MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods.

Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.

FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction.

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was among the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6) and performs as well as much more complex deep learning models such as AlphaMissense. To further demonstrate FiTMuSiC's robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiC's qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at https://babylone.ulb.ac.be/FiTMuSiC , which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community.

pycofitness-Evaluating the fitness landscape of RNA and protein sequences.

MOTIVATION: The accurate prediction of how mutations change biophysical properties of proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue. RESULTS: We present pycofitness, a standalone Python-based software package for the in silico mutagenesis of protein and RNA sequences. It is based on coevolution and, more specifically, on a popular inverse statistical approach, namely direct coupling analysis by pseudo-likelihood maximization. Its efficient implementation and user-friendly command line interface make it an easy-to-use tool even for researchers with no bioinformatics background. To illustrate its strengths, we present three applications in which pycofitness efficiently predicts the deleteriousness of genetic variants and the effect of mutations on protein fitness and thermodynamic stability. AVAILABILITY AND IMPLEMENTATION: https://github.com/KIT-MBS/pycofitness.

Genomic basis of environmental adaptation in the widespread poly-extremophilic Exiguobacterium group.

Delineating cohesive ecological units and determining the genetic basis for their environmental adaptation are among the most important objectives in microbiology. In the last decade, many studies have been devoted to characterizing the genetic diversity in microbial populations to address these issues. However, the impact of extreme environmental conditions, such as temperature and salinity, on microbial ecology and evolution remains unclear so far. In order to better understand the mechanisms of adaptation, we studied the (pan)genome of Exiguobacterium, a poly-extremophile bacterium able to grow in a wide range of environments, from permafrost to hot springs. To have the genome for all known Exiguobacterium type strains, we first sequenced those that were not yet available. Using a reverse-ecology approach, we showed how the integration of phylogenomic information, genomic features, gene and pathway enrichment data, regulatory element analyses, protein amino acid composition, and protein structure analyses of the entire Exiguobacterium pangenome allows to sharply delineate ecological units consisting of mesophilic, psychrophilic, halophilic-mesophilic, and halophilic-thermophilic ecotypes. This in-depth study clarified the genetic basis of the defined ecotypes and identified some key mechanisms driving the environmental adaptation to extreme environments. Our study points the way to organizing the vast microbial diversity into meaningful ecologically units, which, in turn, provides insight into how microbial communities adapt and respond to different environmental conditions in a changing world.

pyScoMotif: discovery of similar 3D structural motifs across proteins.

Motivation: The fast and accurate detection of similar geometrical arrangements of protein residues, known as 3D structural motifs, is highly relevant for many applications such as binding region and catalytic site detection, drug discovery and structure conservation analyses. With the recent publication of new protein structure prediction methods, the number of available protein structures is exploding, which makes efficient and easy-to-use tools for identifying 3D structural motifs essential. Results: We present an open-source Python package that enables the search for both exact and mutated motifs with position-specific residue substitutions. The tool is efficient, flexible, accurate, and suitable to run both on computer clusters and personal laptops. Two successful applications of pyScoMotif for catalytic site identification are showcased. Availability and implementation: The pyScoMotif package can be installed from the PyPI repository and is also available at https://github.com/3BioCompBio/pyScoMotif. It is free to use for non-commercial purposes.

Enzyme Stability-Activity Trade-Off: New Insights from Protein Stability Weaknesses and Evolutionary Conservation.

A general limitation of the use of enzymes in biotechnological processes under sometimes nonphysiological conditions is the complex interplay between two key quantities, enzyme activity and stability, where the increase of one is often associated with the decrease of the other. A precise stability-activity trade-off is necessary for the enzymes to be fully functional, but its weight in different protein regions and its dependence on environmental conditions is not yet elucidated. To advance this issue, we used the formalism that we have recently developed to effectively identify stability strength and weakness regions in protein structures and applied it to a large set of globular enzymes with known experimental structure and catalytic sites. Our analysis showed a striking oscillatory pattern of free energy compensation centered on the catalytic region. Indeed, catalytic residues are usually nonoptimal with respect to stability, but residues in the first shell around the catalytic site are, on the average, stability strengths and thus compensate for this lack of stability; residues in the second shell are weaker again, and so on. This trend is consistent across all enzyme families. It is accompanied by a similar, but less pronounced, pattern of residue conservation across evolution. In addition, we analyzed cold- and heat-adapted enzymes separately and highlighted different patterns of stability strengths and weaknesses, which provide insight into the longstanding problem of catalytic rate enhancement in cold environments. The successful comparison of our stability and conservation results with experimental fitness data, obtained by deep mutagenesis scanning, led us to propose criteria for improving catalytic activity while maintaining enzyme stability, a key goal in enzyme design.

Estimating the Vertical Ionization Potential of Single-Stranded DNA Molecules.

The electronic properties of DNA molecules, defined by the sequence-dependent ionization potentials of nucleobases, enable long-range charge transport along the DNA stacks. This has been linked to a range of key physiological processes in the cells and to the triggering of nucleobase substitutions, some of which may cause diseases. To gain molecular-level understanding of the sequence dependence of these phenomena, we estimated the vertical ionization potential (vIP) of all possible nucleobase stacks in B-conformation, containing one to four Gua, Ade, Thy, Cyt, or methylated Cyt. To do this, we used quantum chemistry calculations and more precisely the second-order Møller-Plesset perturbation theory (MP2) and three double-hybrid density functional theory methods, combined with several basis sets for describing atomic orbitals. The calculated vIP of single nucleobases were compared to experimental data and those of nucleobase pairs, triplets, and quadruplets, to observed mutability frequencies in the human genome, reported to be correlated with vIP values. This comparison selected MP2 with the 6-31G* basis set as the best of the tested calculation levels. These results were exploited to set up a recursive model, called vIPer, which estimates the vIP of all possible single-stranded DNA sequences of any length based on the calculated vIPs of overlapping quadruplets. vIPer's vIP values correlate well with oxidation potentials measured by cyclic voltammetry and activities obtained through photoinduced DNA cleavage experiments, further validating our approach. vIPer is freely available on the github.com/3BioCompBio/vIPer repository.

Critical review of conformational B-cell epitope prediction methods.

Accurate in silico prediction of conformational B-cell epitopes would lead to major improvements in disease diagnostics, drug design and vaccine development. A variety of computational methods, mainly based on machine learning approaches, have been developed in the last decades to tackle this challenging problem. Here, we rigorously benchmarked nine state-of-the-art conformational B-cell epitope prediction webservers, including generic and antibody-specific methods, on a dataset of over 250 antibody-antigen structures. The results of our assessment and statistical analyses show that all the methods achieve very low performances, and some do not perform better than randomly generated patches of surface residues. In addition, we also found that commonly used consensus strategies that combine the results from multiple webservers are at best only marginally better than random. Finally, we applied all the predictors to the SARS-CoV-2 spike protein as an independent case study, and showed that they perform poorly in general, which largely recapitulates our benchmarking conclusions. We hope that these results will lead to greater caution when using these tools until the biases and issues that limit current methods have been addressed, promote the use of state-of-the-art evaluation methodologies in future publications and suggest new strategies to improve the performance of conformational B-cell epitope prediction methods.

Quantification of biases in predictions of protein-protein binding affinity changes upon mutations.

Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein-protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the severe acute respiratory syndrome coronavirus 2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches, while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases toward specific mutation properties, the most marked being toward destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness.

SpikePro: a webserver to predict the fitness of SARS-CoV-2 variants.

MOTIVATION: The SARS-CoV-2 virus has shown a remarkable ability to evolve and spread across the globe through successive waves of variants since the original Wuhan lineage. Despite all the efforts of the last 2 years, the early and accurate prediction of variant severity is still a challenging issue which needs to be addressed to help, for example, the decision of activating COVID-19 plans long before the peak of new waves. Upstream preparation would indeed make it possible to avoid the overflow of health systems and limit the most severe cases. RESULTS: We recently developed SpikePro, a structure-based computational model capable of quickly and accurately predicting the viral fitness of a variant from its spike protein sequence. It is based on the impact of mutations on the stability of the spike protein as well as on its binding affinity for the angiotensin-converting enzyme 2 (ACE2) and for a set of neutralizing antibodies. It yields a precise indication of the virus transmissibility, infectivity, immune escape and basic reproduction rate. We present here an updated version of the model that is now available on an easy-to-use webserver, and illustrate its power in a retrospective study of fitness evolution and reproduction rate of the main viral lineages. SpikePro is thus expected to be great help to assess the fitness of newly emerging SARS-CoV-2 variants in genomic surveillance and viral evolution programs. AVAILABILITY AND IMPLEMENTATION: SpikePro webserver http://babylone.ulb.ac.be/SpikePro/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysis of the Neutralizing Activity of Antibodies Targeting Open or Closed SARS-CoV-2 Spike Protein Conformations.

SARS-CoV-2 infection elicits a polyclonal neutralizing antibody (nAb) response that primarily targets the spike protein, but it is still unclear which nAbs are immunodominant and what distinguishes them from subdominant nAbs. This information would however be crucial to predict the evolutionary trajectory of the virus and design future vaccines. To shed light on this issue, we gathered 83 structures of nAbs in complex with spike protein domains. We analyzed in silico the ability of these nAbs to bind the full spike protein trimer in open and closed conformations, and predicted the change in binding affinity of the most frequently observed spike protein variants in the circulating strains. This led us to define four nAb classes with distinct variant escape fractions. By comparing these fractions with those measured from plasma of infected patients, we showed that the class of nAbs that most contributes to the immune response is able to bind the spike protein in its closed conformation. Although this class of nAbs only partially inhibits the spike protein binding to the host's angiotensin converting enzyme 2 (ACE2), it has been suggested to lock the closed pre-fusion spike protein conformation and therefore prevent its transition to an open state. Furthermore, comparison of our predictions with mRNA-1273 vaccinated patient plasma measurements suggests that spike proteins contained in vaccines elicit a different nAb class than the one elicited by natural SARS-CoV-2 infection and suggests the design of highly stable closed-form spike proteins as next-generation vaccine immunogens.

Using metagenomic data to boost protein structure prediction and discovery.

Over the past decade, metagenomic sequencing approaches have been providing an ever-increasing amount of protein sequence data at an astonishing rate. These constitute an invaluable source of information which has been exploited in various research fields such as the study of the role of the gut microbiota in human diseases and aging. However, only a small fraction of all metagenomic sequences collected have been functionally or structurally characterized, leaving much of them completely unexplored. Here, we review how this information has been used in protein structure prediction and protein discovery. We begin by presenting some widely used metagenomic databases and analyze in detail how metagenomic data has contributed to the impressive improvement in the accuracy of structure prediction methods in recent years. We then examine how metagenomic information can be exploited to annotate protein sequences. More specifically, we focus on the role of metagenomes in the discovery of enzymes and new CRISPR-Cas systems, and in the identification of antibiotic resistance genes. With this review, we provide an overview of how metagenomic data is currently revolutionizing our understanding of protein science.

NPTX1 mutations trigger endoplasmic reticulum stress and cause autosomal dominant cerebellar ataxia.

With more than 40 causative genes identified so far, autosomal dominant cerebellar ataxias exhibit a remarkable genetic heterogeneity. Yet, half the patients are lacking a molecular diagnosis. In a large family with nine sampled affected members, we performed exome sequencing combined with whole-genome linkage analysis. We identified a missense variant in NPTX1, NM_002522.3:c.1165G>A: p.G389R, segregating with the phenotype. Further investigations with whole-exome sequencing and an amplicon-based panel identified four additional unrelated families segregating the same variant, for whom a common founder effect could be excluded. A second missense variant, NM_002522.3:c.980A>G: p.E327G, was identified in a fifth familial case. The NPTX1-associated phenotype consists of a late-onset, slowly progressive, cerebellar ataxia, with downbeat nystagmus, cognitive impairment reminiscent of cerebellar cognitive affective syndrome, myoclonic tremor and mild cerebellar vermian atrophy on brain imaging. NPTX1 encodes the neuronal pentraxin 1, a secreted protein with various cellular and synaptic functions. Both variants affect conserved amino acid residues and are extremely rare or absent from public databases. In COS7 cells, overexpression of both neuronal pentraxin 1 variants altered endoplasmic reticulum morphology and induced ATF6-mediated endoplasmic reticulum stress, associated with cytotoxicity. In addition, the p.E327G variant abolished neuronal pentraxin 1 secretion, as well as its capacity to form a high molecular weight complex with the wild-type protein. Co-immunoprecipitation experiments coupled with mass spectrometry analysis demonstrated abnormal interactions of this variant with the cytoskeleton. In agreement with these observations, in silico modelling of the neuronal pentraxin 1 complex evidenced a destabilizing effect for the p.E327G substitution, located at the interface between monomers. On the contrary, the p.G389 residue, located at the protein surface, had no predictable effect on the complex stability. Our results establish NPTX1 as a new causative gene in autosomal dominant cerebellar ataxias. We suggest that variants in NPTX1 can lead to cerebellar ataxia due to endoplasmic reticulum stress, mediated by ATF6, and associated to a destabilization of NP1 polymers in a dominant-negative manner for one of the variants.

Artificial intelligence challenges for predicting the impact of mutations on protein stability.

Stability is a key ingredient of protein fitness, and its modification through targeted mutations has applications in various fields, such as protein engineering, drug design, and deleterious variant interpretation. Many studies have been devoted over the past decades to build new, more effective methods for predicting the impact of mutations on protein stability based on the latest developments in artificial intelligence. We discuss their features, algorithms, computational efficiency, and accuracy estimated on an independent test set. We focus on a critical analysis of their limitations, the recurrent biases toward the training set, their generalizability, and interpretability. We found that the accuracy of the predictors has stagnated at around 1 kcal/mol for over 15 years. We conclude by discussing the challenges that need to be addressed to reach improved performance.

Perturbing dimer interactions and allosteric communication modulates the immunosuppressive activity of human galectin-7.

The design of allosteric modulators to control protein function is a key objective in drug discovery programs. Altering functionally essential allosteric residue networks provides unique protein family subtype specificity, minimizes unwanted off-target effects, and helps avert resistance acquisition typically plaguing drugs that target orthosteric sites. In this work, we used protein engineering and dimer interface mutations to positively and negatively modulate the immunosuppressive activity of the proapoptotic human galectin-7 (GAL-7). Using the PoPMuSiC and BeAtMuSiC algorithms, mutational sites and residue identity were computationally probed and predicted to either alter or stabilize the GAL-7 dimer interface. By designing a covalent disulfide bridge between protomers to control homodimer strength and stability, we demonstrate the importance of dimer interface perturbations on the allosteric network bridging the two opposite glycan-binding sites on GAL-7, resulting in control of induced apoptosis in Jurkat T cells. Molecular investigation of G16X GAL-7 variants using X-ray crystallography, biophysical, and computational characterization illuminates residues involved in dimer stability and allosteric communication, along with discrete long-range dynamic behaviors involving loops 1, 3, and 5. We show that perturbing the protein-protein interface between GAL-7 protomers can modulate its biological function, even when the overall structure and ligand-binding affinity remains unaltered. This study highlights new avenues for the design of galectin-specific modulators influencing both glycan-dependent and glycan-independent interactions.

Quantifying Renin-Angiotensin-System Alterations in COVID-19.

The renin-angiotensin system (RAS) plays a pivotal role in a wide series of physiological processes, among which inflammation and blood pressure regulation. One of its key components, the angiotensin-converting enzyme 2, has been identified as the entry point of the SARS-CoV-2 virus into the host cells, and therefore a lot of research has been devoted to study RAS dysregulation in COVID-19. Here we discuss the alterations of the regulatory RAS axes due to SARS-CoV-2 infection on the basis of a series of recent clinical investigations and experimental analyzes quantifying, e.g., the levels and activity of RAS components. We performed a comprehensive meta-analysis of these data in view of disentangling the links between the impaired RAS functioning and the pathophysiological characteristics of COVID-19. We also review the effects of several RAS-targeting drugs and how they could potentially help restore the normal RAS functionality and minimize the COVID-19 severity. Finally, we discuss the conflicting evidence found in the literature and the open questions on RAS dysregulation in SARS-CoV-2 infection whose resolution would improve our understanding of COVID-19.

Prediction and Evolution of the Molecular Fitness of SARS-CoV-2 Variants: Introducing SpikePro.

The understanding of the molecular mechanisms driving the fitness of the SARS-CoV-2 virus and its mutational evolution is still a critical issue. We built a simplified computational model, called SpikePro, to predict the SARS-CoV-2 fitness from the amino acid sequence and structure of the spike protein. It contains three contributions: the inter-human transmissibility of the virus predicted from the stability of the spike protein, the infectivity computed in terms of the affinity of the spike protein for the ACE2 receptor, and the ability of the virus to escape from the human immune response based on the binding affinity of the spike protein for a set of neutralizing antibodies. Our model reproduces well the available experimental, epidemiological and clinical data on the impact of variants on the biophysical characteristics of the virus. For example, it is able to identify circulating viral strains that, by increasing their fitness, recently became dominant at the population level. SpikePro is a useful, freely available instrument which predicts rapidly and with good accuracy the dangerousness of new viral strains. It can be integrated and play a fundamental role in the genomic surveillance programs of the SARS-CoV-2 virus that, despite all the efforts, remain time-consuming and expensive.

MutaFrame-an interpretative visualization framework for deleteriousness prediction of missense variants in the human exome.

MOTIVATION: High-throughput experiments are generating ever increasing amounts of various -omics data, so shedding new light on the link between human disorders, their genetic causes and the related impact on protein behavior and structure. While numerous bioinformatics tools now exist that predict which variants in the human exome cause diseases, few tools predict the reasons why they might do so. Yet, understanding the impact of variants at the molecular level is a prerequisite for the rational development of targeted drugs or personalized therapies. RESULTS: We present the updated MutaFrame webserver, which aims to meet this need. It offers two deleteriousness prediction softwares, DEOGEN2 and SNPMuSiC, and is designed for bioinformaticians and medical researchers who want to gain insights into the origins of monogenic diseases. It contains information at two levels for each human protein: its amino acid sequence and its three-dimensional structure; we used the experimental structures whenever available, and modeled structures otherwise. MutaFrame also includes higher-level information, such as protein essentiality and protein-protein interactions. It has a user-friendly interface for the interpretation of results and a convenient visualization system for protein structures, in which the variant positions introduced by the user and other structural information are shown. In this way, MutaFrame aids our understanding of the pathogenic processes caused by single-site mutations and their molecular and contextual interpretation. AVAILABILITY AND IMPLEMENTATION: Mutaframe webserver at http://mutaframe.com/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In Silico Analysis of the Molecular-Level Impact of SMPD1 Variants on Niemann-Pick Disease Severity.

Sphingomyelin phosphodiesterase (SMPD1) is a key enzyme in the sphingolipid metabolism. Genetic SMPD1 variants have been related to the Niemann-Pick lysosomal storage disorder, which has different degrees of phenotypic severity ranging from severe symptomatology involving the central nervous system (type A) to milder ones (type B). They have also been linked to neurodegenerative disorders such as Parkinson and Alzheimer. In this paper, we leveraged structural, evolutionary and stability information on SMPD1 to predict and analyze the impact of variants at the molecular level. We developed the SMPD1-ZooM algorithm, which is able to predict with good accuracy whether variants cause Niemann-Pick disease and its phenotypic severity; the predictor is freely available for download. We performed a large-scale analysis of all possible SMPD1 variants, which led us to identify protein regions that are either robust or fragile with respect to amino acid variations, and show the importance of aromatic-involving interactions in SMPD1 function and stability. Our study also revealed a good correlation between SMPD1-ZooM scores and in vitro loss of SMPD1 activity. The understanding of the molecular effects of SMPD1 variants is of crucial importance to improve genetic screening of SMPD1-related disorders and to develop personalized treatments that restore SMPD1 functionality.

SWOTein: a structure-based approach to predict stability Strengths and Weaknesses of prOTEINs.

MOTIVATION: Although structured proteins adopt their lowest free energy conformation in physiological conditions, the individual residues are generally not in their lowest free energy conformation. Residues that are stability weaknesses are often involved in functional regions, whereas stability strengths ensure local structural stability. The detection of strengths and weaknesses provides key information to guide protein engineering experiments aiming to modulate folding and various functional processes. RESULTS: We developed the SWOTein predictor which identifies strong and weak residues in proteins on the basis of three types of statistical energy functions describing local interactions along the chain, hydrophobic forces and tertiary interactions. The large-scale analysis of the different types of strengths and weaknesses demonstrated their complementarity and the enhancement of the information they provide. Moreover, a good average correlation was observed between predicted and experimental strengths and weaknesses obtained from native hydrogen exchange data. SWOTein application to three test cases further showed its suitability to predict and interpret strong and weak residues in the context of folding, conformational changes and protein-protein binding. In summary, SWOTein is both fast and accurate and can be applied at small and large scale to analyze and modulate folding and molecular recognition processes. AVAILABILITY: The SWOTein webserver provides the list of predicted strengths and weaknesses and a protein structure visualization tool that facilitates the interpretation of the predictions. It is freely available for academic use at http://babylone.ulb.ac.be/SWOTein/.