In Silico Elucidation of Deleterious Non-synonymous SNPs in SHANK3, the Autism Spectrum Disorder Gene

In silico scanning of structural and functional deleterious nsSNPs in Arabidopsis thaliana's SOG1 protein, using molecular dynamic simulation approaches

Suppressor of gamma response 1 (SOG1) is a member of the NAC domain family transcription factors of the DNA damage response (DDR) signaling in the plant's genome. SOG1 is directly involved in transcriptional response to DNA damage, cell cycle checkpoints and ATR or ATM-mediated activation of the DNA damage responses and repair functioning in programmed cell death and regulation of end reduplication. Different mutations in the SOG1 protein lead to severe diseases and, ultimately, cell death. Single nucleotide polymorphisms (SNPs) are an important type of genetic alteration that cause different diseases or programmed cell death. The current study applied different computational approaches to Arabidopsis thaliana L. SOG1 protein to identify the potential deleterious nsSNPs and monitor their impact on the structure, function and protein stability. Various bioinformatics tools were applied to analyze the retrieved 34 nsSNPs and interestingly extracted four deleterious nsSNPs, that is, ensvath13968004 (Q166L), tmp18998388 (P159L), ensvath01103049 (K199N) and tmp18998295 (Y190F). For example, homology modeling, conservation and conformational analysis of the mutant's models were considered to scrutinize the deviations of these variants from the native SOG1 structure. All atoms molecular dynamic simulation confirmed the significance of these mutations on the protein stability, residual and structural conformation, compactness, surface conformation, dominant motion, Gibbs free energy distribution and dynamic effects. Similarly, protein-protein interaction revealed that SOG1 operates as a hub-linking cluster of various proteins, and any changes in the SOG1 might result in the disassociation of several signal transduction cascades.Communicated by Ramaswamy H. Sarma.

Deleterious single nucleotide polymorphisms (SNPs) of human IFNAR2 gene facilitate COVID-19 severity in patients: a comprehensive in silico approach

In humans, the dimeric receptor complex IFNAR2-IFNAR1 accelerates cellular response triggered by type I interferon (IFN) family proteins in response to viral infection including Coronavirus infection. Studies have revealed the association of the IFNAR2 gene with severe illness in Coronavirus infection and indicated the association of genomic variants, i.e. single nucleotide polymorphisms (SNPs). However, comprehensive analysis of SNPs of the IFNAR2 gene has not been performed in both coding and non-coding region to find the causes of loss of function of IFNAR2 in COVID-19 patients. In this study, we have characterized coding SNPs (nsSNPs) of IFNAR2 gene using different bioinformatics tools and identified deleterious SNPs. We found 9 nsSNPs as pathogenic and disease-causing along with a decrease in protein stability. We employed molecular docking analysis that showed 5 nsSNPs to decrease binding affinity to IFN. Later, MD simulations showed that P136R mutant may destabilize crucial binding with the IFN molecule in response to COVID-19. Thus, P136R is likely to have a high impact on disrupting the structure of the IFNAR2 protein. GTEx portal analysis predicted 14 sQTLs and 5 eQTLs SNPs in lung tissues hampering the post-transcriptional modification (splicing) and altering the expression of the IFNAR2 gene. sQTLs and eQTLs SNPs potentially explain the reduced IFNAR2 production leading to severe diseases. These mutants in the coding and non-coding region of the IFNAR2 gene can help to recognize severe illness due to COVID 19 and consequently assist to develop an effective drug against the infection.Communicated by Ramaswamy H. Sarma.

Identification of missense SNP-mediated mutations in the regulatory sites of aldose reductase (ALR2) responsible for treatment failure in diabetic complications

Scientific pieces of evidence indicate that the polymorphism in the ALR2 regulatory gene favors the susceptibility to diabetic complications (DCs). Previous studies have uncovered several single nucleotide polymorphisms (SNPs) in the ALR2 regulatory sites that negatively modulate the activity of this enzyme and eventually increase the risks of DCs. In view of this, the current study aimed at investigating whether the mutation as a resultant of missense SNPs in the regulatory site of ALR2 enzyme can also hamper the interactions of ALR2 inhibitors with the key amino acid residues in the ALR2 binding site. Around 202 SNPs in the ALR2 gene were reported in the dbSNP database. Out of these, eighteen SNPs that are responsible for point mutations in the regulatory sites of ALR2 enzyme were identified and considered for the study. Identified SNPs were then categorized as stabilizing or destabilizing using various in silico tools and webservers. The resulting mutational constructs of ALR2 were further probed for their influence on the binding affinities and binding modes with well-known ALR2 inhibitors using structure-based analyses. This study identified three destabilizing SNPs, i.e., rs779176563 (C298S), rs1392886142 (G16A), and rs1407261115 (A245T), that lead to the compromised response to most of the ALR2 inhibitors which are in clinical trials. On the other hand, treatment with these ALR2 inhibitors may benefit the population which carries missense SNPs rs748119899, rs1402962430, and rs1467939858 that code for W219S, Q183V, and S214A, respectively. Overall findings of the study suggest that one SNP in the inhibitor site and two SNPs in the co-factor site of ALR2 may be responsible for the low efficacy and unsuccessful journey of ALR2 inhibitors in the clinical trials.

SHANK3 genetic polymorphism and susceptibility to ASD: evidence from molecular, in silico, and meta-analysis approaches

BACKGROUND

The SHANK3 gene encodes a master synaptic scaffolding protein at the excitatory synapse's postsynaptic density, which is predominantly responsible for constructing a synapse, maintaining synaptic structure, and functions. Recently, evidence from rare mutations and copy number variation provided an important clue about SHANK3 which acts as a strong candidate gene in the pathogenesis of Autism Spectrum Disorder (ASD).

MATERIALS AND METHODS

To investigate potential allelic variants for the SHANK3 (rs9616915) gene as a genetic risk factor, we performed PCR-RFLP analysis and Sanger sequencing for 90 ASD and 90 healthy subjects. Moreover, to understand the functional and structural impacts of our selected non-synonymous SHANK3 SNP rs9616915, we have performed an in silico analysis. Subsequently, a meta-analysis of rs9616915 with a total of 6 eligible studies (including the present study) containing a total of 795 cases and 12,947 controls was obtained from a comprehensive online database search to evaluate the overall association with ASD.

RESULTS

Our retrieved data, such as Pearson's chi-square test (p = 0.081) as well as logistic regression analysis of co-dominant (p = 0.1131), dominant (p = 0.3656) and recessive models (p = 0.0569) speculated no significant association between rs9616915 and our studied sample. Interestingly, by in silico analysis, we have observed two hydrogen bonds between amino acids instead of one hydrogen bond in the protein structure of rs9616915, which indicates this mutant structure could affect the proteins' stability. The findings of the meta-analysis revealed that four genetic association models were associated with ASD susceptibility.

CONCLUSIONS

Our study suggested that targeted SHANK3 SNP of interest rs9616915 might not be associated with ASD in the southern part of the Bangladeshi population.

Identification of the most damaging nsSNPs in the human CFL1 gene and their functional and structural impacts on cofilin-1 protein

The cofilin-1 protein, encoded by CFL1, is an actin-binding protein that regulates F-actin depolymerization and nucleation activity through phosphorylation and dephosphorylation. CFL1 has been implicated in the development of neurodegenerative diseases (Alzheimer's disease and Huntington's disease), neuronal migration disorders (lissencephaly, epilepsy, and schizophrenia), and neural tube closure defects. Mutations in CFL1 have been associated with impaired neural crest cell migration and neural tube closure defects. In our study, various computational approaches were utilized to explore single-nucleotide polymorphisms (SNPs) in CFL1. The Variation Viewer and gnomAD databases were used to retrieve CFL1 SNPs, including 46 nonsynonymous SNPs (nsSNPs). The functional and structural annotation of SNPs was performed using 12 sequence-based web applications, which identified 20 nsSNPs as being the most likely to be deleterious or disease-causing. The conservation of cofilin-1 protein structures was illustrated using the ConSurf and PROSITE web servers, which projected the 12 most deleterious nsSNPs onto conserved domains, with the potential to disrupt the protein's functionality. These 12 nsSNPs were selected for protein structure construction, and the DynaMut/DUET servers predicted that the protein variants V7G, L84P, and L99A were the most likely to be damaging to the cofilin-1 protein structure or function. The evaluation of molecular docking studies demonstrated that the L99A and L84P cofilin-1 variants reduce the binding affinity for actin compared with the native cofilin-1 structure, and molecular dynamic simulation studies confirmed that these variants might destabilize the protein structure. The consequences of putative mutations on protein-protein interactions and post-translational modification sites in the cofilin-1 protein structure were analyzed. This study represents the first complete approach to understanding the effects of nsSNPs within the actin-depolymerizing factor/cofilin family, which suggested that SNPs resulting in L84P (rs199716082) and L99A (rs267603119) variants represent significant CFL1 mutations associated with disease development.

Comprehensive Characterization of the Coding and Non-Coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools

Single nucleotide polymorphisms (SNPs) help to understand the phenotypic variations in humans. Genome-wide association studies (GWAS) have identified SNPs located in the tumor protein 63 (TP63) locus to be associated with the genetic susceptibility of cancers. However, there is a lack of in-depth characterization of the structural and functional impacts of the SNPs located at the TP63 gene. The current study was designed for the comprehensive characterization of the coding and non-coding SNPs in the human TP63 gene for their functional and structural significance. The functional and structural effects of the SNPs were investigated using a wide variety of computational tools and approaches, including molecular dynamics (MD) simulation. The deleterious impact of eight nonsynonymous SNPs (nsSNPs) affecting protein stability, structure, and functions was measured by using 13 bioinformatics tools. These eight nsSNPs are in highly conserved positions in protein and were predicted to decrease protein stability and have a deleterious impact on the TP63 protein function. Molecular docking analysis showed five nsSNPs to reduce the binding affinity of TP63 protein to DNA with significant results for three SNPs (R319H, G349E, and C347F). Further, MD simulations revealed the possible disruption of TP63 and DNA binding, hampering the essential protein function. PolymiRTS study found five non-coding SNPs in miRNA binding sites, and the GTEx portal recognized five eQTLs SNPs in single tissue of the lung, heart (LV), and cerebral hemisphere (brain). Characterized nsSNPs and non-coding SNPs will help researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review

Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.