Biophysics of protein evolution and evolutionary protein biophysics

Viral Receptor-Binding Protein Evolves New Function through Mutations That Cause Trimer Instability and Functional Heterogeneity

When proteins evolve new activity, a concomitant decrease in stability is often observed because the mutations that confer new activity can destabilize the native fold. In the conventional model of protein evolution, reduced stability is considered a purely deleterious cost of molecular innovation because unstable proteins are prone to aggregation and are sensitive to environmental stressors. However, recent work has revealed that nonnative, often unstable protein conformations play an important role in mediating evolutionary transitions, raising the question of whether instability can itself potentiate the evolution of new activity. We explored this question in a bacteriophage receptor-binding protein during host-range evolution. We studied the properties of the receptor-binding protein of bacteriophage λ before and after host-range evolution and demonstrated that the evolved protein is relatively unstable and may exist in multiple conformations with unique receptor preferences. Through a combination of structural modeling and in vitro oligomeric state analysis, we found that the instability arises from mutations that interfere with trimer formation. This study raises the intriguing possibility that protein instability might play a previously unrecognized role in mediating host-range expansions in viruses.

Modulation of Biophysical Properties of Nucleocapsid Protein in the Mutant Spectrum of SARS-CoV-2

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also exhibiting functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

Deciphering the liquid-liquid phase separation induced modulation in the structure, dynamics, and enzymatic activity of an ordered protein β-lactoglobulin

Owing to the significant role in the subcellular organization of biomolecules, physiology, and the realm of biomimetic materials, studies related to biomolecular condensates formed through liquid-liquid phase separation (LLPS) have emerged as a growing area of research. Despite valuable contributions of prior research, there is untapped potential in exploring the influence of phase separation on the conformational dynamics and enzymatic activities of native proteins. Herein, we investigate the LLPS of β-lactoglobulin (β-LG), a non-intrinsically disordered protein, under crowded conditions. In-depth characterization through spectroscopic and microscopic techniques revealed the formation of dynamic liquid-like droplets, distinct from protein aggregates, driven by hydrophobic interactions. Our analyses revealed that phase separation can alter structural flexibility and photophysical properties. Importantly, the phase-separated β-LG exhibited efficient enzymatic activity as an esterase; a characteristic seemingly exclusive to β-LG droplets. The droplets acted as robust catalytic crucibles, providing an ideal environment for efficient ester hydrolysis. Further investigation into the catalytic mechanism suggested the involvement of specific amino acid residues, rather than general acid or base catalysis. Also, the alteration in conformational distribution caused by phase separation unveils the latent functionality. Our study delineates the understanding of protein phase separation and insights into the diverse catalytic strategies employed by proteins. It opens exciting possibilities for designing functional artificial compartments based on phase-separated biomolecules.

Cellular age explains variation in age-related cell-to-cell transcriptome variability

Organs and tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages, we find that cellular age explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types, the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.

The alteration of structural network upon transient association between proteins studied using graph theory

Proteins such as enzymes perform their function by predominant non-covalent bond interactions between transiently interacting units. There is an impact on the overall structural topology of the protein, albeit transient nature of such interactions, that enable proteins to deactivate or activate. This aspect of the alteration of the structural topology is studied by employing protein structural networks, which are node-edge representative models of protein structure, reported as a robust tool for capturing interactions between residues. Several methods have been optimized to collect meaningful, functionally relevant information by studying alteration of structural networks. In this article, different methods of comparing protein structural networks are employed, along with spectral decomposition of graphs to study the subtle impact of protein-protein interactions. A detailed analysis of the structural network of interacting partners is performed across a dataset of around 900 pairs of bound complexes and corresponding unbound protein structures. The variation in network parameters at, around, and far away from the interface are analyzed. Finally, we present interesting case studies, where an allosteric mechanism of structural impact is understood from communication-path detection methods. The results of this analysis are beneficial in understanding protein stability, for future engineering, and docking studies.

Domain Architecture Based Methods for Comparative Functional Genomics Toward Therapeutic Drug Target Discovery

Genes duplicate, mutate, recombine, fuse or fission to produce new genes, or when genes are formed from de novo, novel functions arise during evolution. Researchers have tried to quantify the causes of these molecular diversification processes to know how these genes increase molecular complexity over a period of time, for instance protein domain organization. In contrast to global sequence similarity, protein domain architectures can capture key structural and functional characteristics, making them better proxies for describing functional equivalence. In Prokaryotes and eukaryotes it has proven that, domain designs are retained over significant evolutionary distances. Protein domain architectures are now being utilized to categorize and distinguish evolutionarily related proteins and find homologs among species that are evolutionarily distant from one another. Additionally, structural information stored in domain structures has accelerated homology identification and sequence search methods. Tools for functional protein annotation have been developed to discover, protein domain content, domain order, domain recurrence, and domain position as all these contribute to the prediction of protein functional accuracy. In this review, an attempt is made to summarise facts and speculations regarding the use of protein domain architecture and modularity to identify possible therapeutic targets among cellular activities based on the understanding their linked biological processes.

Diffusive intracellular interactions: On the role of protein net charge and functional adaptation

A striking feature of nucleic acids and lipid membranes is that they all carry net negative charge and so is true for the majority of intracellular proteins. It is suggested that the role of this negative charge is to assure a basal intermolecular repulsion that keeps the cytosolic content suitably 'fluid' for function. We focus in this review on the experimental, theoretical and genetic findings which serve to underpin this idea and the new questions they raise. Unlike the situation in test tubes, any functional protein-protein interaction in the cytosol is subject to competition from the densely crowded background, i.e. surrounding stickiness. At the nonspecific limit of this stickiness is the 'random' protein-protein association, maintaining profuse populations of transient and constantly interconverting complexes at physiological protein concentrations. The phenomenon is readily quantified in studies of the protein rotational diffusion, showing that the more net negatively charged a protein is the less it is retarded by clustering. It is further evident that this dynamic protein-protein interplay is under evolutionary control and finely tuned across organisms to maintain optimal physicochemical conditions for the cellular processes. The emerging picture is then that specific cellular function relies on close competition between numerous weak and strong interactions, and where all parts of the protein surfaces are involved. The outstanding challenge is now to decipher the very basics of this many-body system: how the detailed patterns of charged, polar and hydrophobic side chains not only control protein-protein interactions at close- and long-range but also the collective properties of the cellular interior as a whole.

Identification of a covert evolutionary pathway between two protein folds

Although homologous protein sequences are expected to adopt similar structures, some amino acid substitutions can interconvert α-helices and β-sheets. Such fold switching may have occurred over evolutionary history, but supporting evidence has been limited by the: (1) abundance and diversity of sequenced genes, (2) quantity of experimentally determined protein structures, and (3) assumptions underlying the statistical methods used to infer homology. Here, we overcome these barriers by applying multiple statistical methods to a family of ~600,000 bacterial response regulator proteins. We find that their homologous DNA-binding subunits assume divergent structures: helix-turn-helix versus α-helix + β-sheet (winged helix). Phylogenetic analyses, ancestral sequence reconstruction, and AlphaFold2 models indicate that amino acid substitutions facilitated a switch from helix-turn-helix into winged helix. This structural transformation likely expanded DNA-binding specificity. Our approach uncovers an evolutionary pathway between two protein folds and provides a methodology to identify secondary structure switching in other protein families.

GUD-VE visualization tool for physicochemical properties of proteins

The physicochemical properties of primary sequences of proteins helps in determining both the structure and biological functions. The sequence analysis of the proteins and nucleic acids is most fundamental element of bioinformatics. Without these elements, it is impossible to gain insight deeper molecular and biochemical mechanisms. For this purpose, the computational methods like bioinformatics tools assist experts and novices alike in resolving issues relating to protein analysis. Similarly, this proposed work, for the graphical user interface (GUI) based prediction and visualization through the computations-based method done on Jupyter Notebook with tkinter package which allows the creation of a program on a local host platform and accessed by the programmer.•When it is queried with a protein sequence, it predicts physicochemical parameters of the peptides.•Users can choose to visualize the findings acquired either anonymously or on the user-specified email address and compare the biophysical properties of one protein with other using amino acids (AA) sequences. The aim of this paper is to meet the requirements of experimentalists, not just hardcore bioinformaticians related to biophysical properties prediction and comparison with other proteins. The code for it has been uploaded on GitHub (an online repository of codes) in private mode.

Conserved folding landscape of monomeric initiator caspases

The apoptotic caspase subfamily evolved into two subfamilies-monomeric initiators and dimeric effectors; both subfamilies share a conserved caspase-hemoglobinase fold with a protease domain containing a large subunit and a small subunit. Sequence variations in the conserved caspase-hemoglobinase fold resulted in changes in oligomerization, enzyme specificity, and regulation, making caspases an excellent model for examining the mechanisms of molecular evolution in fine-tuning structure, function, and allosteric regulation. We examined the urea-induced equilibrium folding/unfolding of two initiator caspases, monomeric caspase-8 and cFLIPL, over a broad pH range. Both proteins unfold by a three-state equilibrium mechanism that includes a partially folded intermediate. In addition, both proteins undergo a conserved pH-dependent conformational change that is controlled by an evolutionarily conserved mechanism. We show that the conformational free energy landscape of the caspase monomer is conserved in the monomeric and dimeric subfamilies. Molecular dynamics simulations in the presence or the absence of urea, coupled with limited trypsin proteolysis and mass spectrometry, show that the small subunit is unstable in the protomer and unfolds prior to the large subunit. In addition, the unfolding of helix 2 in the large subunit results in disruption of a conserved allosteric site. Because the small subunit forms the interface for dimerization, our results highlight an important driving force for the evolution of the dimeric caspase subfamily through stabilizing the small subunit.

Identification of the NA+/K+-ATPase α-Isoforms in Six Species of Poison Dart Frogs and their Sensitivity to Cardiotonic Steroids

Cardiotonic steroids (CTS) are a group of compounds known to be toxic due to their ability to inhibit the Na⁺/K⁺-ATPase (NKA), which is essential to maintain the balance of ions in animal cells. An evolutionary strategy of molecular adaptation to avoid self-intoxication acquired by CTS defended organisms and their predators is the structural modification of their NKA where specific amino acid substitutions confer resistant phenotypes. Several lineages of poison dart frogs (Dendrobatidae) are well known to sequester a wide variety of lipophilic alkaloids from their arthropod diet, however there is no evidence of CTS-sequestration or dietary exposure. Interestingly this study identified the presence of α-NKA isoforms (α₁ and α₂) with amino acid substitutions indicative of CTS-resistant phenotypes in skeletal muscle transcriptomes obtained from six species of dendrobatids: Phyllobates aurotaenia, Oophaga anchicayensis, Epipedobates boulengeri, Andinobates bombetes, Andinobates minutus, and Leucostethus brachistriatus, collected in the Valle del Cauca (Colombia). P. aurotaenia, A. minutus, and E. boulengeri presented two variants for α₁-NKA, with one of them having these substitutions. In contrast, O. anchicayensis and A. bombetes have only one α₁-NKA isoform with an amino acid sequence indicative of CTS susceptibility and an α₂-NKA with one substitution that could confer a reduced affinity for CTS. The α₁ and α₂ isoforms of L. brachistriatus do not contain substitutions imparting CTS resistance. Our findings indicate that poison dart frogs express α-NKA isoforms with different affinities for CTS and the pattern of this expression might be influenced by factors related to evolutionary, physiological, ecological, and geographical burdens.

Structure-informed microbial population genetics elucidate selective pressures that shape protein evolution

Comprehensive sampling of natural genetic diversity with metagenomics enables highly resolved insights into the interplay between ecology and evolution. However, resolving adaptive, neutral, or purifying processes of evolution from intrapopulation genomic variation remains a challenge, partly due to the sole reliance on gene sequences to interpret variants. Here, we describe an approach to analyze genetic variation in the context of predicted protein structures and apply it to a marine microbial population within the SAR11 subclade 1a.3.V, which dominates low-latitude surface oceans. Our analyses reveal a tight association between genetic variation and protein structure. In a central gene in nitrogen metabolism, we observe decreased occurrence of nonsynonymous variants from ligand-binding sites as a function of nitrate concentrations, revealing genetic targets of distinct evolutionary pressures maintained by nutrient availability. Our work yields insights into the governing principles of evolution and enables structure-aware investigations of microbial population genetics.

Design of an artificial phage-display library based on a new scaffold improved for average stability of the randomized proteins

Scaffold-based protein libraries are designed to be both diverse and rich in functional/folded proteins. However, introducing an extended diversity while preserving stability of the initial scaffold remains a challenge. Here we developed an original approach to select the ensemble of folded proteins from an initial library. The thermostable CheY protein from Thermotoga maritima was chosen as scaffold. Four loops of CheY were diversified to create a new binding surface. The subset of the library giving rise to folded proteins was first selected using a natural protein partner of the template scaffold. Then, a gene shuffling approach based on a single restriction enzyme was used to recombine DNA sequences encoding these filtrated variants. Taken together, the filtration strategy and the shuffling of the filtrated sequences were shown to enrich the library in folded and stable sequences while maintaining a large diversity in the final library (Lib-Cheytins 2.1). Binders of the Oplophorus luciferase Kaz domain were then selected by phage display from the final library, showing affinities in the μM range. One of the best variants induced a loss of 92% of luminescent activity, suggesting that this Cheytin preferentially binds to the Kaz active site.

Proteome-Wide Structural Computations Provide Insights into Empirical Amino Acid Substitution Matrices

The relative contribution of mutation and selection to the amino acid substitution rates observed in empirical matrices is unclear. Herein, we present a neutral continuous fitness-stability model, inspired by the Arrhenius law (qij=aije-ΔΔGij). The model postulates that the rate of amino acid substitution (i→j) is determined by the product of a pre-exponential factor, which is influenced by the genetic code structure, and an exponential term reflecting the relative fitness of the amino acid substitutions. To assess the validity of our model, we computed changes in stability of 14,094 proteins, for which 137,073,638 in silico mutants were analyzed. These site-specific data were summarized into a 20 square matrix, whose entries, ΔΔGij, were obtained after averaging through all the sites in all the proteins. We found a significant positive correlation between these energy values and the disease-causing potential of each substitution, suggesting that the exponential term accurately summarizes the fitness effect. A remarkable observation was that amino acids that were highly destabilizing when acting as the source, tended to have little effect when acting as the destination, and vice versa (source → destination). The Arrhenius model accurately reproduced the pattern of substitution rates collected in the empirical matrices, suggesting a relevant role for the genetic code structure and a tuning role for purifying selection exerted via protein stability.

Structures of permuted halves of a modern ribose-binding protein

Periplasmic binding proteins (PBPs) are a class of proteins that participate in the cellular transport of various ligands. They have been used as model systems to study mechanisms in protein evolution, such as duplication, recombination and domain swapping. It has been suggested that PBPs evolved from precursors half their size. Here, the crystal structures of two permuted halves of a modern ribose-binding protein (RBP) from Thermotoga maritima are reported. The overexpressed proteins are well folded and show a monomer-dimer equilibrium in solution. Their crystal structures show partially noncanonical PBP-like fold type I conformations with structural deviations from modern RBPs. One of the half variants forms a dimer via segment swapping, suggesting a high degree of malleability. The structural findings on these permuted halves support the evolutionary hypothesis that PBPs arose via a duplication event of a flavodoxin-like protein and further support a domain-swapping step that might have occurred during the evolution of the PBP-like fold, a process that is necessary to generate the characteristic motion of PBPs essential to perform their functions.

Integrative Bioinformatics Approaches Indicate a Particular Pattern of Some SARS-CoV-2 and Non-SARS-CoV-2 Proteins

Pattern recognition plays a critical role in integrative bioinformatics to determine the structural patterns of proteins of viruses such as SARS-CoV-2. This study identifies the pattern of SARS-CoV-2 proteins to depict the structure-function relationships of the protein alphabets of SARS-CoV-2 and COVID-19. The assembly enumeration algorithm, Anisotropic Network Model, Gaussian Network Model, Markovian Stochastic Model, and image comparison protein-like alphabets were used. The distance score was the lowest with 22 for "I" and highest with 40 for "9". For post-processing and decision, two protein alphabets "C" (PDB ID: 6XC3) and "S" (PDB ID: 7OYG) were evaluated to understand the structural, functional, and evolutionary relationships, and we found uniqueness in the functionality of proteins. Here, models were constructed using "SARS-CoV-2 proteins" (12 numbers) and "non-SARS-CoV-2 proteins" (14 numbers) to create two words, "SARS-CoV-2" and "COVID-19". Similarly, we developed two slogans: "Vaccinate the world against COVID-19" and "Say no to SARS-CoV-2", which were made with the proteins structure. It might generate vaccine-related interest to broad reader categories. Finally, the evolutionary process appears to enhance the protein structure smoothly to provide suitable functionality shaped by natural selection.

In silico investigation of nonsynonymous single nucleotide polymorphisms in BCL2 apoptosis regulator gene to design novel protein-based drugs against cancer

BCL2 apoptosis regulator gene encodes Bcl-2 pro-survival protein, which plays an important role to evade apoptosis in various cancers. Moreover, single nucleotide polymorphisms (SNPs) in the BCL2 gene can be nonsynonymous (nsSNPs), which might affect the protein stability and probably its function. Therefore, we implement cutting-edge computational techniques based on the Spherical Polar Fourier and Monte-Carlo algorithms to investigate the impact of these SNPs on the B cell lymphoma-2 (Bcl-2) stability and therapeutic potential of protein-based molecules to inhibit this protein. As a result, we identified two nsSNPs (Q118R and R129C) to be deleterious and highly conserved, having a negative effect on protein stability. Additionally, molecular docking and molecular dynamics simulations confirmed the decreased binding affinity of mutated Bcl-2 variants to bind three-helix bundle protein inhibitor as these mutations occurred in the protein-protein binding site. Overall, this computational approach investigating nsSNPs provides a useful basis for designing novel molecules to inhibit Bcl-2 pro-survival pathway in malignant cells.

Evolution avoids a pathological stabilizing interaction in the immune protein S100A9

Stability constrains evolution. While much is known about constraints on destabilizing mutations, less is known about the constraints on stabilizing mutations. We recently identified a mutation in the innate immune protein S100A9 that provides insight into such constraints. When introduced into human S100A9, M63F simultaneously increases the stability of the protein and disrupts its natural ability to activate Toll-like receptor 4. Using chemical denaturation, we found that M63F stabilizes a calcium-bound conformation of hS100A9. We then used NMR to solve the structure of the mutant protein, revealing that the mutation distorts the hydrophobic binding surface of hS100A9, explaining its deleterious effect on function. Hydrogen-deuterium exchange (HDX) experiments revealed stabilization of the region around M63F in the structure, notably Phe37. In the structure of the M63F mutant, the Phe37 and Phe63 sidechains are in contact, plausibly forming an edge-face π-stack. Mutating Phe37 to Leu abolished the stabilizing effect of M63F as probed by both chemical denaturation and HDX. It also restored the biological activity of S100A9 disrupted by M63F. These findings reveal that Phe63 creates a molecular staple with Phe37 that stabilizes a nonfunctional conformation of the protein, thus disrupting function. Using a bioinformatic analysis, we found that S100A9 proteins from different organisms rarely have Phe at both positions 37 and 63, suggesting that avoiding a pathological stabilizing interaction indeed constrains S100A9 evolution. This work highlights an important evolutionary constraint on stabilizing mutations, namely, that they must avoid inappropriately stabilizing nonfunctional protein conformations.

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.

A Trait-Based Approach to Predicting Viral Host-Range Evolvability

Predicting the evolution of virus host range has proven to be extremely difficult, in part because of the sheer diversity of viruses, each with unique biology and ecological interactions. We have not solved this problem, but to make the problem more tractable, we narrowed our focus to three traits intrinsic to all viruses that may play a role in host-range evolvability: mutation rate, recombination rate, and phenotypic heterogeneity. Although each trait should increase evolvability, they cannot do so unbounded because fitness trade-offs limit the ability of all three traits to maximize evolvability. By examining these constraints, we can begin to identify groups of viruses with suites of traits that make them especially concerning, as well as ecological and environmental conditions that might push evolution toward accelerating host-range expansion.

Climate change, human health, and the exposome: Utilizing OMIC technologies to navigate an era of uncertainty

Climate change is an anthropogenic phenomenon that is alarming scientists and non-scientists alike. The emission of greenhouse gases is causing the temperature of the earth to rise and this increase is accompanied by a multitude of climate change-induced environmental exposures with potential health impacts. Tracking human exposure has been a major research interest of scientists worldwide. This has led to the development of exposome studies that examine internal and external individual exposures over their lifetime and correlate them to health. The monitoring of health has also benefited from significant technological advances in the field of "omics" technologies that analyze physiological changes on the nucleic acid, protein, and metabolism levels, among others. In this review, we discuss various climate change-induced environmental exposures and their potential health implications. We also highlight the potential integration of the technological advancements in the fields of exposome tracking, climate monitoring, and omics technologies shedding light on important questions that need to be answered.

The Statistical Trends of Protein Evolution: A Lesson from AlphaFold Database

The recent development of artificial intelligence provides us with new and powerful tools for studying the mysterious relationship between organism evolution and protein evolution. In this work, based on the AlphaFold Protein Structure Database (AlphaFold DB), we perform comparative analyses of the proteins of different organisms. The statistics of AlphaFold-predicted structures show that, for organisms with higher complexity, their constituent proteins will have larger radii of gyration, higher coil fractions, and slower vibrations, statistically. By conducting normal mode analysis and scaling analyses, we demonstrate that higher organismal complexity correlates with lower fractal dimensions in both the structure and dynamics of the constituent proteins, suggesting that higher functional specialization is associated with higher organismal complexity. We also uncover the topology and sequence bases of these correlations. As the organismal complexity increases, the residue contact networks of the constituent proteins will be more assortative, and these proteins will have a higher degree of hydrophilic-hydrophobic segregation in the sequences. Furthermore, by comparing the statistical structural proximity across the proteomes with the phylogenetic tree of homologous proteins, we show that, statistical structural proximity across the proteomes may indirectly reflect the phylogenetic proximity, indicating a statistical trend of protein evolution in parallel with organism evolution. This study provides new insights into how the diversity in the functionality of proteins increases and how the dimensionality of the manifold of protein dynamics reduces during evolution, contributing to the understanding of the origin and evolution of lives.

Mechanisms of protein evolution

How do proteins evolve? How do changes in sequence mediate changes in protein structure, and in turn in function? This question has multiple angles, ranging from biochemistry and biophysics to evolutionary biology. This review provides a brief integrated view of some key mechanistic aspects of protein evolution. First, we explain how protein evolution is primarily driven by randomly acquired genetic mutations and selection for function, and how these mutations can even give rise to completely new folds. Then, we also comment on how phenotypic protein variability, including promiscuity, transcriptional and translational errors, may also accelerate this process, possibly via "plasticity-first" mechanisms. Finally, we highlight open questions in the field of protein evolution, with respect to the emergence of more sophisticated protein systems such as protein complexes, pathways, and the emergence of pre-LUCA enzymes.

A Method for Assessing the Robustness of Protein Structures by Randomizing Packing Interactions

Many single-domain proteins are not only stable and water-soluble, but they also populate few to no intermediates during folding. This reduces interactions between partially folded proteins, misfolding, and aggregation, and makes the proteins tractable in biotechnological applications. Natural proteins fold thus, not necessarily only because their structures are well-suited for folding, but because their sequences optimize packing and fit their structures well. In contrast, folding experiments on the de novo designed Top7 suggest that it populates several intermediates. Additionally, in de novo protein design, where sequences are designed for natural and new non-natural structures, tens of sequences still need to be tested before success is achieved. Both these issues may be caused by the specific scaffolds used in design, i.e., some protein scaffolds may be more tolerant to packing perturbations and varied sequences. Here, we report a computational method for assessing the response of protein structures to packing perturbations. We then benchmark this method using designed proteins and find that it can identify scaffolds whose folding gets disrupted upon perturbing packing, leading to the population of intermediates. The method can also isolate regions of both natural and designed scaffolds that are sensitive to such perturbations and identify contacts which when present can rescue folding. Overall, this method can be used to identify protein scaffolds that are more amenable to whole protein design as well as to identify protein regions which are sensitive to perturbations and where further mutations should be avoided during protein engineering.

Physicochemical classification of organisms

The hypervariable residues that compose the major part of proteins’ surfaces are generally considered outside evolutionary control. Yet, these “nonconserved” residues determine the outcome of stochastic encounters in crowded cells. It has recently become apparent that these encounters are not as random as one might imagine, but carefully orchestrated by the intracellular electrostatics to optimize protein diffusion, interactivity, and partner search. The most influential factor here is the protein surface-charge density, which takes different optimal values across organisms with different intracellular conditions. In this study, we examine how far the net-charge density and other physicochemical properties of proteomes will take us in terms of distinguishing organisms in general. The results show that these global proteome properties not only follow the established taxonomical hierarchy, but also provide clues to functional adaptation. In many cases, the proteome–property divergence is even resolved at species level. Accordingly, the variable parts of the genes are not as free to drift as they seem in sequence alignment, but present a complementary tool for functional, taxonomic, and evolutionary assignment.

Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold Structure

Neurexin-1 (NRXN1) is a membrane protein essential in synapse formation and cell signaling as a cell-adhesion molecule and cell-surface receptor. NRXN1 and its binding partner neuroligin have been associated with deficits in cognition. Recent genetics research has linked NRXN1 missense mutations to increased risk for brain disorders, including schizophrenia (SCZ) and autism spectrum disorder (ASD). Investigation of the structure-function relationship in NRXN1 has proven difficult due to a lack of the experimental full-length membrane protein structure. AlphaFold, a deep learning-based predictor, succeeds in high-quality protein structure prediction and offers a solution for membrane protein model construction. In the study, we applied a computational saturation mutagenesis method to analyze the systemic effects of missense mutations on protein functions in a human NRXN1 structure predicted from AlphaFold and an experimental Bos taurus structure. The folding energy changes were calculated to estimate the effects of the 29,540 mutations of AlphaFold model on protein stability. The comparative study on the experimental and computationally predicted structures shows that these energy changes are highly correlated, demonstrating the reliability of the AlphaFold structure for the downstream bioinformatics analysis. The energy calculation revealed that some target mutations associated with SCZ and ASD could make the protein unstable. The study can provide helpful information for characterizing the disease-causing mutations and elucidating the molecular mechanisms by which the variations cause SCZ and ASD. This methodology could provide the bioinformatics protocol to investigate the effects of target mutations on multiple AlphaFold structures.

Exploring the Effect of Enhanced Sampling on Protein Stability Prediction

Changes in protein stability due to side-chain mutations are evaluated by alchemical free-energy calculations based on classical molecular dynamics (MD) simulations in explicit solvent using the GROMOS force field. Three proteins of different complexity with a total number of 93 single-point mutations are analyzed, and the relative free-energy differences are discussed with respect to configurational sampling and (dis)agreement with experimental data. For the smallest protein studied, a 34-residue WW domain, the starting structure dependence of the alchemical free-energy changes, is discussed in detail. Deviations from previous simulations for the two other proteins are shown to result from insufficient sampling in the earlier studies. Hamiltonian replica exchange in combination with multiple starting structures and sufficient sampling time of more than 100 ns per intermediate alchemical state is required in some cases to reach convergence.

Structural-Energetic Basis for Coupling between Equilibrium Fluctuations and Phosphorylation in a Protein Native Ensemble

The functioning of proteins is intimately tied to their fluctuations in the native ensemble. The structural-energetic features that determine fluctuation amplitudes and hence the shape of the underlying landscape, which in turn determine the magnitude of the functional output, are often confounded by multiple variables. Here, we employ the FF1 domain from human p190A RhoGAP protein as a model system to uncover the molecular basis for phosphorylation of a buried tyrosine, which is crucial to the transcriptional activity associated with transcription factor TFII-I. Combining spectroscopy, calorimetry, statistical-mechanical modeling, molecular simulations, and in vitro phosphorylation assays, we show that the FF1 domain samples a diverse array of conformations in its native ensemble, some of which are phosphorylation-competent. Upon eliminating unfavorable charge-charge interactions through a single charge-reversal (K53E) or charge-neutralizing (K53Q) mutation, we observe proportionately lower phosphorylation extents due to the altered structural coupling, damped equilibrium fluctuations, and a more compact native ensemble. We thus establish a conformational selection mechanism for phosphorylation in the FF1 domain with K53 acting as a "gatekeeper", modulating the solvent exposure of the buried tyrosine. Our work demonstrates the role of unfavorable charge-charge interactions in governing functional events through the modulation of native ensemble characteristics, a feature that could be prevalent in ordered protein domains.

The Influence of Codon Usage, Protein Abundance, and Protein Stability on Protein Evolution Vary by Evolutionary Distance and the Type of Protein

In general, the evolutionary rate of proteins is not primarily related to protein and amino acid functions, and factors such as protein abundance, codon usage, and the protein's T_M are more important. To better understand the factors that affect protein evolution, E. coli MG1655 orthologs were compared to those in closely related bacteria and to more distantly related prokaryotes, eukaryotes, and archaea. Also, the evolution of different types of proteins was studied. The analyses indicate that the amino acid conservation of enzymes that do not use macromolecules (e.g. DNA, RNA, and proteins) as substrates and that carry out metabolic processes involving small molecules (i.e. small molecule enzymes) is different than other enzymes. For example, the small molecule enzymes have a lower percent identity than other enzymes when sequences from closely related bacteria are compared. Analyses indicate the lower percent identity is not a result of the amino acid or codon usage of the small molecule enzymes. The small molecule enzymes also don't have a significantly lower protein abundance indicating that is also not likely an important factor driving differences in amino acid conservation. Analyses indicate different methods to measure the T_M of proteins have different relationships between amino acid conservation over different evolutionary distances. In totality, the results demonstrate that the relationship between the factors thought to affect protein evolution (protein abundance, codon usage, and proteins T_Ms) and protein evolution are complex and depend on the factor, the organisms, and the type of proteins being analyzed.

Viral protein instability enhances host-range evolvability

Viruses are highly evolvable, but what traits endow this property? The high mutation rates of viruses certainly play a role, but factors that act above the genetic code, like protein thermostability, are also expected to contribute. We studied how the thermostability of a model virus, bacteriophage λ, affects its ability to evolve to use a new receptor, a key evolutionary transition that can cause host-range evolution. Using directed evolution and synthetic biology techniques we generated a library of host-recognition protein variants with altered stabilities and then tested their capacity to evolve to use a new receptor. Variants fell within three stability classes: stable, unstable, and catastrophically unstable. The most evolvable were the two unstable variants, whereas seven of eight stable variants were significantly less evolvable, and the two catastrophically unstable variants could not grow. The slowly evolving stable variants were delayed because they required an additional destabilizing mutation. These results are particularly noteworthy because they contradict a widely supported contention that thermostabilizing mutations enhance evolvability of proteins by increasing mutational robustness. Our work suggests that the relationship between thermostability and evolvability is more complex than previously thought, provides evidence for a new molecular model of host-range expansion evolution, and identifies instability as a potential predictor of viral host-range evolution.

Understanding how viruses evolve to infect new hosts is critical for predicting host shifts as well as tuning host-range in phage therapy applications. Yet a mechanistic understanding of the molecular steps required to shift hosts has not been achieved. For this study we examined the evolutionary potential of different strains of a model virus, bacteriophage λ, to gain the ability to use a new receptor, a key step in host shifts. We discovered that λ variants with destabilized host-recognition proteins were more likely to evolve the necessary mutations to use the new receptor than stabilized variants. However, destabilization was only beneficial to a certain point and variants with overly unstable proteins lost all function. These results led us to propose a new molecular model for receptor use evolution in λ; 1) destabilizing mutations evolve that provide protein structural flexibility that allows new protein conformations to form that are able to interact with the new receptors, and 2) mutations evolve that alter the binding surface chemical properties to assist interactions with the new receptor. Our work with a model virus-host system, points to the potential use of viral stability as a phenotypic indicator of the capacity for virus host-range evolution.

Conformational dynamics promotes disordered regions from function-dispensable to essential in evolved site-specific DNA recombinases

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New functional regions emerging in evolution of DNA site-specific recombinase tails.

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Transient structural nucleation promotes function-dispensable regions to essential.

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Molecular dynamics reveals conformational diversity and its functional implications.

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Evolved disordered molecular mechanisms of N-term tails for protein stability.

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Structural disorder-based link between protein evolution, stability and function.

Protein intrinsically disordered regions (IDRs) play pivotal roles in molecular recognition and regulatory processes through structural disorder-to-order transitions. To understand and exploit the distinctive functional implications of IDRs and to unravel the underlying molecular mechanisms, structural disorder-to-function relationships need to be deciphered. The DNA site-specific recombinase system Cre/loxP represents an attractive model to investigate functional molecular mechanisms of IDRs. Cre contains a functionally dispensable disordered N-terminal tail, which becomes indispensable in the evolved Tre/loxLTR recombinase system. The difficulty to experimentally obtain structural information about this tail has so far precluded any mechanistic study on its involvement in DNA recombination. Here, we use in vitro and in silico evolution data, conformational dynamics, AI-based folding simulations, thermodynamic stability calculations, mutagenesis and DNA recombination assays to investigate how evolution and the dynamic behavior of this IDR may determine distinct functional properties. Our studies suggest that partial conformational order in the N-terminal tail of Tre recombinase and its packing to a conserved hydrophobic surface on the protein provide thermodynamic stability. Based on our results, we propose a link between protein stability and function, offering new plausible atom-detailed mechanistic insights into disorder-function relationships. Our work highlights the potential of N-terminal tails to be exploited for regulation of the activity of Cre-like tyrosine-type SSRs, which merits future investigations and could be of relevance in future rational engineering for their use in biotechnology and genomic medicine.

Probing ion channel functional architecture and domain recombination compatibility by massively parallel domain insertion profiling

Protein domains are the basic units of protein structure and function. Comparative analysis of genomes and proteomes showed that domain recombination is a main driver of multidomain protein functional diversification and some of the constraining genomic mechanisms are known. Much less is known about biophysical mechanisms that determine whether protein domains can be combined into viable protein folds. Here, we use massively parallel insertional mutagenesis to determine compatibility of over 300,000 domain recombination variants of the Inward Rectifier K⁺ channel Kir2.1 with channel surface expression. Our data suggest that genomic and biophysical mechanisms acted in concert to favor gain of large, structured domain at protein termini during ion channel evolution. We use machine learning to build a quantitative biophysical model of domain compatibility in Kir2.1 that allows us to derive rudimentary rules for designing domain insertion variants that fold and traffic to the cell surface. Positional Kir2.1 responses to motif insertion clusters into distinct groups that correspond to contiguous structural regions of the channel with distinct biophysical properties tuned towards providing either folding stability or gating transitions. This suggests that insertional profiling is a high-throughput method to annotate function of ion channel structural regions.

Evolution of a cytoplasmic determinant: evidence for the biochemical basis of functional evolution of the novel germ line regulator oskar

Germ line specification is essential in sexually reproducing organisms. Despite their critical role, the evolutionary history of the genes that specify animal germ cells is heterogeneous and dynamic. In many insects, the gene oskar is required for the specification of the germ line. However, the germ line role of oskar is thought to be a derived role resulting from co-option from an ancestral somatic role. To address how evolutionary changes in protein sequence could have led to changes in the function of Oskar protein that enabled it to regulate germ line specification, we searched for oskar orthologs in 1,565 publicly available insect genomic and transcriptomic data sets. The earliest-diverging lineage in which we identified an oskar ortholog was the order Zygentoma (silverfish and firebrats), suggesting that oskar originated before the origin of winged insects. We noted some order-specific trends in oskar sequence evolution, including whole gene duplications, clade-specific losses, and rapid divergence. An alignment of all known 379 Oskar sequences revealed new highly conserved residues as candidates that promote dimerization of the LOTUS domain. Moreover, we identified regions of the OSK domain with conserved predicted RNA binding potential. Furthermore, we show that despite a low overall amino acid conservation, the LOTUS domain shows higher conservation of predicted secondary structure than the OSK domain. Finally, we suggest new key amino acids in the LOTUS domain that may be involved in the previously reported Oskar−Vasa physical interaction that is required for its germ line role.

Fuzzle 2.0: Ligand Binding in Natural Protein Building Blocks

Modern proteins have been shown to share evolutionary relationships via subdomain-sized fragments. The assembly of such fragments through duplication and recombination events led to the complex structures and functions we observe today. We previously implemented a pipeline that identified more than 1,000 of these fragments that are shared by different protein folds and developed a web interface to analyze and search for them. This resource named Fuzzle helps structural and evolutionary biologists to identify and analyze conserved parts of a protein but it also provides protein engineers with building blocks for example to design proteins by fragment combination. Here, we describe a new version of this web resource that was extended to include ligand information. This addition is a significant asset to the database since now protein fragments that bind specific ligands can be identified and analyzed. Often the mode of ligand binding is conserved in proteins thereby supporting a common evolutionary origin. The same can now be explored for subdomain-sized fragments within this database. This ligand binding information can also be used in protein engineering to graft binding pockets into other protein scaffolds or to transfer functional sites via recombination of a specific fragment. Fuzzle 2.0 is freely available at https://fuzzle.uni-bayreuth.de/2.0.

Correlated Motions in Structural Biology

Correlated motions in proteins arising from the collective movements of residues have long been proposed to be fundamentally important to key properties of proteins, from allostery and catalysis to evolvability. Recent breakthroughs in structural biology have made it possible to capture proteins undergoing complex conformational changes, yet intrinsic correlated motions within a conformation remain one of the least understood facets of protein structure. For many decades, the analysis of total X-ray scattering held the promise of animating crystal structures with correlated motions. With recent advances in both X-ray detectors and data interpretation methods, this long-held promise can now be met. In this Perspective, we will introduce how correlated motions are captured in total scattering and provide guidelines for the collection, interpretation, and validation of data. As structural biology continues to push the boundaries, we see an opportunity to gain atomistic insight into correlated motions using total scattering as a bridge between theory and experiment.

Evolutionary Tracking of SARS-CoV-2 Genetic Variants Highlights an Intricate Balance of Stabilizing and Destabilizing Mutations

The currently ongoing COVID-19 pandemic caused by SARS-CoV-2 has accounted for millions of infections and deaths across the globe. Genome sequences of SARS-CoV-2 are being published daily in public databases and the availability of these genome data sets has allowed unprecedented access to the mutational patterns of SARS-CoV-2 evolution. We made use of the same genomic information for conducting phylogenetic analysis and identifying lineage-specific mutations. The catalogued lineage-defining mutations were analyzed for their stabilizing or destabilizing impact on viral proteins. We recorded persistence of D614G, S477N, A222V, and V1176F variants and a global expansion of the PANGOLIN variant B.1. In addition, a retention of Q57H (B.1.X), R203K/G204R (B.1.1.X), T85I (B.1.2-B.1.3), G15S+T428I (C.X), and I120F (D.X) variations was observed. Overall, we recorded a striking balance between stabilizing and destabilizing mutations, therefore leading to well-maintained protein structures. With selection pressures in the form of newly developed vaccines and therapeutics to mount in the coming months, the task of mapping viral mutations and recording their impact on key viral proteins should be crucial to preemptively catch any escape mechanism for which SARS-CoV-2 may evolve.

A thermodynamic model of protein structure evolution explains empirical amino acid substitution matrices

Proteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. These evolutionary pressures are sufficiently consistent over time and across protein families to produce substitution patterns, summarized in global amino acid substitution matrices such as BLOSUM, JTT, WAG, and LG, which can be used to successfully detect homologs, infer phylogenies, and reconstruct ancestral sequences. Although the factors that govern the variation of amino acid substitution rates have received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid substitution matrices from evolutionary dynamics controlled by a fitness function that reports on the thermodynamic effects of amino acid mutations in protein structures. This hybrid biophysical and evolutionary model accounts for nucleotide transition/transversion rate bias, multi‐nucleotide codon changes, the number of codons per amino acid, and thermodynamic protein stability. We find that our theoretical model accurately recapitulates the complex yet universal pattern observed in common global amino acid substitution matrices used in phylogenetics. These results suggest that selection for thermodynamically stable proteins, coupled with nucleotide mutation bias filtered by the structure of the genetic code, is the primary driver behind the global amino acid substitution patterns observed in proteins throughout the tree of life.

Evolutionary selectivity of amino acid is inspired from the enhanced structural stability and flexibility of the folded protein

AIM

The present study attempts to decipher the site-specific amino acid alterations at certain positions experiencing preferential selectivity and their effect on proteins' stability and flexibility. The study examines the selection preferences by considering pair-wise non-bonded interaction energies of adjacent and interacting amino acids present at the interacting site, along with their evolutionary history.

MATERIALS AND METHODS

For the study, variations in the interacting residues of spike protein (S-Protein) receptor-binding domain (RBD) of different coronaviruses were examined. The MD simulation trajectory analysis revealed that, though all the variants studied were structurally stable at their native and bound confirmations, the RBD of 2019-nCoV/SARS-CoV-2 was found to be more flexible and more dynamic. Furthermore, a noticeable change observed in the non-bonded interaction energies of the amino acids interacting with the receptor corroborated their selection at respective positions.

KEY FINDINGS

The conformational changes exerted by the altered amino acids could be the reason for a broader range of interacting receptors among the selected proteins.

SIGNIFICANCE

The results envisage a strong indication that the residue selection at certain positions is governed by a well-orchestrated feedback mechanism, which follows increased stability and flexibility in the folded structure compared to its evolutionary predecessor.

Evolution, folding, and design of TIM barrels and related proteins

Proteins are chief actors in life that perform a myriad of exquisite functions. This diversity has been enabled through the evolution and diversification of protein folds. Analysis of sequences and structures strongly suggest that numerous protein pieces have been reused as building blocks and propagated to many modern folds. This information can be traced to understand how the protein world has diversified. In this review, we discuss the latest advances in the analysis of protein evolutionary units, and we use as a model system one of the most abundant and versatile topologies, the TIM-barrel fold, to highlight the existing common principles that interconnect protein evolution, structure, folding, function, and design.

Abundance Imparts Evolutionary Constraints of Similar Magnitude on the Buried, Surface, and Disordered Regions of Proteins

An understanding of the forces shaping protein conservation is key, both for the fundamental knowledge it represents and to allow for optimal use of evolutionary information in practical applications. Sequence conservation is typically examined at one of two levels. The first is a residue-level, where intra-protein differences are analyzed and the second is a protein-level, where inter-protein differences are studied. At a residue level, we know that solvent-accessibility is a prime determinant of conservation. By inverting this logic, we inferred that disordered regions are slightly more solvent-accessible on average than the most exposed surface residues in domains. By integrating abundance information with evolutionary data within and across proteins, we confirmed a previously reported strong surface-core association in the evolution of structured regions, but we found a comparatively weak association between disordered and structured regions. The facts that disordered and structured regions experience different structural constraints and evolve independently provide a unique setup to examine an outstanding question: why is a protein’s abundance the main determinant of its sequence conservation? Indeed, any structural or biophysical property linked to the abundance-conservation relationship should increase the relative conservation of regions concerned with that property (e.g., disordered residues with mis-interactions, domain residues with misfolding). Surprisingly, however, we found the conservation of disordered and structured regions to increase in equal proportion with abundance. This observation implies that either abundance-related constraints are structure-independent, or multiple constraints apply to different regions and perfectly balance each other.

On the Emergence of Orientational Order in Folded Proteins with Implications for Allostery

The beautiful structures of single- and multi-domain proteins are clearly ordered in some fashion but cannot be readily classified using group theory methods that are successfully used to describe periodic crystals. For this reason, protein structures are considered to be aperiodic, and may have evolved this way for functional purposes, especially in instances that require a combination of softness and rigidity within the same molecule. By analyzing the solved protein structures, we show that orientational symmetry is broken in the aperiodic arrangement of the secondary structure elements (SSEs), which we deduce by calculating the nematic order parameter, P2. We find that the folded structures are nematic droplets with a broad distribution of P2. We argue that a non-zero value of P2, leads to an arrangement of the SSEs that can resist external forces, which is a requirement for allosteric proteins. Such proteins, which resist mechanical forces in some regions while being flexible in others, transmit signals from one region of the protein to another (action at a distance) in response to binding of ligands (oxygen, ATP, or other small molecules).

Diversification of DNA-Binding Specificity by Permissive and Specificity-Switching Mutations in the ParB/Noc Protein Family

Specific interactions between proteins and DNA are essential to many biological processes. Yet, it remains unclear how the diversification in DNA-binding specificity was brought about, and the mutational paths that led to changes in specificity are unknown. Using a pair of evolutionarily related DNA-binding proteins, each with a different DNA preference (ParB [Partitioning Protein B] and Noc [Nucleoid Occlusion Factor], which both play roles in bacterial chromosome maintenance), we show that specificity is encoded by a set of four residues at the protein-DNA interface. Combining X-ray crystallography and deep mutational scanning of the interface, we suggest that permissive mutations must be introduced before specificity-switching mutations to reprogram specificity and that mutational paths to new specificity do not necessarily involve dual-specificity intermediates. Overall, our results provide insight into the possible evolutionary history of ParB and Noc and, in a broader context, might be useful for understanding the evolution of other classes of DNA-binding proteins.

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DNA-binding specificity for parS and NBS is conserved within ParB and Noc family

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Specificity is encoded by a set of four residues at the protein-DNA interface

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Mutations must be introduced in a defined order to reprogram specificity

Metamorphic proteins: the Janus proteins of structural biology

The structural paradigm that the sequence of a protein encodes for a unique three-dimensional native fold does not acknowledge the intrinsic plasticity encapsulated in conformational free energy landscapes. Metamorphic proteins are a recently discovered class of biomolecules that illustrate this plasticity by folding into at least two distinct native state structures of comparable stability in the absence of ligands or cofactors to facilitate fold-switching. The expanding list of metamorphic proteins clearly shows that these proteins are not mere aberrations in protein evolution, but may have actually been a consequence of distinctive patterns in selection pressure such as those found in virus–host co-evolution. In this review, we describe the structure–function relationships observed in well-studied metamorphic protein systems, with specific focus on how functional residues are sequestered or exposed in the two folds of the protein. We also discuss the implications of metamorphosis for protein evolution and the efforts that are underway to predict metamorphic systems from sequence properties alone.

Protein phylogenetics with mass spectrometry. A comparison of methods

Advances in protein mass spectrometry have provided the ability to identify and sequence proteins with unprecedented speed, sensitivity and accuracy. These benefits now offer advantages for studies of protein evolution and phylogeny avoiding the need to generate and align DNA sequences which can prove time consuming, costly and difficult in the case of large genomes and for highly diverse organisms. The methods of phylogenetic analysis using protein mass spectrometry can be classified into three categories: (1) de novo protein sequencing followed by multiple sequence alignment for classical phylogenetic reconstruction, (2) direct phylogenetic reconstruction using expressed protein mass profiles exploited in microbial biotyping applications, and (3) the construction of trees using proteolytic peptide mass map or fingerprint data. This review describes the three approaches together with the relevant tools and algorithms required to implement them. It also compares each of these alternative protein based methods alongside conventional gene sequence based phylogenetics.

Structure and function of naturally evolved de novo proteins

Comparative evolutionary genomics has revealed that novel protein coding genes can emerge randomly from non-coding DNA. While most of the myriad of transcripts which continuously emerge vanish rapidly, some attain regulatory regions, become translated and survive. More surprisingly, sequence properties of de novo proteins are almost indistinguishable from randomly obtained sequences, yet de novo proteins may gain functions and integrate into eukaryotic cellular networks quite easily. We here discuss current knowledge on de novo proteins, their structures, functions and evolution. Since the existence of de novo proteins seems at odds with decade-long attempts to construct proteins with novel structures and functions from scratch, we suggest that a better understanding of de novo protein evolution may fuel new strategies for protein design.

Elevated temperature increases genome-wide selection on de novo mutations

Adaptation in new environments depends on the amount of genetic variation available for evolution, and the efficacy by which natural selection discriminates among this variation. However, whether some ecological factors reveal more genetic variation, or impose stronger selection pressures than others, is typically not known. Here, we apply the enzyme kinetic theory to show that rising global temperatures are predicted to intensify natural selection throughout the genome by increasing the effects of DNA sequence variation on protein stability. We test this prediction by (i) estimating temperature-dependent fitness effects of induced mutations in seed beetles adapted to ancestral or elevated temperature, and (ii) calculate 100 paired selection estimates on mutations in benign versus stressful environments from unicellular and multicellular organisms. Environmental stress per se did not increase mean selection on de novo mutation, suggesting that the cost of adaptation does not generally increase in new ecological settings to which the organism is maladapted. However, elevated temperature increased the mean strength of selection on genome-wide polymorphism, signified by increases in both mutation load and mutational variance in fitness. These results have important implications for genetic diversity gradients and the rate and repeatability of evolution under climate change.

Combining structure and genomics to understand antimicrobial resistance

Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.

Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness

Background

How, and the extent to which, evolution acts on DNA and protein sequences to ensure mutational robustness and evolvability is a long-standing open question in the field of molecular evolution. We addressed this issue through the first structurome-scale computational investigation, in which we estimated the change in folding free energy upon all possible single-site mutations introduced in more than 20,000 protein structures, as well as through available experimental stability and fitness data.

Results

At the amino acid level, we found the protein surface to be more robust against random mutations than the core, this difference being stronger for small proteins. The destabilizing and neutral mutations are more numerous in the core and on the surface, respectively, whereas the stabilizing mutations are about 4% in both regions. At the genetic code level, we observed smallest destabilization for mutations that are due to substitutions of base III in the codon, followed by base I, bases I+III, base II, and other multiple base substitutions. This ranking highly anticorrelates with the codon-anticodon mispairing frequency in the translation process. This suggests that the standard genetic code is optimized to limit the impact of random mutations, but even more so to limit translation errors. At the codon level, both the codon usage and the usage bias appear to optimize mutational robustness and translation accuracy, especially for surface residues.

Conclusion

Our results highlight the non-universality of mutational robustness and its multiscale dependence on protein features, the structure of the genetic code, and the codon usage. Our analyses and approach are strongly supported by available experimental mutagenesis data.

Exploring the sequence fitness landscape of a bridge between protein folds

Most foldable protein sequences adopt only a single native fold. Recent protein design studies have, however, created protein sequences which fold into different structures apon changes of environment, or single point mutation, the best characterized example being the switch between the folds of the GA and GB binding domains of streptococcal protein G. To obtain further insight into the design of sequences which can switch folds, we have used a computational model for the fitness landscape of a single fold, built from the observed sequence variation of protein homologues. We have recently shown that such coevolutionary models can be used to design novel foldable sequences. By appropriately combining two of these models to describe the joint fitness landscape of GA and GB, we are able to describe the propensity of a given sequence for each of the two folds. We have successfully tested the combined model against the known series of designed GA/GB hybrids. Using Monte Carlo simulations on this landscape, we are able to identify pathways of mutations connecting the two folds. In the absence of a requirement for domain stability, the most frequent paths go via sequences in which neither domain is stably folded, reminiscent of the propensity for certain intrinsically disordered proteins to fold into different structures according to context. Even if the folded state is required to be stable, we find that there is nonetheless still a wide range of sequences which are close to the transition region and therefore likely fold switches, consistent with recent estimates that fold switching may be more widespread than had been thought.

While most proteins self-assemble (or “fold”) to a unique three-dimensional structure, a few have been identified that can fold into two distinct structures. These so-called “metamorphic” proteins that can switch folds have attracted a lot of recent interest, and it has been suggested that they may be much more widespread than currently appreciated. We have developed a computational model that captures the propensity of a given protein sequence to fold into either one of two specific structures (GA and GB), in order to investigate which sequences are able to fold to both GA and GB (“switch sequences”), versus just one of them. Our model predicts that there is a large number of switch sequences that could fold into both structures, but also that the most likely such sequences are those for which the folded structures have low stability, in agreement with available experimental data. This also suggests that intrinsically disordered proteins which can fold into different structures on binding may provide an evolutionary path in sequence space between protein folds.