A New Insight into Structural and Functional Impact of Single-Nucleotide Polymorphisms in PTEN Gene

PUM2 promoted osteoarthritis progression through PTEN-mediated chondrocyte ferroptosis by facilitating NEDD4 mRNA degradation

Osteoarthritis (OA) is a prevalent degenerative joint disease with a lack of effective therapeutic. Chondrocyte ferroptosis contributes to the progression of OA. PUM2 is shown to exacerbate ischemia-reperfusion-induced neuroinflammation by promoting ferroptosis, but its role in OA remains unexplored. Here, primary mouse chondrocytes were stimulated with IL-1β to mimic OA chondrocyte injury in vitro. And PUM2 was upregulated in OA cartilage tissues and IL-1β-induced chondrocytes. Silencing PUM2 alleviated IL-1β-induced chondrocyte inflammation and ECM degradation. Mechanistically, PUM2 facilitated the degradation of NEDD4 mRNA by binding to the 3'UTR of NEDD4 mRNA, which in turn inhibited NEDD4 induced PTEN ubiquitination and degradation. Consistently, NEDD4 silencing reversed the ameliorative effect of PUM2 knockdown on chondrocyte injury, and overexpression of PTEN abolished the improved role of NEDD4 in chondrocyte injury. Moreover, PTEN aggravated IL-1β-induced ferroptosis in chondrocytes through the Nrf2/HO-1 pathway by increasing the levels of Fe2+, ROS, MDA, and ACSL4 protein, decreasing the activity of SOD and the levels of GSH and GPX4 protein, and aggravating mitochondrial damage. Additionally, destabilized medial meniscus (DMM) were conducted to establish the OA mouse model, and adenovirus-mediated PUM2 shRNA was administered intra-articularly. Silencing PUM2 attenuated OA-induced cartilage damage in vivo. In conclusion, PUM2 promoted OA progression through PTEN-mediated chondrocyte ferroptosis by facilitating NEDD4 mRNA degradation.

In Silico Evaluation of the Potential Association of the Pathogenic Mutations of Alpha Synuclein Protein with Induction of Synucleinopathies

Alpha synuclein (α-Syn) is a neuronal protein encoded by the SNCA gene and is involved in the development of Parkinson's disease (PD). The objective of this study was to examine in silico the functional implications of non-synonymous single nucleotide polymorphisms (nsSNPs) in the SNCA gene. We used a range of computational algorithms such as sequence conservation, structural analysis, physicochemical properties, and machine learning. The sequence of the SNCA gene was analyzed, resulting in the mapping of 42,272 SNPs that are classified into different functional categories. A total of 177 nsSNPs were identified within the coding region; there were 20 variants that may influence the α-Syn protein structure and function. This identification was made by employing different analytical tools including SIFT, PolyPhen2, Mut-pred, SNAP2, PANTHER, PhD-SNP, SNP&Go, MUpro, Cosurf, I-Mut, and HOPE. Three mutations, V82A, K80E, and E46K, were selected for further examinations due to their spatial positioning within the α-Syn as determined by PyMol. Results indicated that these mutations may affect the stability and function of α-Syn. Then, a molecular dynamics simulation was conducted for the SNCA wildtype and the four mutant variants (p.A18G, p.V82A, p.K80E, and p.E46K). The simulation examined temperature, pressure, density, root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg). The data indicate that the mutations p.V82A, p.K80E, and p.E46K reduce the stability and functionality of α-Syn. These findings highlight the importance of understanding the impact of nsSNPs on α-syn structure and function. Our results required verifications in further protein functional and case-control studies. After being verified these findings can be used in genetic testing for the early diagnosis of PD, the evaluation of the risk factors, and therapeutic approaches.

Molecular dynamic simulation and functional analysis of pathogenic PTEN mutations in glioblastoma

PTEN, a dual-phosphatase and scaffold protein, is one of the most commonly mutated tumour suppressor gene across various cancer types in human. The aim of this study therefore was to investigate the stability, structural and functional effects, and pathogenicity of 12 missense PTEN mutations (R15S, E18G, G36R, N49I, Y68H, I101T, C105F, D109N, V133I, C136Y, R173C and N276S) found by next generation sequencing of the PTEN gene in tissue samples obtained from glioblastoma patients. Computational tools and molecular dynamic simulation programs were used to identify the deleterious effects of these mutations. Furthermore, PTEN mRNA and protein expression levels were evaluated by qRT-PCR, Western Blot, and immunohistochemistry staining methods. Various computational tools predicted strong deleterious effects for the G36R, C105F, C136Y and N276S mutations. Molecular dynamic simulation revealed a significant decrease in protein stability for the Y68H and N276S mutations when compared with the wild type protein; whereas, C105F, D109N, V133I and R173C showed partial stability reduction. Significant residual fluctuations were observed in the R15S, N49I and C136Y mutations and radius of gyration graphs revealed the most compact structure for D109N and least for C136Y. In summary, our study is the first one to show the presence of PTEN E18G, N49I, D109N and N276S mutations in glioblastoma patients; where, D109N is neutral and N276S is a damaging and disease-associated mutation.Communicated by Ramaswamy H. Sarma.

An in silico analysis of rpoB mutations to affect Chlamydia trachomatis sensitivity to rifamycin

Background

Chlamydia trachomatis is an obligate intracellular gram-negative pathogen, responsible for diverse affections, mainly trachoma and sexually transmitted diseases. Antibiotics are the commonly used drugs to tackle chlamydiae infections. However, when overused or wrongly used this may lead to strains’ resistance to antibiotics, this phenomenon represents a real health problem worldwide. Numerous studies showed the association of Chlamydia trachomatis resistance with mutations in different genes; these mutations could have a deleterious or neutral impacts on the encoded proteins. The aim of this study is to perform an in silico analysis of C. trachomatis rpoB-encoded proteins using numerous bioinformatics tools and to identify the functional and structural-related effects of the mutations and consequently their impact on the bacteria sensitivity to antibiotics.

Results

The analysis revealed that the prediction of the damaging impact related to the mutations in rpoB-encoded proteins showed eight mutations: V136F, Q458K, V466A, A467T, H471N, H471Y, H471L, and I517M with big deleterious effects. Among them, six mutations, V136F, Q458K, V466A, A467T, H471N, and I517M, are located in a highly conserved regions decreasing the protein’s stability. Furthermore, the structures analysis showed that the mutations A467T, H471N, I517M, and V136F models had a high deviation compared to the wild type. Moreover, the prediction of protein-protein network indicated that rpoB wild type interacts strongly with 10 proteins of C. trachomatis, which are playing different roles at different levels.

Conclusion

As conclusion, the present study revealed that the changes observed in the encoded proteins can affect their functions and structures, in addition to their interactions with other proteins which impact the bacteria sensitivity to antibiotics. Consequently, the information revealed through this in silico analysis would be useful for deeper exploration to understand the mechanisms of C. trachomatis resistance and enable managing the infection to avoid its complications. We recommend further investigations and perform deeper experimental analysis with collaboration between bioinformaticians, physicians, biologists, pharmacists, and chemistry and biochemistry scientists.

Whole proteome screening and identification of potential epitopes of SARS-CoV-2 for vaccine design-an immunoinformatic, molecular docking and molecular dynamics simulation accelerated robust strategy

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the most cryptic pandemic outbreak of the 21st century, has gripped more than 1.8 million people to death and infected almost eighty six million. As it is a new variant of SARS, there is no approved drug or vaccine available against this virus. This study aims to predict some promising cytotoxic T lymphocyte epitopes in the SARS-CoV-2 proteome utilizing immunoinformatic approaches. Firstly, we identified 21 epitopes from 7 different proteins of SARS-CoV-2 inducing immune response and checked for allergenicity and conservancy. Based on these factors, we selected the top three epitopes, namely KAYNVTQAF, ATSRTLSYY, and LTALRLCAY showing functional interactions with the maximum number of MHC alleles and no allergenicity. Secondly, the 3D model of selected epitopes and HLA-A*29:02 were built and Molecular Docking simulation was performed. Most interestingly, the best two epitopes predicted by docking are part of two different structural proteins of SARS-CoV-2, namely Membrane Glycoprotein (ATSRTLSYY) and Nucleocapsid Phosphoprotein (KAYNVTQAF), which are generally target of choice for vaccine designing. Upon Molecular Docking, interactions between selected epitopes and HLA-A*29:02 were further validated by 50 ns Molecular Dynamics (MD) simulation. Analysis of RMSD, Rg, SASA, number of hydrogen bonds, RMSF, MM-PBSA, PCA, and DCCM from MD suggested that ATSRTLSYY is the most stable and promising epitope than KAYNVTQAF epitope. Moreover, we also identified B-cell epitopes for each of the antigenic proteins of SARS CoV-2. Findings of our work will be a good resource for wet lab experiments and will lessen the timeline for vaccine construction.Communicated by Ramaswamy H. Sarma.

Comprehensive in silico mutational-sensitivity analysis of PTEN establishes signature regions implicated in pathogenesis of Autism Spectrum Disorders

An extensively studied cancer and Autism Spectrum Disorders (ASD) gene like PTEN provided an exclusive opportunity to map its mutational-landscape, compare and establish plausible genotypic predictors of ASD-associated phenotypic outcomes. Our exhaustive in silico analysis on 4252 SNPs using >30 tools identified increased mutational-density in exon7. Phosphatase domain, although evolutionarily conserved, had the most nsSNPs localised within signature regions. The evolutionarily variable C-terminal side contained the highest truncating-SNPs outside signature regions of C2 domain and most PTMs within C-tail site which displayed maximum intolerance to polymorphisms, and permitted benign but destabilising nsSNPs that enhanced its intrinsically-disordered nature. ASD-associated SNPs localised within ATP-binding motifs and Nuclear-Localising-Sequences were the most potent triggers of ASD manifestation. These, along with variations within P, WPD and TI loops, M1 within phosphatase domain, M2 and MoRFs of C2 domain, caused severe long-range conformational fluctuations altering PTEN's dynamic stability- not observed in variations outside signature regions. 3'UTR-SNPs affected 44 strong miRNA brain-specific targets; several 5' UTR-SNPs targeted transcription-factor POLR2A and 10 pathogenic Splice-Affecting-Variants were identified.

Predicting the most deleterious missense nsSNPs of the protein isoforms of the human HLA-G gene and in silico evaluation of their structural and functional consequences

BACKGROUND

The Human Leukocyte Antigen G (HLA-G) protein is an immune tolerogenic molecule with 7 isoforms. The change of expression level and some polymorphisms of the HLA-G gene are involved in various pathologies. Therefore, this study aimed to predict the most deleterious missense non-synonymous single nucleotide polymorphisms (nsSNPs) in HLA-G isoforms via in silico analyses and to examine structural and functional effects of the predicted nsSNPs on HLA-G isoforms.

RESULTS

Out of 301 reported SNPs in dbSNP, 35 missense SNPs in isoform 1, 35 missense SNPs in isoform 5, 8 missense SNPs in all membrane-bound HLA-G isoforms and 8 missense SNPs in all soluble HLA-G isoforms were predicted as deleterious by all eight servers (SIFT, PROVEAN, PolyPhen-2, I-Mutant 3.0, SNPs&GO, PhD-SNP, SNAP2, and MUpro). The Structural and functional effects of the predicted nsSNPs on HLA-G isoforms were determined by MutPred2 and HOPE servers, respectively. Consurf analyses showed that the majority of the predicted nsSNPs occur in conserved sites. I-TASSER and Chimera were used for modeling of the predicted nsSNPs. rs182801644 and rs771111444 were related to creating functional patterns in 5'UTR. 5 SNPs in 3'UTR of the HLA-G gene were predicted to affect the miRNA target sites. Kaplan-Meier analysis showed the HLA-G deregulation can serve as a prognostic marker for some cancers.

CONCLUSIONS

The implementation of in silico SNP prioritization methods provides a great framework for the recognition of functional SNPs. The results obtained from the current study would be called laboratory investigations.

Association of phosphatase and tension homologue deleted on chromosome ten polymorphism rs1903858, but not serum levels with the risk of non-small-cell lung cancer: A case-control study

Background

To investigate the association between phosphatase and tension homologue deleted on chromosome ten (PTEN) gene polymorphisms and non–small‐cell lung cancer (NSCLC) and further identify whether these polymorphisms influence serum PTEN levels.

Methods

A total of 152 NSCLC patients and 124 healthy controls were included in the study. PTEN gene rs11202586 (T > C) and rs1903858 (A > G) polymorphisms were detected using the multiple single‐base extension technique (SNaPshot). The serum PTEN levels were determined using an enzyme‐linked immunosorbent assay (ELISA) kit.

Results

The rs1903858 AG, GG genotypes, and G allele were associated with a higher risk of NSCLC (odds ratio (OR) =2.079, 95% confidence interval (CI) = 1.087‐3.974, P = .027; OR = 1.897, 95%CI = 1.053‐3.419, P = .033; OR = 1.505, 95%CI = 1.065‐2.126, P = .020). Stratified analysis reveal that the rs1903858 GG genotype and G allele were associated with an increased risk of squamous cell carcinoma (SCC) (OR = 3.226, 95%CI = 1.075‐9.678, P = .037; OR = 1.873, 95%CI = 1.092‐3.212, P = .023). Among smokers, the rs1903858 G allele carriers have an increased risk of NSCLC (OR = 1.916, 95%CI = 1.023‐3.589, P = .042), but a decreased risk of NSCLC was found with the AT haplotype. With respect to the serum PTEN levels, no significant difference was noted between NSCLC patients and healthy controls in this study.

Conclusions

The study indicated that the rs1903858 gene polymorphism is associated with increased risk of NSCLC, particularly in SCC and smoker, and the haplotype AT was a protective factor for NSCLC. The serum PTEN levels were not associated with NSCLC.

Prediction and Structural Comparison of Deleterious Coding Nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) in Human LEP Gene Associated with Obesity

Leptin is a peptide hormone that regulates fat stores in the body and appetite by controlling the feeling of satiety. This hormone is secreted by the white adipose tissue and plays a role in the storage and mobilization of fatty acids. Mutations of the LEP gene have been associated with obesity in different populations; it is a multifactorial disease that constitutes a major public health problem. In this study, we evaluated the impact of missense SNPs in the LEP gene extracted from dbSNP using 8 computational prediction tools. Out of the total of 4337 SNPs, 93 were nsSNPs (nonsynonymous single nucleotide polymorphisms). Among 93 nsSNPs, 12 (S46L, G59S, D61N, D100N, N103K, C117S, D76V, S88C, P90R, I95N, L161R, and R105W) variants were predicted to be the most deleterious by prediction software. On these 12 deleterious SNPs, 8 variants (S46L, G59S, D61N, D100N, N103K, C117S, L161R, and R105W) were located in the conserved positions and showed a decrease in structure stability which was evaluated by I-Mutant and Mupro. Then, by analyzing the different interactions between different amino acids in wild and mutated proteins, we assessed the structural impact of the deleterious modifications using the YASARA software. Among 8 deleterious nsSNPs, we revealed structure changes in the 6 variants S46L, G59S, D100N, L103K, R105W, L161R, two of which R105W, N103K were previously reported as associated with obesity. Our study suggests 6 deleterious mutations could play an important role in contributing to human obesity and worth to be included in association and functional studies, then may be a drug target.

Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene

In SCF (Skp, Cullin, F-box) ubiquitin-protein ligase complexes, S-phase kinase 2 (SKP2) is one of the major players of F-box family, that is responsible for the degradation of several important cell regulators and tumor suppressor proteins. Despite of having significant evidence for the role of SKP2 on tumorgenesis, there is a lack of available data regarding the effect of non-synonymous polymorphisms. In this communication, the structural and functional consequences of non-synonymous single nucleotide polymorphisms (nsSNPs) of SKP2 have been reported by employing various computational approaches and molecular dynamics simulation. Initially, several computational tools like SIFT, PolyPhen-2, PredictSNP, I-Mutant 2.0 and ConSurf have been implicated in this study to explore the damaging SNPs. In total of 172 nsSNPs, 5 nsSNPs were identified as deleterious and 3 of them were predicted to be decreased the stability of protein. Guided from ConSurf analysis, P101L (rs761253702) and Y346C (rs755010517) were categorized as the highly conserved and functional disrupting mutations. Therefore, these mutations were subjected to three dimensional model building and molecular dynamics simulation study for the detailed structural consequences upon the mutations. The study revealed that P101L and Y346C mutations increased the flexibility and changed the structural dynamics. As both these mutations are located in the most functional regions of SKP2 protein, these computational insights might be helpful to consider these nsSNPs for wet-lab confirmatory analysis as well as in rationalizing future population based studies and structure based drug design against SKP2.

Impact of missense mutations in survival motor neuron protein (SMN1) leading to Spinal Muscular Atrophy (SMA): A computational approach

Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by the mutations in survival motor neuron 1 gene (SMN1). The molecular pathology of missense mutations in SMN1 is not thoroughly investigated so far. Therefore, we collected all missense mutations in the SMN1 protein, using all possible search terms, from three databases (PubMed, PMC and Google Scholar). All missense mutations were subjected to in silico pathogenicity, conservation, and stability analysis tools. We used statistical analysis as a QC measure for validating the specificity and accuracy of these tools. PolyPhen-2 demonstrated the highest specificity and accuracy. While PolyPhen-1 showed the highest sensitivity; overall, PolyPhen2 showed better measures in comparison to other in silico tools. Three mutations (D44V, Y272C, and Y277C) were identified as the most pathogenic and destabilizing. Further, we compared the physiochemical properties of the native and the mutant amino acids and observed loss of H-bonds and aromatic stacking upon the cysteine to tyrosine substitution, which led to the loss of aromatic rings and may reduce protein stability. The three mutations were further subjected to Molecular Dynamics Simulation (MDS) analysis using GROMACS to understand the structural changes. The Y272C and Y277C mutants exhibited maximum deviation pattern from the native protein as compared to D44V mutant. Further MDS analysis predicted changes in the stability that may have been contributed due to the loss of hydrogen bonds as observed in intramolecular hydrogen bond analysis and physiochemical analysis. A loss of function/structural impact was found to be severe in the case of Y272C and Y277C mutants in comparison to D44V mutation. Correlating the results from in silico predictions, physiochemical analysis, and MDS, we were able to observe a loss of stability in all the three mutants. This combinatorial approach could serve as a platform for variant interpretation and drug design for spinal muscular dystrophy resulting from missense mutations.

Single nucleotide polymorphisms rs701848 and rs2735343 in PTEN increases cancer risks in an Asian population

We performed this meta-analysis to analyze the cancer risk to individuals carrying the rs701848 and rs2735343 single nucleotide polymorphisms (SNPs) in the phosphatase and tensin homolog (PTEN) gene. We searched the PubMed, EMBASE, Cochrane library and the national knowledge infrastructure of China (CNKI) databases and identified 18 eligible case-control studies with 5458 cases and 6003 controls for rs701848 as well as 5490 cases and 6209 controls for rs2735343. Our analyses demonstrated that cancer risk was associated with rs701848 in the recessive model (CC vs. CT+TT, OR=1.169, 95% CI: 1.061-1.288) and with rs2735343 in the dominant model (GC+CC vs. GG, OR=0.758, 95% CI: 0.590-0.972). Subgroup analysis showed that in Asian subjects, carrying the C allele of rs701848 or GG genotype of rs2735343 was associated with increased cancer risk. Moreover, Asian subjects carrying the TC/CC genotype or C allele of rs701848 were associated with increased risk of esophageal squamous cell cancer. This meta-analysis indicates that the PTEN rs701848 (CC) and rs2735343 (GG) polymorphisms are associated with increased cancer risk in Asian subjects.

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.

MicroRNA-181 inhibits proliferation and promotes apoptosis of chondrocytes in osteoarthritis by targeting PTEN

Objective: To investigate the effects of microRNA-181 (miR-181) on the proliferation and apoptosis of chondrocytes in osteoarthritis (OA) by targeting PTEN. Methods: The chondrocytes in logarithmic growth phase were selected and divided into 6 test groups: the normal, blank, negative control, miR-181 mimic, miR-181 inhibitor, and miR-181 inhibitor + PTEN-siRNA groups. Reverse transcription qPCR was used to detect the expressions of miR-181 and PTEN mRNA. MTT assay and flow cytometry were performed to detect cell proliferation and apoptosis. The protein expressions of PARP and caspase-3 and the activity of MMP-2 and MMP-9 were detected by Western blotting and gelatin zymography assay. Results: The miR-181 mimic group showed increased miR-181 expression and decreased PTEN expression compared with the other 5 groups. Also, by comparison with the other 5 groups, the cell proliferation rate declined and the rate of cell apoptosis was elevated in the miR-181 mimic group. The MiR-181 mimic group showed remarkably increased protein expression of caspase-3 and PARP compared with the other 5 groups. The activity of MMP-2 and MMP-9 was higher in the miR-181 mimic group than the other 5 groups. Conclusion: MiR-181 could up-regulate the expressions of caspase-3, PARP, MMP-2, and MMP-9, and thereby inhibit cell proliferation and promote apoptosis of chondrocytes in OA by targeting PTEN.

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.

Molecular Dynamics: New Frontier in Personalized Medicine

The field of drug discovery has witnessed infinite development over the last decade with the demand for discovery of novel efficient lead compounds. Although the development of novel compounds in this field has seen large failure, a breakthrough in this area might be the establishment of personalized medicine. The trend of personalized medicine has shown stupendous growth being a hot topic after the successful completion of Human Genome Project and 1000 genomes pilot project. Genomic variant such as SNPs play a vital role with respect to inter individual's disease susceptibility and drug response. Hence, identification of such genetic variants has to be performed before administration of a drug. This process requires high-end techniques to understand the complexity of the molecules which might bring an insight to understand the compounds at their molecular level. To sustenance this, field of bioinformatics plays a crucial role in revealing the molecular mechanism of the mutation and thereby designing a drug for an individual in fast and affordable manner. High-end computational methods, such as molecular dynamics (MD) simulation has proved to be a constitutive approach to detecting the minor changes associated with an SNP for better understanding of the structural and functional relationship. The parameters used in molecular dynamic simulation elucidate different properties of a macromolecule, such as protein stability and flexibility. MD along with docking analysis can reveal the synergetic effect of an SNP in protein-ligand interaction and provides a foundation for designing a particular drug molecule for an individual. This compelling application of computational power and the advent of other technologies have paved a promising way toward personalized medicine. In this in-depth review, we tried to highlight the different wings of MD toward personalized medicine.

Investigating the Inhibitory Effect of Wortmannin in the Hotspot Mutation at Codon 1047 of PIK3CA Kinase Domain: A Molecular Docking and Molecular Dynamics Approach

Oncogenic mutations in phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) are the most frequently reported in association with various forms of cancer. Several studies have reported the significance of hotspot mutations in a catalytic subunit of PIK3CA in association with breast cancer. Mutations are frequently observed in the highly conserved region of the kinase domain (797-1068 amino acids) of PIK3CA are activating or gain-of-function mutations. Mutation in codon 1047 occurs in the C-terminal region of the kinase domain with histidine (H) replaced by arginine (R), lysine (L), and tyrosine (Y). Pathogenicity and protein stability predictors PhD-SNP, Align GVGD, HANSA, iStable, and MUpro classified H1047R as highly deleterious when compared to H1047L and H1047Y. To explore the inhibitory activity of Wortmannin toward PIK3CA, the three-dimensional structure of the mutant protein was determined using homology modeling followed by molecular docking and molecular dynamics analysis. Docking studies were performed for the three mutants and native with Wortmannin to measure the differences in their binding pattern. Comparative docking study revealed that H1047R-Wortmannin complex has a higher number of hydrogen bonds as well as the best binding affinity next to the native protein. Furthermore, 100 ns molecular dynamics simulation was initiated with the docked complexes to understand the various changes induced by the mutation. Though Wortmannin was found to nullify the effect of H1047R over the protein, further studies are required for designing a better compound. As SNPs are major genetic variations observed in disease condition, personalized medicine would provide enhanced drug therapy.

On human disease-causing amino acid variants: statistical study of sequence and structural patterns

Statistical analysis was carried out on large set of naturally occurring human amino acid variations, and it was demonstrated that there is a preference for some amino acid substitutions to be associated with diseases. At an amino acid sequence level, it was shown that the disease-causing variants frequently involve drastic changes in amino acid physicochemical properties of proteins such as charge, hydrophobicity, and geometry. Structural analysis of variants involved in diseases and being frequently observed in human population showed similar trends: disease-causing variants tend to cause more changes in hydrogen bond network and salt bridges as compared with harmless amino acid mutations. Analysis of thermodynamics data reported in the literature, both experimental and computational, indicated that disease-causing variants tend to destabilize proteins and their interactions, which prompted us to investigate the effects of amino acid mutations on large databases of experimentally measured energy changes in unrelated proteins. Although the experimental datasets were linked neither to diseases nor exclusory to human proteins, the observed trends were the same: amino acid mutations tend to destabilize proteins and their interactions. Having in mind that structural and thermodynamics properties are interrelated, it is pointed out that any large change in any of them is anticipated to cause a disease.

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.

Molecular mechanisms of disease-causing missense mutations

Genetic variations resulting in a change of amino acid sequence can have a dramatic effect on stability, hydrogen bond network, conformational dynamics, activity and many other physiologically important properties of proteins. The substitutions of only one residue in a protein sequence, so-called missense mutations, can be related to many pathological conditions and may influence susceptibility to disease and drug treatment. The plausible effects of missense mutations range from affecting the macromolecular stability to perturbing macromolecular interactions and cellular localization. Here we review the individual cases and genome-wide studies that illustrate the association between missense mutations and diseases. In addition, we emphasize that the molecular mechanisms of effects of mutations should be revealed in order to understand the disease origin. Finally, we report the current state-of-the-art methodologies that predict the effects of mutations on protein stability, the hydrogen bond network, pH dependence, conformational dynamics and protein function.