STRUM: structure-based prediction of protein stability changes upon single-point mutation

The W792R HCM missense mutation in the C6 domain of cardiac myosin binding protein-C increases contractility in neonatal mouse myocardium

Missense mutations in cardiac myosin binding protein C (cMyBP-C) are known to cause hypertrophic cardiomyopathy (HCM). The W792R mutation in the C6 domain of cMyBP-C causes severe, early onset HCM in humans, yet its impact on the function of cMyBP-C and the mechanism through which it causes disease remain unknown. To fully characterize the effect of the W792R mutation on cardiac morphology and function in vivo, we generated a murine knock-in model. We crossed heterozygous W792RWR mice to produce homozygous mutant W792RRR, heterozygous W792RWR, and control W792RWW mice. W792RRR mice present with cardiac hypertrophy, myofibrillar disarray and fibrosis by postnatal day 10 (PND10), and do not survive past PND21. Full-length cMyBP-C is present at similar levels in W792RWW, W792RWR and W792RRR mice and is properly incorporated into the sarcomere. Heterozygous W792RWR mice displayed normal heart morphology and contractility. Permeabilized myocardium from PND10 W792RRR mice showed increased Ca2+ sensitivity, accelerated cross-bridge cycling kinetics, decreased cooperativity in the activation of force, and increased expression of hypertrophy-related genes. In silico modeling suggests that the W792R mutation destabilizes the fold of the C6 domain and increases torsion in the C5-C7 region, possibly impacting regulatory interactions of cMyBP-C with myosin and actin. Based on the data presented here, we propose a model in which mutant W792R cMyBP-C preferentially forms Ca2+ sensitizing interactions with actin, rather than inhibitory interactions with myosin. The W792R-cMyBP-C mouse model provides mechanistic insights into the pathology of this mutation and may provide a mechanism by which other central domain missense mutations in cMyBP-C may alter contractility, leading to HCM.

Decoding Missense Variants by Incorporating Phase Separation via Machine Learning

Computational models have made significant progress in predicting the effect of protein variants. However, deciphering numerous variants of uncertain significance (VUS) located within intrinsically disordered regions (IDRs) remains challenging. To address this issue, we introduce phase separation, which is tightly linked to IDRs, into the investigation of missense variants. Phase separation is vital for multiple physiological processes. By leveraging missense variants that alter phase separation propensity, we develop a machine learning approach named PSMutPred to predict the impact of missense mutations on phase separation. PSMutPred demonstrates robust performance in predicting missense variants that affect natural phase separation. In vitro experiments further underscore its validity. By applying PSMutPred on over 522,000 ClinVar missense variants, it significantly contributes to decoding the pathogenesis of disease variants, especially those in IDRs. Our work provides insights into the understanding of a vast number of VUSs in IDRs, expediting clinical interpretation and diagnosis.

BenchStab: a tool for automated querying of web-based stability predictors

SUMMARY

Protein design requires information about how mutations affect protein stability. Many web-based predictors are available for this purpose, yet comparing them or using them en masse is difficult. Here, we present BenchStab, a console tool/Python package for easy and quick execution of 19 predictors and result collection on a list of mutants. Moreover, the tool is easily extensible with additional predictors. We created an independent dataset derived from the FireProtDB and evaluated 24 different prediction methods.

AVAILABILITY AND IMPLEMENTATION

BenchStab is an open-source Python package available at https://github.com/loschmidt/BenchStab with a detailed README and example usage at https://loschmidt.chemi.muni.cz/benchstab. The BenchStab dataset is available on Zenodo: https://zenodo.org/records/10637728.

Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors

BACKGROUND

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

RESULTS

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

CONCLUSIONS

VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at  https://genomeinterpretation.org/vipdb.

Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method

BACKGROUND

Thermostability is a fundamental property of proteins to maintain their biological functions. Predicting protein stability changes upon mutation is important for our understanding protein structure-function relationship, and is also of great interest in protein engineering and pharmaceutical design.

RESULTS

Here we present mutDDG-SSM, a deep learning-based framework that uses the geometric representations encoded in protein structure to predict the mutation-induced protein stability changes. mutDDG-SSM consists of two parts: a graph attention network-based protein structural feature extractor that is trained with a self-supervised learning scheme using large-scale high-resolution protein structures, and an eXtreme Gradient Boosting model-based stability change predictor with an advantage of alleviating overfitting problem. The performance of mutDDG-SSM was tested on several widely-used independent datasets. Then, myoglobin and p53 were used as case studies to illustrate the effectiveness of the model in predicting protein stability changes upon mutations. Our results show that mutDDG-SSM achieved high performance in estimating the effects of mutations on protein stability. In addition, mutDDG-SSM exhibited good unbiasedness, where the prediction accuracy on the inverse mutations is as well as that on the direct mutations.

CONCLUSION

Meaningful features can be extracted from our pre-trained model to build downstream tasks and our model may serve as a valuable tool for protein engineering and drug design.

In Silico Deciphering of the Potential Impact of Variants of Uncertain Significance in Hereditary Colorectal Cancer Syndromes

Colorectal cancer (CRC) ranks third in terms of cancer incidence worldwide and is responsible for 8% of all deaths globally. Approximately 10% of CRC cases are caused by inherited pathogenic mutations in driver genes involved in pathways that are crucial for CRC tumorigenesis and progression. These hereditary mutations significantly increase the risk of initial benign polyps or adenomas developing into cancer. In recent years, the rapid and accurate sequencing of CRC-specific multigene panels by next-generation sequencing (NGS) technologies has enabled the identification of several recurrent pathogenic variants with established functional consequences. In parallel, rare genetic variants that are not characterized and are, therefore, called variants of uncertain significance (VUSs) have also been detected. The classification of VUSs is a challenging task because each amino acid has specific biochemical properties and uniquely contributes to the structural stability and functional activity of proteins. In this scenario, the ability to computationally predict the effect of a VUS is crucial. In particular, in silico prediction methods can provide useful insights to assess the potential impact of a VUS and support additional clinical evaluation. This approach can further benefit from recent advances in artificial intelligence-based technologies. In this review, we describe the main in silico prediction tools that can be used to evaluate the structural and functional impact of VUSs and provide examples of their application in the analysis of gene variants involved in hereditary CRC syndromes.

Enhancing predictions of protein stability changes induced by single mutations using MSA-based Language Models

MOTIVATION

Protein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a Protein Language Model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data pre-processing to mitigate overfitting.

RESULTS

We ensure comprehensive comparisons by fine-tuning various pre-trained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations.

AVAILABILITY AND IMPLEMENTATION

Code and data at https://github.com/RitAreaSciencePark/PLM4Muts.

SUPPLEMENTARY INFORMATION

Supplementary Information is available at Bioinformatics online.

Deep mutational scanning reveals sequence to function constraints for SWEET family transporters

Protein science is entering a transformative phase enabled by deep mutational scans that provide an unbiased view of the residue level interactions that mediate function. However, it has yet to be extensively used to characterize the mutational and evolutionary landscapes of plant proteins. Here, we apply the method to explore sequence-function relationships within the sugar transporter AtSWEET13. DMS results describe how mutational interrogation throughout different regions of the protein affects AtSWEET13 abundance and transport function. Our results identify novel transport-enhancing mutations that are validated using the FRET sensor assays. Extending DMS results to phylogenetic analyses reveal the role of transmembrane helix 4 (TM4) which makes the SWEET family transporters distinct from prokaryotic SemiSWEETs. We show that transmembrane helix 4 is intolerant to motif swapping with other clade-specific SWEET TM4 compositions, despite accommodating single point-mutations towards aromatic and charged polar amino acids. We further show that the transfer learning approaches based on physics and ML based In silico variant prediction tools have limited utility for engineering plant proteins as they were unable to reproduce our experimental results. We conclude that DMS can produce datasets which, when combined with the right predictive computational frameworks, can direct plant engineering efforts through derivative phenotype selection and evolutionary insights.

A compound heterozygote case of glutaric aciduria type II in a patient carrying a novel candidate variant in ETFDH gene: A case report and literature review on compound heterozygote cases

BACKGROUND

Glutaric aciduria type II (GA2) is a rare genetic disorder inherited in an autosomal recessive manner. Double dosage mutations in GA2 corresponding genes, ETFDH, ETFA, and ETFB, lead to defects in the catabolism of fatty acids, and amino acids lead to broad-spectrum phenotypes, including muscle weakness, developmental delay, and seizures. product of these three genes have crucial role in transferring electrons to the electron transport chain (ETC), but are not directly involve in ETC complexes.

METHODS

Here, by using exome sequencing, the cause of periodic cryptic gastrointestinal complications in a 19-year-old girl was resolved after years of diagnostic odyssey. Protein modeling for the novel variant served as another line of validation for it.

RESULTS

Exome Sequencing (ES) identified two variants in ETFDH: ETFDH:c.926T>G and ETFDH:c.1141G>C. These variants are likely contributing to the crisis in this case. To the best of our knowledge at the time of writing this manuscript, variant ETFDH:c.926T>G is reported here for the first time. Clinical manifestations of the case and pathological analysis are in consistent with molecular findings. Protein modeling provided another line of evidence proving the pathogenicity of the novel variant. ETFDH:c.926T>G is reported here for the first time in relation to the causation GA2.

CONCLUSION

Given the milder symptoms in this case, a review of GA2 cases caused by compound heterozygous mutations was conducted, highlighting the range of symptoms observed in these patients, from mild fatigue to more severe outcomes. The results underscore the importance of comprehensive genetic analysis in elucidating the spectrum of clinical presentations in GA2 and guiding personalized treatment strategies.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network

Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we have developed an unbiased effective model: PMSPcnn that is based on a convolutional neural network. We have included an anti-symmetry property to build a balanced training dataset, which improves the prediction, in particular for stabilizing mutations. Persistent homology, which is an effective approach for characterizing protein structures, is used to obtain topological features. Additionally, a regression stratification cross-validation scheme has been proposed to improve the prediction for mutations with extreme ΔΔG. For three test datasets: Ssym, p53, and myoglobin, PMSPcnn achieves a better performance than currently existing predictors. PMSPcnn also outperforms currently available methods for membrane proteins. Overall, PMSPcnn is a promising method for the prediction of protein stability changes caused by single point mutations.

Impact of single-residue mutations on protein thermal stability: The case of threonine 83 of BC2L-CN lectin

The thermal stability of trimeric lectin BC2L-CN was investigated and found to be considerably altered when mutating residue 83, originally a threonine, located at the fucose-binding loop. Mutants were analyzed using differential scanning calorimetry and isothermal microcalorimetry. Although most mutations decreased the affinity of the protein for oligosaccharide H type 1, six mutations increased the melting temperature (Tm) by >5 °C; one mutation, T83P, increased the Tm value by 18.2 °C(T83P, Tm = 96.3 °C). In molecular dynamic simulations, the investigated thermostable mutants, T83P, T83A, and T83S, had decreased fluctuations in the loop containing residue 83. In the T83S mutation, the side-chain hydroxyl group of serine formed a hydrogen bond with a nearby residue, suggesting that the restricted movement of the side-chain resulted in fewer fluctuations and enhanced thermal stability. Residue 83 is located at the interface and near the upstream end of the equivalent loop in a different protomer; therefore, fluctuations by this residue likely propagate throughout the loop. Our study of the dramatic change in thermal stability by a single amino acid mutation provides useful insights into the rational design of protein structures, especially the structures of oligomeric proteins.

Alpha Carbonic Anhydrase from Nitratiruptor tergarcus Engineered for Increased Activity and Thermostability

The development of carbon capture and storage technologies has resulted in a rising interest in the use of carbonic anhydrases (CAs) for CO2 fixation at elevated temperatures. In this study, we chose to rationally engineer the α-CA (NtCA) from the thermophilic bacterium Nitratiruptor tergarcus, which has been previously suggested to be thermostable by in silico studies. Using a combination of analyses with the DEEPDDG software and available structural knowledge, we selected residues in three regions, namely, the catalytic pocket, the dimeric interface and the surface, in order to increase thermostability and CO2 hydration activity. A total of 13 specific mutations, affecting seven amino acids, were assessed. Single, double and quadruple mutants were produced in Escherichia coli and analyzed. The best-performing mutations that led to improvements in both activity and stability were D168K, a surface mutation, and R210L, a mutation in the dimeric interface. Apart from these, most mutants showed improved thermostability, with mutants R210K and N88K_R210L showing substantial improvements in activity, up to 11-fold. Molecular dynamics simulations, focusing particularly on residue fluctuations, conformational changes and hydrogen bond analysis, elucidated the structural changes imposed by the mutations. Successful engineering of NtCA provided valuable lessons for further engineering of α-CAs.

Case report: Novel genotype of ALG2-CDG and confirmation of the heptasaccharide glycan (NeuAc-Gal-GlcNAc-Man2-GlcNAc2) as a specific diagnostic biomarker

This report outlines the case of a child affected by a type of congenital disorder of glycosylation (CDG) known as ALG2-CDG (OMIM 607906), presenting as a congenital myasthenic syndrome (CMS) caused by variants identified in ALG2, which encodes an α1,3-mannosyltransferase (EC 2.4.1.132) involved in the early steps of N-glycosylation. To date, fourteen cases of ALG2-CDG have been documented worldwide. From birth, the child experienced perinatal asphyxia, muscular weakness, feeding difficulties linked to an absence of the sucking reflex, congenital hip dislocation, and hypotonia. Over time, additional complications emerged, such as inspiratory stridor, gastroesophageal reflux, low intake, recurrent seizures, respiratory infections, an inability to maintain the head upright, and a global developmental delay. Whole genome sequencing (WGS) revealed the presence of two ALG2 variants in compound heterozygosity: a novel variant c.1055_1056delinsTGA p.(Ser352Leufs*3) and a variant of uncertain significance (VUS) c.964C>A p.(Pro322Thr). Additional studies, including determination of carbohydrate-deficient transferrin (CDT) revealed a mild type I CDG pattern and the presence of an abnormal transferrin glycoform containing a linear heptasaccharide consisting of one sialic acid, one galactose, one N-acetyl-glucosamine, two mannoses and two N-acetylglucosamines (NeuAc-Gal-GlcNAc-Man2-GlcNAc2), ALG2-CDG diagnostic biomarker, confirming the pathogenicity of these variants.

SPINT2 mutations in the Kunitz domain 2 found in SCSD patients inactivate HAI-2 as prostasin inhibitor via abnormal protein folding and N-glycosylation

Mutations in the Kunitz-type serine protease inhibitor HAI-2, encoded by SPINT2, are responsible for the pathogenesis of syndromic congenital sodium diarrhea (SCSD), an intractable secretory diarrhea of infancy. Some of the mutations cause defects in the functionally required Kunitz domain 1 and/or subcellular targeting signals. Almost all SCSD patients, however, harbor SPINT2 missense mutations that affect the functionally less important Kunitz domain 2. How theses single amino acid substitutions inactivate HAI-2 was, here, investigated by the doxycycline-inducible expression of three of these mutants in HAI-2-knockout Caco-2 human colorectal adenocarcinoma cells. Examining protein expressed from these HAI-2 mutants reveals that roughly 50% of the protein is synthesized as disulfide-linked oligomers that lose protease inhibitory activity due to the distortion of the Kunitz domains by disarrayed disulfide bonding. Although the remaining protein is synthesized as monomers, its glycosylation status suggests that the HAI-2 monomer remains in the immature, lightly glycosylated form, and is not converted to the heavily glycosylated mature form. Heavily glycosylated HAI-2 possesses full anti-protease activity and appropriate subcellular targeting signals, including the one embedded in the complex-type N-glycan. As predicted, these HAI-2 mutants cannot suppress the excessive prostasin proteolysis caused by HAI-2 deletion. The oligomerization and glycosylation defects have also been observed in a colorectal adenocarcinoma line that harbors one of these SPINT2 missense mutations. Our study reveals that the abnormal protein folding and N-glycosylation can cause widespread HAI-2 inactivation in SCSD patents.

Self-incompatibility phenotypes of SRK mutants can be predicted with high accuracy

Only very limited information is available on why some non-synonymous variants severely alter gene function while others have no effect. To identify the characteristic features of mutations that strongly influence gene function, this study focused on S-locus receptor kinase, SRK, which encodes a highly polymorphic receptor kinase expressed in stigma papillary cells that underlies a female determinant of self-incompatibility in Brassicaceae. A set of 299 Arabidopsis thaliana transformants expressing mutated SRKb from A. lyrata was constructed and analyzed to determine the genotype and self-incompatibility phenotype of each transformant. Almost all the transformants showing the self-incompatibility defect contained mutations in AlSRKb that altered localization to the plasma membrane. The observed mutations occurred in amino acid residues that were highly conserved across S haplotypes and whose predicted locations were in the interior of the protein. These mutations were likely to underlie the self-incompatibility defect as they caused significant changes to amino acid properties. Such findings suggested that mutations causing the self-incompatibility defect were more likely to result from changes to AlSRKb biosynthesis than from loss of function. In addition, this study showed the RandomForest and Extreme Gradient Boosting methods could predict self-incompatibility phenotypes of SRK mutants with high accuracy.

Role of deleterious nsSNPs of klotho protein and their drug response: a computational mechanical insights

Worldwide, the burden of chronic kidney disease (CKD) has increased rapidly and is a lethal disease. The klotho protein plays a vital role in the regulatory mechanism in the progression of CKD. Particularly the decreased expression of klothoand its genetic variations might affect the potency of drugs. This study aims to identify a new drug molecule, which works equipotential in all types of klotholike wild and mutant variants. All non-synonymous SNPs were predicted by several SNP tools. Where, two missense variants were examined as vulnerable, significantly damaging, and also involved in the structural conformational changes of the protein. Based on structure-based screening, E-pharmacophore screening, binding mode analysis, binding free energy analysis, QM/MM, and molecular dynamics analysis a lead compound (Lifechemical_F2493-2038) was identified as an effective agonistic molecule hence the identified Lifechemical_F2493-2038 compound is well bound to the wild and mutant proteins which found to increase the expression of klotho.Communicated by Ramaswamy H. Sarma.

TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion

Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs. The model adopts a two-stage architecture: the first stage is designed to fuse different feature embeddings through a transformer encoder. In the second stage, a support vector machine model is employed to quantify the pathogenicity of SAVs after dimensionality reduction. The prediction performance of TransEFVP on blind test data achieves a Matthews correlation coefficient of 0.751, an F1-score of 0.846, and an area under the receiver operating characteristic curve of 0.871, higher than the existing state-of-the-art methods. The benchmark results demonstrate that TransEFVP can be explored as an accurate and effective SAV pathogenicity prediction method. The data and codes for TransEFVP are available at https://github.com/yzh9607/TransEFVP/tree/master for academic use.

FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework

Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer's self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Revolutionizing Synthetic Antibody Design: Harnessing Artificial Intelligence and Deep Sequencing Big Data for Unprecedented Advances

Synthetic antibodies (Abs) represent a category of engineered proteins meticulously crafted to replicate the functions of their natural counterparts. Such Abs are generated in vitro, enabling advanced molecular alterations associated with antigen recognition, paratope site engineering, and biochemical refinements. In a parallel realm, deep sequencing has brought about a paradigm shift in molecular biology. It facilitates the prompt and cost-effective high-throughput sequencing of DNA and RNA molecules, enabling the comprehensive big data analysis of Ab transcriptomes, including specific regions of interest. Significantly, the integration of artificial intelligence (AI), based on machine- and deep- learning approaches, has fundamentally transformed our capacity to discern patterns hidden within deep sequencing big data, including distinctive Ab features and protein folding free energy landscapes. Ultimately, current AI advances can generate approximations of the most stable Ab structural configurations, enabling the prediction of de novo synthetic Abs. As a result, this manuscript comprehensively examines the latest and relevant literature concerning the intersection of deep sequencing big data and AI methodologies for the design and development of synthetic Abs. Together, these advancements have accelerated the exploration of antibody repertoires, contributing to the refinement of synthetic Ab engineering and optimizations, and facilitating advancements in the lead identification process.

ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks

Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster and more accurate method to predict the impact of the mutations on protein stability is helpful for protein design and understanding the phenotypic variation. Recent studies have shown that protein embedding will be particularly powerful at modeling sequence information with context dependence, such as subcellular localization, variant effect, and secondary structure prediction. Herein, we introduce a novel method, ProSTAGE, which is a deep learning method that fuses structure and sequence embedding to predict protein stability changes upon single point mutations. Our model combines graph-based techniques and language models to predict stability changes. Moreover, ProSTAGE is trained on a larger data set, which is almost twice as large as the most used S2648 data set. It consistently outperforms all existing state-of-the-art methods on mutation-affected problems as benchmarked on several independent data sets. The protein embedding as the prediction input achieves better results than the previous results, which shows the potential of protein language models in predicting the effect of mutations on proteins. ProSTAGE is implemented as a user-friendly web server.

OmeDDG: Improved Protein Mutation Stability Prediction Based on Predicted 3D Structures

Determining changes in the protein's thermal stability following mutations is critical in protein engineering and understanding pathogenic missense mutations. Despite the development of various computational methods to predict the effects of single-point mutations, their accuracy remains limited. In this study, we propose a new computational method, OmeDDG, that more accurately predicts mutation-induced Gibbs free energy changes in protein folding (ΔΔG). OmeDDG takes the sequences of wild-type and mutant proteins as input, utilizes OmegaFold to obtain the 3D structure, employs a convolutional neural network to extract structural features, and combines them with protein mutation features and pretraining features to predict the stability of single-point mutations in proteins. We performed a comprehensive comparison between OmeDDG and other available prediction methods on four blind test datasets, confirming that OmeDDG can effectively enhance protein mutation prediction performance. Notably, on the antisymmetric dataset Ssym, OmeDDG achieves the best performance, demonstrating favorable antisymmetry with PCC = 0.79 and RMSE = 0.96 for forward mutations and PCC = 0.77 and RMSE = 0.97 for reverse mutant types.

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation

Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, achieving accuracy and generalization across diverse data sets remains a challenge. This study introduces Geometric Graph Learning for Protein-Protein Interactions (GGL-PPI), a novel approach integrating geometric graph representation and machine learning to forecast mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multiscale weighted colored geometric subgraphs to capture structural features of biomolecules, demonstrating superior performance on three standard data sets, namely, AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 data sets. The model's efficacy extends to predicting protein thermodynamic stability in a blind test set, providing unbiased predictions for both direct and reverse mutations and showcasing notable generalization. GGL-PPI's precision in predicting changes in binding free energy and stability due to mutations enhances our comprehension of protein complexes, offering valuable insights for drug design endeavors.

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks

Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exhibit anti-symmetric bias toward destabilizing situations, whereas others struggle with generalization to unseen examples. To address these issues, we propose a gated graph neural network-based approach to predict changes in protein stability upon mutation. The model uses message passing to encode the links between the molecular structure and property after eliminating the non-mutant structure and creating input feature vectors. While doing so, it also incorporates the coordinates of the raw atoms to provide spatial insights into the chemical systems. We test the model on the Ssym, Myoglobin, Broom, and p53 datasets to demonstrate the generalization performance. Compared to existing approaches, our proposed method achieves improved linearity with symmetry in less time. The code for this study is available at: https://github.com/HongzhouTang/Pros-GNN.

FireProt 2.0: web-based platform for the fully automated design of thermostable proteins

Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron-Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool's availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.

PROSTATA: a framework for protein stability assessment using transformers

MOTIVATION

Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning.

RESULTS

In this work, we introduce PROSTATA, a predictive model built in a knowledge-transfer fashion on a new curated dataset. PROSTATA demonstrates advantage over existing solutions based on neural networks. We show that the large improvement margin is due to both the architecture of the model and the quality of the new training dataset. This work opens up opportunities to develop new lightweight and accurate models for protein stability assessment.

AVAILABILITY AND IMPLEMENTATION

PROSTATA is available at https://github.com/AIRI-Institute/PROSTATA and https://prostata.airi.net.

Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

PURPOSE OF REVIEW

Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low-density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH.

RECENT FINDINGS

In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural alterations, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH-associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH.

Energetic and frustration analysis of SARS-CoV-2 nucleocapsid protein mutations

The ongoing COVID-19 spreads worldwide with the ability to evolve in diverse human populations. The nucleocapsid (N) protein is one of the mutational hotspots in the SARS-CoV-2 genome. The N protein is an abundant RNA-binding protein critical for viral genome packaging. It comprises two large domains including the N-terminal domain (NTD) and the C-terminal domain (CTD) linked by the centrally located linker region. Mutations in N protein have been reported to increase the severity of disease by modulating viral transmissibility, replication efficiency as well as virulence properties of the virus in different parts of the world. To study the effect of N protein missense mutations on protein stability, function, and pathogenicity, we analyzed 228 mutations from each domain of N protein. Further, we have studied the effect of mutations on local residual frustration changes in N protein. Out of 228 mutations, 11 mutations were predicted to be deleterious and destabilized. Among these mutations, R32C, R191C, and R203 M mutations fall into disordered regions and show significant change in frustration state. Overall, this work reveals that by altering the energetics and residual frustration, N protein mutations might affect the stability, function, and pathogenicity of the SARS-CoV-2.

A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations

Background

Genomic variations may cause deleterious effects on protein functionality and perturb biological processes. Elucidating the effects of variations is critical for developing novel treatment strategies for diseases of genetic origin. Computational approaches have been aiding the work in this field by modeling and analyzing the mutational landscape. However, new approaches are required, especially for accurate representation and data-centric analysis of sequence variations.

Method

In this study, we propose ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations), a method for the featurization (i.e., quantitative representation) of single amino acid variations (SAVs), which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. ASCARIS utilizes the direct and spatial correspondence between the location of the SAV on the sequence/structure and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.), along with structural features and physicochemical properties. The main novelty of this method lies in constructing reusable numerical representations of SAVs via functional annotations.

Results

We statistically analyzed the relationship between these features and the consequences of variations and found that each carries information in this regard. To investigate potential applications of ASCARIS, we trained variant effect prediction models that utilize our SAV representations as input. We carried out an ablation study and a comparison against the state-of-the-art methods and observed that ASCARIS has a competing and complementary performance against widely-used predictors. ASCARIS can be used alone or in combination with other approaches to represent SAVs from a functional perspective. ASCARIS is available as a programmatic tool at https://github.com/HUBioDataLab/ASCARIS and as a web-service at https://huggingface.co/spaces/HUBioDataLab/ASCARIS.

LibGENiE - A bioinformatic pipeline for the design of information-enriched enzyme libraries

Enzymes are potent catalysts with high specificity and selectivity. To leverage nature's synthetic potential for industrial applications, various protein engineering techniques have emerged which allow to tailor the catalytic, biophysical, and molecular recognition properties of enzymes. However, the many possible ways a protein can be altered forces researchers to carefully balance between the exhaustiveness of an enzyme screening campaign and the required resources. Consequently, the optimal engineering strategy is often defined on a case-by-case basis. Strikingly, while predicting mutations that lead to an improved target function is challenging, here we show that the prediction and exclusion of deleterious mutations is a much more straightforward task as analyzed for an engineered carbonic acid anhydrase, a transaminase, a squalene-hopene cyclase and a Kemp eliminase. Combining such a pre-selection of allowed residues with advanced gene synthesis methods opens a path toward an efficient and generalizable library construction approach for protein engineering. To give researchers easy access to this methodology, we provide the website LibGENiE containing the bioinformatic tools for the library design workflow.

Topological deep learning based deep mutational scanning

High-throughput deep mutational scanning (DMS) experiments have significantly impacted protein engineering, drug discovery, immunology, cancer biology, and evolutionary biology by enabling the systematic understanding of protein functions. However, the mutational space associated with proteins is astronomically large, making it overwhelming for current experimental capabilities. Therefore, alternative methods for DMS are imperative. We propose a topological deep learning (TDL) paradigm to facilitate in silico DMS. We utilize a new topological data analysis (TDA) technique based on the persistent spectral theory, also known as persistent Laplacian, to capture both topological invariants and the homotopic shape evolution of data. To validate our TDL-DMS model, we use SARS-CoV-2 datasets and show excellent accuracy and reliability for binding interface mutations. This finding is significant for SARS-CoV-2 variant forecasting and designing effective antibodies and vaccines. Our proposed model is expected to have a significant impact on drug discovery, vaccine design, precision medicine, and protein engineering.

Bioinformatic analysis of the effect of SNPs in the pig TERT gene on the structural and functional characteristics of the enzyme to develop new genetic markers of productivity traits

BACKGROUND

Telomerase reverse transcriptase (TERT) plays a crucial role in synthesizing telomeric repeats that safeguard chromosomes from damage and fusion, thereby maintaining genome stability. Mutations in the TERT gene can lead to a deviation in gene expression, impaired enzyme activity, and, as a result, abnormal telomere shortening. Genetic markers of productivity traits in livestock can be developed based on the TERT gene polymorphism for use in marker-associated selection (MAS). In this study, a bioinformatic-based approach is proposed to evaluate the effect of missense single-nucleotide polymorphisms (SNPs) in the pig TERT gene on enzyme function and structure, with the prospect of developing genetic markers.

RESULTS

A comparative analysis of the coding and amino acid sequences of the pig TERT was performed with corresponding sequences of other species. The distribution of polymorphisms in the pig TERT gene, with respect to the enzyme's structural-functional domains, was established. A three-dimensional model of the pig TERT structure was obtained through homological modeling. The potential impact of each of the 23 missense SNPs in the pig TERT gene on telomerase function and stability was assessed using predictive bioinformatic tools utilizing data on the amino acid sequence and structure of pig TERT.

CONCLUSIONS

According to bioinformatic analysis of 23 missense SNPs of the pig TERT gene, a predictive effect of rs789641834 (TEN domain), rs706045634 (TEN domain), rs325294961 (TRBD domain) and rs705602819 (RTD domain) on the structural and functional parameters of the enzyme was established. These SNPs hold the potential to serve as genetic markers of productivity traits. Therefore, the possibility of their application in MAS should be further evaluated in associative analysis studies.

How can we discover developable antibody-based biotherapeutics?

Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.

Basic science methods for the characterization of variants of uncertain significance in hypertrophic cardiomyopathy

With the advent of next-generation whole genome sequencing, many variants of uncertain significance (VUS) have been identified in individuals suffering from inheritable hypertrophic cardiomyopathy (HCM). Unfortunately, this classification of a genetic variant results in ambiguity in interpretation, risk stratification, and clinical practice. Here, we aim to review some basic science methods to gain a more accurate characterization of VUS in HCM. Currently, many genomic data-based computational methods have been developed and validated against each other to provide a robust set of resources for researchers. With the continual improvement in computing speed and accuracy, in silico molecular dynamic simulations can also be applied in mutational studies and provide valuable mechanistic insights. In addition, high throughput in vitro screening can provide more biologically meaningful insights into the structural and functional effects of VUS. Lastly, multi-level mathematical modeling can predict how the mutations could cause clinically significant organ-level dysfunction. We discuss emerging technologies that will aid in better VUS characterization and offer a possible basic science workflow for exploring the pathogenicity of VUS in HCM. Although the focus of this mini review was on HCM, these basic science methods can be applied to research in dilated cardiomyopathy (DCM), restrictive cardiomyopathy (RCM), arrhythmogenic cardiomyopathy (ACM), or other genetic cardiomyopathies.

Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations

The development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein interaction, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods require a database of experimentally measured folding and binding free energy changes for training. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residues on the above-mentioned thermodynamic characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variants (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases, and the distribution of the free energy changes is not the same. It is shown that the Pearson correlation coefficients (PCCs) of folding and binding free energy changes obtained in cases involving SNVs are smaller than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. Furthermore, it is demonstrated that some methods are sensitive to the chemical nature of the mutations, resulting in PCCs that differ by a factor of four across chemically different mutations. All methods are found to underestimate the energy changes by roughly a factor of 2.

Resources and tools for rare disease variant interpretation

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Protein structure-based evaluation of missense variants: Resources, challenges and future directions

We provide an overview of the methods that can be used for protein structure-based evaluation of missense variants. The algorithms can be broadly divided into those that calculate the difference in free energy (ΔΔG) between the wild type and variant structures and those that use structural features to predict the damaging effect of a variant without providing a ΔΔG. A wide range of machine learning approaches have been employed to develop those algorithms. We also discuss challenges and opportunities for variant interpretation in view of the recent breakthrough in three-dimensional structural modelling using deep learning.

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

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

Naturally occurring substitution of an amino acid in a plant virus gene-silencing suppressor enhances viral adaptation to increasing thermal stress

Cereal yellow dwarf virus (CYDV-RPV) encodes a P0 protein that functions as a viral suppressor of RNA silencing (VSR). The strength of silencing suppression is highly variable among CYDV-RPV isolates. In this study, comparison of the P0 sequences of CYDV-RPV isolates and mutational analysis identified a single C-terminal amino acid that influenced P0 RNA-silencing suppressor activity. A serine at position 247 was associated with strong suppressor activity, whereas a proline at position 247 was associated with weak suppressor activity. Amino acid changes at position 247 did not affect the interaction of P0 with SKP1 proteins from Hordeum vulgare (barley) or Nicotiana benthamiana. Subsequent studies found P0 proteins containing a P247 residue were less stable than the P0 proteins containing an S247 residue. Higher temperatures contributed to the lower stability and in planta and the P247 P0 proteins were subject to degradation via the autophagy-mediated pathway. A P247S amino acid residue substitution in P0 increased CYDV-RPV replication after expression in agroinfiltrated plant leaves and increased viral pathogenicity of P0 generated from the heterologous Potato virus X expression vector system. Moreover, an S247 CYDV-RPV could outcompete the P247 CYDV-RPV in a mixed infection in natural host at higher temperature. These traits contributed to increased transmission by aphid vectors and could play a significant role in virus competition in warming climates. Our findings underscore the capacity of a plant RNA virus to adapt to climate warming through minor genetic changes in gene-silencing suppressor, resulting in the potential for disease persistence and prevalence.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Computational insight into in silico analysis and molecular dynamics simulation of the dimer interface residues of ALS-linked hSOD1 forms in apo/holo states: a combined experimental and bioinformatic perspective

The aggregation of misfolded SOD1 proteins in neurodegenerative illnesses is a key pathological hallmark in amyotrophic lateral sclerosis (ALS). SOD1 is stabilized and enzymatically activated after binding to Cu/Zn and forming intramolecular disulfide. SOD1 aggregation/oligomerization is triggered by the dissociation of Cu and/or Zn ions. Therefore, we compared the possible effects of ALS-associated point mutations of the holo/apo forms of WT/I149T/V148G SOD1 variants located at the dimer interface to determine structural characterization using spectroscopic methods, computational approaches as well as molecular dynamics (MD) simulations. Predictive results of computational analysis of single-nucleotide polymorphisms (SNPs) suggested that mutant SOD1 has a deleterious effect on activity and structure destabilization. MD data analysis indicated that changes in flexibility, stability, hydrophobicity of the protein as well as increased intramolecular interactions of apo-SOD1 were more than holo-SOD1. Furthermore, a decrease in enzymatic activity in apo-SOD1 was observed compared to holo-SOD1. Comparative intrinsic and ANS fluorescence results of holo/apo-WT-hSOD1 and mutants indicated structural alterations in the local environment of tryptophan residue and hydrophobic patches, respectively. Experimental and MD data supported that substitution effect and metal deficiency of mutants (apo forms) in the dimer interface may promote the tendency to protein mis-folding and aggregation, consequently disrupting the dimer-monomer equilibrium and increased propensity to dissociation dimer into SOD-monomer ultimately leading to loss of stability and function. Overall, data analysis of apo/holo SOD1 forms on protein structure and function using computational and experimental studies will contribute to a better understanding of ALS pathogenicity.

GUN Mutants: New Weapons To Unravel Ascospore Germination Regulation in the Model Fungus Podospora anserina

In Podospora anserina as in many other Ascomycetes, ascospore germination is a regulated process that requires the breaking of dormancy. Despite its importance in survival and dispersal, ascospore germination in filamentous fungi has been poorly investigated, and little is known about its regulation and genetic control. We have designed a positive genetic screen that led to the isolation of mutants showing uncontrolled germination, the GUN (Germination UNcontrolled) mutants. Here, we report on the characterization of the gun1SG (Spontaneous Germination) mutant. We show that gun1SG is mutated in Pa_6_1340, the ortholog of Magnaporthe oryzae Pth2, which encodes a carnitine-acetyltransferase (CAT) involved in the shuttling of acetyl coenzyme A between peroxisomes and mitochondria and which is required for appressorium development. Bioinformatic analysis revealed that the mutated residue (I441) is highly conserved among Fungi and that the mutation has a deleterious impact on the protein function. We show that GUN1 is essential for ascospore germination and that the protein is localized both in mitochondria and in peroxisomes. Finally, epistasis studies allowed us to place GUN1 together with the PaMpk2 MAPK pathway upstream of the PaNox2/PaPls1 complex in the regulation of ascospore germination. In addition, we show that GUN1 plays a role in appressorium functioning. The pivotal role of GUN1, the ortholog of Pth2, in ascospore germination and in appressorium functioning reinforces the idea of a common genetic regulation governing both appressorium development and melanized ascospore germination. Furthermore, we characterize the second CAT encoded in P. anserina genome, Pa_3_7660/GUP1, and we show that the function of both CATs is conserved in P. anserina. IMPORTANCE The regulation of ascospore germination in filamentous fungi has been poorly investigated so far. To unravel new genes involved in this regulation pathway, we conducted a genetic screen in Podospora anserina, and we isolated 57 mutants affected in ascospore germination. Here, we describe the Germination UNcontrolled One (gun1SG) mutant, and we characterize the gene affected. GUN1 is a peroxisomal/mitochondrial carnitine-acetyltransferase required for acetyl coenzyme A shuttling between both organelles, and we show that GUN1 is a pleiotropic gene also involved in appressorium functioning similarly to its ortholog, the pathogenesis factor Pth2, in the plant pathogen Magnaporthe oryzae. Given the similarities in the regulation of appressorium development and ascospore germination, we speculate that discovering new genes controlling ascospore germination in P. anserina may lead to the discovery of new pathogenesis factors in pathogenic fungi. The characterization of GUN1, the ortholog of M. oryzae Pth2, represents a proof of concept.

Structural prediction of chimeric immunogens to elicit targeted antibodies against betacoronaviruses

Betacoronaviruses pose an ongoing pandemic threat. Antigenic evolution of the SARS-CoV-2 virus has shown that much of the spontaneous antibody response is narrowly focused rather than broadly neutralizing against even SARS-CoV-2 variants, let alone future threats. One way to overcome this is by focusing the antibody response against better-conserved regions of the viral spike protein. Here, we present a design approach to predict stable chimeras between SARS-CoV-2 and other coronaviruses, creating synthetic spike proteins that display a desired conserved region and vary other regions. We leverage AlphaFold to predict chimeric structures and create a new metric for scoring chimera stability based on AlphaFold outputs. We evaluated 114 candidate spike chimeras using this approach. Top chimeras were further evaluated using molecular dynamics simulation as an intermediate validation technique, showing good stability compared to low-scoring controls. This demonstrates the feasibility of the underlying approach, which can be used to design custom immunogens to focus the immune response against a desired viral glycoprotein epitope.

Abnormal Cellular Phenotypes Induced by Three TMPO/LAP2 Variants Identified in Men with Cardiomyopathies

A single missense variant of the TMPO/LAP2α gene, encoding LAP2 proteins, has been associated with cardiomyopathy in two brothers. To further evaluate its role in cardiac muscle, we included TMPO in our cardiomyopathy diagnostic gene panel. A screening of ~5000 patients revealed three novel rare TMPO heterozygous variants in six males diagnosed with hypertrophic or dilated cardiomypathy. We identified in different cellular models that (1) the frameshift variant LAP2α p.(Gly395Glufs*11) induced haploinsufficiency, impeding cell proliferation and/or producing a truncated protein mislocalized in the cytoplasm; (2) the C-ter missense variant LAP2α p.(Ala240Thr) led to a reduced proximity events between LAP2α and the nucleosome binding protein HMGN5; and (3) the LEM-domain missense variant p.(Leu124Phe) decreased both associations of LAP2α/β with the chromatin-associated protein BAF and inhibition of the E2F1 transcription factor activity which is known to be dependent on Rb, partner of LAP2α. Additionally, the LAP2α expression was lower in the left ventricles of male mice compared to females. In conclusion, our study reveals distinct altered properties of LAP2 induced by these TMPO/LAP2 variants, leading to altered cell proliferation, chromatin structure or gene expression-regulation pathways, and suggests a potential sex-dependent role of LAP2 in myocardial function and disease.

Predicting protein stability changes upon mutation using a simple orientational potential

MOTIVATION

Structure-based stability prediction upon mutation is crucial for protein engineering and design, and for understanding genetic diseases or drug resistance events. For this task, we adopted a simple residue-based orientational potential that considers only three backbone atoms, previously applied in protein modeling. Its application to stability prediction only requires parametrizing 12 amino acid-dependent weights using cross-validation strategies on a curated dataset in which we tried to reduce the mutations that belong to protein-protein or protein-ligand interfaces, extreme conditions and the alanine over-representation.

RESULTS

Our method, called KORPM, accurately predicts mutational effects on an independent benchmark dataset, whether the wild-type or mutated structure is used as starting point. Compared with state-of-the-art methods on this balanced dataset, our approach obtained the lowest root mean square error (RMSE) and the highest correlation between predicted and experimental ΔΔG measures, as well as better receiver operating characteristics and precision-recall curves. Our method is almost anti-symmetric by construction, and it performs thus similarly for the direct and reverse mutations with the corresponding wild-type and mutated structures. Despite the strong limitations of the available experimental mutation data in terms of size, variability, and heterogeneity, we show competitive results with a simple sum of energy terms, which is more efficient and less prone to overfitting.

AVAILABILITY AND IMPLEMENTATION

https://github.com/chaconlab/korpm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Energy decomposition and waterswapping analysis to investigate the SNP associated DPD mediated 5-FU resistance

5-fluorouracil is an essential component of systemic chemotherapy for colon, breast, head, and neck cancer patients. However, tumoral overexpression of the dihydropyrimidine dehydrogenase has rendered 5-FU clinically ineffective by inactivating it to 5'-6'-dihydro fluorouracil. The responses to 5-FU in terms of efficacy and toxicity greatly differ depending upon the population group, because of variability in the DPD activity levels. In the current study, key active site amino acids involved in the 5-FU inactivation were investigated by modelling the 3D structure of human DPD in a complex with 5-FU. The identified amino acids were analyzed for their possible missense mutations available in dbSNP database. Out of 12 missense SNPs, four were validated either by sequencing in the 1000 Genomes project or frequency/genotype data. The recorded validated missense SNPs were further considered to analyze the effect of their respective alterations on 5-FU binding. Overall findings suggested that population bearing the Glu611Val DPD mutation (rs762523739) is highly vulnerable to 5-FU resistance. From the docking, electrostatic complementarity, dynamics, and energy decomposition analyses it was found that the above mutation showed superior scores than the wild DPD -5FU complex. Therefore, prescribing prodrug NUC-3373 or DPD inhibitors (Gimeracil/3-Cyano-2,6-Dihydroxypyridines) as adjuvant therapy may overcome the 5-FU resistance.

Beyond sequence: Structure-based machine learning

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.

Prediction of protein stability changes upon single-point variant using 3D structure profile

Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP.