizing mutations of limonene epoxide hydrolase, leading to higher experimental success rates. This exceptional efficiency has enabled us to explore 26 million high-quality protein structures, marking a significant advancement in our ability to navigate the protein sequence space and enhance our understanding of the relationships between protein genotype and phenotype. In addition, we established a web server at https://pythia.wulab.xyz to allow users to easily perform such predictions.

Neutral drift upon threshold-like selection promotes variation in antibiotic resistance phenotype

Heritable phenotypic variation plays a central role in evolution by conferring rapid adaptive capacity to populations. Mechanisms that can explain genetic diversity by describing connections between genotype and organismal fitness have been described. However, the difficulty of acquiring comprehensive data on genotype-phenotype-environment relationships has hindered the efforts to explain how the ubiquitously observed phenotypic variation in populations emerges and is maintained. To address this challenge, we establish an experimental system where we can examine the genotype-phenotype relationships in a controlled environment. We perform long-term experimental evolution on VIM-2 β-lactamase, an antibiotic-resistance enzyme, to explore the conditions that promote the emergence and maintenance of phenotypic variation. We found that evolution in a static environment with low antibiotic concentrations can promote and maintain significant phenotypic variation within populations. Notably, evolution of VIM-2 under selection with a low antibiotic concentration led to variants that conferred resistance to over 100-fold higher antibiotic concentrations than used in selection. A model based on the previously described threshold-like relationship between enzyme phenotype and fitness generated using VIM-2's all single amino acid variants, sufficiently explains the emergence of standing phenotypic variation under static environmental conditions. Overall, our approach provides a tractable model for studying phenotypic variation and evolvability at the population level.

Deep mutational scanning of CYP2C19 in human cells reveals a substrate specificity-abundance tradeoff

The cytochrome P450s enzyme family metabolizes ∼80% of small molecule drugs. Variants in cytochrome P450s can substantially alter drug metabolism, leading to improper dosing and severe adverse drug reactions. Due to low sequence conservation, predicting variant effects across cytochrome P450s is challenging. Even closely related cytochrome P450s like CYP2C9 and CYP2C19, which share 92% amino acid sequence identity, display distinct phenotypic properties. Using variant abundance by massively parallel sequencing, we measured the steady-state protein abundance of 7,660 single amino acid variants in CYP2C19 expressed in cultured human cells. Our findings confirmed critical positions and structural features essential for cytochrome P450 function, and revealed how variants at conserved positions influence abundance. We jointly analyzed 4,670 variants whose abundance was measured in both CYP2C19 and CYP2C9, finding that the homologs have different variant abundances in substrate recognition sites within the hydrophobic core. We also measured the abundance of all single and some multiple wild type amino acid exchanges between CYP2C19 and CYP2C9. While most exchanges had no effect, substitutions in substrate recognition site 4 reduced abundance in CYP2C19. Double and triple mutants showed distinct interactions, highlighting a region that points to differing thermodynamic properties between the 2 homologs. These positions are known contributors to substrate specificity, suggesting an evolutionary tradeoff between stability and enzymatic function. Finally, we analyzed 368 previously unannotated human variants, finding that 43% had decreased abundance. By comparing variant effects between these homologs, we uncovered regions underlying their functional differences, advancing our understanding of this versatile family of enzymes.

Impact of phylogeny on the inference of functional sectors from protein sequence data

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.

Inbreeding in Chinese fir: Insights into the adaptive growth traits of selfed progeny from mRNA, miRNA, and copy number variation

PREMISE

The impact of inbreeding on biological processes is well documented in individuals with severe inbreeding depression. However, the biological processes influencing the adaptive growth of normal selfed individuals are unknown. Here, we aimed to investigate how inbreeding affects gene expression for adaptive growth of normal selfed seedlings from a self-fertilizing parent in Chinese fir (Cunninghamia lanceolata).

METHODS

Using RNA-seq data from above- and underground tissues of abnormal and normal selfed seedlings, we analyzed GO biological processes network. We also sequenced small RNAs in the aboveground tissues and measured the copy number variations (CNV) of the hub genes.

RESULTS

Phenotypic fitness analysis revealed that the normal seedlings were better adapted than their abnormal counterparts. Upregulated differentially expressed genes (DEGs) were associated with development processes, and downregulated DEGs were mainly enriched in fundamental metabolism and stress response. Results of mRNA-miRNA parallel sequencing revealed that upregulated target genes were predominantly associated with development, highlighting their crucial role in phosphorylation in signal transduction networks. We also discovered a moderate correlation (0.1328 < R2 < 0.6257) between CNV and gene expression levels for three hub genes (TMKL1, GT2, and RHY1A).

CONCLUSIONS

We uncovered the key biological processes underpinning the growth of normal selfed seedlings and established the relationship between CNV and the expression levels of hub genes in selfed seedlings. Understanding the candidate genes involved in the growth of selfed seedlings will help us comprehend the genetic mechanisms behind inbreeding depression in the evolutionary biology of plants.

Targeting Efficient Features of Urate Oxidase to Increase Its Solubility

With the demand for mass production of protein drugs, solubility has become a serious issue. Extrinsic and intrinsic factors both affect this property. A homotetrameric cofactor-free urate oxidase (UOX) is not sufficiently soluble. To engineer UOX for optimum solubility, it is important to identify the most effective factor that influences solubility. The most effective feature to target for protein engineering was determined by measuring various solubility-related factors of UOX. A large library of homologous sequences was obtained from the databases. The data was reduced to six enzymes from different organisms. On the basis of various sequence- and structure-derived elements, the most and the least soluble enzymes were defined. To determine the best protein engineering target for modification, features of the most and least soluble enzymes were compared. Metabacillus fastidiosus UOX was the most soluble enzyme, while Agrobacterium globiformis UOX was the least soluble. According to the comparison-constant method, positive surface patches caused by arginine residue distribution are appropriate targets for modification. Two Arg to Ala mutations were introduced to the least soluble enzyme to test this hypothesis. These mutations significantly enhanced the mutant's solubility. While different algorithms produced conflicting results, it was difficult to determine which proteins were most and least soluble. Solubility prediction requires multiple algorithms based on these controversies. Protein surfaces should be investigated regionally rather than globally, and both sequence and structural data should be considered. Several other biotechnological products could be engineered using the data reduction and comparison-constant methods used in this study.

Experimental Evolution Studies in Φ6 Cystovirus

Experimental evolution studies, in which biological populations are evolved in a specific environment over time, can address questions about the nature of spontaneous mutations, responses to selection, and the origins and maintenance of novel traits. Here, we review more than 30 years of experimental evolution studies using the bacteriophage (phage) Φ6 cystovirus. Similar to many lab-studied bacteriophages, Φ6 has a high mutation rate, large population size, fast generation time, and can be genetically engineered or cryogenically frozen, which facilitates its rapid evolution in the laboratory and the subsequent characterization of the effects of its mutations. Moreover, its segmented RNA genome, outer membrane, and capacity for multiple phages to coinfect a single host cell make Φ6 a good non-pathogenic model for investigating the evolution of RNA viruses that infect humans. We describe experiments that used Φ6 to address the fitness effects of spontaneous mutations, the consequences of evolution in the presence of coinfection, the evolution of host ranges, and mechanisms and consequences of the evolution of thermostability. We highlight open areas of inquiry where further experimentation on Φ6 could inform predictions for pathogenic viruses.

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.

Kinetic coevolutionary models predict the temporal emergence of HIV-1 resistance mutations under drug selection pressure

Drug resistance in HIV type 1 (HIV-1) is a pervasive problem that affects the lives of millions of people worldwide. Although records of drug-resistant mutations (DRMs) have been extensively tabulated within public repositories, our understanding of the evolutionary kinetics of DRMs and how they evolve together remains limited. Epistasis, the interaction between a DRM and other residues in HIV-1 protein sequences, is key to the temporal evolution of drug resistance. We use a Potts sequence-covariation statistical-energy model of HIV-1 protein fitness under drug selection pressure, which captures epistatic interactions between all positions, combined with kinetic Monte-Carlo simulations of sequence evolutionary trajectories, to explore the acquisition of DRMs as they arise in an ensemble of drug-naive patient protein sequences. We follow the time course of 52 DRMs in the enzymes protease, RT, and integrase, the primary targets of antiretroviral therapy. The rates at which DRMs emerge are highly correlated with their observed acquisition rates reported in the literature when drug pressure is applied. This result highlights the central role of epistasis in determining the kinetics governing DRM emergence. Whereas rapidly acquired DRMs begin to accumulate as soon as drug pressure is applied, slowly acquired DRMs are contingent on accessory mutations that appear only after prolonged drug pressure. We provide a foundation for using computational methods to determine the temporal evolution of drug resistance using Potts statistical potentials, which can be used to gain mechanistic insights into drug resistance pathways in HIV-1 and other infectious agents.

Cryptic genetic variation shapes the fate of gene duplicates in a protein interaction network

Paralogous genes are often redundant for long periods of time before they diverge in function. While their functions are preserved, paralogous proteins can accumulate mutations that, through epistasis, could impact their fate in the future. By quantifying the impact of all single-amino acid substitutions on the binding of two myosin proteins to their interaction partners, we find that the future evolution of these proteins is highly contingent on their regulatory divergence and the mutations that have silently accumulated in their protein binding domains. Differences in the promoter strength of the two paralogs amplify the impact of mutations on binding in the lowly expressed one. While some mutations would be sufficient to non-functionalize one paralog, they would have minimal impact on the other. Our results reveal how functionally equivalent protein domains could be destined to specific fates by regulatory and cryptic coding sequence changes that currently have little to no functional impact.

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.

ReBaTSA: A simplified CeTSA protocol for studying recombinant mutant proteins in bacterial extracts

INTRODUCTION

The study of protein stability is crucial to biochemistry and relies on different methodologies. Recently, the Cellular Thermal Shift Assay has been introduced to study protein stability in whole cells.

METHODS

We report a novel application of CeTSA named ReBaTSA. This Recombinant Bacterial TSA was performed using clear extracts from bacteria expressing a recombinant protein, incubated at different temperatures, centrifuged and analyzed via SDS-PAGE.

RESULTS AND CONCLUSIONS

We demonstrated the feasibility and reliability of this simplified approach. We validated the method using the protein phosphomannomutase-2 and its common mutants, which were compared in the presence or the absence of a known ligand.

The proteome is a terminal electron acceptor

Microbial metabolism is impressively flexible, enabling growth even when available nutrients differ greatly from biomass in redox state. E. coli, for example, rearranges its physiology to grow on reduced and oxidized carbon sources through several forms of fermentation and respiration. To understand the limits on and evolutionary consequences of metabolic flexibility, we developed a mathematical model coupling redox chemistry with principles of cellular resource allocation. Our integrated model clarifies key phenomena, including demonstrating that autotrophs grow slower than heterotrophs because of constraints imposed by intracellular production of reduced carbon. Our model further indicates that growth is improved by adapting the redox state of biomass to nutrients, revealing an unexpected mode of evolution where proteins accumulate mutations benefiting organismal redox balance.

Empirical validation of ProteinMPNN's efficiency in enhancing protein fitness

Introduction: Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range. Methods: We collected three biological experimental datasets and compared the design results of ProteinMPNN for wild-type proteins with the experimental datasets to verify the ability of ProteinMPNN in improving protein fitness. Results: The validation on biological experimental datasets shows that ProteinMPNN has the ability to design mutation types with higher fitness in single and multi-point mutations. We have verified the high accuracy of ProteinMPNN in protein engineering tasks from both positive and negative perspectives. Discussion: Our research indicates that using large-scale pre trained models to design protein mutants provides a new approach for protein engineering, providing strong support for guiding biological experiments and applications in biotechnology.

Computational multigene interactions in virus growth and infection spread

Viruses persist in nature owing to their extreme genetic heterogeneity and large population sizes, which enable them to evade host immune defenses, escape antiviral drugs, and adapt to new hosts. The persistence of viruses is challenging to study because mutations affect multiple virus genes, interactions among genes in their impacts on virus growth are seldom known, and measures of viral fitness are yet to be standardized. To address these challenges, we employed a data-driven computational model of cell infection by a virus. The infection model accounted for the kinetics of viral gene expression, functional gene-gene interactions, genome replication, and allocation of host cellular resources to produce progeny of vesicular stomatitis virus, a prototype RNA virus. We used this model to computationally probe how interactions among genes carrying up to eleven deleterious mutations affect different measures of virus fitness: single-cycle growth yields and multicycle rates of infection spread. Individual mutations were implemented by perturbing biophysical parameters associated with individual gene functions of the wild-type model. Our analysis revealed synergistic epistasis among deleterious mutations in their effects on virus yield; so adverse effects of single deleterious mutations were amplified by interaction. For the same mutations, multicycle infection spread indicated weak or negligible epistasis, where single mutations act alone in their effects on infection spread. These results were robust to simulation in high- and low-host resource environments. Our work highlights how different types and magnitudes of epistasis can arise for genetically identical virus variants, depending on the fitness measure. More broadly, gene-gene interactions can differently affect how viruses grow and spread.

Coevolution with toxic prey produces functional trade-offs in sodium channels of predatory snakes

Seemingly unrelated traits often share the same underlying molecular mechanisms, potentially generating a pleiotropic relationship whereby selection shaping one trait can simultaneously compromise another. While such functional trade-offs are expected to influence evolutionary outcomes, their actual relevance in nature is masked by obscure links between genotype, phenotype, and fitness. Here, we describe functional trade-offs that likely govern a key adaptation and coevolutionary dynamics in a predator-prey system. Several garter snake (Thamnophis spp.) populations have evolved resistance to tetrodotoxin (TTX), a potent chemical defense in their prey, toxic newts (Taricha spp.). Snakes achieve TTX resistance through mutations occurring at toxin-binding sites in the pore of snake skeletal muscle voltage-gated sodium channels (NaV1.4). We hypothesized that these mutations impair basic NaV functions, producing molecular trade-offs that should ultimately scale up to compromised organismal performance. We investigate biophysical costs in two snake species with unique and independently evolved mutations that confer TTX resistance. We show electrophysiological evidence that skeletal muscle sodium channels encoded by toxin-resistant alleles are functionally compromised. Furthermore, skeletal muscles from snakes with resistance genotypes exhibit reduced mechanical performance. Lastly, modeling the molecular stability of these sodium channel variants partially explains the electrophysiological and muscle impairments. Ultimately, adaptive genetic changes favoring toxin resistance appear to negatively impact sodium channel function, skeletal muscle strength, and organismal performance. These functional trade-offs at the cellular and organ levels appear to underpin locomotor deficits observed in resistant snakes and may explain variation in the population-level success of toxin-resistant alleles across the landscape, ultimately shaping the trajectory of snake-newt coevolution.

Epistasis and pleiotropy shape biophysical protein subspaces associated with drug resistance

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few mentions of protein space consider how protein phenotypes can be described in terms of their biophysical components, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these components. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [k_{cat}, K_{M}, K_{i}, and T_{m} (melting temperature)]. We then examine how combinations of three mutations (eight alleles in total) display pleiotropy, or unique effects on individual subspace traits. We examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that future applications to bioengineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

Comprehensive analysis of mitochondrial DNA variants, mitochondrial DNA copy number and oxidative damage in psoriatic arthritis

Growing evidence suggests that abnormalities in mitochondrial DNA (mtDNA) are involved in the pathogenesis of various inflammatory and immuno-mediated diseases. The present study analysed the entire mitochondrial genome by next-generation sequencing (NGS) in 23 patients with psoriatic arthritis (PsA) and 20 healthy controls to identify PsA-related variants. Changes in mtDNA copy number (mtDNAcn) were also evaluated by quantitative polymerase chain reaction (qPCR) and mtDNA oxidative damage was measured using an 8-hydroxy-2'-deoxyguanosine assay. NGS analysis revealed a total of 435 variants including 187 in patients with PsA only and 122 in controls only. Additionally, 126 common variants were found, of which 2 variants differed significantly in their frequencies among patients and controls (P<0.05), and may be associated with susceptibility to PsA. A total of 33 missense variants in mtDNA-encoded genes for complexes I, III, IV and V were identified only in patients with PsA. Of them, 25 variants were predicted to be deleterious by affecting the functions and structures of encoded proteins, and 13 variants were predicted to affect protein's stability. mtDNAcn analysis revealed decreased mtDNA content in patients with PsA compared with controls (P=0.0001) but the decrease in mtDNAcn was not correlated with patients' age or inflammatory biomarkers (P>0.05). Moreover, a higher level of oxidative damage was observed in patients with PsA compared with controls (P=0.03). The results of the present comprehensive analysis of mtDNA in PsA revealed that certain mtDNA variants may be implicated in the predisposition/pathogenesis of PsA, highlighting the importance of NGS in the identification of mtDNA variants in PsA. The current results also demonstrated that decreased mtDNAcn in PsA may be a consequence of increased oxidative stress. These data provide valuable insights into the contribution of mtDNA defects to the pathogenesis of PsA. Additional studies in larger cohorts are needed to elucidate the role of mtDNA defects in PsA.

Viral Evolution Shaped by Host Proteostasis Networks

Understanding the factors that shape viral evolution is critical for developing effective antiviral strategies, accurately predicting viral evolution, and preventing pandemics. One fundamental determinant of viral evolution is the interplay between viral protein biophysics and the host machineries that regulate protein folding and quality control. Most adaptive mutations in viruses are biophysically deleterious, resulting in a viral protein product with folding defects. In cells, protein folding is assisted by a dynamic system of chaperones and quality control processes known as the proteostasis network. Host proteostasis networks can determine the fates of viral proteins with biophysical defects, either by assisting with folding or by targeting them for degradation. In this review, we discuss and analyze new discoveries revealing that host proteostasis factors can profoundly shape the sequence space accessible to evolving viral proteins. We also discuss the many opportunities for research progress proffered by the proteostasis perspective on viral evolution and adaptation.

In-cell kinetic stability is an essential trait in metallo-β-lactamase evolution

Protein stability is an essential property for biological function. In contrast to the vast knowledge on protein stability in vitro, little is known about the factors governing in-cell stability. Here we show that the metallo-β-lactamase (MBL) New Delhi MBL-1 (NDM-1) is a kinetically unstable protein on metal restriction that has evolved by acquiring different biochemical traits that optimize its in-cell stability. The nonmetalated (apo) NDM-1 is degraded by the periplasmic protease Prc that recognizes its partially unstructured C-terminal domain. Zn(II) binding renders the protein refractory to degradation by quenching the flexibility of this region. Membrane anchoring makes apo-NDM-1 less accessible to Prc and protects it from DegP, a cellular protease degrading misfolded, nonmetalated NDM-1 precursors. NDM variants accumulate substitutions at the C terminus that quench its flexibility, enhancing their kinetic stability and bypassing proteolysis. These observations link MBL-mediated resistance with the essential periplasmic metabolism, highlighting the importance of the cellular protein homeostasis.

Congenital disorders of glycosylation: narration of a story through its patents

Congenital disorders of glycosylation are a group of more than 160 rare genetic defects in protein and lipid glycosylation. Since the first clinical report in 1980 of PMM2-CDG, the most common CDG worldwide, research made great strides, but nearly all of them are still missing a cure. CDG diagnosis has been at a rapid pace since the introduction of whole-exome/whole-genome sequencing as a diagnostic tool. Here, we retrace the history of CDG by analyzing all the patents associated with the topic. To this end, we explored the Espacenet database, extracted a list of patents, and then divided them into three major groups: (1) Drugs/therapeutic approaches for CDG, (2) Drug delivery tools for CDG, (3) Diagnostic tools for CDG. Despite the enormous scientific progress experienced in the last 30 years, diagnostic tools, drugs, and biomarkers are still urgently needed.

Spinocerebellar ataxia 38: structure-function analysis shows ELOVL5 G230V is proteotoxic, conformationally altered and a mutational hotspot

Fatty acid elongase ELOVL5 is part of a protein family of multipass transmembrane proteins that reside in the endoplasmic reticulum where they regulate long-chain fatty acid elongation. A missense variant (c.689G>T p.Gly230Val) in ELOVL5 causes Spinocerebellar Ataxia subtype 38 (SCA38), a neurodegenerative disorder characterized by autosomal dominant inheritance, cerebellar Purkinje cell demise and adult-onset ataxia. Having previously showed aberrant accumulation of p.G230V in the Golgi complex, here we further investigated the pathogenic mechanisms triggered by p.G230V, integrating functional studies with bioinformatic analyses of protein sequence and structure. Biochemical analysis showed that p.G230V enzymatic activity was normal. In contrast, SCA38-derived fibroblasts showed reduced expression of ELOVL5, Golgi complex enlargement and increased proteasomal degradation with respect to controls. By heterologous overexpression, p.G230V was significantly more active than wild-type ELOVL5 in triggering the unfolded protein response and in decreasing viability in mouse cortical neurons. By homology modelling, we generated native and p.G230V protein structures whose superposition revealed a shift in Loop 6 in p.G230V that altered a highly conserved intramolecular disulphide bond. The conformation of this bond, connecting Loop 2 and Loop 6, appears to be elongase-specific. Alteration of this intramolecular interaction was also observed when comparing wild-type ELOVL4 and the p.W246G variant which causes SCA34. We demonstrate by sequence and structure analyses that ELOVL5 p.G230V and ELOVL4 p.W246G are position-equivalent missense variants. We conclude that SCA38 is a conformational disease and propose combined loss of function by mislocalization and gain of toxic function by ER/Golgi stress as early events in SCA38 pathogenesis.

Isothermal Compressibility Perturbation as a Protein Design Principle for T1 Lipase Stability-Activity Trade-Off Counteracting

Given the widely existing stability-activity trade-off in enzyme evolution, it is still a goal to obtain enzymes embracing both high activity and stability. Herein, we employed an isothermal compressibility (β_T) perturbation engineering (ICPE) strategy to comprehensively understand the stability-activity seesaw-like mechanism. The stability and activity of mutants derived from ICPE uncovered a high Pearson correlation (r = 0.93) in a prototypical enzyme T1 lipase. The best variant A186L/L188M/A190Y exhibited a high T_m value up to 78.70 °C, catalytic activity of 474.04 U/mg, and a 73.33% increase in dimethyl sulfoxide resistance compared to the wild type, one of the highest comprehensive performances reported to date. The elastic activation mechanism mediated by conformational change with a Δβ_T range of -6.81 × 10^-6 to -1.90 × 10^-6 bar^-1 may account for the balancing of stability and activity to achieve better performing enzymes. The ICPE strategy deepens our understanding of stability-activity trade-off and boosts its applications in enzyme engineering.

Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few framings of protein space consider how higher-level protein phenotypes can be described in terms of their biophysical dimensions, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these dimensions. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [(kcat, KM, Ki, and Tm (melting temperature)]. We then examine how three mutations (eight alleles in total) display pleiotropy in their interactions across these subspaces. We extend this approach to examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that the process of protein evolution and engineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

Population-based heteropolymer design to mimic protein mixtures

Biological fluids, the most complex blends, have compositions that constantly vary and cannot be molecularly defined1. Despite these uncertainties, proteins fluctuate, fold, function and evolve as programmed2-4. We propose that in addition to the known monomeric sequence requirements, protein sequences encode multi-pair interactions at the segmental level to navigate random encounters5,6; synthetic heteropolymers capable of emulating such interactions can replicate how proteins behave in biological fluids individually and collectively. Here, we extracted the chemical characteristics and sequential arrangement along a protein chain at the segmental level from natural protein libraries and used the information to design heteropolymer ensembles as mixtures of disordered, partially folded and folded proteins. For each heteropolymer ensemble, the level of segmental similarity to that of natural proteins determines its ability to replicate many functions of biological fluids including assisting protein folding during translation, preserving the viability of fetal bovine serum without refrigeration, enhancing the thermal stability of proteins and behaving like synthetic cytosol under biologically relevant conditions. Molecular studies further translated protein sequence information at the segmental level into intermolecular interactions with a defined range, degree of diversity and temporal and spatial availability. This framework provides valuable guiding principles to synthetically realize protein properties, engineer bio/abiotic hybrid materials and, ultimately, realize matter-to-life transformations.

Recent therapeutic approaches to cystathionine beta-synthase-deficient homocystinuria

Cystathionine beta-synthase (CBS)-deficient homocystinuria (HCU) is the most common inborn error of sulfur amino acid metabolism. The pyridoxine non-responsive form of the disease manifests itself by massively increasing plasma and tissue concentrations of homocysteine, a toxic intermediate of methionine metabolism that is thought to be the major cause of clinical complications including skeletal deformities, connective tissue defects, thromboembolism and cognitive impairment. The current standard of care involves significant dietary interventions that, despite being effective, often adversely affect quality of life of HCU patients, leading to poor adherence to therapy and inadequate biochemical control with clinical complications. In recent years, the unmet need for better therapeutic options has resulted in development of novel enzyme and gene therapies and exploration of pharmacological approaches to rescue CBS folding defects caused by missense pathogenic mutations. Here, we review scientific evidence and current state of affairs in development of recent approaches to treat HCU.

A review of visualisations of protein fold networks and their relationship with sequence and function

Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

Analysis of 1276 Haplotype-Resolved Genomes Allows Characterization of Cis- and Trans-Abundant Genes

Many methods for haplotyping have materialized, but their application on a significant scale has been rare to date. Here we summarize analyses that were carried out in 1092 genomes from the 1000 Genomes Consortium and validated in an unprecedented number of 184 PGP genomes that have been experimentally haplotype-resolved by application of the Long-Fragment Read (LFR) technology. These analyses provided first insights into the diplotypic nature of human genomes and its potential functional implications. Thus, protein-changing variants were not randomly distributed between the two homologues of 18,121 autosomal protein-coding genes but occurred significantly more frequently in cis than in trans configurations in virtually each of the 1276 phased genomes. This resulted in global cis/trans ratios of ~60:40, establishing "cis abundance" as a universal characteristic of diploid human genomes. This phenomenon was based on two different classes of genes, a larger one exhibiting cis configurations of protein-changing variants in excess, so-called "cis-abundant" genes, and a smaller one of "trans-abundant" genes. These two gene classes, which together constitute a common diplotypic exome, were further functionally distinguished by means of gene ontology (GO) and pathway enrichment analysis. Moreover, they were distinguishable in terms of their effects on the human interactome, where they constitute distinct cis and trans modules, as shown with network propagation on a large integrated protein-protein interaction network. These analyses, recently performed with updated database and analysis tools, further consolidated the characterization of cis- and trans-abundant genes while expanding previous results. In this chapter, we present the key results along with the materials and methods to motivate readers to investigate these findings independently and gain further insights into the diplotypic nature of genes and genomes.

Analyses of Mutation Displacements from Homology Models

Evaluation of the structural perturbations introduced by a single amino acid mutation is the main issue for protein structural biology. We propose here to present some recent advances in methods, allowing the splitting of distortion between the actual substitution effect and the contribution of the local flexibility of the position where the mutation occurs. Its main drawback is the need of many structures with a single mutation in each of them. To bypass this difficulty, we propose to use molecular modeling tools, with several software enabling us to build a model from a template, given the sequence. As a proof of concept, we rely on a gold standard, the human lysozyme. Both wild-type and three mutant structures are available in the PDB. Two of these mutations result in amyloid fibril formation, and the last one is neutral. As a conclusion, irrespective of the algorithm used for modeling, side chain conformations at the site of mutation are reliable, although long-range effects are out of reach of these tools.

Contingency and Entrenchment of Drug-Resistance Mutations in HIV Viral Proteins

The ability of HIV-1 to rapidly mutate leads to antiretroviral therapy (ART) failure among infected patients. Drug-resistance mutations (DRMs), which cause a fitness penalty to intrinsic viral fitness, are compensated by accessory mutations with favorable epistatic interactions which cause an evolutionary trapping effect, but the kinetics of this overall process has not been well characterized. Here, using a Potts Hamiltonian model describing epistasis combined with kinetic Monte Carlo simulations of evolutionary trajectories, we explore how epistasis modulates the evolutionary dynamics of HIV DRMs. We show how the occurrence of a drug-resistance mutation is contingent on favorable epistatic interactions with many other residues of the sequence background and that subsequent mutations entrench DRMs. We measure the time-autocorrelation of fluctuations in the likelihood of DRMs due to epistatic coupling with the sequence background, which reveals the presence of two evolutionary processes controlling DRM kinetics with two distinct time scales. Further analysis of waiting times for the evolutionary trapping effect to reverse reveals that the sequences which entrench (trap) a DRM are responsible for the slower time scale. We also quantify the overall strength of epistatic effects on the evolutionary kinetics for different mutations and show these are much larger for DRM positions than polymorphic positions, and we also show that trapping of a DRM is often caused by the collective effect of many accessory mutations, rather than a few strongly coupled ones, suggesting the importance of multiresidue sequence variations in HIV evolution. The analysis presented here provides a framework to explore the kinetic pathways through which viral proteins like HIV evolve under drug-selection pressure.

Bacterial Hsp90 predominantly buffers but does not potentiate the phenotypic effects of deleterious mutations during fluorescent protein evolution

Chaperones facilitate the folding of other ("client") proteins and can thus affect the adaptive evolution of these clients. Specifically, chaperones affect the phenotype of proteins via two opposing mechanisms. On the one hand, they can buffer the effects of mutations in proteins and thus help preserve an ancestral, premutation phenotype. On the other hand, they can potentiate the effects of mutations and thus enhance the phenotypic changes caused by a mutation. We study that how the bacterial Hsp90 chaperone (HtpG) affects the evolution of green fluorescent protein. To this end, we performed directed evolution of green fluorescent protein under low and high cellular concentrations of Hsp90. Specifically, we evolved green fluorescent protein under both stabilizing selection for its ancestral (green) phenotype and directional selection toward a new (cyan) phenotype. While Hsp90 did only affect the rate of adaptive evolution transiently, it did affect the phenotypic effects of mutations that occurred during adaptive evolution. Specifically, Hsp90 allowed strongly deleterious mutations to accumulate in evolving populations by buffering their effects. Our observations show that the role of a chaperone for adaptive evolution depends on the organism and the trait being studied.

Directed Evolution of Soluble α-1,2-Fucosyltransferase Using Kanamycin Resistance Protein as a Phenotypic Reporter for Efficient Production of 2'-Fucosyllactose

2'-Fucosyllactose (2'-FL), the most abundant fucosylated oligosaccharide in human milk, has multiple beneficial effects on human health. However, its biosynthesis by metabolically engineered Escherichia coli is often hampered owing to the insolubility and instability of α-1,2-fucosyltransferase (the rate-limiting enzyme). In this study, we aimed to enhance 2'-FL production by increasing the expression of soluble α-1,2-fucosyltransferase from Helicobacter pylori (FucT2). Because structural information regarding FucT2 has not been unveiled, we decided to improve the expression of soluble FucT2 in E. coli via directed evolution using a protein solubility biosensor that links protein solubility to antimicrobial resistance. For such a system to be viable, the activity of kanamycin resistance protein (Kan^R) should be dependent on FucT2 solubility. Kan^R was fused to the C-terminus of mutant libraries of FucT2, which were generated using a combination of error-prone PCR and DNA shuffling. Notably, one round of the directed evolution process, which consisted of mutant library generation and selection based on kanamycin resistance, resulted in a significant increase in the expression level of soluble FucT2. As a result, a batch fermentation with the ΔL M15 pBCGW strain, expressing the FucT2 mutant (F#1-5) isolated from the first round of the directed evolution process, resulted in the production of 0.31 g/l 2'-FL with a yield of 0.22 g 2'-FL/g lactose, showing 1.72- and 1.51-fold increase in the titer and yield, respectively, compared to those of the control strain. The simple and powerful method developed in this study could be applied to enhance the solubility of other unstable enzymes.

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.

Genetics of adaptation

The rediscovery of Mendel’s work showing that the heredity of phenotypes is controlled by discrete genes was followed by the reconciliation of Mendelian genetics with evolution by natural selection in the middle of the last century with the Modern Synthesis. In the past two decades, dramatic advances in genomic methods have facilitated the identification of the loci, genes, and even individual mutations that underlie phenotypic variants that are the putative targets of natural selection. Moreover, these methods have also changed how we can study adaptation by flipping the problem around, allowing us to first examine what loci show evidence of having been under selection, and then connecting these genetic variants to phenotypic variation. As a result, we now have an expanding list of actual genetic changes that underlie potentially adaptive phenotypic variation. Here, we synthesize how considering the effects of these adaptive loci in the context of cellular environments, genomes, organisms, and populations has provided new insights to the genetic architecture of adaptation.

Structural Determinants of Yeast Protein-Protein Interaction Interface Evolution at the Residue Level

Interfaces of contact between proteins play important roles in determining the proper structure and function of protein-protein interactions (PPIs). Therefore, to fully understand PPIs, we need to better understand the evolutionary design principles of PPI interfaces. Previous studies have uncovered that interfacial sites are more evolutionarily conserved than other surface protein sites. Yet, little is known about the nature and relative importance of evolutionary constraints in PPI interfaces. Here, we explore constraints imposed by the structure of the microenvironment surrounding interfacial residues on residue evolutionary rate using a large dataset of over 700 structural models of baker's yeast PPIs. We find that interfacial residues are, on average, systematically more conserved than all other residues with a similar degree of total burial as measured by relative solvent accessibility (RSA). Besides, we find that RSA of the residue when the PPI is formed is a better predictor of interfacial residue evolutionary rate than RSA in the monomer state. Furthermore, we investigate four structure-based measures of residue interfacial involvement, including change in RSA upon binding (ΔRSA), number of residue-residue contacts across the interface, and distance from the center or the periphery of the interface. Integrated modeling for evolutionary rate prediction in interfaces shows that ΔRSA plays a dominant role among the four measures of interfacial involvement, with minor, but independent contributions from other measures. These results yield insight into the evolutionary design of interfaces, improving our understanding of the role that structure plays in the molecular evolution of PPIs at the residue level.

Local and Global Protein Interactions Contribute to Residue Entrenchment in Beta-Lactamase TEM-1

Due to their rapid evolution and their impact on healthcare, beta-lactamases, protein degrading beta-lactam antibiotics, are used as generic models of protein evolution. Therefore, we investigated the mutation effects in two distant beta-lactamases, TEM-1 and CTX-M-15. Interestingly, we found a site with a complex pattern of genetic interactions. Mutation G251W in TEM-1 inactivates the protein’s function, just as the reciprocal mutation, W251G, does in CTX-M-15. The phylogenetic analysis revealed that mutation G has been entrenched in TEM-1’s background: while rarely observed throughout the phylogeny, it is essential in TEM-1. Using a rescue experiment, in the TEM-1 G251W mutant, we identified sites that alleviate the deviation from G to W. While few of these mutations could potentially involve local interactions, most of them were found on distant residues in the 3D structure. Many well-known mutations that have an impact on protein stability, such as M182T, were recovered. Our results therefore suggest that entrenchment of an amino acid may rely on diffuse interactions among multiple sites, with a major impact on protein stability.

New Genome Sequence Detection via Natural Vector Convex Hull Method

It remains challenging how to find existing but undiscovered genome sequence mutations or predict potential genome sequence mutations based on real sequence data. Motivated by this, we develop approaches to detect new, undiscovered genome sequences. Because discovering new genome sequences through biological experiments is resource-intensive, we want to achieve the new genome sequence detection task mathematically. However, little literature tells us how to detect new, undiscovered genome sequence mutations mathematically. We form a new framework based on natural vector convex hull method that conducts alignment-free sequence analysis. Our newly developed two approaches, Random-permutation Algorithm with Penalty (RAP) and Random-permutation Algorithm with Penalty and COstrained Search (RAPCOS), use the geometry properties captured by natural vectors. In our experiment, we discover a mathematically new human immunodeficiency virus (HIV) genome sequence using some real HIV genome sequences. Significantly, the proposed methods are applicable to solve the new genome sequence detection challenge and have many good properties, such as robustness, rapid convergence, and fast computation.

Identification, In Silico Characterization, and Differential Expression Profiles of Carotenoid, Xanthophyll, Apocarotenoid Biosynthetic Pathways Genes, and Analysis of Carotenoid and Xanthophyll Accumulation in Heracleum moellendorffii Hance

Heracleum moellendorffii Hance is a non-woody forest plant widely used in China, Korea, and Japan because of its various therapeutic properties. However, the genetic details of the carotenoid pathway (CP), xanthophyll pathway (XP), and apocarotenoid pathway (AP) genes have not been studied. Thus, the CP, XP, and AP genes of H. moellendorffii were detected and analyzed. A total of fifteen genes were identified, of which eight, four, and three belonged to CP, XP, and AP, respectively. All identified genes possessed full open reading frames. Phylogenetic characterization of the identified gene sequences showed the highest similarity with other higher plants. Multiple alignments and 3D dimensional structures showed several diverse conserved motifs, such as the carotene-binding motif, dinucleotide-binding motif, and aspartate or glutamate residues. The results of real-time PCR showed that the CP, XP, and AP genes were highly expressed in leaves, followed by the stems and roots. In total, eight different individual carotenoids were identified using HPLC analysis. The highest individual and total carotenoid content were achieved in the leaves, followed by the stems and roots. This study will provide more information on the gene structure of the CP, XP, and AP genes, which may help to increase the accumulation of carotenoids in H. moellendorffii through genetic engineering. These results could be helpful for further molecular and functional studies of CP, XP, and AP genes.

Evolutionary Landscapes of Host-Virus Arms Races

Vertebrate immune systems suppress viral infection using both innate restriction factors and adaptive immunity. Viruses mutate to escape these defenses, driving hosts to counterevolve to regain fitness. This cycle recurs repeatedly, resulting in an evolutionary arms race whose outcome depends on the pace and likelihood of adaptation by host and viral genes. Although viruses evolve faster than their vertebrate hosts, their proteins are subject to numerous functional constraints that impact the probability of adaptation. These constraints are globally defined by evolutionary landscapes, which describe the fitness and adaptive potential of all possible mutations. We review deep mutational scanning experiments mapping the evolutionary landscapes of both host and viral proteins engaged in arms races. For restriction factors and some broadly neutralizing antibodies, landscapes favor the host, which may help to level the evolutionary playing field against rapidly evolving viruses. We discuss the biophysical underpinnings of these landscapes and their therapeutic implications.