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. © 2016 Elsevier Inc. All rights reserved.

George Priya Doss C

Department of Integrative Biology

School of Bio Sciences and Technology

Vellore Campus

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Advances in Protein Chemistry and Structural Biology Personalized Medicine

Sjögren-Larsson syndrome (SLS) is an autoimmune disorder inherited in an autosomal recessive pattern. To date, 80 missense mutations have been identified in association with the Aldehyde Dehydrogenase 3 Family Member A2 (ALDH3A2) gene causing SLS. Disruption of the function of ALDH3A2 leads to excessive accumulation of fat in the cells, which interferes with the normal function of protective membranes or materials that are necessary for the body to function normally. We retrieved 54 missense mutations in the ALDH3A2 from the OMIM, UniProt, dbSNP, and HGMD databases that are known to cause SLS. These mutations were examined with various in silico stability tools, which predicted that the mutations p.S308N and p.R423H that are located at the protein-protein interaction domains are the most destabilizing. Furthermore, to determine the atomistic-level differences within the protein-protein interactions owing to mutations, we performed macromolecular simulation (MMS) using GROMACS to validate the motion patterns and dynamic behavior of the biological system. We found that both mutations (p.S380N and p.R423H) had significant effects on the protein-protein interaction and disrupted the dimeric interactions. The computational pipeline provided in this study helps to elucidate the potential structural and functional differences between the ALDH3A2 native and mutant homodimeric proteins, and will pave the way for drug discovery against specific targets in the SLS patients.

Advances in Protein Chemistry and Structural Biology Inflammatory Disorders - Part B

Comprehensive in silico screening and molecular dynamics studies of missense mutations in Sjogren-Larsson syndrome associated with the ALDH3A2 gene

Fulltext

Heliyon

The impact of missense mutation in PIGA associated to paroxysmal nocturnal hemoglobinuria and multiple congenital anomalies-hypotonia-seizures syndrome 2: A computational study

BACKGROUND:The present investigation looks at the most likely possibilities of usage of a naturally occurring photosynthetic pigment, Pheophytin a, from the seagrass, Syringodium isoetifolium, for plausible use as human TSPO ligand. METHODS:Pheophytin a isolated in our laboratory previously was administered to A549 cell lines in vitro to examine its effects on cell migrations, DNA, cell cycle, Mitochondrial Membrane Potential and gene expressions. In silico tools were used to predict the nature of the compound and target binding. RESULTS:Pheophytin a hadIC50 values of 22.9 ± 5.8 µM for cancerous A549 cell lines, whilst not targeting non-cancerous vero cells [IC50: 183.6 ± 1.92 µM]. Pheophytin a hindered cellular migration, fragmented DNA, arrested cell cycle precisely at S phase, reduced ∆ψmit and directed mRNA expressions toward apoptosis. In silico tools indicate that the compound binds to TSPO with high effectiveness to collapse ∆ψmit(which is proved using wet lab experiments) to promote mitophagy. CONCLUSION:Hence Pheophytin a could be seen as a possible TSPO ligand for targeting metastatic alveolar cancers like A549 via intrinsic apoptotic pathway. GENERAL SIGNIFICANCE:Given the inherent non-toxic nature of the compound and easy extractability from almost all autotrophic eukaryotes, one could be confident to testing in animal models.

Phytomedicine

A natural anticancer pigment,Pheophytin a,from a seagrass acts as a high affinity human mitochondrial translocator protein (TSPO) ligand, in silico, to reduce mitochondrial membrane Potential (∆ψ ) in adenocarcinomic A549 cells.

Gaucher’s disease (GD) is a genetic disorder in which glucocerebroside accumulates in cells and specific organs. It is broadly classified into type I, type II and type III. Patients with GD are at high risk of Parkinson’s disease (PD), and the clinical and pathological presentation of GD patients with PD is almost identical to idiopathic PD. Several experimental models like cell culture, animal models, and transgenic mice models were used to understand the molecular mechanism behind GD and PD association; however, such mechanism remains unclear. In this context, based on literature reports, we identified the most common mutations K198T, E326K, T369M, N370S, V394L, D409H, L444P, and R496H, in the Glucosylceramidase (GBA) protein that are known to cause GD1, and represent a risk of developing PD. However, to date, no computational analyses have designed to elucidate the potential functional role of GD mutations with increased risk of PD. The present computational pipeline allows us to understand the structural and functional significance of these GBA mutations with PD. Based on the published data, the most common and severe mutations were E326K, N370S, and L444P, which further selected for our computational analysis. PredictSNP and iStable servers predicted L444P mutant to be the most deleterious and responsible for the protein destabilization, followed by the N370S mutation. Further, we used the structural analysis and molecular dynamics approach to compare the most frequent deleterious mutations (N370S and L444P) with the mild mutation E326K. The structural analysis demonstrated that the location of E326K and N370S in the alpha helix region of the protein whereas the mutant L444P was in the starting region of the beta sheet, which might explain the predicted pathogenicity level and destabilization effect of the L444P mutant. Finally, Molecular Dynamics (MD) at 50 ns showed the highest deviation and fluctuation pattern in the L444P mutant compared to the two mutants E326K and N370S and the native protein. This was consistent with more loss of intramolecular hydrogen bonds and less compaction of the radius of gyration in the L444P mutant. The proposed study is anticipated to serve as a potential platform to understand the mechanism of the association between GD and PD, and might facilitate the process of drug discovery against both GD and PD. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Metabolic Brain Disease

Computational modelling approaches as a potential platform to understand the molecular genetics association between Parkinson’s and Gaucher diseases

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. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

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

We examined 25 breast tumor samples for somatic mutations in exon 20 and exon 9 of PIK3CA gene in South Indian population. Genomic DNA was isolated and amplified for PIK3CA gene, followed by direct sequencing of purified polymerase chain reaction products. We identified PI3K3CA mutations in 5 of 25 (20%), including four of the mutations in p.H1047R and one in p.H1047L. Nucleotide base substitution A to G (c.3140A &gt; G) and A to T (c.3140A &gt; T) results in p.H1047R and p.H1047L mutation in exon 20 of PIK3CA gene. We did not observe any mutation in exon 9 of PIK3CA gene. Furthermore, we investigated the effect of mutations on protein structure and function by the combination of sequence and structure-based in silico prediction methods. This determined the underlying relationship between the mutation and its phenotypic effects. Next step, we complemented by molecular dynamics simulation analysis (30 ns) of native and mutant structures that measured the effect of mutation on protein structure. The obtained results support that the application of computational methods helps predict the biological significance of mutations. © 2015 Taylor and Francis.

Journal of Biomolecular Structure and Dynamics

Deciphering the impact of somatic mutations in exon 20 and exon 9 ofPIK3CAgene in breast tumors among Indian women through molecular dynamics approach

Some individuals with non-small-cell lung cancer (NSCLC) benefit from therapies targeting epidermal growth factor receptor (EGFR), and the characterization of a new mechanism of resistance to the EGFR-specific antibody gefitinib will provide valuable insight into how therapeutic strategies might be designed to overcome this particular resistance mechanism. The G719S and T790M mutations and their combination were involved in causing different conformational redistribution of EGFR. In the present computational study, we analyzed the impact and structural influence of G719S/T790M double mutation (DM) in EGFR with ligand (gefitinib) through molecular dynamic simulation (50â €..ns) and docking analysis. We observed the escalation in distance between the functional loop and activation loop with respect to T790M mutation compared to the G719S mutation. Furthermore, we confirmed that the G719S mutation causes the ligand to move closer to the hinge region, whereas T790M makes the ligand escape from the binding pocket. Obtained results provide with an explanation for the resistance induced by T790M and a vital clue for the design of drugs to combat gefitinib resistance.

Scientific Reports

Structural signature of the G719S-T790M double mutation in the EGFR kinase domain and its response to inhibitors

Single amino acid substitutions (SAPs) belong to a class of SNPs in the coding region, which alter the protein function during the translation process. Storage of more information regarding SAPs in public databases will soon become a major hurdle in characterizing the functional SAPs. In such a demanding era, biology has to rely on bioinformatics, which can work its way through to solve the problems at hand by cutting huge amount of time and resources that are otherwise wasted. Here, we describe an overview of the existing repositories of variant databases and computational methods in predicting the effects of functional SAPs on protein stability, structure, function, drug response, and protein dynamics. This chapter will inspire many biologists with a greater promise in identifying the functional SAPs at the structural level, thereby understanding the molecular effects that are critical for personalized medicine diagnosis, prognosis, and treatment for diseases. © 2014 Elsevier Inc.

Advances in Protein Chemistry and Structural Biology

Computational Approaches and Resources in Single Amino Acid Substitutions Analysis Toward Clinical Research

Over the past decade, advancements in next generation sequencing technology have placed personalized genomic medicine upon horizon. Understanding the likelihood of disease causing mutations in complex diseases as pathogenic or neutral remains as a major task and even impossible in the structural context because of its time consuming and expensive experiments. Among the various diseases causing mutations, single nucleotide polymorphisms (SNPs) play a vital role in defining individual’s susceptibility to disease and drug response. Understanding the genotype-phenotype relationship through SNPs is the first and most important step in drug research and development. Detailed understanding of the effect of SNPs on patient drug response is a key factor in the establishment of personalized medicine. In this paper, we represent a computational pipeline in anaplastic lymphoma kinase (ALK) for SNP-centred study by the application ofin silicoprediction methods, molecular docking, and molecular dynamics simulation approaches. Combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the protein drug targets and eventually leading to variable patient’s drug response. We hope this rapid and cost effective pipeline will also serve as a bridge to connect the clinicians andin silicoresources in tailoring treatments to the patients’ specific genotype.

BioMed Research International

IntegratingIn SilicoPrediction Methods, Molecular Docking, and Molecular Dynamics Simulation to Predict the Impact of ALK Missense Mutations in Structural Perspective

Background: Recent reports suggest the role of nonsynonymous single nucleotide polymorphisms (nsSNPs) in cyclin-dependent kinase 7 (CDK7) gene associated with defect in the DNA repair mechanism that may contribute to cancer risk. Among the various inhibitors developed so far, flavopiridol proved to be a potential antitumor drug in the phase-III clinical trial for chronic lymphocytic leukemia. Here, we described a theoretical assessment for the discovery of new drugs or drug targets in CDK7 protein owing to the changes caused by deleterious nsSNPs. Methods: Three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on protein function by SIFT, PolyPhen2, I-Mutant3, PANTHER, SNPs&amp;GO, PhD-SNP, and screening for non-acceptable polymorphisms (SNAP). Furthermore, we analyzed the native and proposed mutant models in atomic level 10 ns simulation using the molecular dynamics (MD) approach. Finally, with the aid of Autodock 4.0 and PatchDock, we analyzed the binding efficacy of flavopiridol with CDK7 protein with respect to the deleterious mutations. Results: By comparing the results of all seven prediction tools, three nsSNPs (I63R, H135R, and T285M) were predicted to have functional impact on the protein function. The results of protein stability analysis inferred that I63R and H135R exhibited less deviation in root mean square deviation in comparison with the native and T285M protein. The flexibility of all the three mutant models of CDK7 protein is diverse in comparison with the native protein. Following to that, docking study revealed the change in the active site residues and decrease in the binding affinity of flavopiridol with mutant proteins. Conclusion: This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. The identification of disease related SNPs by computational methods has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases. Lay abstract: Cell cycle regulatory protein, CDK7, is linked with DNA repair mechanism which can contribute to cancer risk. The main aim of this study is to extrapolate the relationship between the nsSNPs and their effects in drug-binding capability. In this work, we propose a new methodology which (1) efficiently identified the deleterious nsSNPs that tend to have functional effect on protein function upon mutation by computational tools, (2) analyze d the native protein and proposed mutant models in atomic level using MD approach, and (3) investigated the protein-ligand interactions to analyze the binding ability by docking analysis. This theoretical approach is entirely based on computational methods, which has the ability to identify the disease-related SNPs in complex disorders by contrasting their costs and capabilities with those of the experimental methods. Overall, this approach has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases. © 2013 George Priya Doss et al.

Journal	Data powered by TypesetAdvances in Protein Chemistry and Structural Biology Personalized Medicine
Publisher	Data powered by TypesetElsevier
ISSN	1876-1623
Open Access	No