SDM--a server for predicting effects of mutations on protein stability and malfunction

Computational design of a cutinase for plastic biodegradation by mining molecular dynamics simulations trajectories

Polyethylene terephthalate (PET) has caused serious environmental concerns but could be degraded at high temperature. Previous studies show that cutinase from Thermobifida fusca KW3 (TfCut2) is capable of degrading and upcycling PET but is limited by its thermal stability. Nowadays, Popular protein stability modification methods rely mostly on the crystal structures, but ignore the fact that the actual conformation of protein is complex and constantly changing. To solve these problems, we developed a computational approach to design variants with enhanced protein thermal stability by mining Molecular Dynamics simulation trajectories using Machine Learning methods (MDL). The optimal classification accuracy and the optimal Pearson correlation coefficient of MDL model were 0.780 and 0.716, respectively. And we successfully designed variants with high ΔT_m values using MDL method. The optimal variant S121P/D174S/D204P had the highest ΔT_m value of 9.3 °C, and the PET degradation ratio increased by 46.42-fold at 70℃, compared with that of wild type TfCut2. These results deepen our understanding on the complex conformations of proteins and may enhance the plastic recycling and sustainability at glass transition temperature.

Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion

More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which remains an unresolved challenge. In the present work, a new computational approach, termed MSRes-MutP, is proposed based on ResNet blocks with multi-scale kernel size to predict disease-associated nsSNPs. By feeding the serial concatenation of the extracted four types of features, the performance of MSRes-MutP does not obviously improve. To address this, a second model FFMSRes-MutP is developed, which utilizes deep feature fusion strategy and multi-scale 2D-ResNet and 1D-ResNet blocks to extract relevant two-dimensional features and physicochemical properties. FFMSRes-MutP with the concatenated features achieves a better performance than that with individual features. The performance of FFMSRes-MutP is benchmarked on five different datasets. It achieves the Matthew's correlation coefficient (MCC) of 0.593 and 0.618 on the PredictSNP and MMP datasets, which are 0.101 and 0.210 higher than that of the existing best method PredictSNP1. When tested on the HumDiv and HumVar datasets, it achieves MCC of 0.9605 and 0.9507, and area under curve (AUC) of 0.9796 and 0.9748, which are 0.1747 and 0.2669, 0.0853 and 0.1335, respectively, higher than the existing best methods PolyPhen-2 and FATHMM (weighted). In addition, on blind test using a third-party dataset, FFMSRes-MutP performs as the second-best predictor (with MCC and AUC of 0.5215 and 0.7633, respectively), when compared with the other four predictors. Extensive benchmarking experiments demonstrate that FFMSRes-MutP achieves effective feature fusion and can be explored as a useful approach for predicting disease-associated nsSNPs. The webserver is freely available at http://csbio.njust.edu.cn/bioinf/ffmsresmutp/ for academic use.

MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins

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Prediction of mutations in transmembrane proteins is of significance for diseases diagnosis.

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Building on the evolutionary information, proposed the Gaussian WAPSSM algorithm.

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Based on WAPSSM and sequence and structure-based features, proposed the cascade XGBoost algorithm.

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Webserver is freely at (http://csbio.njust.edu.cn/bioinf/ffmsresmutp/).

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Implement MutTMPredictor to predict mutations in transmembrane proteins.

Transmembrane proteins have critical biological functions and play a role in a multitude of cellular processes including cell signaling, transport of molecules and ions across membranes. Approximately 60% of transmembrane proteins are considered as drug targets. Missense mutations in such proteins can lead to many diverse diseases and disorders, such as neurodegenerative diseases and cystic fibrosis. However, there are limited studies on mutations in transmembrane proteins. In this work, we first design a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information. Then, we propose a new mutation prediction algorithm (cascade XGBoost) by leveraging the idea learned from consensus predictors and gcForest. Multi-level experiments illustrate the effectiveness of WAPSSM and cascade XGBoost algorithms. Finally, based on WAPSSM and other three types of features, in combination with the cascade XGBoost algorithm, we develop a new transmembrane protein mutation predictor, named MutTMPredictor. We benchmark the performance of MutTMPredictor against several existing predictors on seven datasets. On the 546 mutations dataset, MutTMPredictor achieves the accuracy (ACC) of 0.9661 and the Matthew’s Correlation Coefficient (MCC) of 0.8950. While on the 67,584 dataset, MutTMPredictor achieves an MCC of 0.7523 and area under curve (AUC) of 0.8746, which are 0.1625 and 0.0801 respectively higher than those of the existing best predictor (fathmm). Besides, MutTMPredictor also outperforms two specific predictors on the Pred-MutHTP datasets. The results suggest that MutTMPredictor can be used as an effective method for predicting and prioritizing missense mutations in transmembrane proteins. The MutTMPredictor webserver and datasets are freely accessible at http://csbio.njust.edu.cn/bioinf/muttmpredictor/ for academic use.

Differential heterologous neutralisation profile against strains within DENV-3 genotype II

The dengue virus type 3 (DENV-3) homotypic outbreak cycles reported in Klang Valley, Malaysia in 1992–1995 and 2002 demonstrated different epidemic magnitude and duration. These outbreak cycles were caused by two closely related strains of viruses within the DENV-3 genotype II (DENV-3/II). The role of viral genotypic diversity and factors that could have influenced this phenomenon were investigated. The serum neutralisation sensitivity of DEN3/II strains responsible for the DENV-3 outbreak cycles in 1992–1995 and 2002 were examined. Representative virus isolates from the respective outbreaks were subjected to virus neutralisation assay using identified sera of patients with homotypic (DENV-3) or heterotypic dengue infections (DENV-1 and DENV-2). Results from the study suggested that isolates representing DENV-3/II group E (DENV-3/II-E) from the 1992–1995 outbreak and DENV-3/II group F (DENV-3/II-F) from the 2002 outbreak were neutralised at similar capacity (intergenotypic differences <2-fold) by sera of patients infected with DENV-3, DENV-1 and DENV-2/Asian genotypes. Sera of the DENV-2/Cosmopolitan infection efficiently neutralised DENV-3/II-F (FRNT₅₀ = 508.0) at a similar neutralisation capacity against its own homotypic serotype, DENV-2 (FRNT₅₀ = 452.5), but not against DENV-3/II-E (FRNT₅₀ = 100.8). The different neutralisation sensitivities of DENV-3/II strains towards the cross-reacting DENV-2 heterotypic immunity could play a role in shaping the DENV-3 recurring outbreaks pattern in Malaysia. Two genetic variations, E-132 (H/Y) and E-479 (A/V) were identified on the envelope protein of DENV-3/II-E and DENV-3/II-F, respectively. The E-132 variation was predicted to affect the protein stability. A more extensive study, however, on the implication of the naturally occurring genetic variations within closely related DENV genotypes on the neutralisation profile and protective immunity would be needed for a better understanding of the DENV spread pattern in a hyperendemic setting.

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.

Multidimensional Mutational Profiling of the Indian HNSCC Sub-Population Provides IRAK1, a Novel Driver Gene and Potential Druggable Target

Head and neck squamous cell carcinomas (HNSCC) include heterogeneous group of tumors, classified according to their anatomical site. It is the sixth most prevalent cancer globally. Among South Asian countries, India accounts for 40% of HNC malignancies with significant morbidity and mortality. In the present study, we have performed exome sequencing and analysis of 51 Head and Neck squamous cell carcinoma samples. Besides known mutations in the oncogenes and tumour suppressors, we have identified novel gene signatures differentiating buccal, alveolar, and tongue cancers. Around 50% of the patients showed mutation in tumour suppressor genes TP53 and TP63. Apart from the known mutations, we report novel mutations in the genes AKT1, SPECC1, and LRP1B, which are linked with tumour progression and patient survival. A highly curated process was developed to identify survival signatures. 36 survival-related genes were identified based on the correlation of functional impact of variants identified using exome-seq with gene expression from transcriptome data (GEPIA database) and survival. An independent LASSO regression analysis was also performed. Survival signatures common to both the methods led to identification of 4 dead and 3 alive gene signatures, the accuracy of which was confirmed by performing a ROC analysis (AUC=0.79 and 0.91, respectively). Also, machine learning-based driver gene prediction tool resulted in the identification of IRAK1 as the driver (p-value = 9.7 e-08) and also as an actionable mutation. Modelling of the IRAK1 mutation showed a decrease in its binding to known IRAK1 inhibitors.

Identification of Hotspot Residues in Binding of SARS-CoV-2 Spike and Human ACE2 Proteins

Coronaviruses are a large family of viruses that can cause respiratory infections with varying severity from common cold to severe diseases such as novel coronavirus disease (COVID-19). COVID-19 has been declared as a global pandemic by the World Health Organization on March 11, 2020 and with the development of vaccines it slowed down as of this date. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uses its spike glycoprotein (Sgp) to bind human angiotensin-converting enzyme 2 (hACE2) receptor, and mediates membrane fusion and virus entry. The recognition of Sgp to human ACE2 and its high affinity for it has been of great importance since this provides the first step in viral entry to human cells. Therefore, it is crucial to identify key residues (hotspots) in this process. In this study, computational alanine scanning has been performed for Sgp and hACE2. The residues identified with significance in binding and other residues in close proximity were studied further through molecular mechanics-based protein binding free energy change prediction methods. Moreover, the interfacial residues in both proteins were investigated for their cooperative binding. Additionally, folding free energy changes upon mutation to Ala were calculated to assess their effect on stability of Sgp and hACE2. Our results taken together with findings from previous studies revealed the residues that are most significant and are relatively significant in binding of Sgp to human ACE2 protein.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new S^transitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

Biallelic Mutations in ACACA Cause a Disruption in Lipid Homeostasis That Is Associated With Global Developmental Delay, Microcephaly, and Dysmorphic Facial Features

Objective

We proposed that the deficit of ACC1 is the cause of patient symptoms including global developmental delay, microcephaly, hypotonia, and dysmorphic facial features. We evaluated the possible disease-causing role of the ACACA gene in developmental delay and investigated the pathogenesis of ACC1 deficiency.

Methods

A patient who presented with global developmental delay with unknown cause was recruited. Detailed medical records were collected and reviewed. Whole exome sequencing found two variants of ACACA with unknown significance. ACC1 mRNA expression level, protein expression level, and enzyme activity level were detected in patient-derived cells. Lipidomic analysis, and in vitro functional studies including cell proliferation, apoptosis, and the migratory ability of patient-derived cells were evaluated to investigate the possible pathogenic mechanism of ACC1 deficiency. RNAi-induced ACC1 deficiency fibroblasts were established to assess the causative role of ACC1 deficit in cell migratory disability in patient-derived cells. Palmitate supplementation assays were performed to assess the effect of palmitic acid on ACC1 deficiency-induced cell motility deficit.

Results

The patient presented with global developmental delay, microcephaly, hypotonia, and dysmorphic facial features. A decreased level of ACC1 and ACC1 enzyme activity were detected in patient-derived lymphocytes. Lipidomic profiles revealed a disruption in the lipid homeostasis of the patient-derived cell lines. In vitro functional studies revealed a deficit of cell motility in patient-derived cells and the phenotype was further recapitulated in ACC1-knockdown (KD) fibroblasts. The cell motility deficit in both patient-derived cells and ACC1-KD were attenuated by palmitate.

Conclusion

We report an individual with biallelic mutations in ACACA, presenting global development delay. In vitro studies revealed a disruption of lipid homeostasis in patient-derived lymphocytes, further inducing the deficit of cell motility capacity and that the deficiency could be partly attenuated by palmitate.

Towards Understanding the Pathogenicity of DROSHA Mutations in Oncohematology

Myelodysplastic syndrome (MDS) refers to a heterogeneous group of closely related clonal hematopoietic disorders, which are characterized by accumulation of somatic mutations. The acquired mutation burden is suggested to define the pathway and consequent phenotype of the pathology. Recent studies have called attention to the role of miRNA biogenesis genes in MDS progression; in particular, the mutational pressure of the DROSHA gene was determined. Therefore, this highlights the importance of studying the impact of all collected missense mutations found within the DROSHA gene in oncohematology that might affect the functionality of the protein. In this study, the selected mutations were extensively examined by computational screening, and the most deleterious were subjected to a further molecular dynamic simulation in order to uncover the molecular mechanism of the structural damage to the protein altering its biological function. The most significant effect was found for variants I625K, L1047S, and H1170D, presumably affecting the endonuclease activity of DROSHA. Such alterations arisen during MDS progression should be taken into consideration as evoking certain clinical traits in the malignifying clonal evolution.

Packpred: Predicting the Functional Effect of Missense Mutations

Predicting the functional consequences of single point mutations has relevance to protein function annotation and to clinical analysis/diagnosis. We developed and tested Packpred that makes use of a multi-body clique statistical potential in combination with a depth-dependent amino acid substitution matrix (FADHM) and positional Shannon entropy to predict the functional consequences of point mutations in proteins. Parameters were trained over a saturation mutagenesis data set of T4-lysozyme (1,966 mutations). The method was tested over another saturation mutagenesis data set (CcdB; 1,534 mutations) and the Missense3D data set (4,099 mutations). The performance of Packpred was compared against those of six other contemporary methods. With MCC values of 0.42, 0.47, and 0.36 on the training and testing data sets, respectively, Packpred outperforms all methods in all data sets, with the exception of marginally underperforming in comparison to FADHM in the CcdB data set. A meta server analysis was performed that chose best performing methods of wild-type amino acids and for wild-type mutant amino acid pairs. This led to an increase in the MCC value of 0.40 and 0.51 for the two meta predictors, respectively, on the Missense3D data set. We conjecture that it is possible to improve accuracy with better meta predictors as among the seven methods compared, at least one method or another is able to correctly predict ∼99% of the data.

A comprehensive in Silico analysis of the functional and structural impact of single nucleotide polymorphisms (SNPs) in the human IL-33 gene

Interleukin 33 (IL-33) is the latest member of the IL-1 cytokine family, which plays both pro - and anti-inflammatory functions. Numerous Single-nucleotide polymorphisms (SNPs) in the IL-33 gene have been recognized to be associated with a vast variety of inflammatory disorders. SNPs associated studies have become a crucial approach in uncovering the genetic background of human diseases. However, distinguishing the functional SNPs in a disease-related gene from a pool of both functional and neutral SNPs is a major challenge and needs multiple experiments of hundreds or thousands of SNPs in candidate genes. This study aimed to identify the possible deleterious SNPs in the IL-33 gene using bioinformatics predictive tools. The nonsynonymous SNPs (nsSNPs) were analyzed by SIFT, PolyPhen, PROVEAN, SNP&GO, MutPred, SNAP, PhD SNP, and I-Mutant tools. The Non-coding SNPs (ncSNPs) were also analyzed by SNPinfo and RegulomeDB tools. In conclusion, our in-silico analysis predicted 5 nsSNPs and 22 ncSNPs as potential candidates in the IL-33 gene for future genetic association studies.

Molecular Dynamics Simulations in Designing DARPins as Phosphorylation-Specific Protein Binders of ERK2

Extracellular signal-regulated kinases 1 and 2 (ERK1/2) play key roles in promoting cell survival and proliferation through the phosphorylation of various substrates. Remarkable antitumour activity is found in many inhibitors that act upstream of the ERK pathway. However, drug-resistant tumour cells invariably emerge after their use due to the reactivation of ERK1/2 signalling. ERK1/2 inhibitors have shown clinical efficacy as a therapeutic strategy for the treatment of tumours with mitogen-activated protein kinase (MAPK) upstream target mutations. These inhibitors may be used as a possible strategy to overcome acquired resistance to MAPK inhibitors. Here, we report a class of repeat proteins-designed ankyrin repeat protein (DARPin) macromolecules targeting ERK2 as inhibitors. The structural basis of ERK2-DARPin interactions based on molecular dynamics (MD) simulations was studied. The information was then used to predict stabilizing mutations employing a web-based algorithm, MAESTRO. To evaluate whether these design strategies were successfully deployed, we performed all-atom, explicit-solvent molecular dynamics (MD) simulations. Two mutations, Ala → Asp and Ser → Leu, were found to perform better than the original sequence (DARPin E40) based on the associated energy and key residues involved in protein-protein interaction. MD simulations and analysis of the data obtained on these mutations supported our predictions.

The structural, functional, and dynamic effect of Tau tubulin kinase1 upon a mutation: A neuro-degenerative hotspot

Alzheimer's disease (AD) is a progressive disorder that causes brain cells to degenerate and die. AD is one of the common causes of dementia that leads to a decline in thinking, behavioral and social skills that disrupts a person's ability to function independently. Tau-tubulin kinase1 (TTBK1) is a crucial disease regulating AD protein, which is majorly responsible for the phosphorylation and accumulation of tau protein at specific Serine/Threonine residues found in paired helical filaments, suggesting its role in tauopathy. TTBK1 involvement in many diseases and the restricted expression of TTBK1 to the central nervous system (CNS) makes TTBK1 an attractive therapeutic target for tauopathies. The genetic variations in TTBK1 are primarily involved in the TTBK1 pathogenesis. This study highlighted the destabilizing, damaging and deleterious effect of the mutation R142Q on TTBK1 structure through computational predictions and molecular dynamics simulations. The protein deviation, fluctuations, conformational dynamics, solvent accessibility, hydrogen bonding, and the residue-residue mapping confirmed the mutant effect to cause structural aberrations, suggesting overall destabilization due to the protein mutation. The presence of well-defined free energy minima was observed in TTBK1-wild type, as opposed to that in the R142Q mutant, reflecting structural deterioration. The overall findings from the study reveal that the presence of R142Q mutation on TTBK1 is responsible for the structural instability, leading to disruption of its biological functions. The mutation could be used as future diagnostic markers in treating AD.

Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages

Resistance to drugs used to treat tuberculosis disease (TB) continues to remain a public health burden, with missense point mutations in the underlying Mycobacterium tuberculosis bacteria described for nearly all anti-TB drugs. The post-genomics era along with advances in computational and structural biology provide opportunities to understand the interrelationships between the genetic basis and the structural consequences of M. tuberculosis mutations linked to drug resistance. Pyrazinamide (PZA) is a crucial first line antibiotic currently used in TB treatment regimens. The mutational promiscuity exhibited by the pncA gene (target for PZA) necessitates computational approaches to investigate the genetic and structural basis for PZA resistance development. We analysed 424 missense point mutations linked to PZA resistance derived from ∼35K M. tuberculosis clinical isolates sourced globally, which comprised the four main M. tuberculosis lineages (Lineage 1-4). Mutations were annotated to reflect their association with PZA resistance. Genomic measures (minor allele frequency and odds ratio), structural features (surface area, residue depth and hydrophobicity) and biophysical effects (change in stability and ligand affinity) of point mutations on pncA protein stability and ligand affinity were assessed. Missense point mutations within pncA were distributed throughout the gene, with the majority (>80%) of mutations with a destabilising effect on protomer stability and on ligand affinity. Active site residues involved in PZA binding were associated with multiple point mutations highlighting mutational diversity due to selection pressures at these functionally important sites. There were weak associations between genomic measures and biophysical effect of mutations. However, mutations associated with PZA resistance showed statistically significant differences between structural features (surface area and residue depth), but not hydrophobicity score for mutational sites. Most interestingly M. tuberculosis lineage 1 (ancient lineage) exhibited a distinct protein stability profile for mutations associated with PZA resistance, compared to modern lineages.

Molecular Analysis of Streptomycin Resistance Genes in Clinical Strains of Mycobacterium tuberculosis and Biocomputational Analysis of the MtGidB L101F Variant

Globally, tuberculosis (TB) remains a prevalent threat to public health. In 2019, TB affected 10 million people and caused 1.4 million deaths. The major challenge for controlling this infectious disease is the emergence and spread of drug-resistant Mycobacterium tuberculosis, the causative agent of TB. The antibiotic streptomycin is not a current first-line anti-TB drug. However, WHO recommends its use in patients infected with a streptomycin-sensitive strain. Several mutations in the M. tuberculosisrpsL, rrs and gidB genes have proved association with streptomycin resistance. In this study, we performed a molecular analysis of these genes in clinical isolates to determine the prevalence of known or novel mutations. Here, we describe the genetic analysis outcome. Furthermore, a biocomputational analysis of the MtGidB L101F variant, the product of a novel mutation detected in gidB during molecular analysis, is also reported as a theoretical approach to study the apparent genotype-phenotype association.

Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations

Understanding how a mutation might affect protein stability is of significant importance to protein engineering and for understanding protein evolution genetic diseases. While a number of computational tools have been developed to predict the effect of missense mutations on protein stability protein stability upon mutations, they are known to exhibit large biases imparted in part by the data used to train and evaluate them. Here, we provide a comprehensive overview of predictive tools, which has provided an evolving insight into the importance and relevance of features that can discern the effects of mutations on protein stability. A diverse selection of these freely available tools was benchmarked using a large mutation-level blind dataset of 1342 experimentally characterised mutations across 130 proteins from ThermoMutDB, a second test dataset encompassing 630 experimentally characterised mutations across 39 proteins from iStable2.0 and a third blind test dataset consisting of 268 mutations in 27 proteins from the newly published ProThermDB. The performance of the methods was further evaluated with respect to the site of mutation, type of mutant residue and by ranging the pH and temperature. Additionally, the classification performance was also evaluated by classifying the mutations as stabilizing (∆∆G ≥ 0) or destabilizing (∆∆G < 0). The results reveal that the performance of the predictors is affected by the site of mutation and the type of mutant residue. Further, the results show very low performance for pH values 6-8 and temperature higher than 65 for all predictors except iStable2.0 on the S630 dataset. To illustrate how stability and structure change upon single point mutation, we considered four stabilizing, two destabilizing and two stabilizing mutations from two proteins, namely the toxin protein and bovine liver cytochrome. Overall, the results on S268, S630 and S1342 datasets show that the performance of the integrated predictors is better than the mechanistic or individual machine learning predictors. We expect that this paper will provide useful guidance for the design and development of next-generation bioinformatic tools for predicting protein stability changes upon mutations.

Protein analysis and stability: Overcoming trial-and-error by grouping according to physicochemical properties

Today proteins are possibly the most important class of substances. Yet new tasks for proteins are still often solved by trial-and-error approaches. However, in some areas these euphemistically called "screening approaches" are not suitable. E.g. stability tests just take too long and therefore require a more strategic, target-orientated concept. This concept is available by grouping proteins according to their physicochemical properties and then pulling out the right drawer for new tasks. These properties include size, then charge and hydrophobicity as well as their patchinesses, and the degree of order. In addition, solubility, the content of (free) enthalpy, aromatic-amino-acid- and α/β-frequency as well as helix capping, and corresponding patchiness, the number of specific motifs and domains as well as the typical concentration range can be helpful to discriminate between different groups of proteins. Analyzing correlations will reduce the necessary amount of parameters and additional ones, which may be still undiscovered at the present time, can be identified looking at protein subgroups with similar physicochemical properties which still behave heterogeneously. Step-by-step the methodology will be improved. Possibly protein stability will be the driver of this process, but all other areas such as production, purification and analytics including sample pre-treatment and the choice of appropriate separation conditions for e.g. chromatography and electrophoresis will profit from a rational strategy.

Computational modeling and bioinformatic analyses of functional mutations in drug target genes in Mycobacterium tuberculosis

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MycoTRAP-DB, a database of mutations and their impact on normal functionality of protein in M.tb genes.

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Several secondary mutations were identified with significant impact on protein structure and function.

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Comprehensive information gives insight for screening of suspected hotspots in advance to combat drug resistant TB.

Tuberculosis (TB) continues to be the leading cause of deaths due to its persistent drug resistance and the consequent ineffectiveness of anti-TB treatment. Recent years witnessed huge amount of sequencing data, revealing mutations responsible for drug resistance. However, the lack of an up-to-date repository remains a barrier towards utilization of these data and identifying major mutations-associated with resistance. Amongst all mutations, non-synonymous mutations alter the amino acid sequence of a protein and have a much greater effect on pathogenicity. Hence, this type of gene mutation is of prime interest of the present study. The purpose of this study is to develop an updated database comprising almost all reported substitutions within the Mycobacterium tuberculosis (M.tb) drug target genes rpoB, inhA, katG, pncA, gyrA and gyrB. Various bioinformatics prediction tools were used to assess the structural and biophysical impacts of the resistance causing non-synonymous single nucleotide polymorphisms (nsSNPs) at the molecular level. This was followed by evaluating the impact of these mutations on binding affinity of the drugs to target proteins. We have developed a comprehensive online resource named MycoTRAP-DB (Mycobacterium tuberculosis Resistance Associated Polymorphisms Database) that connects mutations in genes with their structural, functional and pathogenic implications on protein. This database is accessible at http://139.59.12.92. This integrated platform would enable comprehensive analysis and prioritization of SNPs for the development of improved diagnostics and antimycobacterial medications. Moreover, our study puts forward secondary mutations that can be important for prognostic assessments of drug-resistance mechanism and actionable anti-TB drugs.

Three Simple Properties Explain Protein Stability Change upon Mutation

Accurate prediction of protein stability upon mutation enables rational engineering of new proteins and insights into protein evolution and monogenetic diseases caused by single-point amino acid substitutions. Many tools have been developed to this aim, ranging from energy-based models to machine-learning methods that use large amounts of experimental data. However, as the methods become more complex, the interpretation of the chemistry underlying the protein stability effects becomes obscure. It is thus of interest to identify the simplest prediction model that retains complete amino acid specific interpretation; for a given number of input descriptors, we expect such a model to be almost universal. In this study, we identify such a limiting model, SimBa, a simple multilinear regression model trained on a substitution-type-balanced experimental data set. The model accounts only for the solvent accessibility of the site, volume difference, and polarity difference caused by mutation. Our results show that this very simple and directly applicable model performs comparably to other much more complex, widely used protein stability prediction methods. This suggests that a hard limit of ∼1 kcal/mol numerical accuracy and an R ∼ 0.5 trend accuracy exists and that new features, such as account of unfolded states, water colocalization, and amino acid correlations, are required to improve accuracy to, e.g., 1/2 kcal/mol.

Increased p53 signaling impairs neural differentiation in HUWE1-promoted intellectual disabilities

Essential E3 ubiquitin ligase HUWE1 (HECT, UBA, and WWE domain containing 1) regulates key factors, such as p53. Although mutations in HUWE1 cause heterogenous neurodevelopmental X-linked intellectual disabilities (XLIDs), the disease mechanisms common to these syndromes remain unknown. In this work, we identify p53 signaling as the central process altered in HUWE1-promoted XLID syndromes. By focusing on Juberg-Marsidi syndrome (JMS), one of the severest XLIDs, we show that increased p53 signaling results from p53 accumulation caused by HUWE1 p.G4310R destabilization. This further alters cell-cycle progression and proliferation in JMS cells. Modeling of JMS neurodevelopment reveals majorly impaired neural differentiation accompanied by increased p53 signaling. The neural differentiation defects can be successfully rescued by reducing p53 levels and restoring the expression of p53 target genes, in particular CDKN1A/p21. In summary, our findings suggest that increased p53 signaling underlies HUWE1-promoted syndromes and impairs XLID JMS neural differentiation.

In silico characterization of mutations circulating in SARS-CoV-2 structural proteins

SARS-CoV-2 has recently emerged as a pandemic that has caused more than 2.4 million deaths worldwide. Since the onset of infections, several full-length sequences of viral genome have been made available which have been used to gain insights into viral dynamics. We utilised a meta-data driven comparative analysis tool for sequences (Meta-CATS) algorithm to identify mutations in 829 SARS-CoV-2 genomes from around the world. The algorithm predicted sixty-one mutations among SARS-CoV-2 genomes. We observed that most of the mutations were concentrated around three protein coding genes viz nsp3 (non-structural protein 3), RdRp (RNA-directed RNA polymerase) and Nucleocapsid (N) proteins of SARS-CoV-2. We used various computational tools including normal mode analysis (NMA), C-α discrete molecular dynamics (DMD) and all-atom molecular dynamic simulations (MD) to study the effect of mutations on functionality, stability and flexibility of SARS-CoV-2 structural proteins including envelope (E), N and spike (S) proteins. PredictSNP predictor suggested that four mutations (L37H in E, R203K and P344S in N and D614G in S) out of seven were predicted to be neutral whilst the remaining ones (P13L, S197L and G204R in N) were predicted to be deleterious in nature thereby impacting protein functionality. NMA, C-α DMD and all-atom MD suggested some mutations to have stabilizing roles (P13L, S197L and R203K in N protein) where remaining ones were predicted to destabilize mutant protein. In summary, we identified significant mutations in SARS-CoV-2 genomes as well as used computational approaches to further characterize the possible effect of highly significant mutations on SARS-CoV-2 structural proteins.

Communicated by Ramaswamy H. Sarma

Prioritization of candidate genes for a South African family with Parkinson's disease using in-silico tools

Parkinson's disease (PD) is a neurodegenerative disorder exhibiting Mendelian inheritance in some families. Next-generation sequencing approaches, including whole exome sequencing (WES), have revolutionized the field of Mendelian disorders and have identified a number of PD genes. We recruited a South African family with autosomal dominant PD and used WES to identify a possible pathogenic mutation. After filtration and prioritization, we found five potential causative variants in CFAP65, RTF1, NRXN2, TEP1 and CCNF. The variant in NRXN2 was selected for further analysis based on consistent prediction of deleteriousness across computational tools, not being present in unaffected family members, ethnic-matched controls or public databases, and its expression in the substantia nigra. A protein model for NRNX2 was created which provided a three-dimensional (3D) structure that satisfied qualitative mean and global model quality assessment scores. Trajectory analysis showed destabilizing effects of the variant on protein structure, indicated by high flexibility of the LNS-6 domain adopting an extended conformation. We also found that the known substrate N-acetyl-D-glucosamine (NAG) contributed to restoration of the structural stability of mutant NRXN2. If NRXN2 is indeed found to be the causal gene, this could reveal a new mechanism for the pathobiology of PD.

Investigation of Cyc1 protein structure stability after H53I mutation using computational approaches to improve redox potential

Acidithiobacillus ferrooxidans (Af) is an acidophilic bacterium that grows in rigid surroundings and gets its own energy from the oxidation of Fe²⁺ to Fe³⁺. These bacteria are involved in the bioleaching process. Cyc₁ is a periplasmic protein with a crucial role in electron transportation in the respiratory chain. His53 of the Cyc₁ protein, involved in electron transfer to CoxB, was selected for mutation and bioinformatics studies. His53 was substituted by Ile using PyMol software. Molecular dynamics simulations were performed for wild and mutant types of Cyc₁ protein. The conformational changes of mutated protein were studied by analyzing RMSD, RMSF, SASA, Rg, H Bond, and DSSP. The results of the RMSF analysis indicated an increase in the flexibility of the ligand in the mutant. Finally, active site instability leads to an increase in the value of E₀ at the mutation point and improving electron transfer. On the other, His53 in Cyc₁ is interconnected to Glu126 in CoxB through the water molecule (W76) and hydrogen bonding. In the H53I mutation, there was a decrease in the distance between H2O 2030, 2033, and isoleucine 53, and subsequently, the distance to the water molecule 76 between the two proteins was reduced and strengthens the hydrogen bond between Cyc₁ and CoxB, finally improves electron transfer and the bioleaching process.

Morquio B disease: From pathophysiology towards diagnosis

Morquio B disease is an attenuated phenotype within the spectrum of beta galactosidase (GLB1) deficiencies. It is characterised by dysostosis multiplex, ligament laxity, mildly coarse facies and heart valve defects due to keratan sulphate accumulation, predominantly in the cartilage. Morquio B patients have normal neurological development, setting them apart from those with the more severe GM1 gangliosidosis. Morquio B disease, with an incidence of 1:250.000 to 1:1.000.000 live births, is very rare. Here we report the clinical-biochemical data of nine patients. High amounts of keratan sulfate were detected using LC-MS/MS in the patients' urinary samples, while electrophoresis, the standard procedure of qualitative glycosaminoglycans analysis, failed to identify this metabolite in any of the patients' samples. We performed molecular analyses at gene, gene expression and protein expression levels, for both isoforms of the GLB1 gene, lysosomal GLB1, and the cell-surface expressed Elastin Binding Protein. We characterised three novel GLB1 mutations [c.75 + 2 T > G, c.575A > G (p.Tyr192Cys) and c.2030 T > G (p.Val677Gly)] identified in three heterozygous patients. We also set up a copy number variation assay by quantitative PCR to evaluate the presence of deletions/ insertions in the GLB1 gene. We propose a diagnostic plan, setting out the specific clinical- biochemical and molecular features of Morquio B, in order to avoid misdiagnoses and improve patients' management.

A base measure of precision for protein stability predictors: structural sensitivity

BACKGROUND

Prediction of the change in fold stability (ΔΔG) of a protein upon mutation is of major importance to protein engineering and screening of disease-causing variants. Many prediction methods can use 3D structural information to predict ΔΔG. While the performance of these methods has been extensively studied, a new problem has arisen due to the abundance of crystal structures: How precise are these methods in terms of structure input used, which structure should be used, and how much does it matter? Thus, there is a need to quantify the structural sensitivity of protein stability prediction methods.

RESULTS

We computed the structural sensitivity of six widely-used prediction methods by use of saturated computational mutagenesis on a diverse set of 87 structures of 25 proteins. Our results show that structural sensitivity varies massively and surprisingly falls into two very distinct groups, with methods that take detailed account of the local environment showing a sensitivity of ~ 0.6 to 0.8 kcal/mol, whereas machine-learning methods display much lower sensitivity (~ 0.1 kcal/mol). We also observe that the precision correlates with the accuracy for mutation-type-balanced data sets but not generally reported accuracy of the methods, indicating the importance of mutation-type balance in both contexts.

CONCLUSIONS

The structural sensitivity of stability prediction methods varies greatly and is caused mainly by the models and less by the actual protein structural differences. As a new recommended standard, we therefore suggest that ΔΔG values are evaluated on three protein structures when available and the associated standard deviation reported, to emphasize not just the accuracy but also the precision of the method in a specific study. Our observation that machine-learning methods deemphasize structure may indicate that folded wild-type structures alone, without the folded mutant and unfolded structures, only add modest value for assessing protein stability effects, and that side-chain-sensitive methods overstate the significance of the folded wild-type structure.

Protein Stability Perturbation Contributes to the Loss of Function in Haploinsufficient Genes

Missense variants are among the most studied genome modifications as disease biomarkers. It has been shown that the “perturbation” of the protein stability upon a missense variant (in terms of absolute ΔΔG value, i.e., |ΔΔG|) has a significant, but not predictive, correlation with the pathogenicity of that variant. However, here we show that this correlation becomes significantly amplified in haploinsufficient genes. Moreover, the enrichment of pathogenic variants increases at the increasing protein stability perturbation value. These findings suggest that protein stability perturbation might be considered as a potential cofactor in diseases associated with haploinsufficient genes reporting missense variants.

TargetMM: Accurate Missense Mutation Prediction by Utilizing Local and Global Sequence Information with Classifier Ensemble

Aim and Objective:

Missense mutation (MM) may lead to various human diseases by disabling proteins. Accurate prediction of MM is important and challenging for both protein function annotation and drug design. Although several computational methods yielded acceptable success rates, there is still room for further enhancing the prediction performance of MM.

Materials and Methods:

In the present study, we designed a new feature extracting method, which considers the impact degree of residues in the microenvironment range to the mutation site. Stringent cross-validation and independent test on benchmark datasets were performed to evaluate the efficacy of the proposed feature extracting method. Furthermore, three heterogeneous prediction models were trained and then ensembled for the final prediction. By combining the feature representation method and classifier ensemble technique, we reported a novel MM predictor called TargetMM for identifying the pathogenic mutations from the neutral ones.

Results:

Comparison outcomes based on statistical evaluation demonstrate that TargetMM outperforms the prior advanced methods on the independent test data. The source codes and benchmark datasets of TargetMM are freely available at https://github.com/sera616/TargetMM.git for academic use.

PremPS: Predicting the impact of missense mutations on protein stability

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.

The development of computational methods to accurately predict the impacts of amino acid substitutions on protein stability is of paramount importance for the field of protein design and understanding the roles of missense mutations in disease. However, most of the available methods have very limited predictive accuracy for mutations increasing stability and few could consistently perform well across different test cases. Here we present a new computational approach PremPS, which is capable of predicting the effects of single point mutations on protein stability. PremPS employs only ten evolutionary- and structure-based features and is trained on a symmetrical dataset consisting of the same number of cases of stabilizing and destabilizing mutations. Our method was tested against numerous blind datasets and shows a considerable improvement especially in evaluating the effects of stabilizing mutations, outperforming previously developed methods. PremPS is freely available as a user-friendly web server at http://lilab.jysw.suda.edu.cn/research/PremPS/, which is fast enough to handle the large number of cases.

Structure-Based Virtual Screening to Discover Potential Lead Molecules for the SARS-CoV-2 Main Protease

The COVID-19 disease is caused by a new strain of the coronavirus family (SARS-CoV-2), and it has affected at present millions of people all over the world. The indispensable role of the main protease (Mpro) in viral replication and gene expression makes this enzyme an attractive drug target. Therefore, inhibition of SARS-CoV-2 Mpro as a proposition to halt virus ingression is being pursued by scientists globally. Here we carried out a study with two objectives: the first being to perform comparative protein sequence and 3D structural analysis to understand the effect of 12 point mutations on the active site. Among these, two mutations, viz., Ser46 and Phe134, were found to cause a significant change at the active sites of SARS-CoV-2. The Ser46 mutation present at the entrance of the S5 subpocket of SARS-CoV-2 increases the contribution of other two hydrophilic residues, while the Phe134 mutation, present in the catalytic cysteine loop, can cause an increase in catalytic efficiency of Mpro by facilitating fast proton transfer from the Cys145 to His41 residue. It was observed that active site remained conserved among Mpro of both SARS-CoVs, except at the entrance of the S5 subpocket, suggesting sustenance of substrate specificity. The second objective was to screen the inhibitory effects of three different data sets (natural products, coronaviruses main protease inhibitors, and FDA-approved drugs) using a structure-based virtual screening approach. A total of 73 hits had a combo score >2.0. Eight different structural scaffold classes were identified, such as one/two tetrahydropyran ring(s), dipeptide/tripeptide/oligopeptide, large (approximately 20 atoms) cyclic peptide, and miscellaneous. The screened hits showed key interactions with subpockets of the active site. Further, molecular dynamics studies of selected screened compounds confirmed their perfect fitting into the subpockets of the active site. This study suggests promising structures that can fit into the SARS-CoV-2 Mpro active site and also offers direction for further lead optimization and rational drug design.

Structural analyses and classification of novel isoniazid resistance coupled mutational landscapes in Mycobacterium tuberculosis: a combined molecular docking and MD simulation study

Drug resistance in Mycobacterium tuberculosis has become a major challenge to the current regime of treatment as well as to the containment of the disease globally. The molecular and genetic studies identified frequently occurring point mutations in the virulent protein such as KatG of M. tuberculosis resulted in the development of isoniazid tolerance in the pathogen. This study aims to analyze the structural basis of the disease mutations available in the literature as well as to predict novel alteration in the KatG which may cause similar deleterious effects. Around 15 experimentally derived mutations were included in this study and pathogenic mutational landscapes containing 60 site-specific alterations were predicted using the available in silico techniques. The effects of these mutations on the stability of the protein were studied and an exhaustive docking study was conducted for each classified perturbations, which identify the highest changes in the binding energies in p.Meth255Ile among experimental and p.Ala222Arg in computationally predicted mutations. Furthermore, the structural effects on these substitutions were analyzed using the principles of molecular dynamic simulations each for a 100 ns time scale, which validated the interaction studies. The outcome of this study may enable the identification of the novel drug resistance-associated point mutations which were not previously reported and may contribute significantly in a variety of experimental studies as well as facilitate the process of drug design and discovery.Communicated by Ramaswamy H. Sarma.

Structural and functional impacts of E5 genetic variants of human papillomavirus type 31

Persistent infections caused by high-risk human papillomavirus (HR-HPV) are important, for the development of cervical lesions, but environmental and genetic factors are also related in the process of carcinogenesis. Among the genetic factors, the genetic variants of HR-HPV appear to be related to the risk of persistent infections. Therefore, the present study investigates variants of HPV31 E5 oncogene in cervical scraping samples from Brazilian women to assess their functional and structural effects, in order to identify possible repercussions of these variants on the infectious and carcinogenic process. Our results detected nucleotide changes previously described in the HPV31 E5 oncogene, which may play a critical role in the development of cancer due to its ability to promote cell proliferation and signal transmission. In our study, the interaction percentage of the 31E5 sequence generated by the Immune Epitope Server database and the Analysis Resource (IEDB) allowed us to include possible immunogenic epitopes with the MHC-I and MHC-II molecules, which may represent a possible relationship between protein suppression of the immune system. In the structural analysis of the HPV31 E5 oncoprotein, the N5D, I48 V, P56A, F80I and V64I polymorphisms can be found inserted within transmembrane regions. The P56A mutation has been predicted to be highly stabilizing and, therefore, can cause a change in protein function. Regarding the interaction of the E5 protein from HPV31 with the signaling of NF-kB pathway, we observed that in all variants of the E5 gene from HPV-31, the activity of the NF-kB pathway was increased compared to the prototype. Our study contributes to a more refined design of studies with the E5 gene from HPV31 and provides important data for a better understanding of how variants can be distinguished under their clinical consequences.

Effect of Y50H and S187G substitutions on thermostability and exonuclease activity of TK1646 from Thermococcus kodakarensis

TK1646 is a highly thermostable single strand specific 3'-5' exonuclease. Exonucleases play important role in maintaining the genome integrity at elevated temperatures. Therefore, it is important to examine the factors contributing to thermostability of these exonucleases. In this study we report on production, purification and characterization of S187G and Y50H mutants of TK1646, focusing on the factors leading to thermostability of TK1646. Characterization of the recombinant proteins indicated that these substitutions did not drastically affect the catalysis of single stranded DNA. However, both of these substitutions reduced the thermostability of the recombinant proteins. Half-lives of Y50H and S187G mutants were 95 and 155 min, respectively, at 100 °C in comparison to 180 min of the wild type. Bioinformatics analysis indicated an increase in solvent accessibility of the mutated residues and disruption of hydrogens bonds. Molecular modelling and superimposition of the 3D structures of the mutants and the wild type demonstrated that one of the active site residues, Glu145, was shifted away from the metal ion in both the mutants which may be responsible for the decrease in catalytic activity. Compact secondary structure, hydrophobicity and hydrogen bonding might be the major factors contributing to the thermostability of TK1646.

Mutation analysis of MFSD8 in an amyotrophic lateral sclerosis cohort from mainland China

Recent studies have suggested that rare variants in MFSD8 contribute to risk for frontotemporal dementia (FTD). Considering the common underlying pathogenesis and the shared genetic risk between amyotrophic lateral sclerosis (ALS) and FTD, we screened the coding region of MFSD8 in 551 unrelated patients with ALS (510 unrelated sporadic ALS and 41 familial ALS probands) from mainland China by whole-exome sequencing to assess its mutation frequency in patients with ALS and evaluate its association. Two rare deleterious variants, c.343G>A (p. V115M) and c.695T>C (p.L232P), were identified in this study. The variant c.695T>C (p.L232P) has not been previously reported and the carrier of this variant exhibits a relatively younger age of disease onset. Our studies provide some independent evidence showing that the rare variant p.L232P in MFSD8 might be a candidate risk factor for ALS. However, the relatively small sample size and the lack of patient-derived cells limit the power of the genetic exploration of this study, further robust multicenter studies with larger sizes and biological experiments with patient-derived cells are needed to elucidate the pathogenesis of the rare variant in MFSD8 in ALS.

Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks

Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network (3D-CNN) designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in CNNs, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D-CNNs can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.

Effects of Single-Nucleotide Polymorphisms in Calmodulin-Dependent Protein Kinase Kinase 2 (CAMKK2): A Comprehensive Study

Calmodulin-dependent protein kinase kinase 2 (CAMKK2) is a protein kinase that belongs to the serine/threonine kinase family. It phosphorylates kinases like CAMK1, CAMK2, and AMP, and this signaling cascade is involved in various biological processes including cell proliferation, apoptosis, and proliferation. Also, the CAMKK2 signaling activity is required for the healthy activity of the brain which otherwise can cause diseases like bipolar disorders and anxiety. The current study is based on in silico bioinformatics analysis that combines sequence- and structure-based predictions to mark a SNP as damaging or neutral. The combined results from sequence-based, evolutionary conservation-based, and consensus-based tools have predicted a total of 18 nsSNPs as deleterious, and these nsSNPs were further subjected to structure-based analysis. The six mutant models of V195A, V249M, R311C, F366Y, P389T, and W445C showed a higher deviation from the wildtype protein model and hence were further taken for docking studies. The molecular docking analysis has predicted that these mutations will also be disruptive to the protein-protein interactions between CAMKK2 and PRKAG1 which will create an evident reduction in the kinase activity. The current study has enlightened us that a few of the significant mutations are prime candidates in CAMKK2 which could be the fundamental cause of various bipolar and psychiatric disorders. This is the first detailed study that predicts the deleterious nsSNPs in CAMKK2 and contributes positively in providing a better understanding of disease mechanisms.

Pharmacoresistant Epilepsy in Childhood: Think of the Cerebral Folate Deficiency, a Treatable Disease

Cerebral folate deficiency (CFD) is a neurological disorder characterized by low levels of 5-methyltetrahydrofolate (5-MTHF) in the cerebrospinal fluid (CSF). The prevalence of this autosomal recessive disorder is estimated to be <1/1,000,000. Fifteen different pathogenic variants in the folate receptor 1 gene (FOLR1) encoding the receptor of folate α (FRα) have already been described. We present a new pathogenic variation in the FOLR1 in a childhood-stage patient. We aim to establish the core structure of the FRα protein mandatory for its activity. A three-year-old child was admitted at hospital for a first febrile convulsions episode. Recurrent seizures without fever also occurred a few months later, associated with motor and cognitive impairment. Various antiepileptic drugs failed to control seizures. Magnetic resonance imaging (MRI) showed central hypomyelination and biological analysis revealed markedly low levels of 5-MTHF in CSF. Next generation sequencing (NGS) confirmed a CFD with a FOLR1 homozygous variation (c.197 G > A, p.Cys66Tyr). This variation induces an altered folate receptor α protein and underlines the role of a disulfide bond: Cys66-Cys109, essential to transport 5-MTHF into the central nervous system. Fortunately, this severe form of CFD had remarkably responded to high doses of oral folinic acid combined with intravenous administrations.

A Novel Missense Single Nucleotide Polymorphism in the GREM1 Gene is Highly Associated with Higher Reproductive Traits in Awassi Sheep

GREM1 (gremlin1) is a known inhibitor for BMP15 (bone morphogenetic protein 15) family, but its genetic diversity in sheep is unknown. The present study was conducted to analyze the polymorphism of GREM1 gene using PCR- single-strand conformation polymorphism (SSCP) and DNA sequencing methods and to assess the possible association of GREM1 gene polymorphism with reproductive traits in Awassi ewes. A total of 224 ewes, 124 producing singles and 100 producing twins, were included in the study. Two SSCP patterns were detected in two amplified loci within the exon 2. Two exonic novel single nucleotide polymorphism (SNP)s were identified, c.74 T > G (the silent SNP p.Met123 =) and c.30 T > A with (the missense SNP p.Ile237Phe). Statistical analyses indicated a non-significant (P > 0.05) association of p.Met123 = with the analyzed reproductive traits of fecundity, prolificacy, litter size, and twinning rate. Meanwhile, p.Ile237Phe SNP exhibited a highly significant (P < 0.01) association with the measured reproductive traits, in which ewes with TA genotype (with p.Ile237Phe SNP) exhibited higher litter size, twinning ratio, fecundity, and prolificacy than those with TT genotype (without p.Ile237Phe SNP). The deleterious impact of p.Ile237Phe SNP was observed by the means of ten different state-of-the-art in silico tools that predicted a highly damaging effect of p.Ile237Phe SNP on the structure, function, and stability of gremlin1. In conclusion, the results of our study suggest that p.Ile237Phe SNP has a remarkable negative impact on the gremlin1 structure, function, and stability. Since gremlin1 is a known inhibitor of reproductive performance, a consequent higher reproductive performance was observed in ewes with damaged gremlin1 (with p.Ile237Phe SNP) than those with non-damaged gremlin1 (without p.Ile237Phe SNP). Therefore, it can be stated that the implementation of the novel p.Ile237Phe SNP in the GREM1 gene could be a useful marker in marker-assisted selection. This manuscript is the first one to describe GREM1 gene variations in sheep.

Postmortem diagnosis of PPA2-associated sudden cardiac death from dried blood spot in a neonate presenting with vocal cord paralysis

Biallelic variants in inorganic pyrophosphatase 2 (PPA2) are known to cause infantile sudden cardiac failure (OMIM #617222), but relatively little is known about phenotypic variability of these patients prior to their death. We report a 5-wk-old male with bilateral vocal cord paralysis and hypertension who had a sudden unexpected cardiac death. Subsequently, molecular autopsy via whole-genome sequencing from newborn dried blood spot identified compound heterozygous mutations in PPA2, with a paternally inherited, pathogenic missense variant (c.514G > A; p.Glu172Lys) and a novel, maternally inherited missense variant of uncertain significance (c.442A > T; p.Thr148Ser). This report expands the presenting phenotype of patients with PPA2 variants. It also highlights the utility of dried blood spots for postmortem molecular diagnosis.

Deciphering Molecular Virulence Mechanism of Mycobacterium tuberculosis Dop isopeptidase Based on Its Sequence-Structure-Function Linkage

The pupylation pathway marks proteins for prokaryotic ubiquitin-like protein (Pup)-proteasomal degradation and survival strategy of mycobacteria inside of the host macrophages. Deamidase of Pup (Dop) plays a central role in the pupylation pathway. It is still a matter of investigation to know the function of Dop in virulence of mycobacterial lineage. Hence, the present study was intended to describe the sequence-structure-function-virulence link of Dop for understanding the molecular virulence mechanism of Mycobacterium tuberculosis H37Rv (Mtb). Phylogenetic analysis of this study indicated that Dop has extensively diverged across the proteasome-harboring bacteria. The functional part of Dop was converged across the pathogenic mycobacterial lineage. The genome-wide analysis pointed out that the pupylation gene locus was identical to each other, but its genome neighborhood differed from species to species. Molecular modeling and dynamic studies proved that the predicted structure of Mtb Dop was energetically stable and low conformational freedom. Moreover, evolutionary constraints in Mtb Dop were intensively analyzed for inferring its sequence-structure-function relationships for the full virulence of Mtb. It indicated that evolutionary optimization was extensively required to stabilize its local structural environment at the side chains of mutable residues. The sequence-structure-function-virulence link of Dop might have retained in Mtb by reordering hydrophobic and hydrogen bonding patterns in the local structural environment. Thus, the results of our study provide a quest to understand the molecular virulence and pathogenesis mechanisms of Mtb during the infection process.

Identification of pathogenic missense mutations using protein stability predictors

Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 13 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, significantly outperforms all other predictors in the identification of disease variants. Moreover, we demonstrate that employing predicted absolute energy change scores improves performance of nearly all predictors in distinguishing pathogenic from benign variants. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.

MPTherm-pred: Analysis and Prediction of Thermal Stability Changes upon Mutations in Transmembrane Proteins

The stability of membrane proteins differs from globular proteins due to the presence of nonpolar membrane-spanning regions. Using a dataset of 929 membrane protein mutations whose effects on thermal stability (ΔT_m) were experimentally determined, we found that the average ΔT_m due to 190 stabilizing and 232 destabilizing mutations occurring in membrane-spanning regions are 2.43(3.1) °C and -5.48(5.5) °C, respectively. The ΔT_m values for mutations occurring in solvent-exposed regions are 2.56(2.82) and - 6.8(7.2) °C. We have systematically analyzed the factors influencing the stability of mutants and observed that changes in hydrophobicity, number of contacts between Cα atoms and frequency of aliphatic residues are important determinants of the stability change induced by mutations occurring in membrane-spanning regions. We have developed structure- and sequence-based machine learning predictors of ΔT_m due to mutations specifically for membrane proteins. They showed a correlation and mean absolute error (MAE) of 0.72 and 2.85 °C, respectively, between experimental and predicted ΔT_m for mutations in membrane-spanning regions on 10-fold group-wise cross-validation. The average correlation and MAE for mutations in aqueous regions are 0.73 and 3.7 °C, respectively. These MAE values are about 50% lower than standard deviations from the mean ΔT_m values. The reliability of the method was affirmed on a test set of mutations occurring in evolutionary independent protein sequences. The developed MPTherm-pred server for predicting thermal stability changes upon mutations in membrane proteins is available at https://web.iitm.ac.in/bioinfo2/mpthermpred/. Our results provide insights into factors influencing the stability of membrane proteins and can aid in designing mutants that are more resistant to thermal stress.

Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance

Tuberculosis (TB) and leprosy are mycobacterial infections caused by Mycobacterium tuberculosis and Mycobacterium leprae respectively. These diseases continue to be endemic in developing countries where the cost of new medicines presents major challenges. The situation is further exacerbated by the emergence of resistance to many front-line antibiotics. A priority now is to design new antimycobacterials that are not only effective in combatting the diseases but are also less likely to give rise to resistance. In both these respects understanding the structure of drug targets in M. tuberculosis and M. leprae is crucial. In this review we describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.

Investigating the Role of Missense SNPs on ALDH 1A1 mediated pharmacokinetic resistance to cyclophosphamide

Cyclophosphamide (CP) is a well-known anti-cancer drug, which exerts its therapeutic effect by DNA cross-linking, both within and between DNA strands. Earlier, a single dose of CP was enough for an effective treatment however due to overexpression of ALDH 1A1 in cancer cells and consequent drug inactivation, the quality of treatment suffered a lot. Drug inactivation via Drug Metabolizing Enzyme (DME) like Aldehyde dehydrogenase 1A1 (ALDH 1A1) is one of the resistance mechanism which is least considered and somewhat overlooked. The current study focused on investigating the impact of missense SNPs on ALDH 1A1 mediated pharmacokinetic resistance to CP. To achieve this aim, we selected 14 missense SNPs from the large pool of SNPs database. The stability of the mutants corresponding to selected SNPs was then determined using web-based tools like I-Mutant, CUPSAT, Maestro-web, STRUM, Eris, SDM, DUET, I-Stable. The obtained results from the mentioned web tools were later validated by molecular dynamic simulations. Furthermore, to find out the optimal range in terms of geometrical parameters and binding affinity for a molecule to be a good substrate for ALDH 1A1, some well-reported substrates of ALDH1A1 were pooled from the literature. Subsequently, similar parameters were calculated for each aldophosphamide (Active metabolite of CP) - mutant complexes to determine if these parameters lie within the optimal range. Based on this analyses population which is most or least susceptible to resistance was suggested. Our results demonstrated that the population group corresponding to rs11554423 (Gly125Arg) and rs763363983 (Val460Leu) mutation may be least vulnerable to CP resistance. Whereas, the population corresponding to rs1049981 (Asn121Ser) and rs774967243 (Val295Leu) SNPs may be most vulnerable to CP resistance.

ATP7A Clinical Genetics Resource - A comprehensive clinically annotated database and resource for genetic variants in ATP7A gene

ATP7A is a critical copper transporter involved in Menkes Disease, Occipital horn Syndrome and X-linked distal spinal muscular atrophy type 3 which are X linked genetic disorders. These are rare diseases and their genetic epidemiology of the diseases is unknown. A number of genetic variants in the genes have been reported in published literature as well as databases, however, understanding the pathogenicity of variants and genetic epidemiology requires the data to be compiled in a unified format. To this end, we systematically compiled genetic variants from published literature and datasets. Each of the variants were systematically evaluated for evidences with respect to their pathogenicity and classified as per the American College of Medical Genetics and the Association of Molecular Pathologists (ACMG-AMP) guidelines into Pathogenic, Likely Pathogenic, Benign, Likely Benign and Variants of Uncertain Significance. Additional integrative analysis of population genomic datasets provides insights into the genetic epidemiology of the disease through estimation of carrier frequencies in global populations. To deliver a mechanistic explanation for the pathogenicity of selected variants, we also performed molecular modeling studies. Our modeling studies concluded that the small structural distortions observed in the local structures of the protein may lead to the destabilization of the global structure. To the best of our knowledge, ATP7A Clinical Genetics Resource is one of the most comprehensive compendium of variants in the gene providing clinically relevant annotations in gene.

Impact of amino acid substitution in the kinase domain of Bruton tyrosine kinase and its association with X-linked agammaglobulinemia

X-linked agammaglobulinemia (XLA) is a rare disease that affects the immune system, characterized by a serial development of bacterial infection from the onset of infantile age. Bruton tyrosine kinase (BTK) is a non-receptor cytoplasmic kinase that plays a crucial role in the B-lymphocyte maturation. The altered expression, mutation and/or structural variations of BTK are responsible for causing XLA. Here, we have performed extensive sequence and structure analyses of BTK to find deleterious variations and their pathogenic association with XLA. First, we screened the pathogenic variations in the BTK from a pool of publicly available resources, and their pathogenicity/tolerance and stability predictions were carried out. Finally, two pathogenic variations (E589G and M630K) were studied in detail and subjected to all-atom molecular dynamics simulation for 200 ns. Intramolecular hydrogen bonds (H-bonds), secondary structure, and principal component analysis revealed significant conformational changes in variants that support the structural basis of BTK dysfunction in XLA. The free energy landscape analysis revealed the presence of multiple energy minima, suggests that E589G brings a large destabilization and consequently unfolding behavior compared to M630K. Overall, our study suggests that amino acid substitutions, E589G, and M630K, significantly alter the structural conformation and stability of BTK.