Computational SNP analysis: current approaches and future prospects

Elucidating the role of MLL1 nsSNPs: Structural and functional alterations and their contribution to leukemia development

(1) BACKGROUND

The Mixed lineage leukemia 1 (MLL1) gene, located on chromosome 11q23, plays a pivotal role in histone lysine-specific methylation and is consistently associated with various types of leukemia. Non-synonymous Single Nucleotide Polymorphisms (nsSNPs) have been tied to numerous diseases, including cancers, and have become valuable cancer biomarkers. There's a notable gap in studies probing the influence of SNPs on MLL1 protein structure, function, and subsequent modifications.

(2) METHODS

We utilized an array of bioinformatics tools, including PredictSNP, InterPro, ConSurf, I-Mutant2.0, MUpro, Musitedeep, Project HOPE, RegulomeDB, Mutpred2, and both CScape and CScape Somatic, to meticulously analyze the consequences of nsSNPs in the MLL1 gene.

(3) RESULTS

Out of 2,097 nsSNPs analyzed, 62 were determined to be significantly pathogenic by the PredictSNP tool, with ten crucial MLL1 functional domains identified using InterPro. Additionally, 50 of these nsSNPs had high conservation scores, hinting at potential effects on protein structure and function, while 32 were found to undermine MLL1 protein stability. Notably, four nsSNPs were deemed oncogenic, with two identified as cancer drivers. The nsSNP, D2724G, between the MLL1 protein's FY-rich domains, could disrupt proteolytic cleavage, altering gene expression patterns and potentially promoting cancer.

(4) CONCLUSIONS

Our research provides a comprehensive assessment of nsSNPs' impact in the MLL1 protein structure and function and consequently on leukemia development, suggesting potential avenues for personalized treatment, early detection, improved prognosis, and a deeper understanding of hematological malignancy genesis.

Stack-HDAC3i: A high-precision identification of HDAC3 inhibitors by exploiting a stacked ensemble-learning framework

Epigenetics involves reversible modifications in gene expression without altering the genetic code itself. Among these modifications, histone deacetylases (HDACs) play a key role by removing acetyl groups from lysine residues on histones. Overexpression of HDACs is linked to the proliferation and survival of tumor cells. To combat this, HDAC inhibitors (HDACi) are commonly used in cancer treatments. However, pan-HDAC inhibition can lead to numerous side effects. Therefore, isoform-selective HDAC inhibitors, such as HDAC3i, could be advantageous for treating various medical conditions while minimizing off-target effects. To date, computational approaches that use only the SMILES notation without any experimental evidence have become increasingly popular and necessary for the initial discovery of novel potential therapeutic drugs. In this study, we develop an innovative and high-precision stacked-ensemble framework, called Stack-HDAC3i, which can directly identify HDAC3i using only the SMILES notation. Using an up-to-date benchmark dataset, we first employed both molecular descriptors and Mol2Vec embeddings to generate feature representations that cover multi-view information embedded in HDAC3i, such as structural and contextual information. Subsequently, these feature representations were used to train baseline models using nine popular ML algorithms. Finally, the probabilistic features derived from the selected baseline models were fused to construct the final stacked model. Both cross-validation and independent tests showed that Stack-HDAC3i is a high-accuracy prediction model with great generalization ability for identifying HDAC3i. Furthermore, in the independent test, Stack-HDAC3i achieved an accuracy of 0.926 and Matthew's correlation coefficient of 0.850, which are 0.44-6.11% and 0.83-11.90% higher than its constituent baseline models, respectively.

Integrated metabolomics and serum-feces pharmacochemistry-based network pharmacology to reveal the mechanisms of an herbal prescription against ulcerative colitis

BACKGROUND

CDD-2103 is an herbal prescription used to treat ulcerative colitis (UC). This study aimed to uncover its mechanism by integrating metabolomics and serum-feces pharmacochemistry-based network pharmacology.

METHODS

A DSS-induced chronic colitis mice model was used to evaluate the anti-colitis effect of CDD-2103. Serum and feces metabolomics were conducted to identify differential metabolites and pathways. In the serum-feces pharmacochemistry study, biological samples were collected from rats treated with CDD-2103. Then, network pharmacology was utilized to predict the targets of the identified compounds. Critical genes were extracted through the above-integrated analysis. The interactions between targets, CDD-2103, and its compounds were validated through molecular docking, immunoblotting, and enzyme activity assays.

RESULTS

CDD-2103 alleviated ulcerous symptoms and colonic injuries in colitis mice. Metabolomics study identified differential metabolites associated with tryptophan, glycerophospholipid, and linoleic acid metabolisms. The serum-feces pharmacochemistry study revealed twenty-three compounds, which were subjected to network pharmacology analysis. Integration of these results identified three key targets (AHR, PLA2, and PTGS2). Molecular docking showed strong affinities between the compounds and targets. PTGS2 was identified as a hub gene targeted by most CDD-2103 compounds. Immunoblotting and enzyme activity assays provided further evidence that CDD-2103 alleviates UC, potentially through its inhibitory effect on cyclooxygenase-2 (COX-2, encoded by PTGS2), with alkaloids and curcuminoids speculated as crucial anti-inflammatory compounds.

CONCLUSION

This integrated strategy reveals the mechanism of CDD-2103 and provides insights for developing herbal medicine-based therapies for UC.

Molecular insights into the interactions between PEG carriers and drug molecules from Celastrus hindsii: a multi-scale simulation study

Efficient drug delivery is crucial for the creation of effective pharmaceutical treatments, and polyethylene glycol (PEG) carriers have been emerged as promising candidates for this purpose due to their bio-compatibility, enhancement of drug solubility, and stability. In this study, we utilized molecular simulations to examine the interactions between PEG carriers and selected drug molecules extracted from Celastrus hindsii: Hindsiilactone A, Hindsiiquinoflavan B, Maytenfolone A, and Celasdin B. The simulations provided detailed insights into the binding affinity, stability, and structural properties of these drug molecules when complexed with PEG carriers. A multi-scale approach combining density functional theory (DFT), extended tight-binding (xTB), and molecular dynamics (MD) simulations was conducted to investigate both unbound and bound states of PEG/drug systems. The results from DFT and xTB calculations revealed that the unbound complex has an unfavorable binding free energy, primarily due to negative contributions of delta solvation free energy and entropy. The MD simulations provided more detailed insights into the interactions between PEG and drug molecules in water solutions. By integrating the findings from the multi-scale simulations, a comprehensive picture of the unbound and bound states of PEG and drug systems were obtained. This information is valuable for understanding the molecular mechanisms governing the binding of drugs in PEG-based delivery platforms, and it contributes to the rational design and optimization of these systems.

Stilbenoids from fenugreek seeds alleviate insulin resistance by regulating the PI3K/AKT/mTOR signaling pathway in a type 2 diabetes zebrafish model

Insulin resistance (IR) is the main cause of type 2 diabetes mellitus (T2DM). The specific targets and underlying mechanisms responsible for the ameliorative effects of the stilbenoid compounds found in fenugreek seeds for ameliorating IR require further study. Here, we were predicted by using the network pharmacology prediction, molecular docking and molecular dynamics simulation approach the targets in common and the potential mechanismsof three stilbenoid compounds (rhaponticin, desoxyrhaponticin, and rhapontigenin) in relation to T2DM and IR. The results showed that the compounds may improve IR through the phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) signaling pathway. Molecular docking studies revealed that they exhibit high binding affinity with the structural domains of peroxisome proliferator-activated receptor gamma (PPARG), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), PI3K, and AKT. These results suggest that PPARG and GAPDH may be the potential targets for these three compounds in the treatment of T2DM.Subsequently, experiments using the zebrafish T2DM model showed that the stilbenoid compounds had varying degrees of efficacy in improving IR through the PI3K/AKT/mTOR signaling pathway, and rhaponticin had the most promising effects. The findings implicate a potential mechanism of action for the three stilbenoid compounds in enhancing insulin resistance (IR) through modulation of the PI3K/AKT/mTOR pathway.

Transcriptomic and proteomic investigations identify PI3K-akt pathway targets for hyperthyroidism management in rats via polar iridoids from radix Scrophularia

High-polarity iridoids from Radix Scrophulariae (R. Scrophulariae) offer a range of benefits, including anti-inflammatory, antioxidant, antitumour, antibacterial, antiviral, and antiallergic effects. Although previous studies have indicated the potential of R. Scrophulariae for hyperthyroidism prevention and treatment, the specific active compounds involved and their mechanisms of action are not fully understood. This study explored the effects of high-polarity iridoid glycosides from R. Scrophulariae on hyperthyroidism induced in rats by levothyroxine sodium. The experimental design included a control group, a hyperthyroidism model group, and a group treated with iridoid glycosides. Serum triiodothyronine (T3) and thyroxine (T4) levels were quantified using an enzyme-linked immunosorbent assay (ELISA). Transcriptomic and proteomic analyses were applied to liver samples to identify differentially expressed genes and proteins. These analyses were complemented by trend analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The effectiveness of key factors was further examined through molecular biology techniques. ELISA results indicated a notable increase in T3 and T4 in the hyperthyroid rats, which was significantly mitigated by treatment with iridoid glycosides. Transcriptomic analysis revealed 6 upregulated and 6 downregulated genes in the model group, showing marked improvement following treatment. Proteomic analysis revealed changes in 30 upregulated and 50 downregulated proteins, with improvements observed upon treatment. The PI3K-Akt signalling pathway was investigated through KEGG enrichment analysis. Molecular biology methods verified the upregulation of Spp1, Thbs1, PI3K, and Akt in the model group, which was reversed in the treatment group. This study revealed that highly polar iridoids from R. Scrophulariae can modulate the Spp1 gene and Thbs1 protein via the PI3K-Akt signalling pathway, suggesting a therapeutic benefit for hyperthyroidism and providing a basis for drug development targeting this condition.

Exploring the effect of disease causing mutations in metal binding sites of human ARSA in metachromatic leukodystrophy

The arylsulfatase A (ARSA) gene is observed to be deficient in patients with metachromatic leukodystrophy (MLD), a type of lysosomal storage disease. MLD is a severe neurodegenerative disorder characterized by an autosomal recessive inheritance pattern. This study aimed to map the most deleterious mutations at the metal binding sites of ARSA and the amino acids in proximity to the mutated positions. We utilized an array of computational tools, including PredictSNP, MAPP, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT, SNAP, and ConSurf, to identify the most detrimental mutations potentially implicated in MLD collected from UniProt, ClinVar, and HGMD. Two mutations, D29N and D30H, as being extremely deleterious based on assessments of pathogenicity, conservation, biophysical characteristics, and stability analysis. The D29 and D30 are located at the metal-interacting regions of ARSA and found to undergo post-translational modification, specifically phosphorylation. Henceforth, the in-depth effect of metal binding upon mutation was examined using molecular dynamics simulations (MDS) before and after phosphorylation. The MDS results exhibited high deviation for the D29N and D30H mutations in comparison to the native, and the same was confirmed by significant residue fluctuation and reduced compactness. These structural alterations suggest that such mutations may influence protein functionality, offering potential avenues for personalized therapeutic and providing a basis for potential mutation-specific treatments for severe MLD patients.

Molecular Mechanism of Yangshen Maidong Decoction in the Treatment of Chronic Heart Failure based on Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations

Chronic heart failure (CHF) is a complex multifactorial clinical syndrome leading to abnormal cardiac structure and function. The severe form of this ailment is characterized by high disability, high mortality, and morbidity. Worldwide, 2-17% of patients die at first admission, of which 17-45% die within 1 year of admission and >50% within 5 years. Yangshen Maidong Decoction (YSMDD) is frequently used to treat the deficiency and pain of the heart. The specific mechanism of action of YSMDD in treating CHF, however, remains unclear. Therefore, a network pharmacology-based strategy combined with molecular docking and molecular dynamics simulations was employed to investigate the potential molecular mechanism of YSMDD against CHF. The effective components and their targets of YSMDD and related targets of CHF were predicted and screened based on the public database. The network pharmacology was used to explore the potential targets and possible pathways that involved in YSMDD treated CHF. Molecular docking and molecular dynamics simulations were performed to elucidate the binding affinity between the YSMDD and CHF targets. Screen results, 10 main active ingredients, and 6 key targets were acquired through network pharmacology analysis. Pathway enrichment analysis showed that intersectional targets associated pathways were enriched in the Prostate cancer pathway, Hepatitis B pathway, and C-type lectin receptor signaling pathways. Molecular docking and molecular dynamics simulations analysis suggested 5 critical active ingredients have high binding affinity to the 5 key targets. This research shows the multiple active components and molecular mechanisms of YSMDD in the treatment of CHF and offers resources and suggestions for future studies.

Role of CSF1R 550th-tryptophan in kusunokinin and CSF1R inhibitor binding and ligand-induced structural effect

Binding affinity is an important factor in drug design to improve drug-target selectivity and specificity. In this study, in silico techniques based on molecular docking followed by molecular dynamics (MD) simulations were utilized to identify the key residue(s) for CSF1R binding affinity among 14 pan-tyrosine kinase inhibitors and 15 CSF1R-specific inhibitors. We found tryptophan at position 550 (W550) on the CSF1R binding site interacted with the inhibitors' aromatic ring in a π-π way that made the ligands better at binding. Upon W550-Alanine substitution (W550A), the binding affinity of trans-(-)-kusunokinin and imatinib to CSF1R was significantly decreased. However, in terms of structural features, W550 did not significantly affect overall CSF1R structure, but provided destabilizing effect upon mutation. The W550A also did not either cause ligand to change its binding site or conformational changes due to ligand binding. As a result of our findings, the π-π interaction with W550's aromatic ring could be still the choice for increasing binding affinity to CSF1R. Nevertheless, our study showed that the increasing binding to W550 of the design ligand may not ensure CSF1R specificity and inhibition since W550-ligand bound state did not induce significantly conformational change into inactive state.

Ligand-based drug design against Herpes Simplex Virus-1 capsid protein by modification of limonene through in silico approaches

The pharmacological effects of limonene, especially their derivatives, are currently at the forefront of research for drug development and discovery as well and structure-based drug design using huge chemical libraries are already widespread in the early stages of therapeutic and drug development. Here, various limonene derivatives are studied computationally for their potential utilization against the capsid protein of Herpes Simplex Virus-1. Firstly, limonene derivatives were designed by structural modification followed by conducting a molecular docking experiment against the capsid protein of Herpes Simplex Virus-1. In this research, the obtained molecular docking score exhibited better efficiency against the capsid protein of Herpes Simplex Virus-1 and hence we conducted further in silico investigation including molecular dynamic simulation, quantum calculation, and ADMET analysis. Molecular docking experiment has documented that Ligands 02 and 03 had much better binding affinities (- 7.4 kcal/mol and - 7.1 kcal/mol) to capsid protein of Herpes Simplex Virus-1 than Standard Acyclovir (- 6.5 kcal/mol). Upon further investigation, the binding affinities of primary limonene were observed to be slightly poor. But including the various functional groups also increases the affinities and capacity to prevent viral infection of the capsid protein of Herpes Simplex Virus-1. Then, the molecular dynamic simulation confirmed that the mentioned ligands might be stable during the formation of drug-protein complexes. Finally, the analysis of ADMET was essential in establishing them as safe and human-useable prospective chemicals. According to the present findings, limonene derivatives might be a promising candidate against the capsid protein of Herpes Simplex Virus-1 which ultimately inhibits Herpes Simplex Virus-induced encephalitis that causes interventions in brain inflammation. Our findings suggested further experimental screening to determine their practical value and utility.

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.

Structural analysis and conformational dynamics of SOCS1 gene mutations involved in diffuse large B-cell lymphoma

OBJECTIVES

The SOCS1 gene is frequently mutated in primary Diffuse Large B-Cell Lymphoma (DLBCL) patients and is associated with a reduced survival rate. Using various computational techniques, the current study aims to identify Single Nucleotide Polymorphisms (SNPs) in the SOCS1 gene that are associated with the mortality rate of DLBCL patients. This study also evaluates the effects of SNPs on the structural instability of the SOCS1 protein in DLBCL patient.

METHODS

The cBioPortal webserver was used for mutations and determining how the SNP mutations affect the SOCS1 protein with various algorithms (PolyPhen-2.0, Provean, PhD-SNPg, SNPs&GO, SIFT, FATHMM, Predict SNP and SNAP). Five webservers (I-Mutant 2.0, MUpro, mCSM, DUET and SDM) were used for protein instability and the conserved status and were also predicted through different tools (ConSurf, Expasy, SOMPA). Lastly, MD simulations were run on the two chosen mutations (S116N and V128G) using GROMACS 5.0.1 to study how the mutations change the structure of SOCS1.

RESULTS

Among the 93 SOCS1 mutations detected in DLBCL patients, nine mutations were found to have a detrimental effect (damaging/deleterious/pathogenic/altered) on the SOCS1 protein. All the nine selected mutations are in the conserved region and four are on the extended strand site, four on the random coil site and one on the alpha helix position of the secondary protein structure. After anticipating the structural effects of these nine mutations, two were chosen (S116N and V128G) based on mutational frequency, location within the protein, structural effect (primary, secondary and tertiary) on stability and conservation status within the SOCS1 protein. The simulation of a 50 ns time interval revealed that the Rg value of S116N (2.17 nm) is higher than that of WT (1.98 nm), indicating a loss of structural compactness. In the case of the RMSD value, this mutated type (V128G) shows more deviation (1.54 nm) in comparison to the wild-type (2.14 nm) and another mutant type (S116N) (2.12 nm). The average RMSF values of wild-type and mutant types (V128G and S116N) were 0.88 nm, 0.49 nm, and 0.93 nm, respectively. The RMSF result shows that the mutant V128G structure is more stable than the wild-type and mutant S116N structures.

CONCLUSION

Based on all these computational predictions, this study finds that certain mutations, particularly S116N, have a destabilising and robust effect on the SOCS1 protein. These results can be used to learn more about the importance of SOCS1 mutations in DLBCL patients and to develop new ways to treat DLBCL.

In silico comprehensive analysis of coding and non-coding SNPs in human mTOR protein

The mammalian/mechanistic target of rapamycin (mTOR) protein is an important growth regulator and has been linked with multiple diseases including cancer and diabetes. Non-synonymous mutations of this gene have already been found in patients with renal clear cell carcinoma, melanoma, and acute lymphoid leukemia among many others. Such mutations can potentially affect a protein’s structure and hence its functions. In this study, therefore, the most deleterious SNPs of mTOR protein have been determined to identify potential biomarkers for various disease treatments. The aim is to generate a structured dataset of the mTOR gene’s SNPs that may prove to be an asset for the identification and treatment of multiple diseases associated with the target gene. Both sequence and structure-based approaches were adopted and a wide variety of bioinformatics tools were applied to analyze the SNPs of mTOR protein. In total 11 nsSNPs have been filtered out of 2178 nsSNPs along with two non-coding variations. All of the nsSNPs were found to destabilize the protein structure and disrupt its function. While R619C, A1513D, and T1977R mutations were shown to alter C alpha distances and bond angles of the mTOR protein, L509Q, R619C and N2043S were predicted to disrupt the mTOR protein’s interaction with NBS1 protein and FKBP1A/rapamycin complex. In addition, one of the non-coding SNPs was shown to alter miRNA binding sites. Characterizing nsSNPs and non-coding SNPs and their harmful effects on a protein’s structure and functions will enable researchers to understand the critical impact of mutations on the molecular mechanisms of various diseases. This will ultimately lead to the identification of potential targets for disease diagnosis and therapeutic interventions.

High-Risk Polymorphisms Associated with the Molecular Function of Human HMGCR Gene Infer the Inhibition of Cholesterol Biosynthesis

HMG-CoA reductase or HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase) is a rate-limiting enzyme involved in cholesterol biosynthesis. HMGCR plays an important role in the possible occurrence of hypercholesterolemia leading to atherosclerosis and coronary heart disease. This enzyme is a major target for cholesterol-lowering drugs such as "statin" which blocks the synthesis of mevalonate, a precursor for cholesterol biosynthesis. This study is aimed at characterizing deleterious mutations and classifying functional single nucleotide polymorphisms (SNPs) of the HMGCR gene through analysis of functional and structural evaluation, domain association, solvent accessibility, and energy minimization studies. The functional and characterization tools such as SIFT, PolyPhen, SNPs and GO, Panther, I-Mutant, and Pfam along with programming were employed to explore all the available SNPs in the HMGCR gene in the database. Among 6815 SNP entries from different databases, approximately 388 SNPs were found to be missense. Analysis showed that seven missense SNPs are more likely to have deleterious effects. A tertiary model of the mutant protein was constructed to determine the functional and structural effects of the HMGCR mutation. In addition, the location of the mutations suggests that they may have deleterious effects because most of the mutations are residing in the functional domain of the protein. The findings from the analysis predicted that rs147043821 and rs193026499 missense SNPs could cause significant structural and functional instability in the mutated proteins of the HMGCR gene. The findings of the current study will likely be useful in future efforts to uncover the mechanism and cause of hypercholesterolemia. In addition, the identified SNPs of HMGCR gene could set up a strong foundation for further therapeutic discovery.

Isolation and structure elucidation of a steroidal moiety from Withania somnifera and in silico evaluation of antimalarial efficacy against artemisinin resistance Plasmodium falciparum kelch 13 protein

According to the 2021 Malaria report, 241 million clinical episodes with 627000 deaths penalty was estimated across the worldwide. However, mutation in the propeller domain of Plasmodium falciparum kelch 13 protein resulted in longer parasite clearance time following an artemisinin-based treatment and had a greater survival rate of ring-stage parasites even after a brief exposure to a high dose of artesunate. Clinical manifestations become more complex and worse with the emerging trend of drug resistance against artemisinin derivatives and the poor effectiveness of malaria vaccination drive. Steroidal lactone (withanolide) moiety (C-28) isolated from methanolic leaf extract Withania somnifera show a greater affinity towards Pfkelch 13 protein in comparison to the artemisinin derivatives (artesunate, artemether). The isolated compound was characterized to be withaferin A with a percentage yield of 29.01% w/w in chloroform fraction, 1.75% w/w in methanolic extract, and 0.29% w/w in raw leaf powder. Structure-based analysis shows that withaferin A (docking score -8.253, -9.802) has a higher affinity for two distinct binding pockets I and II of the Plasmodium falciparum kelch 13 protein than artesunate (docking score -4.470, -3.656). Further, Gibbs binding free energy signifies thermodynamic stability of the docked complex of withaferin A (-43.25, -43.76 Kcal/mol) in comparison to artesunate docked complex (-8.49, -5.75 Kcal/mol). The pharmacokinetic profile of withaferin A shows more drug-likeness characteristics without violating Jorgensen's rule of three, and Lipinski's rule of five. Hence above experimental findings suggest withaferin A could be a suitable therapeutic adjunct for preclinical evaluation of antimalarial potentiality in artemisinin-resistant malaria.

Identification, analysis of deleterious SNPs of the human GSR gene and their effects on the structure and functions of associated proteins and other diseases

Hereditary glutathione reductase deficiency, caused by mutations of the GSR gene, is an autosomal recessive disorder characterized by decreased glutathione disulfide (GSSG) reduction activity and increased thermal instability. This study implemented computational analysis to screen the most likely mutation that might be associated with hereditary glutathione reductase deficiency and other diseases. Using ten online computational tools, the study revealed four nsSNPs among the 17 nsSNPs identified as most deleterious and disease associated. Structural analyses and evolutionary confirmation study of native and mutant GSR proteins using the HOPE project and ConSruf. HOPE revealed more flexibility in the native GSR structure than in the mutant structure. The mutation in GSR might be responsible for changes in the structural conformation and function of the GSR protein and might also play a significant role in inducing hereditary glutathione reductase deficiency. LD and haplotype studies of the gene revealed that the identified variations rs2978663 and rs8190955 may be responsible for obstructive heart defects (OHDs) and hereditary anemia, respectively. These interethnic differences in the frequencies of SNPs and haplotypes might help explain the unpredictability that has been reported in association studies and can contribute to predicting the pharmacokinetics and pharmacodynamics of drugs that make use of GSR.

Structural modeling of human AKAP3 protein and in silico analysis of single nucleotide polymorphisms associated with sperm motility

AKAP3 is a member of the A-kinase anchoring proteins and it is a constituent of the sperm fibrous sheath. AKAP3 is needed for the formation of sperm flagellum structure, sperm motility, and male fertility. This study aims to model the AKAP3 tertiary structure and identify the probable impact of four mutations characterized in infertile men on the AKAP3 structure. The T464S, I500T, E525K, and I661T substitutions were analyzed using in silico methods. The secondary structure and three-dimensional model of AKAP3 were determined using PSI-BLAST based secondary structure prediction and Robetta servers. The TM-score was used to quantitatively measure the structural similarities between native and mutated models. All of the desired substitutions were classified as benign. I-Mutant results showed all of the substitutions decreased AKAP3 stability; however, the I500T and I661T were more effective. Superposition and secondary structure comparisons between native and mutants showed no dramatic deviations. Our study provided an appropriate model for AKAP3. Destabilization of AKAP3 caused by these substitutions did not appear to induce structural disturbances. As AKAP3 is involved in male infertility, providing more structural insights and the impact of mutations that cause protein functional diversity could elucidate the etiology of male fertility problems at molecular level.

In-silico phenotype prediction by normal mode variant analysis in TUBB4A-related disease

TUBB4A-associated disorder is a rare condition affecting the central nervous system. It displays a wide phenotypic spectrum, ranging from isolated late-onset torsion dystonia to a severe early-onset disease with developmental delay, neurological deficits, and atrophy of the basal ganglia and cerebellum, therefore complicating variant interpretation and phenotype prediction in patients carrying TUBB4A variants. We applied entropy-based normal mode analysis (NMA) to investigate genotype–phenotype correlations in TUBB4A-releated disease and to develop an in-silico approach to assist in variant interpretation and phenotype prediction in this disorder. Variants included in our analysis were those reported prior to the conclusion of data collection for this study in October 2019. All TUBB4A pathogenic missense variants reported in ClinVar and Pubmed, for which associated clinical information was available, and all benign/likely benign TUBB4A missense variants reported in ClinVar, were included in the analysis. Pathogenic variants were divided into five phenotypic subgroups. In-silico point mutagenesis in the wild-type modeled protein structure was performed for each variant. Wild-type and mutated structures were analyzed by coarse-grained NMA to quantify protein stability as entropy difference value (ΔG) for each variant. Pairwise ΔG differences between all variant pairs in each structural cluster were calculated and clustered into dendrograms. Our search yielded 41 TUBB4A pathogenic variants in 126 patients, divided into 11 partially overlapping structural clusters across the TUBB4A protein. ΔG-based cluster analysis of the NMA results revealed a continuum of genotype–phenotype correlation across each structural cluster, as well as in transition areas of partially overlapping structural clusters. Benign/likely benign variants were integrated into the genotype–phenotype continuum as expected and were clearly separated from pathogenic variants. We conclude that our results support the incorporation of the NMA-based approach used in this study in the interpretation of variant pathogenicity and phenotype prediction in TUBB4A-related disease. Moreover, our results suggest that NMA may be of value in variant interpretation in additional monogenic conditions.

Functional and structural analysis of non-synonymous single nucleotide polymorphisms (nsSNPs) in the MYB oncoproteins associated with human cancer

MYB proteins are highly conserved DNA-binding domains (DBD) and mutations in MYB oncoproteins have been reported to cause aberrant and augmented cancer progression. Identification of MYB molecular biomarkers predictive of cancer progression can be used for improving cancer management. To address this, a biomarker discovery pipeline was employed in investigating deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in predicting damaging and potential alterations on the properties of proteins. The nsSNP of the MYB family; MYB, MYBL1, and MYBL2 was extracted from the NCBI database. Five in silico tools (PROVEAN, SIFT, PolyPhen-2, SNPs&GO and PhD-SNP) were utilized to investigate the outcomes of nsSNPs. A total of 45 nsSNPs were predicted as high-risk and damaging, and were subjected to PMut and I-Mutant 2.0 for protein stability analysis. This resulted in 32 nsSNPs with decreased stability with a DDG score lower than - 0.5, indicating damaging effect. G111S, N183S, G122S, and S178C located within the helix-turn-helix (HTH) domain were predicted to be conserved, further posttranslational modifications and 3-D protein analysis indicated these nsSNPs to shift DNA-binding specificity of the protein thus altering the protein function. Findings from this study would help in the field of pharmacogenomic and cancer therapy towards better intervention and management of cancer.

Integrative Bioinformatics approaches to therapeutic gene target selection in various cancers for Nitroglycerin

Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.

Missense mutations involvement in COX-2 structure, and protein-substrate binding affinity: in-silico study

Cyclooxygenase-2 (COX-2) is an inducible inflammatory enzyme, which produces prostanoids from arachidonic acid. COX-2 overexpression and over-activity can cause inflammation, tumorigenesis, and angiogenesis. Prostanoids are the main reason for the inflammation, and increase of mitogenesis by COX-2. So, any change such as mutations that can lead to COX-2 over-activity could ignite the tumor situations with increase of prostanoids production is one of its ways. The aim of this study was to check the effect of 166 missense mutations of COX-2 on protein features that can affect the COX-2 activity such as protein stability, fluctuation, 2D structure, and its binding affinity with the substrate by in silico methods, network modeling, and docking calculations, by which 44 of them shown to be deleterious. Among them, the S124I and S474F mutations can increase the stability of the protein. 11.36% of deleterious nsSNPs were part of the substrate-binding region among which the M508T, H337R, and V511G have the potential to affect the protein by 2D structure alteration. V511G can improve binding affinity and H337R showed a small decrease in the deformation overall energy that can represent a decrease in the stability of COX-2. Also, L517S showed a significant decrease in the binding power of COX-2/substrate but based on the anisotropic network modeling this mutation has a dual effect on COX-2 stability. These nsSNPs/mutations have the potential causing an increase or decrease of tumorigenesis because increasing of COX-2 stability and its binding affinity can lead to altering its activity.

Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays

Single-nucleotide polymorphisms (SNPs) are the most common genetic variations for various complex human diseases, including cancers. Genome-wide association studies (GWAS) have identified numerous SNPs that increase cancer risks, such as breast cancer, colorectal cancer, and leukemia. These SNPs were cataloged for scientific use. However, GWAS are often conducted on certain populations in which the Orang Asli and Malays were not included. Therefore, we have developed a bioinformatic pipeline to mine the whole-genome sequence databases of the Orang Asli and Malays to determine the presence of pathogenic SNPs that might increase the risks of cancers among them. Five different in silico tools, SIFT, PROVEAN, Poly-Phen-2, Condel, and PANTHER, were used to predict and assess the functional impacts of the SNPs. Out of the 80 cancer-related nsSNPs from the GWAS dataset, 52 nsSNPs were found among the Orang Asli and Malays. They were further analyzed using the bioinformatic pipeline to identify the pathogenic variants. Three nsSNPs; rs1126809 (TYR), rs10936600 (LRRC34), and rs757978 (FARP2), were found as the most damaging cancer pathogenic variants. These mutations alter the protein interface and change the allosteric sites of the respective proteins. As TYR, LRRC34, and FARP2 genes play important roles in numerous cellular processes such as cell proliferation, differentiation, growth, and cell survival; therefore, any impairment on the protein function could be involved in the development of cancer. rs1126809, rs10936600, and rs757978 are the important pathogenic variants that increase the risks of cancers among the Orang Asli and Malays. The roles and impacts of these variants in cancers will require further investigations using in vitro cancer models.

CARD8 polymorphism rs2043211 protects against noise-induced hearing loss by causing the dysfunction of CARD8 protein

Inflammation is a complicated process and is considered to be responsible for the development of noise-induced hearing loss (NIHL). CARD8 is an important component of inflammasome that has been implicated in inflammation. To decide the relationship between the polymorphisms of CARD8 gene and NIHL risk and deduce the potential mechanism, three SNPs (rs2043211, rs1062808, and rs12459322) were genotyped in a Chinese population consisting of 610 NIHL cases and 612 normal hearing controls. The possible impacts of SNPs on CARD8 structure and function were assessed using a variety of bioinformatics tools. Plasmids expressing wild-type and/or mutated CARD8 were transfected into HEK293 cells to verify the effect of SNPs on CARD8 protein expression level by western blot. The results revealed that rs2043211 AA genotype and A allele were associated with decreased risk of NIHL. Stratified analysis found that the male, drinking and exposed to noise ≥ 92 dB, subjects harboring rs2043211 A allele had a low risk of NIHL. The haplotype AGG (rs2043211-rs1062808-rs12459322) was significantly associated with a decreased risk of NIHL. SNP rs2043211 was predicted to be deleterious and affects CARD8 protein structure and stability. Furthermore, the functional experiment showed the mutant CARD8 could significantly decrease the CARD8 protein expression level. This study confirms that rs2043211 A allele may reduce NIHL risk by causing the loss of PPI combined with the decreased CARD8 expression level leading to CARD8 functional changes, and it may be one valuable genetic biomarker of NIHL susceptibility for Chinese noise-exposed workers.

Mutational landscape of K-Ras substitutions at 12th position-a systematic molecular dynamics approach

K-Ras is a small GTPase and acts as a molecular switch by recruiting GEFs and GAPs, and alternates between the inert GDP-bound and the dynamic GTP-bound forms. The amino acid at position 12 of K-Ras is a hot spot for oncogenic mutations (G12A, G12C, G12D, G12R, G12S, and G12V), disturbing the active fold of the protein, leading to cancer development. This study aimed to investigate the potential conformational changes induced by these oncogenic mutations at the 12^th position, impairing GAP-mediated GTP hydrolysis. Comprehensive computational tools (iStable, FoldX, SNPeffect, DynaMut, and CUPSAT) were used to evaluate the effect of these six mutations on the stability of wild type K-Ras protein. The docking of GTP with K-Ras was carried out using AutoDock4.2, followed by molecular dynamics simulations. Furthermore, on comparison of binding energies between the wild type K-Ras and the six mutants, we have demonstrated that the G12A and G12V mutants exhibited the strongest binding efficiency compared to the other four mutants. Trajectory analyses of these mutations revealed that G12A encountered the least deviation, fluctuation, intermolecular H-bonds, and compactness compared to the wildtype, which was supported by the lower Gibbs free energy value. Our study investigates the molecular dynamics simulations of the mutant K-Ras forms at the 12^th position, which expects to provide insights about the molecular mechanisms involved in cancer development, and may serve as a platform for targeted therapies against cancer. Communicated by Ramaswamy H. Sarma.

The relationships of genetic polymorphisms of the long noncoding RNA growth arrest-specific transcript 5 with uterine cervical cancer

The purposes of the investigation were to examine the implications of long noncoding RNA growth arrest-specific transcript 5 (GAS5) in progression and clinicopathological factors of uterine cervical cancer, and patient survival in Taiwan. Genotypic distributions of two GAS5 genetic variants rs145204276 and rs55829688 were detected in 208 patients including 111 patients with invasive cancer, 97 with precancerous lesions as well as 307 control women using real-time polymerase chain reaction. It explored that patients with cervical precancerous lesion had lower rate of AGGCA deletion (Del) in both alleles (Del/Del) of GAS5 rs145204276 as compared with control women. Patients with invasive cancer did not exhibit higher rate of Del/Del. Meanwhile, there were no different genotypic distributions in rs55829688 among patients with cervical invasive cancer and those with precancerous lesions as well as control women. Moreover, cervical cancer patients with Ins (insertion, AGGCA)/Del and Del/Del (-/-) in GAS5 rs55829688 tended to have poorer hazard ratio (HR) of 5 years survival. In addition, lymph node metastasis status exerted the most significantly predictive of 5 years survival rate. Conclusively, GAS5 polymorphism rs145204276 is probably applicable to predict 5 years survival HR of cervical cancer patients. However, the mechanism elucidating the methylation status and transcription function of rs145204276 in uterine cervical cancer needs to be delineated for its unique implication in uterine cervical cancer.

Altered expression of long non-coding RNA GAS5 in digestive tumors

Cancer has become one of the most important diseases that affect human health and life. The effects of cancer in the digestive system are particularly prominent. Recently, long non-coding RNA (lncRNA) has attracted the attention of more and more researchers and has become an emerging field of gene research. The lncRNA growth arrest-specific 5 (GAS5) is a novel lncRNA that has attracted the attention of researchers in recent years and plays an important role in the development of tumors, especially in digestive system tumors. GAS5 was first identified in a mouse cDNA library. It was generally considered that it has the role of tumor suppressor genes, but there are still studies that have a certain ability to promote cancer. Furthermore, the 5-bp indel polymorphism (rs145204276) in the GAS5 promoter region also has a carcinogenic effect. The discovery of GAS5 and in-depth study of single nucleotide polymorphism (SNP) mechanism can provide a new way for the prevention and treatment of digestive system tumors.

Exploring the Impact of Single-Nucleotide Polymorphisms on Translation

Over the past 15 years, sequencing of the human genome and The Cancer Genome Atlas (TCGA) project have led to comprehensive lists of single-nucleotide polymorphisms (SNPs) and gene mutations across a large number of human samples. However, our ability to predict the functional impact of SNPs and mutations on gene expression is still in its infancy. Here, we provide key examples to help understand how mutations present in genes can affect translational output.

Identification of candidate aberrantly methylated and differentially expressed genes in thyroid cancer

Aberrant methylation of DNA sequences plays a criticle role in finding novel aberrantly methylated genes and pathways in thyroid cancer (THCA). This study aimed to integrate three cohorts profile datasets to find novel aberrantly methylated genes and pathways in THCA. Data of gene expression profiling microarrays (GSE33630 and GSE65144) and gene methylation profiling microarrays (GSE51090) were downloaded from the Gene Expression Omnibus database. Aberrantly methylated and differentially expressed genes were sorted and pathways were analyzed. Functional and enrichment analyses of selected genes were performed using the String database. A protein‐protein interaction network was constructed using the Cytoscape software, and module analysis was performed using Molecular Complex detection. In total, we identified 12 hypomethylation/high‐expression genes and 30 hypermethylation/low‐expression genes at the screening step and, finally, found 6 mostly changed hub genes including PPARGC1A, CREBBP, EP300, CD44, SPP1, and MMP9. Pathway analysis showed that aberrantly methylated differentially expressed genes were mainly associated with the thyroid hormone signaling pathway, AMP‐activated protein kinase (AMPK) signaling pathway, and cell cycle process in THCA. After validation in the Cancer Genome Atlas database, the methylation and expression status of hub genes was significantly altered and the same with our results. Taken together, we identified novel aberrantly methylated genes and pathways in THCA, which could improve our understanding of the cause and underlying molecular events, and these candidate genes could serve as aberrant methylation‐based biomarkers for precise diagnosis and treatment of THCA.

Identification and characterization of functional single nucleotide polymorphisms (SNPs) in Axin 1 gene: a molecular dynamics approach

Wnt signaling pathway has been reported to play crucial role in intestinal crypt formation and deregulation of this pathway is responsible for colorectal cancer initiation and progression. Axin 1, a scaffold protein, play pivotal role in the regulation of Wnt/β-catenin signaling pathway and has been found to be mutated in several cancers; primarily in colon cancer. Considering its crucial role, a structural and functional analysis of missense mutations in Axin 1 gene was performed in this study. Initially, one hundred non-synonymous single nucleotide polymorphisms in the coding regions of Axin 1 gene were selected for in silico analysis. Six variants (G820S, G856S, E830K, L811V, L847V, and R767C) were predicted to be deleterious by combinatorial prediction. Further investigation of structural attributes confirmed two highly deleterious single nucleotide polymorphisms (G820S and G856S). Molecular dynamics simulation demonstrated variation in different structural attributes between native and two highly deleterious Axin 1 mutant models. Finally, docking analysis showed variation in binding affinity of mutant Axin 1 proteins with two destruction complex members, GSK3β and adenomatous polyposis. The results collectively showed the deleterious effect of the above predicted single nucleotide polymorphisms on the Axin 1 protein structure and could prove to be an adjunct in the disease genotype-phenotype correlation studies.

Prediction of a highly deleterious mutation E17K in AKT-1 gene: An in silico approach

The AKT1 (v-akt murine thymoma viral oncogene homologue 1) kinase is a member of most frequently activated proliferation and survival signaling pathway in cancer. Recently, hyperactivation of AKT1, due to functional point mutation in the pleckstrin homology (PH) domain of AKT1 gene, has been found to be associated with human colorectal, breast and ovarian cancer. Thus, considering its crucial role in cellular signaling pathway, a functional analysis of missense mutations of AKT1 gene was undertaken in this study. Twenty nine nsSNPs (non-synonymous single nucleotide polymorphism) within coding region of AKT1 gene were selected for our investigation and six SNPs were found to be deleterious by combinatorial predictions of various computational tools. RMSD values were calculated for the mutant models which predicted four substitutions (E17K, E319G, D32E and A255T) to be highly deleterious. The insight of the structural attribute was gained through analysis of, secondary structures, solvent accessibility and intermolecular hydrogen bond analysis which confirmed one missense mutation (E17K) to be highly deleterious nsSNPs. In conclusion, the investigated gene AKT1 has twenty nine SNPs in the coding region and through progressive analysis using different bioinformatics tools one highly deleterious SNP with rs121434592 was profiled. Thus, results of this study can pave a new platform to sort nsSNPs for several important regulatory genes that can be undertaken for the confirmation of their phenotype and their correlation with diseased status in case control studies.

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We have added a small portion of text in introduction part as per reviewers comment.

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We have added a schematic representation of methodology used (Fig. 1).

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We have added text in the discussion portion as per the comment of reviewer.

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We have also corrected the conclusion as per reviewer's comments.

Coupling between Protein Stability and Catalytic Activity Determines Pathogenicity of G6PD Variants

G6PD deficiency, an enzymopathy affecting 7% of the world population, is caused by over 160 identified amino acid variants in glucose-6-phosphate dehydrogenase (G6PD). The clinical presentation of G6PD deficiency is diverse, likely due to the broad distribution of variants across the protein and the potential for multidimensional biochemical effects. In this study, we use bioinformatic and biochemical analyses to interpret the relationship between G6PD variants and their clinical phenotype. Using structural information and statistical analyses of known G6PD variants, we predict the molecular phenotype of five uncharacterized variants from a reference population database. Through multidimensional analysis of biochemical data, we demonstrate that the clinical phenotypes of G6PD variants are largely determined by a trade-off between protein stability and catalytic activity. This work expands the current understanding of the biochemical underpinnings of G6PD variant pathogenicity and suggests a promising avenue for correcting G6PD deficiency by targeting essential structural features of G6PD.

MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm

BACKGROUND

This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable. The target-decoy database strategy is largely used for error estimation in PSM assessment. However, in general, that strategy does not account for sensitivity.

RESULTS

In a previous study, we proposed the method MUMAL that applies an artificial neural network to effectively generate a model to classify PSMs using decoy hits with increased sensitivity. Nevertheless, the present approach shows that the sensitivity can be further improved with the use of a cost matrix associated with the learning algorithm. We also demonstrate that using a threshold selector algorithm for probability adjustment leads to more coherent probability values assigned to the PSMs. Our new approach, termed MUMAL2, provides a two-fold contribution to shotgun proteomics. First, the increase in the number of correctly interpreted spectra in the peptide level augments the chance of identifying more proteins. Second, the more appropriate PSM probability values that are produced by the threshold selector algorithm impact the protein inference stage performed by programs that take probabilities into account, such as ProteinProphet. Our experiments demonstrate that MUMAL2 reached around 15% of improvement in sensitivity compared to the best current method. Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the probabilities generated by our model are in fact appropriate. Finally, Venn diagrams comparing MUMAL2 with the best current method show that the number of exclusive peptides found by our method was nearly 4-fold higher, which directly impacts the proteome coverage.

CONCLUSIONS

The inclusion of a cost matrix and a probability threshold selector algorithm to the learning task further improves the target-decoy database analysis for identifying peptides, which optimally contributes to the challenging task of protein level identification, resulting in a powerful computational tool for shotgun proteomics.

In Silico Analysis of FMR1 Gene Missense SNPs

The FMR1 gene, a member of the fragile X-related gene family, is responsible for fragile X syndrome (FXS). Missense single-nucleotide polymorphisms (SNPs) are responsible for many complex diseases. The effect of FMR1 gene missense SNPs is unknown. The aim of this study, using in silico techniques, was to analyze all known missense mutations that can affect the functionality of the FMR1 gene, leading to mental retardation (MR) and FXS. Data on the human FMR1 gene were collected from the Ensembl database (release 81), National Centre for Biological Information dbSNP Short Genetic Variations database, 1000 Genomes Browser, and NHLBI Exome Sequencing Project Exome Variant Server. In silico analysis was then performed. One hundred-twenty different missense SNPs of the FMR1 gene were determined. Of these, 11.66 % of the FMR1 gene missense SNPs were in highly conserved domains, and 83.33 % were in domains with high variety. The results of the in silico prediction analysis showed that 31.66 % of the FMR1 gene SNPs were disease related and that 50 % of SNPs had a pathogenic effect. The results of the structural and functional analysis revealed that although the R138Q mutation did not seem to have a damaging effect on the protein, the G266E and I304N SNPs appeared to disturb the interaction between the domains and affect the function of the protein. This is the first study to analyze all missense SNPs of the FMR1 gene. The results indicate the applicability of a bioinformatics approach to FXS and other FMR1-related diseases. I think that the analysis of FMR1 gene missense SNPs using bioinformatics methods would help diagnosis of FXS and other FMR1-related diseases.

An in silico approach to investigate the source of the controversial interpretations about the phenotypic results of the human AhR-gene G1661A polymorphism

Aryl hydrocarbon receptor (AhR) acts as an enhancer binding ligand-activated intracellular receptor. Chromatin remodeling components and general transcription factors such as TATA-binding protein (TBP) are evoked on AhR-target genes by interaction with its flexible transactivation domain (TAD). AhR-G1661A single nucleotide polymorphism (SNP: rs2066853) causes an arginine to lysine substitution in the acidic sub-domain of TAD at position 554 (R554K). Although, numerous studies associate the SNP with some abnormalities such as cancer, other reliable investigations refuse the associations. Consequently, the interpretation of the phenotypic results of G1661A-transition has been controversial. In this study, an in silico analysis were performed to investigate the possible effects of the transition on AhR-mRNA, protein structure, interaction properties and modifications. The analysis revealed that the R554K substitution affects secondary structure and solvent accessibility of adjacent residues. Also, it causes to decreasing of the AhR stability; altering the hydropathy features of the local sequence and changing the pattern of the residues at the binding site of the TAD-acidic sub-domain. Generating of new sites for ubiquitination and acetylation for AhR-K554 variant respectively at positions 544 and 560 was predicted. Our findings intensify the idea that the AhR-G1661A transition may affects AhR-TAD interactions, especially with the TBP, which influence AhR-target genes expression. However, the previously reported flexibility of the modular TAD could act as an intervening factor, moderate the SNP effects and causes distinct outcomes in different individuals and tissues.

A Novel Mutation of SMAD3 Identified in a Chinese Family with Aneurysms-Osteoarthritis Syndrome

Aneurysms-osteoarthritis syndrome (AOS) is a recently delineated autosomal dominant disorder characterized by aneurysms, dissections, and tortuosity throughout the arterial tree in association with early onset osteoarthritis, mild craniofacial features, and skeletal and cutaneous anomalies. Previous studies have demonstrated that mutations in SMAD3, a key regulator of TGF-β signal transduction, contribute to AOS. Here, we investigated a family of three generations affected by AOS. A novel SMAD3 mutation, c.266G>A (p.C89Y), was identified and cosegregated with the affected individuals in this family. Our finding expands the mutation spectrum of SMAD3 gene and further strengthens the connection between the presence of aneurysms-osteoarthritis phenotype and SMAD3 mutations, which facilitates the understanding of the genotype-phenotype correlation of AOS.

Prediction of the damage-associated non-synonymous single nucleotide polymorphisms in the human MC1R gene

The melanocortin 1 receptor (MC1R) is involved in the control of melanogenesis. Polymorphisms in this gene have been associated with variation in skin and hair color and with elevated risk for the development of melanoma. Here we used 11 computational tools based on different approaches to predict the damage-associated non-synonymous single nucleotide polymorphisms (nsSNPs) in the coding region of the human MC1R gene. Among the 92 nsSNPs arranged according to the predictions 62% were classified as damaging in more than five tools. The classification was significantly correlated with the scores of two consensus programs. Alleles associated with the red hair color (RHC) phenotype and with the risk of melanoma were examined. The R variants D84E, R142H, R151C, I155T, R160W and D294H were classified as damaging by the majority of the tools while the r variants V60L, V92M and R163Q have been predicted as neutral in most of the programs The combination of the prediction tools results in 14 nsSNPs indicated as the most damaging mutations in MC1R (L48P, R67W, H70Y, P72L, S83P, R151H, S172I, L206P, T242I, G255R, P256S, C273Y, C289R and R306H); C273Y showed to be highly damaging in SIFT, Polyphen-2, MutPred, PANTHER and PROVEAN scores. The computational analysis proved capable of identifying the potentially damaging nsSNPs in MC1R, which are candidates for further laboratory studies of the functional and pharmacological significance of the alterations in the receptor and the phenotypic outcomes.

An integrated computational approach can classify VHL missense mutations according to risk of clear cell renal carcinoma

Mutations in the von Hippel-Lindau (VHL) gene are pathogenic in VHL disease, congenital polycythaemia and clear cell renal carcinoma (ccRCC). pVHL forms a ternary complex with elongin C and elongin B, critical for pVHL stability and function, which interacts with Cullin-2 and RING-box protein 1 to target hypoxia-inducible factor for polyubiquitination and proteasomal degradation. We describe a comprehensive database of missense VHL mutations linked to experimental and clinical data. We use predictions from in silico tools to link the functional effects of missense VHL mutations to phenotype. The risk of ccRCC in VHL disease is linked to the degree of destabilization resulting from missense mutations. An optimized binary classification system (symphony), which integrates predictions from five in silico methods, can predict the risk of ccRCC associated with VHL missense mutations with high sensitivity and specificity. We use symphony to generate predictions for risk of ccRCC for all possible VHL missense mutations and present these predictions, in association with clinical and experimental data, in a publically available, searchable web server.

Structure-function correlation analysis of connexin50 missense mutations causing congenital cataract: electrostatic potential alteration could determine intracellular trafficking fate of mutants

Connexin50 (Cx50) mutations are reported to cause congenital cataract probably through the disruption of intercellular transport in the lens. Cx50 mutants that undergo mistrafficking have generally been associated with failure to form functional gap junction channels; however, sometimes even properly trafficked mutants were found to undergo similar consequences. We hereby wanted to elucidate any structural bases of the varied functional consequences of Cx50 missense mutations through in silico approach. Computational studies have been done based on a Cx50 homology model to assess conservation, solvent accessibility, and 3-dimensional localization of mutated residues as well as mutation-induced changes in surface electrostatic potential, H-bonding, and steric clash. This was supplemented with meta-analysis of published literature on the functional properties of connexin missense mutations. Analyses revealed that the mutation-induced critical alterations of surface electrostatic potential in Cx50 mutants could determine their fate in intracellular trafficking. A similar pattern was observed in case of mutations involving corresponding conserved residues in other connexins also. Based on these results the trafficking fates of 10 uncharacterized Cx50 mutations have been predicted. Further experimental analyses are needed to validate the observed correlation.

AKT kinase pathway: a leading target in cancer research

AKT1, a serine/threonine-protein kinase also known as AKT kinase, is involved in the regulation of various signalling downstream pathways including metabolism, cell proliferation, survival, growth, and angiogenesis. The AKT kinases pathway stands among the most important components of cell proliferation mechanism. Several approaches have been implemented to design an efficient drug molecule to target AKT kinases, although the promising results have not been confirmed. In this paper we have documented the detailed molecular insight of AKT kinase protein and proposed a probable doxorubicin based approach in inhibiting miR-21 based cancer cell proliferation. Moreover, the inhibition of miR-21 activation by raising the FOXO3A concentration seems promising in reducing miR-21 mediated cancer activation in cell. Furthermore, the use of next generation sequencing and computational drug design approaches will greatly assist in designing a potent drug molecule against the associated cancer cases.

Molecular dynamic simulation reveals damaging impact of RAC1 F28L mutation in the switch I region

Ras-related C3 botulinum toxin substrate 1 (RAC1) is a plasma membrane-associated small GTPase which cycles between the active GTP-bound and inactive GDP-bound states. There is wide range of evidences indicating its active participation in inducing cancer-associated phenotypes. RAC1 F28L mutation (RAC^F28L) is a fast recycling mutation which has been implicated in several cancer associated cases. In this work we have performed molecular docking and molecular dynamics simulation (~0.3 μs) to investigate the conformational changes occurring in the mutant protein. The RMSD, RMSF and NHbonds results strongly suggested that the loss of native conformation in the Switch I region in RAC1 mutant protein could be the reason behind its oncogenic transformation. The overall results suggested that the mutant protein attained compact conformation as compared to the native. The major impact of mutation was observed in the Switch I region which might be the crucial reason behind the loss of interaction between the guanine ring and F28 residue.