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

Computational Analysis of Non-synonymous SNPs in ATM Kinase: Structural Insights, Functional Implications, and Inhibitor Discovery

Ataxia telangiectasia-mutated (ATM) protein kinase, a key player in cellular integrity regulation, is known for its role in DNA damage response. This study investigates the broader impact of ATM on cellular processes and potential clinical manifestations arising from mutations, aiming to expand our understanding of ATM's diverse functions beyond conventional roles. The research employs a comprehensive set of computational techniques for a thorough analysis of ATM mutations. The mutation data are curated from dbSNP and HuVarBase databases. A meticulous assessment is conducted, considering factors such as deleterious effects, protein stability, oncogenic potential, and biophysical characteristics of the identified mutations. Conservation analysis, utilizing diverse computational tools, provides insights into the evolutionary significance of these mutations. Molecular docking and dynamic simulation analyses are carried out for selected mutations, investigating their interactions with Y2080D, AZD0156, and quercetin inhibitors to gauge potential therapeutic implications. Among the 419 mutations scrutinized, five (V1913C, Y2080D, L2656P, C2770G, and C2930G) are identified as both disease causing and protein destabilizing. The study reveals the oncogenic potential of these mutations, supported by findings from the COSMIC database. Notably, Y2080D is associated with haematopoietic and lymphoid cancers, while C2770G shows a correlation with squamous cell carcinomas. Molecular docking and dynamic simulation analyses highlight strong binding affinities of quercetin for Y2080D and AZD0156 for C2770G, suggesting potential therapeutic options. In summary, this computational analysis provides a comprehensive understanding of ATM mutations, revealing their potential implications in cellular integrity and cancer development. The study underscores the significance of Y2080D and C2770G mutations, offering valuable insights for future precision medicine targeting-specific ATM. Despite informative computational analyses, a significant research gap exists, necessitating essential in vitro and in vivo studies to validate the predicted effects of ATM mutations on protein structure and function.

Keratin variants in Pyoderma Gangrenosum: pathogenetic insights from a Whole Exome Sequencing-based bioinformatic analysis

Pyoderma gangrenosum (PG) is an inflammatory skin disorder belonging to the group of neutrophilic dermatoses. Clinically, it is typified by cutaneous ulcers with distinctive erythemato-violaceous borders and may occur alone or in association with other inflammatory, autoinflammatory or neoplastic conditions. Although its pathophysiology remains incompletely understood, mounting evidence points towards a predisposing genetic background as well as a dysregulation of both the innate and adaptive immune responses, with follicular or epidermal structures as putative initial targets. To investigate genetic factors associated with PG susceptibility and severity (arbitrarily defined as unilesional and multilesional), whole exome sequencing was performed on eleven unrelated PG patients. Eight carried at least one variant in keratin encoding genes, including, KRT18, KRT20, KRT23, KRT32, and KRT33B. Strikingly, a recurrent variant (rs77999286) in the KRT18 gene was identified in 5/6 patients with multilesional and in 1/5 of those with unilesional PG. AlphaFold modelling and mutation analysis showed the destabilizing effect of the KRT18 rs77999286 variant on protein structure. Furthermore, immunohistochemistry revealed undetectable KRT18 staining in lesional as compared with healthy control skin. Overall, these findings suggest keratin variants may have a role in PG pathogenesis and indicate the KRT18 rs77999286 variant as a potential genetic factor linked to multilesional disease.

Insights Into Genetic Variations of the OCT1 Gene in Metformin Poor Responders Among Bangladeshi Type 2 Diabetic Patients

Metformin is the most widely prescribed drug for the treatment of Type 2 diabetes mellitus (T2DM), but its response varies from person to person. This study aims to analyze the complete mutation spectrum of the OCT1 gene in metformin poor responders and to explore the potential pathogenic effects of the identified mutations. Clinical features of 56 Bangladeshi T2DM patients (who showed altered response to metformin) were analyzed, and genomic DNA was extracted from their blood samples. Subsequently, the entire exons (1-11), along with flanking introns of the OCT1 gene were amplified and sequenced. Molecular consequences of the identified mutations on OCT1 protein activity were determined through in silico analyses. In this study, 29 mutations of the OCT1 gene were identified; among which 5 mutations (c.412-86G>T, c.970G>C, c.1386-3088_1386-3083delGAATCA, c.1498+66G>T, and c.1653C>A) were novel. It was found that nsSNPs c.181C>T, c.1022C>T, c.493G>T, c.1207A>G, and c.970G>C (novel) as well as frameshift deletions have potential deleterious effects on OCT1 protein stability and function. Some of these mutations also cause alternative splicing, as per the HSF tool. In addition, alteration of interatomic bonding in the OCT1 protein due to two high-risk mutations (c.181C>T and c.1022C>T) was found from web-based analysis. The mutations, as mentioned earlier, are the most probable causative factor of decreased metformin effectiveness and adverse side effects in T2DM patients who are poor responders. Understanding the OCT1 gene variations of patients can help tailor treatment strategies for optimal metformin response or identify alternative medications.

Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study

The cytotoxic T-lymphocyte antigen-4 (CTLA4) is essential in controlling T cell activity within the immune system. Thus, uncovering the molecular dynamics of single nucleotide polymorphisms (SNPs) within the CTLA4 gene is critical. We identified the non-synonymous SNPs (nsSNPs), examined their impact on protein stability, and identified the protein sequences associated with them in the human CTLA4 gene. There were 3134 SNPs (rsIDs) in our study. Out of these, 186 missense variants (5.93%), 1491 intron variants (47.57%), and 91 synonymous variants (2.90%), while the remaining SNPs were unspecified. We utilized SIFT, PolyPhen-2, PROVEAN, and SNAP for identifying deleterious nsSNPs, and SNPs&GO, PhD SNP, and PANTHER for verifying risk nsSNPs in the CTLA4 gene. Following SIFT analysis, six nsSNPs were identified as deleterious and reporting second and third nsSNPs as probably damaging and one as benign, respectively. From upstream analysis, rs138279736, rs201778935, rs369567630, and rs376038796 were found to be deleterious, probably damaging, and disease associated. ConSurf predicted conservation scores for four nsSNPs, and Project Hope suggested that all mutations could disrupt protein interactions. Furthermore, mCSM and DynaMut2 analyses indicated a decrease in ΔΔG stability for the mutants. GeneMANIA and STRING networks highlighted correlations with CD86 and CD80 genes. Finally, MD simulation revealed consistent fluctuation in RMSD and RMSF, consequently Rg, hydrogen bonds, and PCA in the mutant proteins compared with wild-type, which might alter the functional and structural stability of CTLA4 protein. The current comprehensive study shows how various nsSNPs in the CTLA4 gene can modify the structural and functional characteristics of the protein, potentially influencing the pathogenesis of diseases in humans. Further, experimental studies are needed to analyze the effect of these nsSNPs on the susceptibility of pathological phenotype populations.

A rare haplotype of the GJD3 gene segregating in familial Meniere's disease interferes with connexin assembly

BACKGROUND

Familial Meniere's disease (FMD) is a rare polygenic disorder of the inner ear. Mutations in the connexin gene family, which encodes gap junction proteins, can also cause hearing loss, but their role in FMD is largely unknown.

METHODS

We retrieved exome sequencing data from 94 individuals in 70 Meniere's disease (MD) families. Through gene burden analysis, we calculated the enrichment of rare variants (allele frequency < 0.05) in connexins genes in FMD individuals compared with the reference population. The connexin monomer and the homomeric connexon structural models were predicted using AlphaFold2 and HDOCK. RT-qPCR and immunofluorescence were done in mice cochleae to identify expression of the mouse ortholog candidate gene Gjd3.

RESULTS

We found an enrichment of rare missense variants in the GJD3 gene when comparing allelic frequencies in FMD (N = 94) with the Spanish reference population (OR = 3.9[1.92-7.91], FDR = 2.36E-03). In the GJD3 sequence, we identified a rare haplotype (TGAGT) composed of two missense, two synonymous, and one downstream variant. This haplotype was found in five individuals with FMD, segregating in three unrelated families with a total of ten individuals; and in another eight MD individuals. GJD3 encodes the gap junction protein delta 3, also known as human connexin 31.9 (Cx31.9). The protein model predicted that the NP_689343.3:p.(His175Tyr) missense variant could modify the interaction between connexins and the connexon assembly, affecting the homotypic GJD3 gap junction between cells. Our studies in mice revealed that Gjd3-encoding Gjd3 or mouse connexin 30.2 (Cx30.2)-was expressed in the organ of Corti and vestibular organs, particularly in the tectorial membrane, the base of inner and outer hair cells and the nerve fibers.

CONCLUSIONS

The present results describe a novel association between GJD3 and FMD, providing evidence that FMD is related to changes in the inner ear channels, and supporting a new role of tectorial membrane proteins in MD.

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

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

Characterization of the E26H Mutant Schistosoma japonicum Glutathione S-Transferase

Glutathione-S-transferase, such as that of Schistosoma japonicum (sjGST) belongs to the most widely utilized fusion tags in the recombinant protein technology. The E26H mutation of sjGST has already been found to remarkably improve its ability for binding divalent ions, enabling its purification with immobilized metal affinity chromatography (IMAC). Nevertheless, most characteristics of this mutant remained unexplored to date. In this study, we performed a comparative analysis of the wild-type and the E26H mutant sjGST by using in vitro as well as in silico approaches. We confirmed that the sjGST(E26H) protein exhibits significantly increased affinity for binding nickel ions as compared to the wild-type. In addition, we proved that the sjGST(E26H) can be purified efficiently either with glutathione- or immobilized metal ion-affinity chromatography, even in consecutive purification steps. The human retroviral-like aspartic protease 1 (ASPRV1) conjugated with the sjGST(E26H) fusion tag was also successfully purified by using both of these affinity chromatographic approaches. Our studies revealed that the E26H mutant sjGST can be used as a versatile affinity tag because the modified protein retains the kinetic features of the wild-type and its affinity towards glutathione, while can be purified efficiently by IMAC, as well.

DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning

MOTIVATION

The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimental approaches and allow a fast screening of large datasets of variations.

RESULTS

In this work, we present DDGemb, a novel method combining protein language model embeddings and transformer architectures to predict protein ΔΔG upon both single- and multi-point variations. DDGemb has been trained on a high-quality dataset derived from literature and tested on available benchmark datasets of single- and multi-point variations. DDGemb performs at the state of the art in both single- and multi-point variations.

AVAILABILITY AND IMPLEMENTATION

DDGemb is available as web server at https://ddgemb.biocomp.unibo.it. Datasets used in this study are available at https://ddgemb.biocomp.unibo.it/datasets.

Bioinformatic approach to identifying causative missense polymorphisms in animal genomes

BACKGROUND

Trends in the development of genetic markers for the purposes of genomic and marker-assisted selection primarily focus on identifying causative polymorphisms. Using these polymorphisms as markers enables a more accurate association between genotype and phenotype. Bioinformatic analysis allows calculating the impact of missense polymorphisms on the structural and functional characteristics of proteins, which makes it promising for identifying causative polymorphisms. In this study, a bioinformatic approach is applied to evaluate and differentiate polymorphisms based on their causality in genes that affect the production traits of pigs and cows, which are two important livestock species.

RESULTS

The influence of both known causative and candidate missense polymorphisms in the MC4R, NR6A1, PRKAG3, RYR1, and SYNGR2 genes of pigs, as well as the ABCG2, DGAT1, GHR, and MSTN genes of cows, was assessed. The study included an evaluation of the effect of polymorphisms on protein functions, considering the evolutionary and physicochemical characteristics of amino acids at polymorphic sites. Additionally, it examined the impact of polymorphisms on the stability of tertiary protein structures, including changes in folding, binding of protein monomers, and interaction with ligands.

CONCLUSIONS

The comprehensive bioinformatic analysis used in this study enables the differentiation of polymorphisms into neutral, where both amino acids in the polymorphic site do not significantly affect the structure and function of the protein, and causative, where one of the amino acids significantly impacts the protein's properties. This approach can be employed in future research to screen extensive sets of polymorphisms in animal genomes, identifying the most promising polymorphisms for further investigation in association studies.

Investigating the effects of the ARG258HIS mutation on RAD51C in inherited Fanconi Anemia and cancer disease

Fanconi anemia is a rare chromosomal instability disorder associated with developmental abnormalities, bone marrow failure, and a heightened susceptibility to leukemia and other cancers. It is an autosomal recessive genetic disorder, necessitating both parents to carry the faulty gene. Diagnostic methods include blood tests, chromosome breakage assessments, and genetic testing. While there is no cure, treatments encompass blood transfusions, bone marrow transplants, and gene therapy, with patients requiring regular check-ups, supportive care, and cancer screening to enhance their quality of life. In this study, we identify a specific substitution (R258H) targeting the crucial binding site of the alpha-helix region in RAD51C. This substitution induces structural disorder in distinct regions, as indicated by the near absence of electron density for multiple amino acids. Intriguingly, these disordered regions do not follow a continuous sequence from the mutation site and extend across domain boundaries. We utilized computational prediction algorithms and Molecular Dynamics (MD) simulations to model RAD51C and its mutation (R258H) structurally. These simulations highlighted alterations in conformational dynamics, the Free Energy Landscape (FEL), and intrinsic molecular motions induced by the mutation, suggesting structural destabilization that could disrupt its function. This observed destabilization in RAD51C due to mutations offers valuable insights that may serve as diagnostic markers for individuals carrying these mutations, particularly in Fanconi anemia.

A protein fitness predictive framework based on feature combination and intelligent searching

Machine learning (ML) constructs predictive models by understanding the relationship between protein sequences and their functions, enabling efficient identification of protein sequences with high fitness values without falling into local optima, like directional evolution. However, how to extract the most pertinent functional feature information from a limited number of protein sequences is vital for optimizing the performance of ML models. Here, we propose scut_ProFP (Protein Fitness Predictor), a predictive framework that integrates feature combination and feature selection techniques. Feature combination offers comprehensive sequence information, while feature selection searches for the most beneficial features to enhance model performance, enabling accurate sequence-to-function mapping. Compared to similar frameworks, scut_ProFP demonstrates superior performance and is also competitive with more complex deep learning models-ECNet, EVmutation, and UniRep. In addition, scut_ProFP enables generalization from low-order mutants to high-order mutants. Finally, we utilized scut_ProFP to simulate the engineering of the fluorescent protein CreiLOV and highly enriched mutants with high fluorescence based on only a small number of low-fluorescence mutants. Essentially, the developed method is advantageous for ML in protein engineering, providing an effective approach to data-driven protein engineering. The code and datasets for scut_ProFP are available at https://github.com/Zhang66-star/scut_ProFP.

USP8 Mutations Associated with Cushing's Disease Alter Protein Structure Dynamics

The adenomas in Cushing's disease frequently exhibit mutations in exon 14, within a binding motif for the regulatory protein 14-3-3 located between the catalytic domain (DUB), responsible for ubiquitin hydrolysis, and the WW-like domain that mediates autoinhibition, resulting in constantly active USP8. The exact molecular mechanism of deubiquitinase activity disruption in Cushing's disease remains unclear. To address this, Sanger sequencing of USP8 was performed to identify mutations in corticotropinomas. These mutations were subjected to computational screening, followed by molecular dynamics simulations to assess the structural alterations that might change the biological activity of USP8. Eight different variants of the USP8 gene were identified both within and outside the "hotspot" region. Six of these had previously been reported in Cushing's disease, while two were detected for the first time in our patients with CD. One of the two new variants, initially classified as benign during screening, was found in the neighboring SH3 binding motif at a distance of 20 amino acids. This variant demonstrated pathogenicity patterns similar to those of known pathogenic variants. All USP8 variants identified in our patients caused conformational changes in the USP8 protein in a similar manner. The identified mutations, despite differences in annotation results-including evolutionary conservation assessments, automated predictor data, and variations in localization within exon 14-exhibit similar patterns of protein conformational change. This suggests a pathogenic effect that contributes to the development of CD.

Rabbit and Human Angiotensin-Converting Enzyme-2: Structure and Electric Properties

The angiotensin-converting enzyme-2 (ACE2) is a transmembrane glycoprotein, consisting of two segments: a large carboxypeptidase catalytic domain and a small transmembrane collectrin-like segment. This protein plays an essential role in blood pressure regulation, transforming the peptides angiotensin-I and angiotensin-II (vasoconstrictors) into angiotensin-1-9 and angiotensin-1-7 (vasodilators). During the COVID-19 pandemic, ACE2 became best known as the receptor of the S-protein of SARS-CoV-2 coronavirus. The purpose of the following research is to reconstruct the 3D structure of the catalytic domain of the rabbit enzyme rACE2 using its primary amino acid sequence, and then to compare it with the human analog hACE2. For this purpose, we have calculated the electric properties and thermodynamic stability of the two protein globules employing computer programs for protein electrostatics. The analysis of the amino acid content and sequence demonstrates an 85% identity between the two polypeptide chains. The 3D alignment of the catalytic domains of the two enzymes shows coincidence of the α-helix segments, and a small difference in two unstructured segments of the chain. The electric charge of the catalytic domain of rACE2, determined by 70 positively chargeable amino acid residues, 114 negatively chargeable ones, and two positive charges of the Zn2+ atom in the active center exceeds that of hACE2 by one positively and four negatively chargeable groups; however, in 3D conformation, their isoelectric points pI 5.21 coincide. The surface electrostatic potential is similarly distributed on the surface of the two catalytic globules, but it strongly depends on the pH of the extracellular medium: it is almost positive at pH 5.0 but strongly negative at pH 7.4. The pH dependence of the electrostatic component of the free energy discloses that the 3D structure of the two enzymes is maximally stable at pH 6.5. The high similarity in the 3D structure, as well as in the electrostatic and thermodynamic properties, suggests that rabbit can be successfully used as an animal model to study blood pressure regulation and coronavirus infection, and the results can be extrapolated to humans.

Structural and functional consequences of non-synonymous SNPs within the LAMA2 protein: a molecular dynamics perspective

Clinical phenotypic presentations associated with LAMA2 deficiency have shown a variety of manifestations. LAMA2 mutations are mainly linked to congenital muscular dystrophy, but there is also mounting evidence suggesting their presence in inflammatory breast cancer, laryngopharyngeal squamous cell carcinoma, and ventricular tachycardia related to coronary artery disease and cardiomyopathy. This study examined the structural and functional impacts of 144 non-synonymous single nucleotide polymorphisms (nsSNPs) within the LAMA2 gene. Through multi-tiered sequence and structure-based methods, 11 deleterious and destabilizing mutations were identified (A1362T, E1308Q, E1360G, I1276S, L1195P, M1359T, P1232H, P1238A, P1272L, Y1234H, Y1338C). Further, four mutations (L1195P, Y1234H, P1238A, A1362T), which aligned with conserved positions, were subjected to 500 ns molecular dynamics (MD) simulations. RMSD calculated from MD trajectories highlighted structural disparities between wild-type and mutant forms, with the latter showing greater flexibility. Radius of gyration analysis indicated reduced compactness, solvent accessibility changes suggested unfolding, and hydrogen bond (HB) analysis demonstrated disrupted integrity. The HB analysis revealed disruptions in structural integrity due to diminished hydrogen bonds in mutants. Secondary structure analysis revealed significant alterations in secondary structural content. Principal Component Analysis unveiled increased dynamic behavior in mutants. Gibbs free energy landscape analysis reflected distinct energy minima regions in mutants, indicating structural destabilization. Overall, this study revealed the functional and structural ramifications of nsSNPs in the LAMA2 gene, providing valuable insights into potential disease-causing mutations and warranting future research on understanding LAMA2 associated diseases and disorders.

Computational insights into NIMA-related kinase 6: unraveling mutational effects on structure and function

The NEK6 (NIMA-related kinase 6) serine/threonine kinase is a pivotal player in a multitude of cellular processes, including the regulation of the cell cycle and the response to DNA damage. Its significance extends to disease pathogenesis, as changes in NEK6 activity have been linked to the development of cancer. Non-synonymous single nucleotide polymorphisms (nsSNPs) in NEK6 have been linked to cancer as they alter the protein's native structure and function. The association between NEK6 activity and cancer development has prompted researchers to explore the effects of genetic variations within the NEK6 gene. Therefore, we utilized advanced computational tools to analyze 155 high-confidence nsSNPs in the NEK6 gene. From this analysis, 21 nsSNPs were identified as potentially harmful, raising concerns about their impact on NEK6 activity and cancer risk. These 21 mutations were then examined for structural alterations, and eight of nsSNPs (I51M, V76A, I134N, Y152D, R171Q, V186G, L237R, and C285S) were found to destabilize the protein. Among the destabilizing mutations screened, a specific mutation, R171Q, stood out due to its conserved nature. To understand its impact on the protein and conformation, all-atom molecular dynamics simulations (MDS) for 100 ns were performed for both Wildtype NEK6 (WT-NEK6) and R171Q. The simulations revealed that the R171Q variant was unstable and led to significant conformational changes in NEK6. This study provides valuable insights into NEK6 dysfunction caused by single amino acid alterations, offering a novel understanding of the molecular mechanisms underlying NEK6-related cancer progression.

QSOX2 Deficiency-induced short stature, gastrointestinal dysmotility and immune dysfunction

Postnatal growth failure is often attributed to dysregulated somatotropin action, however marked genetic and phenotypic heterogeneity exist. We report five patients from three families who present with short stature, immune dysfunction, atopic eczema and gastrointestinal pathology associated with recessive variants in QSOX2. QSOX2 encodes a nuclear membrane protein linked to disulphide isomerase and oxidoreductase activity. Loss of QSOX2 disrupts Growth hormone-mediated STAT5B nuclear translocation despite enhanced Growth hormone-induced STAT5B phosphorylation. Moreover, patient-derived dermal fibroblasts demonstrate Growth hormone-induced mitochondriopathy and reduced mitochondrial membrane potential. Located at the nuclear membrane, QSOX2 acts as a gatekeeper for regulating stabilisation and import of phosphorylated-STAT5B. Altogether, QSOX2 deficiency modulates human growth by impairing Growth hormone-STAT5B downstream activities and mitochondrial dynamics, which contribute to multi-system dysfunction. Furthermore, our work suggests that therapeutic recombinant insulin-like growth factor-1 may circumvent the Growth hormone-STAT5B dysregulation induced by pathological QSOX2 variants and potentially alleviate organ specific disease.

Identification of novel natural compounds against CFTR p.Gly628Arg pathogenic variant

Cystic fibrosis transmembrane conductance regulator (CFTR) protein is an ion channel found in numerous epithelia and controls the flow of water and salt across the epithelium. The aim of our study to find natural compounds that can improve lung function for people with cystic fibrosis (CF) caused by the p.Gly628Arg (rs397508316) mutation of CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands that included Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and one Trikafta (R*) reference drug were screened out from PubChem database. Autodock vina software carried out docking, and binding energies between the drug and the target were included using docking-score. The following tools examined binding energy, interaction, stability, toxicity, and visualize protein-ligand complexes. The compounds having binding energies of -6.4, -5.1, -6.6, -5.1, and - 6.5 kcal/mol for Armepavine, Osthole, Curcumin, Plumbagine, Quercetin, and R*-drug, respectively with mutated CFTR (Gly628Arg) structure were chosen as the most promising ligands. The ligands bind to the mutated CFTR protein structure active sites in hydrophobic bonds, hydrogen bonds, and electrostatic interactions. According to ADMET analyses, the ligands Armepavine and Quercetin also displayed good pharmacokinetic and toxicity characteristics. An MD simulation for 200 ns was also established to ensure that Armepavine and Quercetin ligands attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that Armepavine and Quercetin have stronger capacity to inhibit the effect of mutated CFTR protein through improved trafficking and restoration of original function.

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.

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 Functional and Structural Analysis of Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in Human Paired Box 4 Gene

In human genome, members of Paired box (PAX) transcription factor family are highly sequence-specific DNA-binding proteins. Among PAX gene family members, PAX4 gene has significant role in growth, proliferation, differentiation, and insulin secretion of pancreatic β-cells. Single nucleotide polymorphisms (SNPs) in PAX4 gene progress in the pathogenesis of various human diseases. Hence, the molecular mechanism of how these SNPs in PAX4 gene significantly progress diseases pathogenesis needs to be elucidated. For the reason, a series of bioinformatic analyzes were done to identify the SNPs of PAX4 gene that contribute in diseases pathogenesis. From the analyzes, 4145 SNPs (rsIDs) in PAX4 gene were obtained, where, 362 missense (8.73%), 169 synonymous (4.08%), and 2323 intron variants (56.04%). The rest SNPs were unspecified. Among the 362 missense variants, 118 nsSNPs were found as deleterious in SIFT analysis. Among those, 25 nsSNPs were most probably damaging and 23 were deleterious as observed in PolyPhen-2 and PROVEAN analyzes, respectively. Following all analyzes, 14 nsSNPs (rs149708455, rs115887120, rs147279315, rs35155575, rs370095957, rs373939873, rs145468905, rs121917718, rs2233580, rs3824004, rs372751660, rs369459316, rs375472849, rs372497946) were common and observed as deleterious, probably damaging, affective and diseases associated. Following structural analyzes, 11 nsSNPs guided proteins were found as most unstable and highly conserved. Among these, R20W, R39Q, R45Q, R60H, G65D, and A223D mutated proteins were highly harmful. Hence, the results from above-mentioned integrated comprehensive bioinformatic analyzes guide how different nsSNPs in PAX4 gene alter structural and functional characteristics of the protein that might progress diseases pathogenesis in human including type 2 diabetes.

Silibinins and curcumin as promising ligands against mutant cystic fibrosis transmembrane regulator protein

Cystic Fibrosis Transmembrane Regulator (CFTR) is a significant protein that is responsible for the movement of ions across cell membranes. The cystic fibrosis (CF) occur due to the mutations in the CFTR gene as it produces the dysfunctional CFTR protein. The sequence of CFTR protein as a target structure was retrieved from UniProt and PDB database. The ligands selection was performed through virtual screening and top 3 ligands choose out of 65 ligands silibinins, curcumin, demethoxycurcumin were selected with a reference drug Trikafta (R*). According to docking, ADMET analyses, the natural ligands (Silibinins and Curcumin) displayed best binding energy, pharmacokinetic and free toxicity than other natural compounds and reference drug (R*). An MD simulation for 200 ns was also established to ensure that natural ligands (Silibinins and Curcumin) attached to the target protein favorably and dynamically, and that protein-ligand complex stability was maintained. It is concluded that silibinins and curcumins have a better capacity to decrease the effect of mutant CFTR protein through improved trafficking and the restoration of original function. In conclusion, in silico studies demonstrate the potential of silibinins and curcumin as therapeutic agents for cystic fibrosis, particularly for the D614G mutated protein. Their ability to increase CFTR function while reducing cellular stress and inflammation, together with their favorable safety profile and accessibility could make them valuable additions to cystic fibrosis treatment options. Further experimental and clinical validation will be required to fully realize their potential and include them into effective therapy regimens.

Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations

Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies.

Identifying Oncogenic Missense Single Nucleotide Polymorphisms in Human SAT1 Gene Using Computational Algorithms and Molecular Dynamics Tools

BACKGROUND

The human SAT1 gene encodes spermidine/spermine N1-acetyltransferase 1 (SSAT1), a regulatory biological catalyst of polyamine catabolism. Numerous essential biological processes, such as cellular proliferation, differentiation, and survival, depend on polyamines like spermidine and spermine. Thus, SSAT1 is involved in key cellular activities such as proliferation and survival of cells and mediates various diseases including cancer. A plethora of studies established the involvement of missense single nucleotide polymorphisms (SNPs) in numerous pathological conditions due to their ability to adversely affect the structure and subsequent function of the protein.

AIMS

To date, an in silico study to identify the pathogenic missense SNPs of the human SAT1 gene has not been accomplished yet. This study aimed to filter the missense SNPs that were functionally detrimental and pathogenic.

METHODS AND RESULTS

The rs757435207 (I21N) was ascertained to be the most deleterious and pathogenic by all algorithmic tools. Stability and evolutionary conservation analysis tools also stated that I21N variant decreased the stability and was located in the highly conserved residue. Molecular dynamics simulation revealed that I21N caused substantial alterations in the conformational stability and dynamics of the SSAT1 protein. Consequently, the I21N variant could disrupt the native functional roles of the SSAT1 enzyme.

CONCLUSION

Therefore, the I21N variant was identified and concluded to be an oncogenic missense variant of the human SAT1 gene. Overall, the findings of this study would be a great directory of future experimental research to develop personalized medicine.

Prediction and assessment of deleterious and disease causing nonsynonymous single nucleotide polymorphisms (nsSNPs) in human FOXP4 gene: An in - silico study

In humans, FOXP gene family is involved in embryonic development and cancer progression. The FOXP4 (Forkhead box protein P4) gene belongs to this FOXP gene family. FOXP4 gene plays a crucial role in oncogenesis. Single nucleotide polymorphisms are biological markers and common determinants of human diseases. Mutations can largely affect the function of the corresponding protein. Therefore, the molecular mechanism of nsSNPs in the FOXP4 gene needs to be elucidated. Initially, the SNPs of the FOXP4 gene were extracted from the dbSNP database and a total of 23124 SNPs was found, where 555 nonsynonymous, 20525 intronic, 1114 noncoding transcript, 334 synonymous were obtained and the rest were unspecified. Then, a series of bioinformatics tools (SIFT, PolyPhen2, SNAP2, PhD SNP, PANTHER, I-Mutant2.0, MUpro, GOR IV, ConSurf, NetSurfP 2.0, HOPE, DynaMut2, GeneMANIA, STRING and Schrodinger) were used to explore the effect of nsSNPs on FOXP4 protein function and structural stability. First, 555 nsSNPs were analyzed using SIFT, of which 57 were found as deleterious. Following, PolyPhen2, SNAP2, PhD SNP and PANTHER analyses, 10 nsSNPs (rs372762294, rs141899153, rs142575732, rs376938850, rs367607523, rs112517943, rs140387832, rs373949416, rs373949416 and rs376160648) were common and observed as deleterious, damaging and diseases associated. Following that, using I-Mutant2.0 and MUpro servers, 7 nsSNPs were found to be the most unstable. GOR IV predicted that these seven nsSNPs affect protein structure by altering the protein contents of alpha helixes, extended strands, and random coils. Following DynaMut2, 5 nsSNPs showed a decrease in the ΔΔG value compared with the wild-type and were found to be responsible for destabilizing the corresponding protein. GeneMANIA and STRING network revealed interaction of FOXP4 with other genes. Finally, molecular dynamics simulation analysis revealed consistent fluctuation in RMSD and RMSF values, Rg and hydrogen bonds in the mutant proteins compared with WT, which might alter the functional and structural stability of the corresponding protein. As a result, the aforementioned integrated comprehensive bioinformatic analyses provide insight into how various nsSNPs of the FOXP4 gene change the structural and functional properties of the corresponding protein, potentially proceeding with the pathophysiology of human diseases.

Identification and Functional Implications of the E5 Oncogene Polymorphisms of Human Papillomavirus Type 16

The persistence of the human papillomavirus type 16 (HPV16) infection on the cervical epithelium contributes to the progression of cervical cancer. Studies have demonstrated that HPV16 genetic variants may be associated with different risks of developing cervical cancer. However, the E5 oncoprotein of HPV16, which is related to several cellular mechanisms in the initial phases of the infection and thus contributes to carcinogenesis, is still little studied. Here we investigate the HPV16 E5 oncogene variants to assess the effects of different mutations on the biological function of the E5 protein. We detected and analyzed the HPV16 E5 oncogene polymorphisms and their phylogenetic relationships. After that, we proposed a tertiary structure analysis of the protein variants, preferential codon usage, and functional activity of the HPV16 E5 protein. Intra-type variants were grouped in the lineages A and D using in silico analysis. The mutations in E5 were located in the T-cell epitopes region. We therefore analyzed the interference of the HPV16 E5 protein in the NF-kB pathway. Our results showed that the variants HPV16E5_49PE and HPV16E5_85PE did not increase the potential of the pathway activation capacity. This study provides additional knowledge about the mechanisms of dispersion of the HPV16 E5 variants, providing evidence that these variants may be relevant to the modulation of the NF-κB signaling pathway.

Roles of the Arabidopsis KEULE Gene in Postembryonic Development

Cytokinesis in plant cells begins with the fusion of vesicles that transport cell wall materials to the center of the cell division plane, where the cell plate forms and expands radially until it fuses with the parental cell wall. Vesicle fusion is facilitated by trans-SNARE complexes, with assistance from Sec1/Munc18 (SM) proteins. The SNARE protein KNOLLE and the SM protein KEULE are required for membrane fusion at the cell plate. Due to the crucial function of KEULE, all Arabidopsis (Arabidopsis thaliana) keule mutants identified to date are seedling lethal. Here, we identified the Arabidopsis serrata4-1 (sea4-1) and sea4-2 mutants, which carry recessive, hypomorphic alleles of KEULE. Homozygous sea4-1 and sea4-2 plants are viable and fertile but have smaller rosettes and fewer leaves at bolting than the wild type. Their leaves are serrated, small, and wavy, with a complex venation pattern. The mutant leaves also develop necrotic patches and undergo premature senescence. RNA-seq revealed transcriptome changes likely leading to reduced cell wall integrity and an increase in the unfolded protein response. These findings shed light on the roles of KEULE in postembryonic development, particularly in the patterning of rosette leaves and leaf margins.

Membranome-based identification of amino acid substitution in Haemophilus influenzae multidrug efflux pump HmrM for reduced chloramphenicol susceptibility

A decreased chloramphenicol susceptibility in Haemophilus influenzae is commonly caused by the activity of chloramphenicol acetyltransferases (CATs). However, the involvement of membrane proteins in chloramphenicol susceptibility in H. influenzae remains unclear. In this study, chloramphenicol susceptibility testing, whole-genome sequencing, and analyses of membrane-related genes were performed in 51 H. influenzae isolates. Functional complementation assays and structure-based protein analyses were conducted to assess the effect of proteins with sequence substitutions on the minimum inhibitory concentration (MIC) of chloramphenicol in CAT-negative H. influenzae isolates. Six isolates were resistant to chloramphenicol and positive for type A-2 CATs. Of these isolates, A3256 had a similar level of CAT activity but a higher chloramphenicol MIC relative to the other resistant isolates; it also had 163 specific variations in 58 membrane genes. Regarding the CAT-negative isolates, logistic regression and receiver operator characteristic curve analyses revealed that 48T > G (Asn16Lys), 85 C > T (Leu29Phe), and 88 C > A (Leu30Ile) in HI_0898 (emrA), and 86T > G (Phe29Cys) and 141T > A (Ser47Arg) in HI_1177 (artM) were associated with enhanced chloramphenicol susceptibility, whereas 997G > A (Val333Ile) in HI_1612 (hmrM) was associated with reduced chloramphenicol susceptibility. Furthermore, the chloramphenicol MIC was lower in the CAT-negative isolates with EmrA-Leu29Phe/Leu30Ile or ArtM-Ser47Arg substitution and higher in those with HmrM-Val333Ile substitution, relative to their counterparts. The Val333Ile substitution was associated with enhanced HmrM protein stability and flexibility and increased chloramphenicol MICs in CAT-negative H. influenzae isolates. In conclusion, the substitution in H. influenzae multidrug efflux pump HmrM associated with reduced chloramphenicol susceptibility was characterised.

Understanding Genetic Risks: Computational Exploration of Human β-Synuclein nsSNPs and their Potential Impact on Structural Alteration

Synucleins are pivotal in neurodegenerative conditions. Beta-synuclein (β-synuclein) is part of the synuclein protein family alongside alpha-synuclein (α-synuclein) and gamma-synuclein (γ-synuclein). These proteins, found mainly in brain tissue and cancers, are soluble and unstructured. β-synuclein shares significant similarity with α-synuclein, especially in their N-terminus, with a 90% match. However, their aggregation tendencies differ significantly. While α-synuclein aggregation is believed to be counteracted by β-synuclein, which occurs in conditions like Parkinson's disease, β-synuclein may counteract α-synuclein's toxic effects on the nervous system, offering potential treatment for neurodegenerative diseases. Under normal circumstances, β-synuclein may guard against disease by interacting with α-synuclein. Yet, in pathological environments with heightened levels or toxic substances, it might contribute to disease. Our research aims to explore potential harmful mutations in the β-synuclein using computational tools to predict their destabilizing impact on protein structure. Consensus analysis revealed rs1207608813 (A63P), rs1340051870 (S72F), and rs1581178262 (G36C) as deleterious. These findings highlight the intricate relationship between nsSNPs and protein function, shedding light on their potential implications in disease pathways. Understanding the structural consequences of nsSNPs is crucial for elucidating their role in pathogenesis and developing targeted therapeutic interventions. Our results offer a robust computational framework for identifying neurodegenerative disorder-related mutations from SNP datasets, potentially reducing the costs associated with experimental characterization.

Engineering G protein-coupled receptors for stabilization

G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.

Review of predicting protein stability changes upon variations

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.

MutL Activates UvrD by Interaction Between the MutL C-terminal Domain and the UvrD 2B Domain

UvrD is a helicase vital for DNA replication and quality control processes. In its monomeric state, UvrD exhibits limited helicase activity, necessitating either dimerization or assistance from an accessory protein to efficiently unwind DNA. Within the DNA mismatch repair pathway, MutL plays a pivotal role in relaying the repair signal, enabling UvrD to unwind DNA from the strand incision site up to and beyond the mismatch. Although this interdependence is well-established, the precise mechanism of activation and the specific MutL-UvrD interactions that trigger helicase activity remain elusive. To address these questions, we employed site-specific crosslinking techniques using single-cysteine variants of MutL and UvrD followed by functional assays. Our investigation unveils that the C-terminal domain of MutL not only engages with UvrD but also acts as a self-sufficient activator of UvrD helicase activity on DNA substrates with 3'-single-stranded tails. Especially when MutL is covalently attached to the 2B or 1B domain the tail length can be reduced to a minimal substrate of 5 nucleotides without affecting unwinding efficiency.

A Novel Missense SNP in the Fatty Acid-Binding Protein 4 (FABP4) Gene is Associated with Growth Traits in Karakul and Awassi Sheep

The fatty acid-binding protein 4 (FABP4) plays a crucial role in the transportation and metabolism of fatty acids. It binds to long-chain fatty acids and facilitates their transport within cells. FABP4 is involved in lipid metabolism, insulin sensitivity, inflammation, and energy homeostasis. This study was conducted to assess the association between the FABP4 gene and growth traits in Karakul and Awassi sheep. A PCR-single strand conformation polymorphism (SSCP) protocol was utilized to assess the polymorphism of FABP4 PCR products with growth traits measured at birth, three, six, nine, and twelve-month intervals. One non-synonymous SNP was identified in the second exon, in which threonine was converted to aspartate in the 61st position in FABP4 (p.61Thr > Asp). This novel SNP showed significant associations with all growth traits measured at all age intervals. The results showed that lambs with the TT genotype exhibited higher growth traits than those with the GT and GG genotypes, respectively. The conducted prediction showed a clearly deleterious effect of p.61Thr > Asp on FABP4 structure, which was accompanied by reduced fatty acid binding efficiency. Owing to the predicted damaging effects caused by p.61Thr > Asp on FABP, lower levels of lipid transport and its consequent increased weight gain and other growth trait indices are expected. Therefore, a putative mechanism through which lambs with these genotypes exhibit higher growth traits is proposed. The FABP4 gene is suggested as a promising marker to improve growth traits in Karakul and Awassi sheep. However, more research is required to validate this mechanism.

A computational approach for structural and functional analyses of disease-associated mutations in the human CYLD gene

Tumor suppressor cylindromatosis protein (CYLD) regulates NF-κB and JNK signaling pathways by cleaving K63-linked poly-ubiquitin chain from its substrate molecules and thus preventing the progression of tumorigenesis and metastasis of the cancer cells. Mutations in CYLD can cause aberrant structure and abnormal functionality leading to tumor formation. In this study, we utilized several computational tools such as PANTHER, PROVEAN, PredictSNP, PolyPhen-2, PhD-SNP, PON-P2, and SIFT to find out deleterious nsSNPs. We also highlighted the damaging impact of those deleterious nsSNPs on the structure and function of the CYLD utilizing ConSurf, I-Mutant, SDM, Phyre2, HOPE, Swiss-PdbViewer, and Mutation 3D. We shortlisted 18 high-risk nsSNPs from a total of 446 nsSNPs recorded in the NCBI database. Based on the conservation profile, stability status, and structural impact analysis, we finalized 13 nsSNPs. Molecular docking analysis and molecular dynamic simulation concluded the study with the findings of two significant nsSNPs (R830K, H827R) which have a remarkable impact on binding affinity, RMSD, RMSF, radius of gyration, and hydrogen bond formation during CYLD-ubiquitin interaction. The principal component analysis compared native and two mutants R830K and H827R of CYLD that signify structural and energy profile fluctuations during molecular dynamic (MD) simulation. Finally, the protein-protein interaction network showed CYLD interacts with 20 proteins involved in several biological pathways that mutations can impair. Considering all these in silico analyses, our study recommended conducting large-scale association studies of nsSNPs of CYLD with cancer as well as designing precise medications against diseases associated with these polymorphisms.

In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene

Single nucleotide polymorphisms (SNPs) are one of the most common determinants and potential biomarkers of human disease pathogenesis. SNPs could alter amino acid residues, leading to the loss of structural and functional integrity of the encoded protein. In humans, members of the minichromosome maintenance (MCM) family play a vital role in cell proliferation and have a significant impact on tumorigenesis. Among the MCM members, the molecular mechanism of how missense SNPs of minichromosome maintenance complex component 6 (MCM6) contribute to DNA replication and tumor pathogenesis is underexplored and needs to be elucidated. Hence, a series of sequence and structure-based computational tools were utilized to determine how mutations affect the corresponding MCM6 protein. From the dbSNP database, among 15,009 SNPs in the MCM6 gene, 642 missense SNPs (4.28%), 291 synonymous SNPs (1.94%), and 12,500 intron SNPs (83.28%) were observed. Out of the 642 missense SNPs, 33 were found to be deleterious during the SIFT analysis. Among these, 11 missense SNPs (I123S, R207C, R222C, L449F, V456M, D463G, H556Y, R602H, R633W, R658C, and P815T) were found as deleterious, probably damaging, affective and disease-associated. Then, I123S, R207C, R222C, V456M, D463G, R602H, R633W, and R658C missense SNPs were found to be highly harmful. Six missense SNPs (I123S, R207C, V456M, D463G, R602H, and R633W) had the potential to destabilize the corresponding protein as predicted by DynaMut2. Interestingly, five high-risk mutations (I123S, V456M, D463G, R602H, and R633W) were distributed in two domains (PF00493 and PF14551). During molecular dynamics simulations analysis, consistent fluctuation in RMSD and RMSF values, high Rg and hydrogen bonds in mutant proteins compared to wild-type revealed that these mutations might alter the protein structure and stability of the corresponding protein. Hence, the results from the analyses guide the exploration of the mechanism by which these missense SNPs of the MCM6 gene alter the structural integrity and functional properties of the protein, which could guide the identification of ways to minimize the harmful effects of these mutations in humans.

Computational analysis of non-synonymous single nucleotide polymorphism in the bovine PKLR geneComputational analysis of bovine PKLR gene

Pyruvate kinase (PKLR) is a potential candidate gene for milk production traits in cows. The main aim of this work is to investigate the potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in the PKLR gene by using several computational tools. In silico tools including SIFT, Polyphen-2, SNAP2 and Panther indicated only 18 nsSNPs out of 170 were considered deleterious. The analysis of proteins' stability change due to amino acid substitution performed by the use of the I-mutant, MUpro, CUPSTAT, SDM and Dynamut confirmed that 9 nsSNPs decreased protein stability. ConSurf analysis predicted that all 18 nsSNPs were evolutionary moderately or highly conserved. Two different domains of PKLR protein were revealed by the InterPro tool with 12 nsSNPs positioned in the Pyruvate Kinase barrel domain and 6 nsSNP present in the Pyruvate Kinase C Terminal. The PKLR 3D model was predicted by MODELLER software and validated via Ramachandran plot and Prosa which indicated a good quality model. The analysis of energy minimizations for the native and mutated structures was performed by SWISS PDB viewer with GROMOS 96 program and showed that 3 structural and 4 functional residues had total energy higher than the native model. These findings indicate that these mutant structures (rs441424814, rs449326723, rs476805413, rs472263384, rs474320860, rs475521477, rs441633284) were less stable than the native model. Molecular Dynamics simulations were performed to confirm the impact of nsSNPs on the protein structure and function. The present study provides useful information about functional SNPs that have an impact on PKLR protein in cattle.Communicated by Ramaswamy H. Sarma.

Comprehensive in silico analysis of prolactin receptor (PRLR) gene nonsynonymous single nucleotide polymorphisms (nsSNPs) reveals multifaceted impact on protein structure, function, and interactions

This study delves into the functional and structural implications of non-synonymous single nucleotide polymorphisms (nsSNPs) within the Prolactin Receptor (PRLR) gene. Thirteen deleterious nsSNPs were identified through bioinformatics tools, with SIFT predicting 168 out of 395 nsSNPs as detrimental, exhibiting tolerance index (TI) scores ranging from 0 to 0.05. Polyphen2 assigned likelihood scores >0.99 to all 13 nsSNPs, indicating high probability of harm, while Panther scores classified most nsSNPs as 'probably damaging', with specific mutations like W218R scoring 0.74, suggesting a higher impact. Stability analysis using DDG I-Mutant and DDG Mupro consistently predicted decreased stability for all mutations, with CUPSAT indicating mutations like V125G and W218R significantly decreasing stability. Structural analysis through DynaMut predicted destabilization for all mutations except L196I and L292H. MutPred2 highlighted structural alterations for all nsSNPs except L196I, L293V, R315W, and S353N. Domain analysis revealed key mutations within essential functional domains, with five nsSNPs located within Fibronectin type-III domains. Bayesian analysis through ConSurf identified 9 critical residues, with 11 nsSNPs exhibiting notably high conservation. STRING analysis unveiled a complex interaction network, indicating involvement in vital biological processes like lactation. Molecular dynamics (MD) simulations, spanning 100 nanoseconds, elucidated structural dynamics induced by detrimental missense SNPs. Post-translational modification (PTM) analysis identified specific mutations, such as R351, involved in methylation, while S353 was implicated in phosphorylation and glycosylation. These findings offer comprehensive insights into the molecular and phenotypic effects of deleterious nsSNPs in the PRLR gene, crucial for selective breeding.Communicated by Ramaswamy H. Sarma.

Emerging insight of whole genome sequencing coupled with protein structure prediction into the pyrazinamide-resistance signature of Mycobacterium tuberculosis

Pyrazinamide (PZA) is considered to be a pivotal drug to shorten the treatment of both drug-susceptible and drug-resistant tuberculosis, but its use is challenged by the reliability of drug-susceptibility testing (DST). PZA resistance in Mycobacterium tuberculosis (MTB) is relevant to the amino acid substitution of pyrazinamidase that is responsible for the conversion of PZA to active pyrazinoic acid (POA). The single nucleotide variants (SNVs) within ribosomal protein S1 (rpsA) or aspartate decarboxylase (panD), the binding targets of POA, has been reported to drive the PZA-resistance signature of MTB. In this study, whole genome sequencing (WGS) was used to identify SNVs within the pncA, rpsA and panD genes in 100 clinical MTB isolates associated with DST results for PZA. The potential influence of high-confidence, interim-confidence or emerging variants on the interplay between target genes and PZA or POA was simulated computationally, and predicted with a protein structure modelling approach. The DST results showed weak agreement with the identification of high-confidence variants within the pncA gene (Cohen's kappa coefficient=0.58), the analytic results of WGS coupled with protein structure modelling on pncA mutants (Cohen's kappa coefficient=0.524) or related genes (Cohen's kappa coefficient=0.504). Taken together, these results suggest the practicable application of a genotypic-coupled bioinformatic approach to manage PZA-containing regimens for patients with MTB.

Effects of genetic variation on the structure of RNA and protein

Changes in the structure of RNA and protein, have an important impact on biological functions and are even important determinants of disease pathogenesis and treatment. Some genetic variations, including copy number variation, single nucleotide variation, and so on, can lead to changes in biological function and increased susceptibility to certain diseases by changing the structure of RNA or protein. With the development of structural biology and sequencing technology, a large amount of RNA and protein structure data and genetic variation data resources has emerged to be used to explain biological processes. Here, we reviewed the effects of genetic variation on the structure of RNAs and proteins, and investigated their impact on several diseases. An online resource (http://www.onethird-lab.com/gems/) to support convenient retrieval of common tools is also built. Finally, the challenges and future development of the effects of genetic variation on RNA and protein were discussed.

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.

Cytokines inhibitory mechanism of Prunus domestica L. (Plum) peptides as potential immunomodulators against systemic lupus erythematosus: an in-silico screening

Natural bioactive peptides exhibit various chemical and structural properties to enhance the immune response against multiple inflammatory and autoimmune related disorders. The immunomodulatory function and bioactivity of seed peptides show the capability for the development of biotherapeutics that could prevent autoimmune diseases. The aim of current study is to determine the immunomodulatory function of bioactive peptides derived from the seed of plum (Prunus domestica L.) by applying various immunoinformatic approaches. A thorough analysis of forty-one peptides was performed including drug likeliness, pharmacokinetic, and bioactivity profiling studies. Further, molecular docking and molecular dynamics (MD) simulations of screened peptides were carried out with the two interleukin targets (IL-17A and IL-23) of systemic lupus erythematosus (SLE). After the systematic screening, four peptides, namely HLLP, LPLL, LPAGV, and NLPL, were found as potential inhibitors against SLE. Additionally, site-directed mutagenesis analysis was conducted to explore the role of essential amino acid residues in the binding pattern/energy change. Computational alanine screening analysis found that CYS123, CYS121 of IL-17A and ASP270, and SER249 of IL-23 as hot spot residues that could play an important role in the inhibition property of screened peptides. Overall, the methodology described in the study can be utilized for developing unique peptide inhibitors that have a preventative role against SLE.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00188-8.

Three-Dimensional Structural Stability and Local Electrostatic Potential at Point Mutations in Spike Protein of SARS-CoV-2 Coronavirus

The contagiousness of SARS-CoV-2 β-coronavirus is determined by the virus-receptor electrostatic association of its positively charged spike (S) protein with the negatively charged angiotensin converting enzyme-2 (ACE2 receptor) of the epithelial cells. If some mutations occur, the electrostatic potential on the surface of the receptor-binding domain (RBD) could be altered, and the S-ACE2 association could become stronger or weaker. The aim of the current research is to investigate whether point mutations can noticeably alter the electrostatic potential on the RBD and the 3D stability of the S1-subunit of the S-protein. For this purpose, 15 mutants with different hydrophilicity and electric charge (positive, negative, or uncharged) of the substituted and substituting amino acid residues, located on the RBD at the S1-ACE2 interface, are selected, and the 3D structure of the S1-subunit is reconstructed on the base of the crystallographic structure of the S-protein of the wild-type strain and the amino acid sequence of the unfolded polypeptide chain of the mutants. Then, the Gibbs free energy of folding, isoelectric point, and pH-dependent surface electrostatic potential of the S1-subunit are computed using programs for protein electrostatics. The results show alterations in the local electrostatic potential in the vicinity of the mutant amino acid residue, which can influence the S-ACE2 association. This approach allows prediction of the relative infectivity, transmissibility, and contagiousness (at equal social immune status) of new SARS-CoV-2 mutants by reconstruction of the 3D structure of the S1-subunit and calculation of the surface electrostatic potential.

A novel SERPINA12 variant and first European patients with diffuse palmoplantar keratoderma

BACKGROUND

Hereditary palmoplantar keratodermas (hPPKs) comprise a heterogeneous group of skin disorders characterized by persistent palmoplantar hyperkeratosis. Loss-of-function variants in a serine peptidase inhibitor, SERPINA12, have recently been implicated in autosomal recessive diffuse hPPK. The disorder appears to share similarities with another hPPK associated with protease overactivity, namely Nagashima-type PPK (NPPK) caused by biallelic variants in SERPINB7.

OBJECTIVES

The aim of this study was to enhance the understanding of the clinical and genetic characteristics of serine protease-related hPPKs caused by variants in SERPINA12 and SERPINB7.

METHODS

Whole-exome sequencing (WES) was performed for hPPK patients. Haplotype analysis was completed for the patients with identified recessive SERPINA12 variants and their available family members. In addition, the current literature of SERPINA12- and SERPINB7-related hPPKs was summarized.

RESULTS

The phenotype of SERPINA12-related hPPK was confirmed by reporting three new SERPINA12 patients, the first of European origin. A novel SERPINA12 c.1100G>A p.(Gly367Glu) missense variant was identified confirming that the variant spectrum of SERPINA12 include both truncating and missense variants. The previously reported SERPINA12 c.631C>T p.(Arg211*) was indicated enriched in the Finnish population due to a plausible founder effect. In addition, SERPINA12 hPPK patients were shown to share a similar phenotype to patients with recessive variants in SERPINB7. The shared phenotype included diffuse transgradient PPK since birth or early childhood and frequent palmoplantar hyperhidrosis, aquagenic whitening and additional hyperkeratotic lesions in non-palmoplantar areas. SERPINA12 and SERPINB7 hPPK patients cannot be distinguished without genetic analysis.

CONCLUSIONS

Recessive variants in SERPINA12 and SERPINB7 leading to protease overactivity and hPPK produce a similar phenotype, indistinguishable without genetic analysis. SERPINA12 variants should be assessed also in non-Asian patients with diffuse transgradient PPK. Understanding the role of serine protease inhibitors will provide insights into the complex proteolytic network in epidermal homeostasis.

Computational identification and analysis of deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in the human POR gene: a structural and functional impact

Cytochrome P450 oxidoreductase (POR) protein is essential for steroidogenesis, and POR gene mutations are frequently associated with P450 Oxidoreductase Deficiency (PORD), a disorder of hormone production. To our knowledge, no previous attempt has been made to identify and analyze the deleterious/pathogenic non-synonymous single nucleotide polymorphisms (nsSNPs) in the human POR gene through an extensive computational approach. Computational algorithms and tools were employed to identify, characterize, and validate the pathogenic SNPs associated with certain diseases. To begin with, all the high-confidence SNPs were collected, and their structural and functional impacts on the protein structures were explored. The results of various in silico analyses affirm that the A287P and R457H variants of POR could destabilize the interactions between the amino acids and the hydrogen bond networks, resulting in functional deviations of POR. The literature study further confirms that the pathogenic mutations (A287P and R457H) are associated with the onset of PORD. Molecular dynamics simulations (MDS) and essential dynamics (ED) studies characterized the structural consequences of prioritized deleterious mutations, representing the structural destabilization that might disrupt POR biological function. The identified deleterious mutations at the cofactor's binding domains might interfere with the essential interactions between the protein and cofactors, thus inhibiting POR catalytic activity. The consolidated insights from the computational analyses can be used to predict potential deleterious mutants and understand the disease's pathological basis and the molecular mechanism of drug metabolism for the application of personalized medication. HIGHLIGHTSNADPH cytochrome P450 oxidoreductase (POR) mutations are associated with a broad spectrum of human diseasesIdentified and analyzed the most deleterious nsSNPs of POR through the sequence and structure-based prediction toolsInvestigated the structural and functional impacts of the most significant mutations (A287P and R457H) associated with PORDMolecular dynamics and PCA-based FEL analysis were utilized to probe the mutation-induced structural alterations in PORCommunicated by Ramaswamy H. Sarma.

Pharmacogenomics of poor drug metabolism in greyhounds: Canine P450 oxidoreductase genetic variation, breed heterogeneity, and functional characterization

Greyhounds metabolize cytochrome P450 (CYP) 2B11 substrates more slowly than other dog breeds. However, CYP2B11 gene variants associated with decreased CYP2B11 expression do not fully explain reduced CYP2B11 activity in this breed. P450 oxidoreductase (POR) is an essential redox partner for all CYPs. POR protein variants can enhance or repress CYP enzyme function in a CYP isoform and substrate dependent manner. The study objectives were to identify POR protein variants in greyhounds and determine their effect on coexpressed CYP2B11 and CYP2D15 enzyme function. Gene sequencing identified two missense variants (Glu315Gln and Asp570Glu) forming four alleles, POR-H1 (reference), POR-H2 (570Glu), POR-H3 (315Gln, 570Glu) and POR-H4 (315Gln). Out of 68 dog breeds surveyed, POR-H2 was widely distributed across multiple breeds, while POR-H3 was largely restricted to greyhounds and Scottish deerhounds (35% allele frequencies), and POR-H4 was rare. Three-dimensional protein structure modelling indicated significant effects of Glu315Gln (but not Asp570Glu) on protein flexibility through loss of a salt bridge between Glu315 and Arg519. Recombinant POR-H1 (reference) and each POR variant (H2-H4) were expressed alone or with CYP2B11 or CYP2D15 in insect cells. No substantial effects on POR protein expression or enzyme activity (cytochrome c reduction) were observed for any POR variant (versus POR-H1) when expressed alone or with CYP2B11 or CYP2D15. Furthermore, there were no effects on CYP2B11 or CYP2D15 protein expression, or on CYP2D15 enzyme kinetics by any POR variant (versus POR-H1). However, Vmax values for 7-benzyloxyresorufin, propofol and bupropion oxidation by CYP2B11 were significantly reduced by coexpression with POR-H3 (by 34-37%) and POR-H4 (by 65-72%) compared with POR-H1. Km values were unaffected. Our results indicate that the Glu315Gln mutation (common to POR-H3 and POR-H4) reduces CYP2B11 enzyme function without affecting at least one other major canine hepatic P450 (CYP2D15). Additional in vivo studies are warranted to confirm these findings.

Exploring the effects of missense mutations on protein thermodynamics through structure-based approaches: findings from the CAGI6 challenges

Missense mutations are known contributors to diverse genetic disorders, due to their subtle, single amino acid changes imparted on the resultant protein. Because of this, understanding the impact of these mutations on protein stability and function is crucial for unravelling disease mechanisms and developing targeted therapies. The Critical Assessment of Genome Interpretation (CAGI) provides a valuable platform for benchmarking state-of-the-art computational methods in predicting the impact of disease-related mutations on protein thermodynamics. Here we report the performance of our comprehensive platform of structure-based computational approaches to evaluate mutations impacting protein structure and function on 3 challenges from CAGI6: Calmodulin, MAPK1 and MAPK3. Our stability predictors have achieved correlations of up to 0.74 and AUCs of 1 when predicting changes in ΔΔG for MAPK1 and MAPK3, respectively, and AUC of up to 0.75 in the Calmodulin challenge. Overall, our study highlights the importance of structure-based approaches in understanding the effects of missense mutations on protein thermodynamics. The results obtained from the CAGI6 challenges contribute to the ongoing efforts to enhance our understanding of disease mechanisms and facilitate the development of personalised medicine approaches.

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.

Proposal of pharmacophore model for HIV reverse transcriptase inhibitors: Combined mutational effect analysis, molecular dynamics, molecular docking and pharmacophore modeling study

OBJECTIVES

Antiretroviral therapy (ART) efficacy is jeopardized by the emergence of drug resistance mutations in HIV, compromising treatment effectiveness. This study aims to propose novel analogs of Effavirenz (EFV) as potential direct inhibitors of HIV reverse transcriptase, employing computer-aided drug design methodologies.

METHODS

Three key approaches were applied: a mutational profile study, molecular dynamics simulations, and pharmacophore development. The impact of mutations on the stability, flexibility, function, and affinity of target proteins, especially those associated with NRTI, was assessed. Molecular dynamics analysis identified G190E as a mutation significantly altering protein properties, potentially leading to therapeutic failure. Comparative analysis revealed that among six first-line antiretroviral drugs, EFV exhibited notably low affinity with viral reverse transcriptase, further reduced by the G190E mutation. Subsequently, a search for EFV-similar inhibitors yielded 12 promising molecules based on their affinity, forming the basis for generating a pharmacophore model.

RESULTS

Mutational analysis pinpointed G190E as a crucial mutation impacting protein properties, potentially undermining therapeutic efficacy. EFV demonstrated diminished affinity with viral reverse transcriptase, exacerbated by the G190E mutation. The search for EFV analogs identified 12 high-affinity molecules, culminating in a pharmacophore model elucidating key structural features crucial for potent inhibition.

CONCLUSION

This study underscores the significance of EFV analogs as potential inhibitors of HIV reverse transcriptase. The findings highlight the impact of mutations on drug efficacy, particularly the detrimental effect of G190E. The generated pharmacophore model serves as a pivotal reference for future drug development efforts targeting HIV, providing essential structural insights for the design of potent inhibitors based on EFV analogs identified in vitro.

Protocol for establishing a protein interactome based on close physical proximity to a target protein within live budding yeast

Here, we present a protocol for establishing a protein interactome based on close physical proximity to a target protein within live yeast cells. We describe steps for capturing both transient and stable binders by integrating a non-natural amino acid. We detail procedures for employing a site-directed method for labeling the surface that mediates protein associations and uncovers the binding sites on the interactors. Combined with mass spectrometry, our approach proves valuable in discovering binding partners and constructing a comprehensive protein-interaction network.

Classic Galactosemia: Clinical and Computational Characterization of a Novel GALT Missense Variant (p.A303D) and a Literature Review

Classic galactosemia is an autosomal recessive inherited liver disorder of carbohydrate metabolism caused by deficient activity of galactose-1-phosphate uridylyltransferase (GALT). While a galactose-restricted diet is lifesaving, most patients still develop long-term complications. In this study, we report on a two-week-old female patient who is a compound heterozygote for a known pathogenic variant (p.K285N) and a novel missense variant (p.A303D) in the GALT gene. Segregation analysis showed that the patient inherited the p.K285N pathogenic variant from her father and the p.A303D variant from her mother. A bioinformatics analysis to predict the impact of the p.A303D missense variant on the structure and stability of the GALT protein revealed that it may be pathogenic. Based on this finding, we performed a literature review of all GALT missense variants identified in homozygous and compound heterozygous galactosemia patients carrying the p.K285N pathogenic variant to explore their molecular effects on the clinical phenotype of the disease. Our analysis revealed that these missense variants are responsible for a wide range of molecular defects. This study expands the clinical and mutational spectrum in classic galactosemia and reinforces the importance of understanding the molecular consequences of genetic variants to incorporate genetic analysis into clinical care.

A missense SNP in the proopiomelanocortin (POMC) gene is associated with growth traits in Awassi and Karakul sheep

This study was conducted to assess the association between proopiomelanocortin (POMC) gene and growth traits in Awassi and Karakul sheep. PCR-single strand conformation polymorphism (SSCP) method was utilized to assess the polymorphism of POMC PCR amplicons with body weight and length, wither and rump height, chest and abdominal circumference measured at birth, 3, 6, 9, and 12 months intervals. Only one missense SNP (rs424417456:C > A) was detected in exon-2, in which glycine was converted to cysteine in the 65th position in POMC (p.65Gly > Cys). rs424417456 SNP showed significant associations with all growth traits in the third, sixth, ninth, and twelfth months. At the age of 3 months onward, lambs with CC genotype showed higher body weight, body length, wither and rump heights, and chest and abdominal circumferences than lambs with CA and AA genotypes, respectively. Prediction analyses indicated a deleterious effect of p.65Gly > Cys on POMC structure, function, and stability. Owing to the strong correlation between rs424417456:CC and better growth characteristics, this genotype is proposed as a promising marker to enhance growth traits in Awassi and Karakul sheep. The predicted damaging effects caused by rs424417456:CA and rs424417456:AA genotypes may entail a putative mechanism through which lambs with these genotypes exhibit lower growth traits.