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.

Molecular Dynamics Simulation of Kir6.2 Variants Reveals Potential Association with Diabetes Mellitus

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.

The deleterious variants of N-acetylgalactosamine-6-sulfatase (GalN6S) enzyme trigger Morquio a syndrome by disrupting protein foldings

Lysosomal enzymes degrade cellular macromolecules, while their inactivation causes human hereditary metabolic disorders. Mucopolysaccharidosis IVA (MPS IVA; Moquio A syndrome) is one of the lysosomal storage disorders caused by a defective Galactosamine-6-sulfatase (GalN6S) enzyme. In several populations, disease incidence is elevated due to missense mutations brought on by non-synonymous allelic variation in the GalN6S enzyme. Here, we studied the effect of non-synonymous single nucleotide polymorphism (nsSNPs) on the structural dynamics of the GalN6S enzyme and its binding with N-acetylgalactosamine (GalNAc) using all-atom molecular dynamics simulation and an essential dynamics approach. Consequently, in this study, we have identified three functionally disruptive mutations in domain-I and domain-II, that is, S80L, R90W, and S162F, which presumably contribute to post-translational modifications. The study delineated that both domains work cooperatively, and alteration in domain II (S80L, R90W) leads to conformational changes in the catalytic site in domain-I, while mutation S162F mainly provokes higher residual flexibility of domain II. These results show that these mutations impair the hydrophobic core, implying that Morquio A syndrome is caused by misfolding of the GalN6S enzyme. The results also show the instability of the GalN6S-GalNAc complex upon substitution. Overall, the structural dynamics resulting from point mutations give the molecular rationale for Moquio A syndrome and, more importantly, the Mucopolysaccharidoses (MPS) family of diseases, re-establishing MPS IVA as a protein-folding disease.Communicated by Ramaswamy H. Sarma.

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

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

Insights from the SNP analysis of TYMP gene linking MNGIE

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.

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.

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.

In Silico Evaluation of Coding and Non-Coding nsSNPs in the Thrombopoietin Receptor (MPL) Proto-Oncogene: Assessing Their Influence on Protein Stability, Structure, and Function

The thrombopoietin receptor (MPL) gene is a critical regulator of hematopoiesis, and any alterations in its structure or function can result in a range of hematological disorders. Non-synonymous single nucleotide polymorphisms (nsSNPs) in MPL have the potential to disrupt normal protein function, prompting our investigation into the most deleterious MPL SNPs and the associated structural changes affecting protein-protein interactions. We employed a comprehensive suite of bioinformatics tools, including PredictSNP, InterPro, ConSurf, I-Mutant2.0, MUpro, Musitedeep, Project HOPE, STRING, RegulomeDB, Mutpred2, CScape, and CScape Somatic, to analyze 635 nsSNPs within the MPL gene. Among the analyzed nsSNPs, PredictSNP identified 28 as significantly pathogenic, revealing three critical functional domains within MPL. Ten of these nsSNPs exhibited high conservation scores, indicating potential effects on protein structure and function, while 14 were found to compromise MPL protein stability. Although the most harmful nsSNPs did not directly impact post-translational modification sites, 13 had the capacity to substantially alter the protein's physicochemical properties. Some mutations posed a risk to vital protein-protein interactions crucial for hematological functions, and three non-coding region nsSNPs displayed significant regulatory potential with potential implications for hematopoiesis. Furthermore, 13 out of 21 nsSNPs evaluated were classified as high-risk pathogenic variants by Mutpred2. Notably, amino acid alterations such as C291S, T293N, D295G, and W435C, while impactful on protein stability and function, were deemed non-oncogenic "passenger" mutations. Our study underscores the substantial impact of missense nsSNPs on MPL protein structure and function. Given MPL's central role in hematopoiesis, these mutations can significantly disrupt hematological processes, potentially leading to a variety of disorders. The identified high-risk pathogenic nsSNPs may hold promise as potential biomarkers or therapeutic targets for hematological diseases. This research lays the foundation for future investigations into the MPL gene's role in the realm of hematological health and diseases.

Structural impact of pathogenic SNPs on β-tubulin using molecular dynamics study

Single nucleotide polymorphisms (SNPs) in the TUBB1 (β-tubulin) gene have been implicated as the primary cause of macro thrombocytopenia. Therefore it is essential to identify the potential SNPs which are harmful to cause diseases such as macro thrombocytopenia. The impact caused by these variants on β-tubulin is twofold, both structural and functional. Multiple in-silico tools were used to scrutinise the most deleterious nsSNPs (non-synonymous SNPs) via sequence and structure-based approaches. Further, the β-tubulin protein model incorporating identified mutants was subjected to MD (molecular dynamic) simulations to analyse the impact on protein structure. A total of 2974 SNPs of TUBB1 were retrieved from various sources, and 32 nsSNPs were identified. By screening through sequence-based technique, 13 variants were detected as deleterious and further structure-based filtration was carried out to find thermally destabilising variants. Finally, three variants have been detected as highly destabilising by the mCSM server and chosen for the MD study. All three variants are present in the N-terminal, Intermediate, and C-terminal regions, breaking the spatial arrangement required for microtubule assembly. The spatial arrangement of these variants is in deviation with respect to WT (wild type) β-tubulin. The protein model was subjected to a simulation period of 100 ns. The FEL analysis revealed multiple clusters with minor populations indicating the unstable conformation adapted by the β-tubulin. The normal mode vector analysis exhibited high-intensity flexible motions at the C-terminal end, responsible for binding with MAPs (microtubule-associated proteins), an essential region in microtubule assembly. All these results reveal that the SNP's predicted eventually influence the spatial arrangement of β-tubulin, which would disturb the stacking arrangement of αβ tubulin dimer in microtubule assembly. The present study may set a path to cure the diseases like macro thrombocytopenia.Communicated by Ramaswamy H. Sarma.

Computational screening and analysis of deleterious nsSNPs in human p14ARF (CDKN2A gene) protein using molecular dynamic simulation approach

Cyclin-dependent kinase inhibitor 2 A (CDKN2A) gene belongs to the cyclin-dependent kinase family that code for two transcripts (p16INK4A and p14ARF), both work as tumor suppressors proteins. The mutation that occurs in the p14ARF protein can lead to different types of cancers. Single nucleotide polymorphisms (SNPs) are an important type of genetic alteration that can lead to different types of diseases. In this study, we applied the computational strategy on human p14ARF protein to identify the potential deleterious nsSNPs and check their impact on the structure, function, and protein stability. We applied more than ten prediction tools to screen the retrieved 288 nsSNPs, consequently extracting four deleterious nsSNPs i.e., rs139725688 (R10G), rs139725688 (R21W), rs374360796 (F23L) and rs747717236 (L124R). Homology modeling, conservation and conformational analysis of mutant models were performed to examine the divergence of these variants from the native p14ARF structure. All-atom molecular dynamics simulation revealed a significant impact of these mutations on protein stability, compactness, globularity, solvent accessibility and secondary structure elements. Protein-protein interactions indicated that p14ARF operates as a hub linking clusters of different proteins and that changes in p14ARF may result in the disassociation of numerous signal cascades. Our current study is the first survey of computational analysis on p14ARF protein that determines the association of these nsSNPs with the altered function of p14ARF protein and leads to the development of various types of cancers. This research proposes the described functional SNPs as possible targets for proteomic investigations, diagnostic procedures, and treatments.Communicated by Ramaswamy H. Sarma.

Comprehensive In Silico Characterization of the Coding and Non-Coding SNPs in Human Dectin-1 Gene with the Potential of High-Risk Pathogenicity Associated with Fungal Infections

The human C-type lectin domain family 7 member A (CLEC7A) gene encodes a Dectin-1 protein that recognizes beta-1,3-linked and beta-1,6-linked glucans, which form the cell walls of pathogenic bacteria and fungi. It plays a role in immunity against fungal infections through pathogen recognition and immune signaling. This study aimed to explore the impact of nsSNPs in the human CLEC7A gene through computational tools (MAPP, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT, SNAP, and PredictSNP) to identify the most deleterious and damaging nsSNPs. Further, their effect on protein stability was checked along with conservation and solvent accessibility analysis by I-Mutant 2.0, ConSurf, and Project HOPE and post-translational modification analysis using MusiteDEEP. Out of the 28 nsSNPs that were found to be deleterious, 25 nsSNPs affected protein stability. Some SNPs were finalized for structural analysis with Missense 3D. Seven nsSNPs affected protein stability. Results from this study predicted that C54R, L64P, C120G, C120S, S135C, W141R, W141S, C148G, L155P, L155V, I158M, I158T, D159G, D159R, I167T, W180R, L183F, W192R, G197E, G197V, C220S, C233Y, I240T, E242G, and Y3D were the most structurally and functionally significant nsSNPs in the human CLEC7A gene. No nsSNPs were found in the predicted sites for post-translational modifications. In the 5' untranslated region, two SNPs, rs536465890 and rs527258220, showed possible miRNA target sites and DNA binding sites. The present study identified structurally and functionally significant nsSNPs in the CLEC7A gene. These nsSNPs may potentially be used for further evaluation as diagnostic and prognostic biomarkers.

Comprehensive in-silico analysis of deleterious SNPs in APOC2 and APOA5 and their differential expression in cancer and cardiovascular diseases conditions

Genetic variations in APOC2 and APOA5 genes involve activating lipoprotein lipase (LPL), responsible for the hydrolysis of triglycerides (TG) in blood and whose impaired functions affect the TG metabolism and are associated with metabolic diseases. In this study, we investigate the biological significance of genetic variations at the DNA sequence and structural level using various computational tools. Subsequently, 8 (APOC2) and 17 (APOA5) non-synonymous SNPs (nsSNPs) were identified as high-confidence deleterious SNPs based on the effects of the mutations on protein conservation, stability, and solvent accessibility. Furthermore, based on our docking results, the interaction of native and mutant forms of the corresponding proteins with LPL depicts differences in root mean square deviation (RMSD), and binding affinities suggest that these mutations may affect their function. Furthermore, in vivo, and in vitro studies have shown that differential expression of these genes in disease conditions due to the influence of nsSNPs abundance may be associated with promoting the development of cancer and cardiovascular diseases. Preliminary screening using computational methods can be a helpful start in understanding the effects of mutations in APOC2 and APOA5 on lipid metabolism; however, further wet-lab experiments would further strengthen the conclusions drawn from the computational study.

Structural and Dynamic Investigation of non-synonymous variations in Renin-AGT complex revealed altered binding via hydrogen bonding network reprogramming to accelerate the hypertension pathway

Hypertension is one of the major issues worldwide and one of the main factors involved in heart and kidney failure. Angiotensinogen and renin are key components of the renin-angiotensin-aldosterone system (RAAS), which plays an indispensable role in hypertension. The aimed of this study to find out the non-synonymous mutations and structure-based mutation-function correlation in the Renin-AGT complex and reveal the most deleterious mutations to accelerated hypertension. In the current study, we employed computational modelling and molecular simulation approaches to demonstrate the impact of specific mutations in the REN-AGT interface in hypertension. Computational algorithms i.e. PhD-SNP, PolyPhen-1, MAPP, SIFT, SNAP, PredictSNP, PolyPhen-2, and PANTHER predicted 20 mutations as deleterious in AGT while only five mutations were conformed as deleterious in the Renin protein. Investigation of the bonding analysis revealed that two mutations S107L and V193F in Renin altered the hydrogen-bonding paradigm at the interface site. Furthermore, exploration of structural-dynamic behaviors demonstrated by that these mutations also increases the structural stability to regulate the expression of disease pathway. The flexibility index of each residues and structural compactness analysis further validated the findings by portraying the difference in the dynamic behavior in contrast to the wild type. Binding energy calculations based on molecular mechanics/generalized Born surface area (MM/GBSA) methods were used which further established the binding differences between the wild type, S107L, and V193F mutant variants. The total binding energy for wild type, S107L, and V193F were reported to be -27.79 kcal/mol, -47.72 kcal/mol, and -38.25 kcal/mol respectively. In conclusion, these two mutations increase the binding free energy alongside the docking score to enhance the binding between Renin and AGT to overexpress this pathway in a hypertension disease condition. Patients with these mutations may be screened for potential therapeutic intervention.

Exploring the Structural and Functional Effects of Nonsynonymous SNPs in the Human Serotonin Transporter Gene Through In Silico Approaches

The sodium-dependent serotonin transporter SLC6A4 (solute carrier family 6 member 4) gene encodes an intrinsic membrane protein that transmits the serotonin neurotransmitter from synaptic clefts into presynaptic neurons. The product of the SLC6A4 gene is related to the regulation of mood and social behavior, sleep, appetite, memory, digestion, and sexual desire. This protein is a target for antidepressant and psychostimulant drugs, thus prolonged neurotransmitter signaling remains blocked. In this study, the functional consequences of nsSNPs in the human SLC6A4 gene were explored through computational tools: PhD-SNP, SIFT, Align GVGD, PROVEAN, PMut, nsSNP Analyzer, SNPs&GO, SNAP2, PolyPhen2, and PANTHER to identify the most deleterious and damaging nsSNPs. Then the mutant protein stabilities were assessed using I-Mutant, MUpro, and MutPred2; amino acid conservation using ConSurf, and posttranslational modification analysis using MusiteDEEP and PROSPER. Furthermore, the 3-dimensional (3D) model of the mutated proteins was predicted and validated using SPARKS-X, Verify3D, and PROCHECK. The protein–ligand binding sites were analyzed using the COACH meta-server. Results from this study predicted that T192M, G342E, R607C, W282S, R104C, P131L, P156L, and N351S were the most structurally and functionally significant nsSNPs in the human SLC6A4 gene. Arg607 and Pro156 were the predicted sites for posttranslational modifications, and Thr192 and Try282 were the ligand-binding sites in the human SLC6A4 gene. The analyzed data also suggested that R104C, P131L, P156L, T192M, G342E, and W282S mutants might affect the binding of sodium ions with this protein. Taken together, this study provided important information on structurally and functionally important nsSNPs of the human SLC6A4 gene for further experimental validation. In the future, these damaging nsSNPs of the SLC6A4 gene have the potential to be evaluated as prognostic biomarkers for SLC6A4-related disorder diagnosis and research.

Emerging of composition variations of SARS-CoV-2 spike protein and human ACE2 contribute to the level of infection: in silico approaches

SARS-CoV-2 is causative of pandemic COVID-19. There is a sequence similarity between SARS-CoV-2 and SARS-CoV; however, SARS-CoV-2 RBDs (receptor-binding domain) binds 20-fold strongly with human angiotensin-converting enzyme 2 (hACE2) than SARS-CoV. The study aims to investigate protein-protein interactions (PPI) of hACE2 with SARS-CoV-2 RBD between wild and variants to detect the most influential interaction. Variants of hACE2 were retrieved from NCBI and subjected to determine the most pathogenic nsSNPs. Probability of PPIs determines the binding affinity of hACE2 genetic variants with RBD was investigated. Composition variations at the hACE2 and RBD were processed for PatchDock and refined by FireDock for the PPIs. Twelve nsSNPs were identified as the top pathogenic from SNPs (n = 7489) in hACE2 using eight bioinformatics tools. Eight RBD variants were complexed with 12 nSNPS of hACE2, and the global energy scores (Kcal/mol) were calculated and classified as very weak (-3.93 to -18.43), weak (-18.42 to -32.94), moderate (-32.94 to -47.44), strong (-47.44 to -61.95) and very strong (-61.95 to -76.46) zones. Seven composition variants in the very strong zone [G726R-G476S; R768W-V367F; Y252N-V483A; Y252N-V367F; G726R-V367F; N720D-V367F and N720D-F486L], and three in very weak [P263S-S383C; RBD-H378R; G726R-A348T] are significantly (p < 0.00001) varied for global energy score. Zonation of the five zones was established based on the scores to differentiate the effect of hACE2 and RBD variants on the binding affinity. Moreover, our findings support that the combination of hACE2 and RBD is key players for the risk of infection that should be done by further laboratory studies.Communicated by Ramaswamy H. Sarma.

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.

Computational algorithmic and molecular dynamics study of functional and structural impacts of non-synonymous single nucleotide polymorphisms in human DHFR gene

Human dihydrofolate reductase (DHFR) is a conserved enzyme that is central to folate metabolism and is widely targeted in pathogenic diseases as well as cancers. Although studies have reported the fact that genetic mutations in DHFR leads to a rare autosomal recessive inborn error of folate metabolism and drug resistance, there is a lack of an extensive study on how the deleterious non-synonymous SNPs (nsSNPs) disrupt its phenotypic effects. In this study, we aim at discovering the structural and functional consequences of nsSNPs in DHFR by employing a combined computational approach consisting of ten recently developed in silico tools for identification of damaging nsSNPs and molecular dynamics (MD) simulation for getting deeper insights into the magnitudes of damaging effects. Our study revealed the presence of 12 most deleterious nsSNPs affecting the native phenotypic effects, with three (R71T, G118D, Y122D) identified in the co-factor and ligand binding active sites. MD simulations also suggested that these three SNPs particularly Y122D, alter the overall structural flexibility and dynamics of the native DHFR protein which can provide more understandings into the crucial roles of these mutants in influencing the loss of DHFR function.

The investigation of nonsynonymous SNPs of human SLC6A4 gene associated with depression: An in silico approach

Genetic polymorphism of the SLC6A4 gene is associated with several behavioral disorders, including depression. Since studying the total nonsynonymous single nucleotide polymorphisms (nsSNPs) of the SLC6A4 gene at the population level is a difficult task, we aim to utilize in silico approach to detect the most deleterious nsSNPs of the SLC6A4 gene. In our study, 7 computational tools were used in the initial stage, including SIFT, Polyphen-2, PROVEAN, SNAP2, PhD-SNP, PANTHER, and SNPs&GO to find out the most damaging nsSNPs. In the second phase, we performed structural, functional, and stability analysis of SLC6A4 protein by popular computation tools, including I-Mutant 2.0 and MutPred2. Also, the ConSurf server was utilized to find the conserved region of the SLC6A4 protein to determine the relationship between these conserved regions with high-risk nsSNPs. Based on these analyses, 5 high-risk mutations of the SLC6A4 protein were selected. Then, we carried out comparative modeling by using the Robetta server and aligned the mutant protein model with the native protein structure. Later, we performed the post-translational modification and functional domain analysis of the SLC6A4 protein. This study concludes that Arginine → Tryptophan at position 79 and Arginine → Cysteine at position 104 are the two significant mutations in SLC6A4 protein which might play an essential role in causing diseases. Future studies should take these high-risk nsSNPs (rs1221448303, rs200953188) into consideration while exploring diseases related to the SLC6A4 gene. Besides, our research is the first-ever comprehensive in silico investigation of the SLC6A4 gene. Thus, the findings of this study could be beneficial for developing precision medicines against diseases caused by SLC6A4 malfunction. Furthermore, extensive wet-lab research and experiments on various model organisms might be helpful to investigate the precise role of these damaging nsSNPs of the SLC6A4 gene.

Computational Insights into the Deleterious Impacts of Missense Variants on N-Acetyl-d-glucosamine Kinase Structure and Function

An enzyme of the mammalian amino-sugar metabolism pathway, N-acetylglucosamine kinase (NAGK), that synthesizes N-acetylglucosamine (GlcNAc)-6-phosphate, is reported to promote dynein functions during mitosis, axonal and dendritic growth, cell migration, and selective autophagy, which all are unrelated to its enzyme activity. As non-enzymatic structural functions can be altered by genetic variation, we made an effort in this study aimed at deciphering the pathological effect of nonsynonymous single-nucleotide polymorphisms (nsSNPs) in NAGK gene. An integrated computational approach, including molecular dynamics (MD) simulation and protein-protein docking simulation, was used to identify the damaging nsSNPs and their detailed structural and functional consequences. The analysis revealed the four most damaging variants (G11R, G32R, G120E, and A156D), which are highly conserved and functional, positioned in both small (G11R and G32R) and large (G120E and A156D) domains of NAGK. G11R is located in the ATP binding region, while variants present in the large domain (G120E and A156D) were found to induce substantial alterations in the structural organizations of both domains, including the ATP and substrate binding sites. Furthermore, all variants were found to reduce binding energy between NAGK and dynein subunit DYNLRB1, as revealed by protein-protein docking and MM-GBSA binding energy calculation supporting their deleteriousness on non-canonical function. We hope these findings will direct future studies to gain more insight into the role of these variants in the loss of NAGK function and their role in neurodevelopmental disorders.

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

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

Structural and functional insights on vitamin D receptor and CYP24A1 deleterious single nucleotide polymorphisms: A computational and pharmacogenomics perpetual approach

The development of chronic kidney disease (CKD) drugs remains a challenge due to the variations in the genes. The vitamin D receptor (VDR) and Cytochrome 24A1 (CYP24A1) genetic variants might affect the drug potency, efficacy and pathway. Here we have to analyse and determine the deleterious single-nucleotide polymorphisms (nsSNPs) of VDR and CYP24A1 genes and their different population's drug responses in different populations to understand the key role in CKD. Among that the large scale of nsSNP, we used certain computational tools that predicted six missense variants are observed to be significantly damaging effect and SNP variability with large differences in various populations. Molecular docking studies were carried out by clinical and our screened compounds to VDR and CYP24A1. Docking results revealed all the compounds have a good binding affinity (Score). The screened compounds (TCM_2868 and UNPD_141613) show good binding affinity when compared to known compounds. The QM/MM study revealed that the compounds have electron transfer ability and act as a donor/acceptor to mutated proteins. The structural and conformational changes of protein complexes were analysed by molecular dynamics study. Hence, this study helps to identify suitable drugs through drug discovery in CKD treatment. The abovementioned compounds have more binding affinity, efficacy, and potency of both wild and mutant of VDR and CYP24A1.

Mutational Landscape of Pirin and Elucidation of the Impact of Most Detrimental Missense Variants That Accelerate the Breast Cancer Pathways: A Computational Modelling Study

Pirin (PIR) protein is highly conserved in both prokaryotic and eukaryotic organisms. Recently, it has been identified that PIR positively regulates breast cancer cell proliferation, xenograft tumor formation, and metastasis, through an enforced transition of G1/S phase of the cell cycle by upregulation of E2F1 expression at the transcriptional level. Keeping in view the importance of PIR in many crucial cellular processes in humans, we used a variety of computational tools to identify non-synonymous single-nucleotide polymorphisms (SNPs) in the PIR gene that are highly deleterious for the structure and function of PIR protein. Out of 173 SNPs identified in the protein, 119 are non-synonymous, and by consensus, 24 mutations were confirmed to be deleterious in nature. Mutations such as V257A, I28T, and I264S were unveiled as highly destabilizing due to a significant stability fold change on the protein structure. This observation was further established through molecular dynamics (MD) simulation that demonstrated the role of the mutation in protein structure destability and affecting its internal dynamics. The findings of this study are believed to open doors to investigate the biological relevance of the mutations and drugability potential of the protein.

Mechanistic insights on nsSNPs on binding site of renin and cytochrome P450 proteins: A computational perceptual study for pharmacogenomics evaluation

Past several decades, therapeutic investigations lead to the discovery of numerous antihypertensive drugs. Although it has been proved for their potency, altered efficacy is common norms in several conditions due to genetic variations. Cytochrome P450 plays a crucial role in drug metabolism and responsible for the pharmacokinetic and pharmacodynamic properties of the drug molecules. Here, we report the deleterious point mutations in the genes associated with the altered response of antihypertensive drug molecules and their metabolizers. Missense variants were filtered as potential nonsynonymous single nucleotide polymorphisms among the available data for the target genes (REN, CYP2D6, CYP3A4). The key objective of the work is to identify the deleterious single nucleotide polymorphisms (SNPs) responsible for the drug response and metabolism for the application of personalized medication. The molecular docking studies revealed that Aliskiren and other clinically approved drug molecules have a high binding affinity with both wild and mutant structures of renin, CYP2D6, and CYP3A4 proteins. The docking (Glide XP) score was observed to have in the range of -8.896 to -11.693 kcal/mol. The molecular dynamics simulation studies were employed to perceive the structural changes and conformational deviation through various analyses. Each studied SNPs was observed to have disparate scoring in the binding affinity to the specific drug molecules. As a prospective plan, we assume this study might be applied to identify the risky SNPs associated with hypertension from the patients to recommend the suitable drug for personalized hypertensive treatment. Further, extensive clinical pharmacogenomics studies are required to support the findings.

Implementation of computational approaches to explore the deleterious effects of non-synonymous SNPs on pRB protein

Retinoblastoma 1 (RB1) is the first discovered tumor suppressor gene and recognized as the simple model system whose encoded defective protein can cause a pediatric cancer retinoblastoma. It functions as a negative regulator of the cell cycle through the interactions with members of the E2F transcription factors family. The protein of the RB1 gene (pRB) is engaged in various cell cycle processes including apoptosis, cell cycle arrest and chromatin remodeling. Recent studies on Retinoblastoma also exhibited multiple sets of point mutation in the associated protein due to its large polymorphic information in the local database. In this study, we identified the list of disease associated non-synonymous single nucleotide polymorphisms (nsSNPs) in RB1 by incorporating different computational algorithms, web servers, modeling of the mutants and finally superimposing it. Out of 826 nsSNPs, W516G and W563G were predicted to be highly deleterious variants in the conserved regions and found to have an impact on protein structure and protein-protein interaction. Moreover, our study concludes the effect of W516G variant was more detrimental in destabilizing protein's nature as compared to W563G variant. We also found defective binding of pRB having W516G mutation with E2F2 protein. Findings of this study will aid in shortening of the expensive experimental cost of identifying disease associated SNPs in retinoblastoma for which specialized personalized treatment or therapy can be formulated.Communicated by Ramaswamy H. Sarma.

Computational prediction of the effects of non-synonymous single nucleotide polymorphisms on the GPI-anchor transamidase subunit GPI8p of Plasmodium falciparum

Drug resistance is increasingly evolving in malaria parasites; hence, it is important to discover and establish alternative drug targets. In this context, GPI-anchor transamidase (GPI-T) is a potential drug target primarily of its crucial role in the development and survival of the parasite in the GPI anchor biosynthesis pathway. The present investigation was undertaken to explore the plausible effects of nsSNP on the structure and functions of GPI-T subunit GPI8p of Plasmodium falciparum. The GPI8p (PF3D7_1128700) was analyzed using various sequence-based and structure-based computational tools such as SIFT, PROVEAN, PredictSNP, SNAP2, I-Mutant, MuPro, ConSurf, NetSurfP, MUSTER, COACH server and STRING server. Of the 34 nsSNPs submitted for functional analysis, 18 nsSNPs (R124 L, N143 K, Y145 F, V157I, T195S, K379E, I392 K, I437 T, Y438H, N439D, Y441H, N442D, N448D, N451D, D457A, D457Y, I458 L and N460 K) were predicted to have deleterious effects on the protein GPI8p. Additionally, I-Mutant 2.0 and MuPro both showed a decrease in stability after mutation as a result of these nsSNPs, suggesting the destabilization of protein. ConSurf findings suggest that most of the regions were highly conserved. In addition, COACH server was used to predict the ligand binding sites. It was found that no mutation was present at the predicted ligand binding site. The results of the STRING database showed that the protein GPI8p interacts with those proteins which either involve the biosynthetic process of attaching GPI anchor to protein or GPI anchor. The present study suggested that the GPI8p could be a novel target for anti-malarial drugs, which provides significant details for further experimentation.

Computational SNP Analysis and Molecular Simulation Revealed the Most Deleterious Missense Variants in the NBD1 Domain of Human ABCA1 Transporter

The ATP-binding cassette transporter A1 (ABCA1) is a membrane-bound exporter protein involved in regulating serum HDL level by exporting cholesterol and phospholipids to load up in lipid-poor ApoA-I and ApoE, which allows the formation of nascent HDL. Mutations in the ABCA1 gene, when presents in both alleles, disrupt the canonical function of ABCA1, which associates with many disorders related to lipid transport. Although many studies have reported the phenotypic effects of a large number of ABCA1 variants, the pathological effect of non-synonymous polymorphisms (nsSNPs) in ABCA1 remains elusive. Therefore, aiming at exploring the structural and functional consequences of nsSNPs in ABCA1, in this study, we employed an integrated computational approach consisting of nine well-known in silico tools to identify damaging SNPs and molecular dynamics (MD) simulation to get insights into the magnitudes of the damaging effects. In silico tools revealed four nsSNPs as being most deleterious, where the two SNPs (G1050V and S1067C) are identified as the highly conserved and functional disrupting mutations located in the NBD1 domain. MD simulation suggested that both SNPs, G1050V and S1067C, changed the overall structural flexibility and dynamics of NBD1, and induced substantial alteration in the structural organization of ATP binding site. Taken together, these findings direct future studies to get more insights into the role of these variants in the loss of the ABCA1 function.

CYP2R1 and CYP27A1 genes: An in silico approach to identify the deleterious mutations, impact on structure and their differential expression in disease conditions

Mutations in CYP2R1 and CYP27A1 involved in the conversion of Cholecalciferol into Calcidiol were associated with the impaired 25-hydroxylase activity therefore affecting the Vitamin D metabolism. Hence, this study attempted to understand the influence of genetic variations at the sequence and structural level via computational approach. The non-synonymous mutations retrieved from dbSNP database were assessed for their pathogenicity, stability as well as conservancy using various computational tools. The above analysis predicted 11/260 and 35/489 non-synonymous mutations to be deleterious in CYP2R1 and CYP27A1 genes respectively. Native and mutant forms of the corresponding proteins were modeled. Further, interacting native and mutant proteins with cholecalciferol showed difference in hydrogen bonds, hydrophobic bonds and their binding affinities suggesting the possible influence of these mutations in their function. Also, expression of these genes in various disease conditions was investigated using GEO datasets which predicted that there is a differential expression in cancer and arthritis.

Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA

Mucopolysaccharidosis (MPS) IIIA, also known as Sanfilippo syndrome type A, is a severe, progressive disease that affects the central nervous system (CNS). MPS IIIA is inherited in an autosomal recessive manner and is caused by a deficiency in the lysosomal enzyme sulfamidase, which is required for the degradation of heparan sulfate. The sulfamidase is produced by the N-sulphoglucosamine sulphohydrolase (SGSH) gene. In MPS IIIA patients, the excess of lysosomal storage of heparan sulfate often leads to mental retardation, hyperactive behavior, and connective tissue impairments, which occur due to various known missense mutations in the SGSH, leading to protein dysfunction. In this study, we focused on three mutations (R74C, S66W, and R245H) based on in silico pathogenic, conservation, and stability prediction tool studies. The three mutations were further subjected to molecular dynamic simulation (MDS) analysis using GROMACS simulation software to observe the structural changes they induced, and all the mutants exhibited maximum deviation patterns compared with the native protein. Conformational changes were observed in the mutants based on various geometrical parameters, such as conformational stability, fluctuation, and compactness, followed by hydrogen bonding, physicochemical properties, principal component analysis (PCA), and salt bridge analyses, which further validated the underlying cause of the protein instability. Additionally, secondary structure and surrounding amino acid analyses further confirmed the above results indicating the loss of protein function in the mutants compared with the native protein. The present results reveal the effects of three mutations on the enzymatic activity of sulfamidase, providing a molecular explanation for the cause of the disease. Thus, this study allows for a better understanding of the effect of SGSH mutations through the use of various computational approaches in terms of both structure and functions and provides a platform for the development of therapeutic drugs and potential disease treatments.

Identification of most damaging nsSNPs in human CCR6 gene: In silico analyses

Single nucleotide polymorphisms in CCR6 (C-C chemokine receptor type 6) gene have been found to be the possible cause of many diseases like rheumatoid arthritis, psoriasis, lupus nephritis and systemic sclerosis and other autoimmune diseases. Therefore, identification of structurally and functionally important polymorphisms in CCR6 is important in order to study its potential malfunctioning and discovering therapeutic targets. Several bioinformatics tools were used to identify most damaging nsSNPs that might be vital for CCR6 structure and function. The in silico tools included PROVEAN, SIFT, SNP&GO and PolyPhen2 followed by I-Mutant MutPred and ConSurf. Phyre2 and I-TASSER were used for protein 3-D Modelling while gene-gene interaction was predicted by STRING and GeneMANIA. Our study suggested that three nsSNPs rs1376162684, rs751102128 and rs1185426631 are the most damaging in CCR6 gene while 7 missense SNPs rs1438637216, rs139697820, rs768420505, rs1282264186, rs1394647982, rs769360638 and rs1263402382 are found to revert into stop codons. Prediction of post-transcriptional modifications highlighted the significance of rs1376162684 because it effected potential phosphorylation site. Gene-gene interactions showed relation of CCR6 with other genes depicting its importance in several pathways and co-expressions. In future, studying diseases related to CCR6 should include investigation of these 10 nsSNPs. Being the first of its type, this study also proposes future perspectives that will help in precision medicines. For such purposes, CCR6 proteins from patients of autoimmune diseases should be explored. Animal models can also be of significance find out the effects of CCR6 in diseases.

In silico screening of deleterious single nucleotide polymorphisms (SNPs) and molecular dynamics simulation of disease associated mutations in gene responsible for oculocutaneous albinism type 6 (OCA 6) disorder

Solute carrier family 24 member 5 (SLC24A5) is a gene that is associated with oculocutaneous albinism type 6 (OCA6) disorder and is involved in skin and hair pigmentation. It is involved in the maturation of melanosomes and melanin synthesis. SLC24A5 gene is located in the chromosomal position of 15q21.1. The present study involves the use of computational techniques in order to obtain a detailed picture of the most probable mutations that are associated with SLC24A5. From the observed result it was found that the mutation S145F is most deleterious and disease associated is predicted using several bioinformatics tools. The 3-D structures of native and mutant (S145F) were modeled in order to understand protein functionality using ab initio Robetta server. The modeled structure validation was done with ERRAT, Verify-3D, Procheck and RAMPAGE Ramachandran plot analysis. The most validated structure undergoes molecular dynamics simulations (MDS) study to understand the structural and functional behaviour of the native and mutant proteins. The MDS result showed the more flexibility in the native SLC24A5 structure. Due to mutation in the SLC24A5 protein structure it became more rigid and might disturb the conformational changes and glycosylation function of protein structure and might play role in inducing the OCA6. This study provides a significant insight into the underlying molecular mechanism involved in albinism associated with OCA6. It further helps scientists to develop a drug therapy against OCA 6 disease. Communicated by Ramaswamy H. Sarma.

In silico approach to identify non-synonymous SNPs with highest predicted deleterious effect on protein function in human obesity related gene, neuronal growth regulator 1 (NEGR1)

Neuronal growth regulator 1 (NEGR1) is a candidate gene for human obesity, which encodes the neural cell adhesion and growth molecule. The aim of the current study was to recognize the non-synonymous SNPs (nsSNPs) with the highest predicted deleterious effect on protein function of the NEGR1 gene. We have used five computational tools, namely, PolyPhen, SIFT, PROVEAN, MutPred and M-CAP, to predict the deleterious and pathogenic nsSNPs of the NEGR1 gene. Homology modeling approach was used to model the native and mutant NEGR1 protein models. Furthermore, structural validation was performed by the PROCHECK server to interpret the stability of the predicted models. We have predicted four potential deleterious nsSNPs, i.e., rs145524630 (Ala70Thr), rs267598710 (Pro168Leu), rs373419972 (Arg239Cys) and rs375352213 (Leu158Phe), which might be involved in causing obesity phenotypes. The predicted mutant models showed higher root mean square deviation and free energy values under the PyMoL and SWISS-PDB viewer, respectively. Additionally, the FTSite server predicted one nsSNP, i.e., rs145524630 (Ala70Thr) out of four identified nsSNPs found in the NEGR1 protein-binding site. There were four potential deleterious and pathogenic nsSNPs, i.e., rs145524630, rs267598710, rs373419972 and rs375352213, identified from the above-mentioned tools. In future, further functional in vitro and in vivo analysis could lead to better knowledge about these nsSNPs on the influence of the NEGR1 gene in causing human obesity. Hence, the present computational examination suggest that predicated nsSNPs may feasibly be a drug target and play an important role in contributing to human obesity.

The Nonsynonymous Polymorphisms Val276Met and Gly393Ser of E2F1 Gene are Strongly Associated with Lung, and Head and Neck Cancers

AIM

The early gene factor-2 (E2F), a family of transcription factors, is involved in cell cycle regulation. Deregulated expression of most of the members of the E2F family is associated with various human cancers. In this study, we investigated the association between the E2F1 genetic variants rs3213173 (C/T) (Val276Met) and rs3213176 (G/A) (Gly393Ser) with the risk of lung cancer (LC) and head and neck cancer (HNC) in 190 patients and 230 control samples.

MATERIALS AND METHODS

We used polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and mutagenic primer-based PCR-RFLP methods to genotype all target polymorphisms.

RESULTS

The rs3213173 (C/T) polymorphism was associated with LC risk in the homozygous model (odds ratio [OR] = 2.954, 95% confidence interval [CI] 1.366-6.386; p = 0.004) as well as in heterozygous model (OR = 2.314; 95% CI = 1.369-3.912; p = 0.001). A significant association was also observed for the rs3213176 (G/A) polymorphism with LC risk in homozygous model, GG versus AA (OR = 2.750; 95% CI = 1.236-6.118; p = 0.01); in heterozygous model, GG versus GA (OR = 2.111; 95% CI = 1.256-3.549; p = 0.004); and in combined mutant GG versus GA+AA (OR = 2.214; 95% CI = 1.343-3.650; p = 0.001). The rs3213176 (G/A) marker was also associated with HNC risk.

CONCLUSIONS

Our findings reveal that the rs3213173 (C/T) and rs3213176 (G/A) polymorphisms of the E2F1 gene are genetic risk factors for susceptibility to LC and HNC in the North Indian Population.

An Integrated Computational Framework to Assess the Mutational Landscape of α-L-Iduronidase IDUA Gene

Mucopolysaccharidosis type I is a lysosomal genetic disorder caused due to the deficiency of the α-L-iduronidase enzyme (IDUA). Mutations associated with IDUA lead to mild to severe forms of diseases characterized by different clinical features. In the present study, we first performed a comprehensive analysis using various in silico prediction tools to screen and prioritize the missense mutations or nonsynonymous SNPs (nsSNPs) associated with IDUA. Subsequently, statistical analysis was empowered to examine the predictive ability and accuracy of the in silico prediction tool results supporting the disease phenotype ranging from mild to severe. Till date, no study has been carried out in IDUA in analyzing the impact of the nsSNPs at the structural level. In this context with the aid of pathogenic and stability prediction in silico tools, we identified nsSNPs R89Q, R89W, and P533R to be most deleterious and disease-causing having impact on the function of the protein. Extensive molecular dynamics analysis was performed using Gromacs to understand the deleterious nature of the mutants. Variations observed between the trajectory files of native and mutants R89Q, R89W, and P533R using Gromacs utilities enabled us to measure the adverse effects on the protein and could be the underlying reasons for the disease pathogenesis. These findings may be helpful in understanding the genotype-phenotype relationship and molecular basis of the disease to design drugs for better treatment. J. Cell. Biochem. 119: 555-565, 2018. © 2017 Wiley Periodicals, Inc.

Mutation near the binding interfaces at α-hemoglobin stabilizing protein is highly pathogenic

Aggregation of free alpha-hemoglobin proteins forms harmful reactive oxygen radicals during the development of normal erythroid cell, which can be prevented by a chaperone, alpha hemoglobin stabilizing protein (AHSP). Mutations at the AHSP gene may affect its interacting ability with other globin proteins. Various state-of-the-art tools have been extensively used to identify the most deleterious nsSNPs at the AHSP and their pathogenic effect during AHSP-globin interaction. Comprehensive analysis revealed that the V56G of the AHS protein is the most pathogenic amino acid substitution, agreed consistently and significantly (P=1.27E-13) by all the state-of-the-art tools (PROVEAN <-2.5, SIFT=0, SNAP2 >50, SNPs&GO >0.5, PolyPhen >0.5, FATHMM >0.6, PANTHER <-3, VEST P<0.05) and protein-protein interaction analysis. The V56G exists near the hot spot and was found to be the highly pathogenic and it forms an extra helix on mutation. The unchaperoned HBA2 and KLF1 proteins with the AHSP mutant (V56G) chains denote the non-interactive nature. Binding energies were significantly varied upon highly deleterious mutation at AHSP and/or HBA1 gene. The study endorses the mutated AHSP protein, p.val56Gly for detailed confirmatory wet lab analysis.

Investigating regulatory signatures of human autophagy related gene 5 (ATG5) through functional in silico analysis

Autophagy is an essential, homeostatic process which removes damaged cellular proteins and organelles for cellular renewal. ATG5, a part of E3 ubiquitin ligase-like complex (Atg12-Atg5/Atg16L1), is a key regulator involved in autophagosome formation - a crucial phase of autophagy. In this study, we used different in silico methods for comprehensive analysis of ATG5 to investigate its less explored regulatory activity. We have predicted various physico-chemical parameters and two possible transmembrane models that helped in exposing its functional regions. Twenty four PTM sites and 44 TFBS were identified which could be targeted to modulate the autophagy pathway. Furthermore, LD analysis identified 3 blocks of genotyped SNPs and 2 deleterious nsSNPs that may have damaging impact on protein function and thus could be employed for carrying genome-wide association studies. In conclusion, the information obtained in this study could be helpful for better understanding of regulatory roles of ATG5 and provides a base for its implication in population-based studies.

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ATG5 phylogenetic analysis shows its evolutionary relationship with other species.

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Two possible models for transmembrane regions detected in ATG5.

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24 Post-translational modification sites were annotated over ATG5 domain structure.

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44 Transcription factor binding sites were identified in ATG5.

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2 nsSNPs were predicted to have damaging impact on ATG5 protein function.

In silico Analysis Revealed High-risk Single Nucleotide Polymorphisms in Human Pentraxin-3 Gene and their Impact on Innate Immune Response against Microbial Pathogens

Pentraxin-3 (PTX-3) protein is an evolutionary conserved protein that acts as a soluble pattern-recognition receptor for pathogens and plays important role in innate immune response. It recognizes various pathogens by interacting with extracellular moieties such as glactomannan of conidia (Aspergillus fumigatus), lipopolysaccharide of Pseudomonas aeruginosa, Streptococcus pneumonia and Salmonella typhimurium. Thus, PTX-3 protein helps to clear these pathogens by activating downstream innate immune process. In this study, computational methods were used to analyze various non-synonymous single nucleotide polymorphisms (nsSNPs) in PTX-3 gene. Three different databases were used to retrieve SNP data sets followed by seven different in silico algorithms to screen nsSNPs in PTX-3 gene. Sequence homology based approach was used to identify nsSNPs. Conservation profile of PTX-3 protein amino acid residues were predicted by ConSurf web server. In total, 10 high-risk nsSNPs were identified in pentraxin-domain of PTX-3 gene. Out of these 10 high-risk nsSNPs, 4 were present in the conserved structural and functional residues of the pentraxin-domain, hence, selected for structural analyses. The results showed alteration in the putative structure of pentraxin-domain. Prediction of protein–protein interactions analysis showed association of PTX-3 protein with C1q component of complement pathway. Different functional and structural residues along with various putative phosphorylation sites and evolutionary relationship were also predicted for PTX-3 protein. This is the first extensive computational analyses of pentraxin protein family with nsSNPs and will serve as a valuable resource for future population based studies.

Identification of hub glycogenes and their nsSNP analysis from mouse RNA-Seq data

Glycogenes regulate a large number of biological processes such as cancer and development. In this work, we created an interaction network of 923 glycogenes to detect potential hubs from different mouse tissues using RNA-Seq data. DAVID functional cluster analysis revealed enrichment of immune response, glycoprotein and cholesterol metabolic processes. We also explored nsSNPs that may modify the expression and function of identified hubs using computational methods. We observe that the number of nsSNPs predicted by any two methods to affect protein function is 4, 7 and 2 for FLT1, NID2 and TNFRSF1B. Residues in the native and mutant proteins were analyzed for solvent accessibility and secondary structure change. Analysis of hubs can help in determining their degree of conservation and understanding their functions in biological processes. The nsSNPs proposed in this work may be further targeted through experimental methods for understanding structural and functional relationships of hub mutants.

Performance of In Silico Tools for the Evaluation of UGT1A1 Missense Variants

Variations in the gene encoding uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) are particularly important because they have been associated with hyperbilirubinemia in Gilbert's and Crigler-Najjar syndromes as well as with changes in drug metabolism. Several variants associated with these phenotypes are nonsynonymous single-nucleotide polymorphisms (nsSNPs). Bioinformatics approaches have gained increasing importance in predicting the functional significance of these variants. This study was focused on the predictive ability of bioinformatics approaches to determine the pathogenicity of human UGT1A1 nsSNPs, which were previously characterized at the protein level by in vivo and in vitro studies. Using 16 Web algorithms, we evaluated 48 nsSNPs described in the literature and databases. Eight of these algorithms reached or exceeded 90% sensitivity and six presented a Matthews correlation coefficient above 0.46. The best-performing method was MutPred, followed by Sorting Intolerant from Tolerant (SIFT). The prediction measures varied significantly when predictors such us SIFT, polyphen-2, and Prediction of Pathological Mutations on Proteins were run with their native alignment generated by the tool, or with an input alignment that was strictly built with UGT1A1 orthologs and manually curated. Our results showed that the prediction performance of some methods based on sequence conservation analysis can be negatively affected when nsSNPs are positioned at the hypervariable or constant regions of UGT1A1 ortholog sequences.

The Contribution of Missense Mutations in Core and Rim Residues of Protein-Protein Interfaces to Human Disease

Missense mutations at protein–protein interaction sites, called interfaces, are important contributors to human disease. Interfaces are non-uniform surface areas characterized by two main regions, “core” and “rim”, which differ in terms of evolutionary conservation and physicochemical properties. Moreover, within interfaces, only a small subset of residues (“hot spots”) is crucial for the binding free energy of the protein–protein complex.

We performed a large-scale structural analysis of human single amino acid variations (SAVs) and demonstrated that disease-causing mutations are preferentially located within the interface core, as opposed to the rim (p < 0.01). In contrast, the interface rim is significantly enriched in polymorphisms, similar to the remaining non-interacting surface. Energetic hot spots tend to be enriched in disease-causing mutations compared to non-hot spots (p = 0.05), regardless of their occurrence in core or rim residues. For individual amino acids, the frequency of substitution into a polymorphism or disease-causing mutation differed to other amino acids and was related to its structural location, as was the type of physicochemical change introduced by the SAV.

In conclusion, this study demonstrated the different distribution and properties of disease-causing SAVs and polymorphisms within different structural regions and in relation to the energetic contribution of amino acid in protein–protein interfaces, thus highlighting the importance of a structural system biology approach for predicting the effect of SAVs.

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Protein–protein interactions are fundamental in all biological processes.

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The distribution of deleterious and non-SAVs within protein interfaces is unknown.

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The distribution of deleterious SAVs differs within different interface structural regions.

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The distribution of SAVs differs in relation to interface residues energetic contribution.

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Structural analysis of protein complexes enhances the understanding of deleterious SAVs.

Abundant sequence divergence in the native Japanese cattle Mishima-Ushi (Bos taurus) detected using whole-genome sequencing

The native Japanese cattle Mishima-Ushi, a designated national natural treasure, are bred on a remote island, which has resulted in the conservation of their genealogy. We examined the genetic characteristics of 8 Mishima-Ushi individuals by using single nucleotide polymorphisms (SNPs), insertions, and deletions obtained by whole-genome sequencing. Mapping analysis with various criteria showed that predicted heterozygous SNPs were more prevalent than predicted homozygous SNPs in the exonic region, especially non-synonymous SNPs. From the identified 6.54 million polymorphisms, we found 400 non-synonymous SNPs in 313 genes specific to each of the 8 Mishima-Ushi individuals. Additionally, 3,170,833 polymorphisms were found between the 8 Mishima-Ushi individuals. Phylogenetic analysis confirmed that the Mishima-Ushi population diverged from another strain of Japanese cattle. This study provides a framework for further genetic studies of Mishima-Ushi and research on the function of SNP-containing genes as well as understanding the genetic relationship between the domestic and native Japanese cattle breeds.