OBJECTIVE: The luteinizing hormone (LH), produced by gonadal and nongonadal cells in the anterior pituitary gland play a critical role in human sexual development and reproduction. It is required for the induction of ovulation in females and sex steroid hormone production in both males and females. It is also an important player in early pregnancy events in oviducts and in absence of LH signalling, the uterus cannot initiate pregnancy. LH works through its receptor LHCGR. Therefore, it is quite important to figure out those mutations that have the potential to affect the structure and function of both LH and LHR.
METHODS: Various in silico tools were employed in the study for the data mining of SNPs and predicting their possible impact on the structure and function of the protein. ConSurf analysis predicted V454I and I161K are exposed residues in the 2D structure of protein and highly conserved in protein structure. PSIPRED and Swiss Modeller were employed to predict the 2D and 3D structure of mutated receptor protein. FT site server predicted both substitutions were involved in the ligand-binding site RESULTS: By present analysis, we have found that R59G in LHα, Q74R and T78N in LHβ and V454I and I161K in LHCGR are the most deleterious nsSNPs affecting the structure and function of the protein.
CONCLUSIONS: These SNPs are still uncharacterised; hence providing a baseline for validation of their association with the susceptibility of diseases and develop personalised therapeutics.

UNASSIGNED: The Alternative Lengthening of Telomeres (ALT) pathway represents a non-canonical mechanism of telomere maintenance that operates independently of the conventional telomerase activity. The three biologically significant proteins, designated as SMARCAL1 (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A-like protein 1), DAXX (Death domain-associated protein 6) and ATRX (alpha-thalassemia/mental retardation, X-linked) are associated with ALT in certain cancer types. The purpose of this study was to identify the most high-risk nsSNPs (non-synonymous Single Nucleotide Polymorphisms) within these three genes and assess their impacts on the structure and function of the proteins they encode.
UNASSIGNED: The reported genetic polymorphisms of SMARCAL1, DAXX and ATRX genes were retrieved from the Ensembl database. Later, various computational tools like PROVEAN, PolyPhen2, SNPs and GO, SNAP2, Predict-SNP, Panther and PMut were used to predict the most deleterious nsSNPs. MutPred was used to understand the underlying molecular reasons of those nsSNPs being deleterious, followed by prediction of Post Translational Modification Sites (PTMs) using ModPred. I-Mutant and MUpro were used to predict the effect of SNP on energy stability. Later, 3D clustering analysis was done using Mutation 3D server. Moreover, ConSurf was utilized to identify the conservation scores of wild-type amino acids. Additionally, the NCBI conserved domain search tool was employed to pinpoint conserved domains within these three proteins. Project-Hope helped for biophysical validation, followed by prediction of these genes\' interaction and function by using GeneMANIA.
UNASSIGNED: Analysis on SMARCAL1 protein revealed that among 665 nsSNPs, four were identified as the most deleterious: L578S, T581S, P582A, and P582S. Similarly, within the DAXX protein, among a pool of 480 nsSNPs, P284S, R230C, and R230S were found out to be the most deleterious variants. In case of ATRX protein, V178D, R246C, and V277G, from the total of 1009 nsSNPs, were predicted to be the most deleterious. All these nsSNPs were found to occur at residue positions that are 100 % conserved within protein domains and were predicted to be most damaging from both structural and functional perspectives and highly destabilizing to their corresponding proteins.
UNASSIGNED: Computational investigation on the 3 proteins-SMARCAL1, DAXX and ATRX through different bioinformatics analysis tools concludes that the identified high risk nsSNPs of these proteins are pathogenic SNPs. These variants potentially exert functional and structural influences, thus making them valuable candidates for future genetic studies.

Delta-opioid receptor protein (OPRD1) is one of the potential targets for treating pain. The presently available opioid agonists are known to cause unnecessary side effects. To discover a novel opioid agonist, our research group has synthesized a chimeric peptide MCRT and proved its potential activity through in vivo analysis. Non-synonymous SNPs (nsSNPs) missense mutations affect the functionality and stability of proteins leading to diseases. The current research was focused on understanding the role of MCRT in restoring the binding tendency of OPRD1 nsSNPs missense mutations on dynamic nature in comparison with Deltorphin-II and morphiceptin. The deleterious effects of nsSNPs were analyzed using various bioinformatics tools for predicting structural, functional, and oncogenic influence. The shortlisted nine nsSNPs were predicted for allergic reactions, domain changes, post-translation modification, multiple sequence alignment, secondary structure, molecular dynamic simulation (MDS), and peptide docking influence. Further, the docked complex of three shortlisted deleterious nsSNPs was analyzed using an MDS study, and the highly deleterious shortlisted nsSNP A149T was further analyzed for higher trajectory analysis. MCRT restored the binding tendency influence caused by nsSNPs on the dynamics of stability, functionality, binding affinity, secondary structure, residues connection, motion, and folding of OPRD1 protein.

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.

Male infertility is a significant reproductive issue affecting a considerable number of couples worldwide. While there are various causes of male infertility, genetic factors play a crucial role in its development. We focused on identifying and analyzing the high-risk nsSNPs in DNAH1 and DNAH17 genes, which encode proteins involved in sperm motility. A total of 20 nsSNPs for DNAH1 and 10 nsSNPs for DNAH17 were analyzed using various bioinformatics tools including SIFT, PolyPhen-2, CADD, PhD-SNPg, VEST-4, and MutPred2. As a result, V1287G, L2071R, R2356W, R3169C, R3229C, E3284K, R4096L, R4133C, and A4174T in DNAH1 gene and C1803Y, C1829Y, R1903C, and L3595P in DNAH17 gene were identified as high-risk nsSNPs. These nsSNPs were predicted to decrease protein stability, and almost all were found in highly conserved amino acid positions. Additionally, 4 nsSNPs were observed to alter post-translational modification status. Furthermore, the interaction network analysis revealed that DNAH1 and DNAH17 interact with DNAH2, DNAH3, DNAH5, DNAH7, DNAH8, DNAI2, DNAL1, CFAP70, DNAI3, DNAI4, ODAD1, and DNAI7, demonstrating the importance of DNAH1 and DNAH17 proteins in the overall functioning of the sperm motility machinery. Taken together, these findings revealed the detrimental effects of identified high-risk nsSNPs on protein structure and function and highlighted their potential relevance to male infertility. Further studies are warranted to validate these findings and to elucidate the underlying mechanisms.

BACKGROUND: Polymorphisms in IL1B play a significant role in depression, multiple inflammatory-associated disorders, and susceptibility to infection. Functional non-synonymous SNPs (nsSNPs) result in changes in the encoded amino acids, potentially leading to structural and functional alterations in the mutant proteins. So far, most genetic studies have concentrated on SNPs located in the IL1B promoter region, without addressing nsSNPs and their association with multifactorial diseases. Therefore, this study aimed to explore the impact of deleterious nsSNPs retrieved from the dbSNP database on the structure and functions of the IL1B protein.
RESULTS: Six web servers (SIFT, PolyPhen-2, PROVEAN, SNPs&GO, PHD-SNP, PANTHER) were used to analyze the impact of 222 missense SNPs on the function and structure of IL1B protein. Five novel nsSNPs (E100K, T240I, S53Y, D128Y, and F228S) were found to be deleterious and had a mutational impact on the structure and function of the IL1B protein. The I-mutant v2.0 and MUPro servers predicted that these mutations decreased the stability of the IL1B protein. Additionally, these five mutations were found to be conserved, underscoring their significance in protein structure and function. Three of them (T240I, D128Y, and F228S) were predicted to be cancer-causing nsSNPs. To analyze the behavior of the mutant structures under physiological conditions, we conducted a 50 ns molecular dynamics simulation using the WebGro online tool. Our findings indicate that the mutant values differ from those of the IL1B wild type in terms of RMSD, RMSF, Rg, SASA, and the number of hydrogen bonds.
CONCLUSIONS: This study provides valuable insights into nsSNPs located in the coding regions of IL1B, which lead to direct deleterious effects on the functional and structural aspects of the IL1B protein. Thus, these nsSNPs could be considered significant candidates in the pathogenesis of disorders caused by IL1B dysfunction, contributing to effective drug discovery and the development of precision medications. Thorough research and wet lab experiments are required to verify our findings. Moreover, bioinformatic tools were found valuable in the prediction of deleterious nsSNPs.

BACKGROUND: N-ras protein is encoded by the NRAS gene and operates as GDP-GTP-controlled on/off switching. N-ras interacts with cellular signaling networks that regulate various cellular activities including cell proliferation and survival. The nonsynonymous single nucleotide polymorphism (nsSNPs)-mediated alteration can substantially disrupt the structure and activity of the corresponding protein. N-ras has been reported to be associated with numerous diseases including cancers due to the nsSNPs. A comprehensive study on the NRAS gene to unveil the potentially damaging and oncogenic nsSNPs is yet to be accomplished. Hence, this extensive in silico study is intended to identify the disease-associated, specifically oncogenic nsSNPs of the NRAS gene.
RESULTS: Out of 140 missense variants, 7 nsSNPs (I55R, G60E, G60R, Y64D, L79F, D119G, and V152F) were identified to be damaging utilizing 10 computational tools that works based on different algorithms with high accuracy. Among those, G60E, G60R, and D119G variants were further filtered considering their location in the highly conserved region and later identified as oncogenic variants. Interestingly, G60E and G60R variants were revealed to be particularly associated with lung adenocarcinoma, rhabdomyosarcoma, and prostate adenocarcinoma. Therefore, D119G could be subjected to detailed investigation for identifying its association with specific cancer.
CONCLUSIONS: This in silico study identified the deleterious and oncogenic missense variants of the human NRAS gene that could be utilized for designing further experimental investigation. The outcomes of this study would be worthwhile in future research for developing personalized medicine.

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.

TYMP gene, which codes for thymidine phosphorylase (TP) is also known as platelet-derived endothelial cell growth factor (PD-ECGF). TP plays crucial roles in nucleotide metabolism and angiogenesis. Mutations in the TYMP gene can lead to Mitochondrial Neurogastrointestinal Encephalopathy (MNGIE) syndrome, a rare genetic disorder. Our main objective was to evaluate the impact of detrimental non-synonymous single nucleotide polymorphisms (nsSNPs) on TP protein structure and predict harmful variants in untranslated regions (UTR). We employed a combination of predictive algorithms to identify nsSNPs with potential deleterious effects, followed by molecular modeling analysis to understand their effects on protein structure and function. Using 13 algorithms, we identified 119 potentially deleterious nsSNPs, with 82 located in highly conserved regions. Of these, 53 nsSNPs were functional and exposed, while 79 nsSNPs reduced TP protein stability. Further analysis of 18 nsSNPs through 3D protein structure analysis revealed alterations in amino acid interactions, indicating their potential impact on protein function. This will help in the development of faster and more efficient genetic tests for detecting TYMP gene mutations.

Diabetes mellitus (DM) represents a problem for the healthcare system worldwide. DM has very serious complications such as blindness, kidney failure, and cardiovascular disease. In addition to the very bad socioeconomic impacts, it influences patients and their families and communities. The global costs of DM and its complications are huge and expected to rise by the year 2030. DM is caused by genetic and environmental risk factors. Genetic testing will aid in early diagnosis and identification of susceptible individuals or populations using ATP-sensitive potassium (KATP) channels present in different tissues such as the pancreas, myocardium, myocytes, and nervous tissues. The channels respond to different concentrations of blood sugar, stimulation by hormones, or ischemic conditions. In pancreatic cells, they regulate the secretion of insulin and glucagon. Mutations in the KCNJ11 gene that encodes the Kir6.2 protein (a major constituent of KATP channels) were reported to be associated with Type 2 DM, neonatal diabetes mellitus (NDM), and maturity-onset diabetes of the young (MODY). Kir6.2 harbors binding sites for ATP and phosphatidylinositol 4,5-diphosphate (PIP2). The ATP inhibits the KATP channel, while the (PIP2) activates it. A Kir6.2 mutation at tyrosine330 (Y330) was demonstrated to reduce ATP inhibition and predisposes to NDM. In this study, we examined the effect of mutations on the Kir6.2 structure using bioinformatics tools and molecular dynamic simulations (SIFT, PolyPhen, SNAP2, PANTHER, PhD&SNP, SNP&Go, I-Mutant, MuPro, MutPred, ConSurf, HOPE, and GROMACS). Our results indicated that M199R, R201H, R206H, and Y330H mutations influence Kir6.2 structure and function and therefore may cause DM. We conclude that MD simulations are useful techniques to predict the effects of mutations on protein structure. In addition, the M199R, R201H, R206H, and Y330H variant in the Kir6.2 protein may be associated with DM. These results require further verification in protein-protein interactions, Kir6.2 function, and case-control studies.