Path to facilitate the prediction of functional amino acid substitutions in red blood cell disorders--a computational approach. PLoS One. 2011;6:e24607. [PMID: 21931771 PMCID: PMC3172254 DOI: 10.1371/journal.pone.0024607]

Exploring the impact of variations in the mucolipin1 protein that result in mucolipidosis type 4 using the technique of molecular docking and dynamics simulation

Mucolipidosis type IV (MLIV) is classified as an exceptionally autosomal recessive condition due to a change in MCOLN1 that encodes the mucolipin-1 protein. ML-1 is a membrane protein comprising 6 Trans regions, which are situated at the LELs, a serine lipase area, and a nuclear localization sign. The characteristic features of the ML4 patients are mental retardation and skeletal deformities due to an increase in lipid molecules in the brain, other tissues, and organs. The fundamental goal of the work is to identify the most significant amino acid variants via a computational pipeline. The twenty-three amino acid variants that are responsible for the condition were retrieved from the public domain: L106P and L447P amino acid variants, with the following categories: extremely conserved, highly pathogenic, most interfering with protein function, more structurally unstable, and having promising Phenotyping characteristics was scrutinized from the series of bioinformatics tools that denote its significant nature. A docking and dynamics study was initiated to identify the interaction profiling and interatomic simulation between the Native, L106P, and L447P and the ligand ML-SA1 (it was known to ease the fatty acid buildup in lysosomes of cellular models of Mucolipidosis type IV) and had a score of -6.19, -5.12, and -5.21 kcal/mol, followed by a duplicate 100-ns run trajectory results, which assisted in detecting the stable nature of all the complex structures. Hence, this work helps to recognize the significant role of the scrutinized amino acid variants and, on the other hand, the stable nature of the ligand using standard computational tools.

Mutational analysis of flavonol synthase of M. pinnata towards enhancement of binding affinity: a computational approach

Millettia pinnata is an important medicinal plant that has been used as a treatment of various diseases due to presence of wide range of pharmacological properties. The plant contains quercetin, kaempferol, karanjin, pongaglabrone, kanjone, kanugin, gammatin, pongaglabol, and other bioflavonoids. Kaempferol is a natural flavonol that shows many pharmacological properties including anti-inflammatory, antioxidant, anticancer, and antidiabetic activities etc. The enzyme flavonol synthase (FLS, EC 1.14.20.6) catalyses the conversion of dihydroflavonols to flavonols, i.e. biosynthesis of kaempferol from dihydrokaempferol. The current work examined the binding affinity-based approach to improve the enzyme catalytic activity using computational methods. Sequential site-directed mutagenesis was used to create four mutants with the goal to increase hydrogen bonds and further improving the ligand (dihydrokaempferol) binding efficiency. Simulations were done to monitor the stability of the mutants followed by molecular docking to confirm interactions with ligand. For structure validation, various dynamic analysis like RMSD, RMSF, ROG, SASA, H-bond, PCA, DCCM, and FEL were performed, which predicts the stability of wild-type (WT) proteins and mutants. The Mutant_2 and Mutant_3 showed maximum H-bonding and better stability than other mutants and WT that proved higher affinity suggesting improved catalysis. Mutant_2 and Mutant_3 exhibited binding affinities of -7.6 and -8.2 kcal/mol, respectively for the ligand. The outcome of present study will provide significant improvement in synthesis of kaempferol and other plant-based flavonoids.Communicated by Ramaswamy H. Sarma.

Unravelling the Relacatib activity against the CTSK proteins causing pycnodysostosis: a molecular docking and dynamics approach

Pycnodysostosis is an atypical autosomal recessive condition of Lysosomal storage disorder that originated due to the deficit of the enzyme Cathepsin K which is vital for normal osteoclast action in bone resorption. Abnormal degradation of type 1 collagen and accumulation of toxic undigested collagen fibers in lysosomes of the osteoclast cells resulting in high bone density, brittle bones, and a short stature is caused in CTSK protein-carrying individuals. The broad aim of this study is to identify the most significant variant through various computational pipelines. This study was initiated by retrieving a total number of thirty-six variants from NCBI, HGMD, and UniProt databases, and the Y283C variant was found to be more significant by various standard computational tools. A structural investigation was performed to understand and gain a better knowledge about the interaction profile for the native (1BY8) and variant (Y283C) with Relacatib (a small-molecule drug that blocks the function of Cathepsin K, an enzyme that has been linked to osteoporosis, osteoarthritis, and other bone-degrading diseases). The interaction profile was analyzed using molecular docking. Relacatib (ligand) had an average binding affinity for both native (-7.16 kcal/mol) and Y283C (-6.76 kcal/mol). Finally, Molecular dynamics simulations were done in duplicates to recognize the variant (Y283C) activity of the protein structure against Relacatib for 100 ns. This study assists in comprehending the most pathogenic amino-acid variant, the ligand interaction with the protein structure, and paves the way for understanding the steadiness of the ligand with the native and selected significant amino-acid variant.Communicated by Ramaswamy H. Sarma.

A computational approach to analyzing the functional and structural impacts of Tripeptidyl-Peptidase 1 missense mutations in neuronal ceroid lipofuscinosis

Neuronal ceroid-lipofuscinosis (NCLs) are a group of severe neurodegenerative conditions, most likely present in infantile, late infantile, juvenile, and adult-onset forms. Their phenotypic characteristics comprise eyesight damage, reduced motor activity and cognitive function, and sometimes tend to die in the initial stage. In recent studies, NCLs have been categorized into at least 14 genetic collections (CLN1-14). CLN2 gene encodes Tripeptidyl peptidase 1 (TPP1), which affects late infantile-onset form. In this study, we retrieved a mutational dataset screening for TPP1 protein from various databases (ClinVar, UniProt, HGMD). Fifty-six missense mutants were enumerated with computational methods to perceive the significant mutants (G475R and G501C) and correlated with clinical and literature data. A structure-based screening method was initiated to understand protein-ligand interaction and dynamic simulation. The docking procedure was performed for the native (3EDY) and mutant (G473R and G501C) structures with Gemfibrozil (gem), which lowers the lipid level, decreases the triglycerides amount in the blood circulation, and controls hyperlipidemia. The Native had an interaction score of -5.57 kcal/mol, and the mutants had respective average binding scores of -6.24 (G473R) and - 5.17 (G501C) kcal/mol. Finally, molecular dynamics simulation showed that G473R and G501C mutants had better flexible and stable orientation in all trajectory analyses. Therefore, this work gives an extended understanding of both functional and structural levels of influence for the mutant form that leads to NCL disorder.

Instrumental and transcriptome analysis reveals the chemotherapeutic effects of doxorubicin-loaded black phosphate nanosheets on abiraterone-resistant prostate cancer

Prostate cancer is the second most common cause of cancer-related deaths in men and is common in most developed countries. Androgen deprivation therapy (ADT) that uses abiraterone acetate (AA) is an effective second-line treatment for prostate cancer. However, approximately 20-40% of patients develop primary resistance to abiraterone post-treatment. In this study, we aimed to understand the molecular mechanisms underlying the development of abiraterone resistance in prostate cancer cells and the potential use of black phosphorus nanosheets (BPNS) for treating abiraterone-resistant prostate cancer. We first established abiraterone-resistant prostate cancer PC-3 cells and found that these cells have higher migration ability than normal prostate cancer cells. Using comparative transcriptomic and bioinformatics analyses between abiraterone-sensitive PC-3 and abiraterone-resistant PC-3 cells, we highlighted the differentially expressed genes (DEGs) involved in the biological processes related to prostate gland morphogenesis, drug response, immune response, angiogenesis. We further studied the therapeutic effects of BPNS. Our results show that BPNS reduced the proliferation and migration of abiraterone-resistant PC-3 cells. Bioinformatics analysis, including gene ontology, Kyoto encyclopedia of genes and genomes enrichment analysis, and ingenuity pathway analysis (IPA) of the DEGs, suggested that BPNS treatment controlled cancer cell proliferation, metastasis, and oncogenic signaling pathways. Furthermore, the IPA gene network highlighted the involvement of the MMP family, ATF, and notch families in the anti-prostate cancer function of BPNS. Our findings suggest that BPNS may have a chemotherapeutic function in treating abiraterone-resistant prostate cancer.

Structural Consequences of IRS-2 nsSNPs and Implication for Insulin Receptor Substrate-2 Protein Stability

Single-Nucleotide Polymorphisms (SNPs) are common genetic variations implicated in human diseases. The non-synonymous SNPs (nsSNPs) affect the proteins' structures and their molecular interactions with other interacting proteins during the accomplishment of biochemical processes. This ultimately causes proteins functional perturbation and disease phenotypes. The Insulin receptor substrate-2 (IRS-2) protein promotes glucose absorption and participates in the biological regulation of glucose metabolism and energy production. Several IRS-2 SNPs are reported in association with type 2 diabetes and obesity in human populations. However, there are no comprehensive reports about the protein structural consequences of these nsSNPs. Keeping in view the pathophysiological consequences of the IRS-2 nsSNPs, we designed the current study to understand their possible structural impact on coding protein. The prioritized list of the deleterious IRS-2 nsSNPs was acquired from multiple bioinformatics resources, including VEP (SIFT, PolyPhen, and Condel), PROVEAN, SNPs&GO, PMut, and SNAP2. The protein structure stability assessment of these nsSNPs was performed by MuPro and I-Mutant-3.0 servers via structural modeling approaches. The atomic-level structural and molecular dynamics (MD) impact of these nsSNPs were examined using GROMACS 2019.2 software package. The analyses initially predicted 8 high-risk nsSNPs located in the highly conserved regions of IRS-2. The MD simulation analysis eventually prioritized the N232Y, R218C, and R104H nsSNPs that predicted to significantly compromise the structure stability and may affect the biological function of IRS-2. These nsSNPs are predicted as high-risk candidates for diabetes and obesity. The validation of protein structural impact of these shortlisted nsSNPs may provide biochemical insight into the IRS-2-mediated type-2 diabetes.

An In silico Approach towards Finding the Cancer-Causing Mutations in Human MET Gene

Mesenchymal-epithelial transition (MET) factor is a proto-oncogene encoding tyrosine kinase receptor with hepatocyte growth factor (HGF) or scatter factor (SF). It is found on the human chromosome number 7 and regulates the diverse cellular mechanisms of the human body. The impact of mutations occurring in the MET gene is demonstrated by their detrimental effects on normal cellular functions. These mutations can change the structure and function of MET leading to different diseases such as lung cancer, neck cancer, colorectal cancer, and many other complex syndromes. Hence, the current study focused on finding deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) and their subsequent impact on the protein's structure and functions, which may contribute to the emergence of cancers. These nsSNPs were first identified utilizing computational tools like SIFT, PROVEAN, PANTHER-PSEP, PolyPhen-2, I-Mutant 2.0, and MUpro. A total of 45359 SNPs of MET gene were accumulated from the database of dbSNP, and among them, 1306 SNPs were identified as non-synonymous or missense variants. Out of all 1306 nsSNPs, 18 were found to be the most deleterious. Moreover, these nsSNPs exhibited substantial effects on structure, binding affinity with ligand, phylogenetic conservation, secondary structure, and post-translational modification sites of MET, which were evaluated using MutPred2, RaptorX, ConSurf, PSIPRED, and MusiteDeep, respectively. Also, these deleterious nsSNPs were accompanied by changes in properties of MET like residue charge, size, and hydrophobicity. These findings along with the docking results are indicating the potency of the identified SNPs to alter the structure and function of the protein, which may lead to the development of cancers. Nonetheless, Genome-wide association study (GWAS) studies and experimental research are required to confirm the analysis of these nsSNPs.

The molecular docking and molecular dynamics study of flavonol synthase and flavonoid 3'-monooxygenase enzymes involved for the enrichment of kaempferol

Kaempferol is a natural flavonol that shows many pharmacological properties including anti-inflammatory, antioxidant, anticancer, antidiabetic activities etc. It has been reported in many vegetables, fruits, herbs and medicinal plants. The enzyme flavonol synthase (FLS, EC 1.14.20.6) catalyses the conversion of dihydroflavonols to flavonols. Whereas flavonoid 3'-monooxygenase (F3'H, EC 1.14.14.82) catalyses the hydroxylation of dihydroflavonol, and flavonol. FLS is involved in the synthesis of the kaempferol whereas F3'H causes degradation of kaempferol. The present study aimed to analyse the binding affinity, stability and activating activity of enzyme FLS as well as inhibitory activity of enzyme F3'H involved in the enrichment of the kaempferol using the in-silico approaches. Computational study for physico-chemical properties, conserved domain identification, 3-D structure prediction and its validation, conservation analysis, molecular docking followed by molecular dynamics analysis of FLS and F3'H, protein-activator (FLS-LIG Complex) and protein-inhibitor (F3'H-LIG Complex) complexes have been performed. Other structural analyses like root mean square fluctuation (RMSF), root mean square deviation (RMSD), surface area solvent accessibility (SASA), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA), Poisson-Boltzmann analysis (MM_PBSA) and the dynamic cross correlation map (DCCM) analysis to explore the structural, functional and thermodynamic stability of the proteins and the complexes were also studied. The molecular docking result showed that FLS binds strongly with the activator ascorbate (CID _54670067) while F3'H binds with the inhibitor ketoconazole (CID_456201). The most powerful inhibitor (ketoconazole for F3'H) and activator (ascorbate for FLS) is determined by computing the thermodynamic binding free energy through MM_PBSA analysis. The current work provides wide-ranging structural and functional information about FLS and F3'H enzymes showing detailed molecular mechanism of kaempferol biosynthesis and its degradation and hence kaempferol enrichment. Finding of the present work opens up new possibilities for future research towards enrichment of kaempferol by using activator (ascorbate) for FLS and inhibitor (ketoconazole) for F3'H as well as for its large-scale production using in vitro approaches.Communicated by Ramaswamy H. Sarma.

Putative pathogen-selected polymorphisms in the PKLR gene are associated with mycobacterial susceptibility in Brazilian and African populations

Pyruvate kinase (PK), encoded by the PKLR gene, is a key player in glycolysis controlling the integrity of erythrocytes. Due to Plasmodium selection, mutations for PK deficiency, which leads to hemolytic anemia, are associated with resistance to malaria in sub-Saharan Africa and with susceptibility to intracellular pathogens in experimental models. In this case-control study, we enrolled 4,555 individuals and investigated whether PKLR single nucleotide polymorphisms (SNPs) putatively selected for malaria resistance are associated with susceptibility to leprosy across Brazil (Manaus-North; Salvador-Northeast; Rondonópolis-Midwest and Rio de Janeiro-Southeast) and with tuberculosis in Mozambique. Haplotype T/G/G (rs1052176/rs4971072/rs11264359) was associated with leprosy susceptibility in Rio de Janeiro (OR = 2.46, p = 0.00001) and Salvador (OR = 1.57, p = 0.04), and with tuberculosis in Mozambique (OR = 1.52, p = 0.07). This haplotype downregulates PKLR expression in nerve and skin, accordingly to GTEx, and might subtly modulate ferritin and haptoglobin levels in serum. Furthermore, we observed genetic signatures of positive selection in the HCN3 gene (xpEHH>2 -recent selection) in Europe but not in Africa, involving 6 SNPs which are PKLR/HCN3 eQTLs. However, this evidence was not corroborated by the other tests (FST, Tajima's D and iHS). Altogether, we provide evidence that a common PKLR locus in Africans contribute to mycobacterial susceptibility in African descent populations and also highlight, for first, PKLR as a susceptibility gene for leprosy and TB.

Himalayan bioactive molecules as potential entry inhibitors for the human immunodeficiency virus

The human immunodeficiency virus interacts with the cluster of differentiation 4 receptors and one of the two chemokine receptors (CCR5 and CXCR4) to gain entry in human cells. Both the co-receptors are essential for viral entry, replication, and are considered critical targets for antiviral drugs. In this study, bioactive molecules from different Himalayan plants were screened considering their potential to bind with the CCR5 and CXCR4 co-receptors. We utilized computational and thermodynamic parameters to validate the binding of the selected biomolecules to the active site of the co-receptors. The molecules Butyl 2-ethylhexyl phthalate and Dactylorhin-A showed a higher binding affinity with CCR5 co-receptor than the standard antagonist Maraviroc. Moreover, Pseudohypericin, Amarogentin, and Dactylorhin-E exhibited stronger interactions with CXCR4 than the co-crystallized inhibitor Isothiourea-1 t. Hence, we suggest that these molecules could be developed as potential inhibitors of the CCR5 and CXCR4 co-receptors. However, this require further in-vitro and in-vivo validation.

Prediction of functionally significant single nucleotide polymorphisms in PTEN tumor suppressor gene: An in silico approach

The phosphatase and tensin homolog (PTEN) gene plays a crucial role in signal transduction by negatively regulating the PI3K signaling pathway. It is the most frequent mutated gene in many human-related cancers. Considering its critical role, a functional analysis of missense mutations of PTEN gene was undertaken in this study. Thirty five nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of the PTEN gene were selected for our in silico investigation, and five nsSNPs (G129E, C124R, D252G, H61D, and R130G) were found to be deleterious based on combinatorial predictions of different computational tools. Moreover, molecular dynamics (MD) simulation was performed to investigate the conformational variation between native and all the five mutant PTEN proteins having predicted deleterious nsSNPs. The results of MD simulation of all mutant models illustrated variation in structural attributes such as root-mean-square deviation, root-mean-square fluctuation, radius of gyration, and total energy; which depicts the structural stability of PTEN protein. Furthermore, mutant PTEN protein structures also showed a significant variation in the solvent accessible surface area and hydrogen bond frequencies from the native PTEN structure. In conclusion, results of this study have established the deleterious effect of the all the five predicted nsSNPs on the PTEN protein structure. Thus, results of the current study can pave a new platform to sort out nsSNPs that can be undertaken for the confirmation of their phenotype and their correlation with diseased status in case of control studies.

Notch1 suppresses prostate cancer cell invasion via the metastasis-associated 1-KiSS-1 metastasis-suppressor pathway

Notch1 is a type-1 transmembrane receptor which has been demonstrated to be involved in proliferation in various organisms. A number of studies have proposed that Notch signaling may be aberrantly activated, thus contributing to development, invasion and metastasis in a variety of human cancers. In the present study, the function and mechanism of Notch1 in human prostate cancer (PCa) LNCaP cells in vitro was investigated. Notch1 and cleaved-Notch1 expression were evaluated in human PCa cell lines, including LNCaP, PC-3 and DU 145, and the human prostate epithelial RWPE-1 cell line. LNCaP cells were transfected with Notch1-targeting short hairpin RNAs (shRNAs) and the level of proliferation, the ability to invade and the expression of genes associated with cancer cell invasion were subsequently investigated. Notch1 was highly expressed in LNCaP, PC-3 and DU 145 cells compared with RWPE-1 cells, while cleaved-Notch1 was expressed in LNCaP, PC-3 and DU 145 cells, and only to a minimal extent in RWPE-1 cells. Knockdown of Notch1 by shRNA in LNCaP cells markedly decreased cell invasion through Matrigel and inhibited cell proliferation 48 h following transfection. Reverse transcription-quantitative polymerase chain reaction analysis indicated that Notch1-knockdown resulted in a significant reduction of metastasis-associated 1 (MTA1) and increase of KiSS-1 metastasis-suppressor (KISS-1), mitogen-activated protein kinase 4 (MKK4) and cluster of differentiation 82 (KAI1). The present data demonstrated that expression of Notch1 was significantly associated with the invasion of prostate cancer. Knockdown of Notch1 decreased the invasive ability of LNCaP cells, which may be caused by downregulating MTA1 and upregulating KISS-1, MKK4 and KAI1. These findings indicated that targeting Notch1 may provide a novel method of suppressing or treating metastasis in prostate cancer.

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.

Phenotype prediction for mucopolysaccharidosis type I by in silico analysis

BACKGROUND

Mucopolysaccharidosis type I (MPS I) is an autosomal recessive disease due to deficiency of α-L-iduronidase (IDUA), a lysosomal enzyme that degrades glycosaminoglycans (GAG) heparan and dermatan sulfate. To achieve optimal clinical outcomes, early and proper treatment is essential, which requires early diagnosis and phenotype severity prediction.

RESULTS

To establish a genotype/phenotype correlation of MPS I disease, a combination of bioinformatics tools including SIFT, PolyPhen, I-Mutant, PROVEAN, PANTHER, SNPs&GO and PHD-SNP are utilized. Through analyzing single nucleotide polymorphisms (SNPs) by these in silico approaches, 28 out of 285 missense SNPs were predicted to be damaging. By integrating outcomes from these in silico approaches, a prediction algorithm (sensitivity 94%, specificity 80%) was thereby developed. Three dimensional structural analysis of 5 candidate SNPs (P533R, P496R, L346R, D349G, T374P) were performed by SWISS PDB viewer, which revealed specific structural changes responsible for the functional impacts of these SNPs. Additionally, SNPs in the untranslated region were analyzed by UTRscan and PolymiRTS. Moreover, by investigating known pathogenic mutations and relevant patient phenotypes in previous publications, phenotype severity (severe, intermediate or mild) of each mutation was deduced.

CONCLUSIONS

Collectively, these results identified potential candidate SNPs with functional significance for studying MPS I disease. This study also demonstrates the effectiveness, reliability and simplicity of these in silico approaches in addressing complexity of underlying genetic basis of MPS I disease. Further, a step-by-step guideline for phenotype prediction of MPS I disease is established, which can be broadly applied in other lysosomal diseases or genetic disorders.

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.

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

In silico analysis of deleterious single nucleotide polymorphisms in human BUB1 mitotic checkpoint serine/threonine kinase B gene

One of the major challenges in the analysis of human genetic variation is to distinguish mutations that are functionally neutral from those that contribute to disease. BubR1 is a key protein mediating spindle-checkpoint activation that plays a role in the inhibition of the anaphase-promoting complex/cyclosome (APC/C), delaying the onset of anaphase and ensuring proper chromosome segregation. Owing to the importance of BUB1B gene in mitotic checkpoint a functional analysis using different in silico approaches was undertaken to explore the possible associations between genetic mutations and phenotypic variation. In this work we found that 3 nsSNPs I82N, P334L and R814H have a functional effect on protein function and stability. A literature search revealed that R814H was already implicated in human diseases. Additionally, 2 SNPs in the 5' UTR region was predicted to exhibit a pattern change in the internal ribosome entry site (IRES), and eight MicroRNA binding sites were found to be highly affected due to 3' UTR SNPs. These in silico predictions will provide useful information in selecting the target SNPs that are likely to have functional impact on the BUB1B gene.

Identification of Deleterious Mutations in Myostatin Gene of Rohu Carp (Labeo rohita) Using Modeling and Molecular Dynamic Simulation Approaches

The myostatin (MSTN) is a known negative growth regulator of skeletal muscle. The mutated myostatin showed a double-muscular phenotype having a positive significance for the farmed animals. Consequently, adequate information is not available in the teleosts, including farmed rohu carp, Labeo rohita. In the absence of experimental evidence, computational algorithms were utilized in predicting the impact of point mutation of rohu myostatin, especially its structural and functional relationships. The four mutations were generated at different positions (p.D76A, p.Q204P, p.C312Y, and p.D313A) of MSTN protein of rohu. The impacts of each mutant were analyzed using SIFT, I-Mutant 2.0, PANTHER, and PROVEAN, wherein two substitutions (p.D76A and p.Q204P) were predicted as deleterious. The comparative structural analysis of each mutant protein with the native was explored using 3D modeling as well as molecular-dynamic simulation techniques. The simulation showed altered dynamic behaviors concerning RMSD and RMSF, for either p.D76A or p.Q204P substitution, when compared with the native counterpart. Interestingly, incorporated two mutations imposed a significant negative impact on protein structure and stability. The present study provided the first-hand information in identifying possible amino acids, where mutations could be incorporated into MSTN gene of rohu carp including other carps for undertaking further in vivo studies.

Value of predictive bioinformatics in inherited metabolic diseases

Typically, inherited metabolic diseases arise from point mutations in genes encoding metabolic enzymes. Although some of these mutations directly affect amino acid residues in the active sites of these enzymes, the majority do not. It is now well accepted that the majority of these disease-associated mutations exert their effects through alteration of protein stability, which causes a reduction in enzymatic activity. This finding suggests a way to predict the severity of newly discovered mutations. In silico prediction of the effects of amino acid sequence alterations on protein stability often correlates with disease severity. However, no stability prediction tool is perfect and, in general, better results are obtained if the predictions from a variety of tools are combined and then interpreted. In addition to predicted alterations to stability, the degree of conservation of a particular residue can also be a factor which needs to be taken into account: alterations to highly conserved residues are more likely to be associated with severe forms of the disease. The approach has been successfully applied in a variety of inherited metabolic diseases, but further improvements are necessary to enable robust translation into clinically useful tools.

Analysis of consequences of non-synonymous SNP in feed conversion ratio associated TGF-β receptor type 3 gene in chicken

The recent advances in high throughput sequencing technology accelerate possible ways for the study of genome wide variation in several organisms and associated consequences. In the present study, mutations in TGFBR3 showing significant association with FCR trait in chicken during exome sequencing were further analyzed. Out of four SNPs, one nsSNP p.Val451Leu was found in the coding region of TGFBR3. In silico tools such as SnpSift and PANTHER predicted it as deleterious (0.04) and to be tolerated, respectively, while I-Mutant revealed that protein stability decreased. The TGFBR3 I-TASSER model has a C-score of 0.85, which was validated using PROCHECK. Based on MD simulation, mutant protein structure deviated from native with RMSD 0.08 Å due to change in the H-bonding distances of mutant residue. The docking of TGFBR3 with interacting TGFBR2 inferred that mutant required more global energy. Therefore, the present study will provide useful information about functional SNPs that have an impact on FCR traits.

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Investigated functional nsSNP p.Val451Leu (rs312979494) in feed conversion ratio (FCR) associated TGFBR3 of chicken

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Computational tools (SIFT and I-Mutant 2.0) predicted that this nsSNP was deleterious.

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Mutant structure of TGFBR3 showed high energies and RMS deviations compared to native using MD simulation.

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Molecular docking of TGFBR3 with interacting protein TGFBR2 showed an increase in global energy of mutant compared to native.

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We have predicted that functional SNP has an impact on TGFBR3 of chicken and thus can be treated as candidate SNP in screening.

Computational analysis of functional single nucleotide polymorphisms associated with the CYP11B2 gene

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variations in humans and play a major role in the genetics of human phenotype variation and the genetic basis of human complex diseases. Recently, there is considerable interest in understanding the possible role of the CYP11B2 gene with corticosterone methyl oxidase deficiency, primary aldosteronism, and cardio-cerebro-vascular diseases. Hence, the elucidation of the function and molecular dynamic behavior of CYP11B2 mutations is crucial in current genomics. In this study, we investigated the pathogenic effect of 51 nsSNPs and 26 UTR SNPs in the CYP11B2 gene through computational platforms. Using a combination of SIFT, PolyPhen, I-Mutant Suite, and ConSurf server, four nsSNPs (F487V, V129M, T498A, and V403E) were identified to potentially affect the structure, function, and activity of the CYP11B2 protein. Furthermore, molecular dynamics simulation and structure analyses also confirmed the impact of these nsSNPs on the stability and secondary properties of the CYP11B2 protein. Additionally, utilizing the UTRscan, MirSNP, PolymiRTS and miRNASNP, three SNPs in the 3'UTR region were predicted to exhibit a pattern change in the upstream open reading frames (uORF), and eight microRNA binding sites were found to be highly affected due to 3'UTR SNPs. This cataloguing of deleterious SNPs is essential for narrowing down the number of CYP11B2 mutations to be screened in genetic association studies and for a better understanding of the functional and structural aspects of the CYP11B2 protein.

A novel and a previously described compound heterozygous PKLR gene mutations causing pyruvate kinase deficiency in a Chinese child

BACKGROUND

Pyruvate kinase deficiency (PKD) is one of the most common enzymatic defects in humans and it is an autosomal recessive disorder causing chronic nonspherocytic hemolytic anemia.

METHODS

A two-year-old male baby with severe hemolytic anemia and low level of pyruvate kinase (PK) activity was enrolled in this study. All exons of PKLR gene and their flanking sequences were amplified from the patient's genomic DNA using PCR. Bioinformatics software was used to evaluate the functional impacts of the mutations found in this study.

RESULTS

It was here demonstrated that the boy harbored a previously described mutation (c. 941T>C) in exon 7 and a novel mutation (c. 1183 G>C) in exon 9 of PKLR gene. Both mutations led to significant structural alterations and decreased enzymatic activity of PK, as predicted by tool software.

CONCLUSIONS

The compound heterozygous mutations in the PKLR gene were the cause of inherited PKD for this patient.

Evolution- and structure-based computational strategy reveals the impact of deleterious missense mutations on MODY 2 (maturity-onset diabetes of the young, type 2)

Heterozygous mutations in the central glycolytic enzyme glucokinase (GCK) can result in an autosomal dominant inherited disease, namely maturity-onset diabetes of the young, type 2 (MODY 2). MODY 2 is characterised by early onset: it usually appears before 25 years of age and presents as a mild form of hyperglycaemia. In recent years, the number of known GCK mutations has markedly increased. As a result, interpreting which mutations cause a disease or confer susceptibility to a disease and characterising these deleterious mutations can be a difficult task in large-scale analyses and may be impossible when using a structural perspective. The laborious and time-consuming nature of the experimental analysis led us to attempt to develop a cost-effective computational pipeline for diabetic research that is based on the fundamentals of protein biophysics and that facilitates our understanding of the relationship between phenotypic effects and evolutionary processes. In this study, we investigate missense mutations in the GCK gene by using a wide array of evolution- and structure-based computational methods, such as SIFT, PolyPhen2, PhD-SNP, SNAP, SNPs&GO, fathmm, and Align GVGD. Based on the computational prediction scores obtained using these methods, three mutations, namely E70K, A188T, and W257R, were identified as highly deleterious on the basis of their effects on protein structure and function. Using the evolutionary conservation predictors Consurf and Scorecons, we further demonstrated that most of the predicted deleterious mutations, including E70K, A188T, and W257R, occur in highly conserved regions of GCK. The effects of the mutations on protein stability were computed using PoPMusic 2.1, I-mutant 3.0, and Dmutant. We also conducted molecular dynamics (MD) simulation analysis through in silico modelling to investigate the conformational differences between the native and the mutant proteins and found that the identified deleterious mutations alter the stability, flexibility, and solvent-accessible surface area of the protein. Furthermore, the functional role of each SNP in GCK was identified and characterised using SNPeffect 4.0, F-SNP, and FASTSNP. We hope that the observed results aid in the identification of disease-associated mutations that affect protein structure and function. Our in silico findings provide a new perspective on the role of GCK mutations in MODY2 from an evolution-based structure-centric point of view. The computational architecture described in this paper can be used to predict the most appropriate disease phenotypes for large-genome sequencing projects and to provide individualised drug therapy for complex diseases such as diabetes.

In silico profiling and structural insights of missense mutations in RET protein kinase domain by molecular dynamics and docking approach

A major challenge remaining in drug design efforts towards protein kinase is due to the development of drug resistance initiated by the missense mutations in the kinase catalytic domain. Gain or loss of function mutations in the REarranged during Transfection (RET) tyrosine kinase gene have been associated with the development of a wide range of human associated cancers and Hirschsprung's disease. However, to what extent these mutations might affect bio-molecular functions remains unclear. In this article, the functionally significant mutations in RET were screened with the aid of various sequence and structure based in silico prediction methods. We mapped the deleterious mutants, modelled mutant proteins and deciphered the impact of mutations on drug binding mechanisms in the RET crystal structure of PDB ID: with the potential inhibitor vandetanib by docking analysis. Furthermore, molecular dynamics simulations were undertaken to understand the mechanistic action of cancer associated mutations in altering the protein kinase structure, dynamics, and stability. According to our results, the overall effect of V804M, M918T and S922Y were destabilizing and mostly alter the electrostatic component of the binding energy. Specifically, the mutation of gatekeeper residue valine 804 present in the ATP binding pocket affects the protein stability and confers resistance to the drug vandetanib, which was consistent with previously published experimental results. Overall, our findings may provide useful structural insights for in-depth understanding of the molecular mechanism underlying RET mutation and developing effective drugs.

Predicting the impact of deleterious mutations in the protein kinase domain of FGFR2 in the context of function, structure, and pathogenesis--a bioinformatics approach

Fibroblast growth factor receptor 2 (FGFR2) controls a wide range of biological functions by regulating the cellular proliferation, survival, migration and differentiation. A growing body of preclinical data demonstrated that deregulation of the FGFR signalling through genetic modification was observed in various types of cancers. However, the extent to which genetic modifications interfere with gene regulation and their involvement in cancer susceptibility remains largely unknown. In this work, we performed in silico profiling of harmful non-synonymous single nucleotide polymorphisms (SNPs) in the protein kinase domain of FGFR2. Tolerance index, position-specific independent count score, change in free energy score (ΔΔG), Eris and FoldX indicated that seven mutations were found to be deleterious and may alter the protein function and structure. Furthermore, based on physico-chemical properties, two mutations K659N and R747H were found to be most deleterious in protein kinase domain and taken for further structural analysis. Docking study showed a complete loss of binding affinity followed by interference in hydrogen bonding and surrounding residues due to K659N and R747H mutations. In order to elucidate the mechanism behind the impact of mutation that can generate a ripple effect throughout the protein structure and ultimately affect the function, in-depth molecular dynamics simulation and principal component analysis were performed. The obtained results indicate that K659N and R747H mutations have a distinct effect on the dynamic behaviour of FGFR2 protein. Our strategy may be helpful for understanding SNP effects on proteins with function and their role in human genetic diseases and for the development of novel pharmacological strategies.

In silico discrimination of nsSNPs in hTERT gene by means of local DNA sequence context and regularity

Understanding and predicting the significance of novel genetic variants revealed by DNA sequencing is a major challenge to integrate and interpret in medical genetics with medical practice. Recent studies have afforded significant advances in characterization and predicting the association of single nucleotide polymorphisms in human TERT with various disorders, but the results remain inconclusive. In this context, a comparative study between disease causing and novel mutations in hTERT gene was performed computationally. Out of 59 missense mutations, five variants were predicted to be less stable with the most deleterious effect on hTERT gene by in silico tools, in which two mutations (L584W and M970T) were not previously reported to be involved in any of the human disorders. To get insight into the structural and functional impact due to the mutation, docking study and interaction analysis was performed followed by 6 ns molecular dynamics simulation. These results may provide new perspectives for the targeted drug discovery in the coming future.

Screening of mutations affecting protein stability and dynamics of FGFR1-A simulation analysis

Single amino acid substitutions in Fibroblast Growth Factor Receptor 1 (FGFR1) destabilize protein and have been implicated in several genetic disorders like various forms of cancer, Kallamann syndrome, Pfeiffer syndrome, Jackson Weiss syndrome, etc. In order to gain functional insight into mutation caused by amino acid substitution to protein function and expression, special emphasis was laid on molecular dynamics simulation techniques in combination with in silico tools such as SIFT, PolyPhen 2.0, I-Mutant 3.0 and SNAP. It has been estimated that 68% nsSNPs were predicted to be deleterious by I-Mutant, slightly higher than SIFT (37%), PolyPhen 2.0 (61%) and SNAP (58%). From the observed results, P722S mutation was found to be most deleterious by comparing results of all in silico tools. By molecular dynamics approach, we have shown that P722S mutation leads to increase in flexibility, and deviated more from the native structure which was supported by the decrease in the number of hydrogen bonds. In addition, biophysical analysis revealed a clear insight of stability loss due to P722S mutation in FGFR1 protein. Majority of mutations predicted by these in silico tools were in good concordance with the experimental results.

Computational refinement of functional single nucleotide polymorphisms associated with ATM gene

BACKGROUND

Understanding and predicting molecular basis of disease is one of the major challenges in modern biology and medicine. SNPs associated with complex disorders can create, destroy, or modify protein coding sites. Single amino acid substitutions in the ATM gene are the most common forms of genetic variations that account for various forms of cancer. However, the extent to which SNPs interferes with the gene regulation and affects cancer susceptibility remains largely unknown.

PRINCIPAL FINDINGS

We analyzed the deleterious nsSNPs associated with ATM gene based on different computational methods. An integrative scoring system and sequence conservation of amino acid residues was adapted for a priori nsSNP analysis of variants associated with cancer. We further extended our approach on SNPs that could potentially influence protein Post Translational Modifications in ATM gene.

SIGNIFICANCE

In the lack of adequate prior reports on the possible deleterious effects of nsSNPs, we have systematically analyzed and characterized the functional variants in both coding and non coding region that can alter the expression and function of ATM gene. In silico characterization of nsSNPs affecting ATM gene function can aid in better understanding of genetic differences in disease susceptibility.

In silico profiling of deleterious amino acid substitutions of potential pathological importance in haemophlia A and haemophlia B

Background

In this study, instead of current biochemical methods, the effects of deleterious amino acid substitutions in F8 and F9 gene upon protein structure and function were assayed by means of computational methods and information from the databases. Deleterious substitutions of F8 and F9 are responsible for Haemophilia A and Haemophilia B which is the most common genetic disease of coagulation disorders in blood. Yet, distinguishing deleterious variants of F8 and F9 from the massive amount of nonfunctional variants that occur within a single genome is a significant challenge.

Methods

We performed an in silico analysis of deleterious mutations and their protein structure changes in order to analyze the correlation between mutation and disease. Deleterious nsSNPs were categorized based on empirical based and support vector machine based methods to predict the impact on protein functions. Furthermore, we modeled mutant proteins and compared them with the native protein for analysis of protein structure stability.

Results

Out of 510 nsSNPs in F8, 378 nsSNPs (74%) were predicted to be 'intolerant' by SIFT, 371 nsSNPs (73%) were predicted to be 'damaging' by PolyPhen and 445 nsSNPs (87%) as 'less stable' by I-Mutant2.0. In F9, 129 nsSNPs (78%) were predicted to be intolerant by SIFT, 131 nsSNPs (79%) were predicted to be damaging by PolyPhen and 150 nsSNPs (90%) as less stable by I-Mutant2.0. Overall, we found that I-Mutant which emphasizes support vector machine based method outperformed SIFT and PolyPhen in prediction of deleterious nsSNPs in both F8 and F9.

Conclusions

The models built in this work would be appropriate for predicting the deleterious amino acid substitutions and their functions in gene regulation which would be useful for further genotype-phenotype researches as well as the pharmacogenetics studies. These in silico tools, despite being helpful in providing information about the nature of mutations, may also function as a first-pass filter to determine the substitutions worth pursuing for further experimental research in other coagulation disorder causing genes.