Cyclic-dependent kinase 2 (CDK2) is one of the primary protein kinases involved in the regulation of cell cycle progression. Flavopiridol is a flavonoid derived from an indigenous plant act as a potent antitumor drug showing increased inhibitory activity toward CDK2. The presence of deleterious variations in CDK2 may produce different effects in drug-binding adaptability. Studies on nsSNPs of CDK2 gene will provide information on the most likely variants associated with the disease. Furthermore, investigating the relationship between deleterious variants and its ripple effect in the inhibitory action with drug will provide fundamental information for the development of personalized therapies. In this study, we predicted four variants Y15S, V18L, P45L, and V69A of CDK2 as highly deleterious. Occurrence of these variations seriously affected the normal binding capacity of flavopiridol with CDK2. Analysis of 10-ns molecular dynamics (MD) simulation trajectories indicated that the predicted deleterious variants altered the CDK2 stability, flexibility, and surface area. Notably, we noticed the decrease in number of hydrogen bonds between CDK2 and flavopiridol mutant complexes in the whole dynamic period. Overall, this study explores the possible relationship between the CDK2 deleterious variants and the drug-binding ability with the help of molecular docking and MD approaches. © 2013 Springer Science+Business Media New York.

George Priya Doss C

Department of Integrative Biology

School of Bio Sciences and Technology

Vellore Campus

Nagasundaram N

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

Cell Biochemistry and Biophysics

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. © 2016 Elsevier Inc. All rights reserved.

Advances in Protein Chemistry and Structural Biology Personalized Medicine

Investigating the Inhibitory Effect of Wortmannin in the Hotspot Mutation at Codon 1047 of PIK3CA Kinase Domain

The cyclin-dependent kinase 4 (CDK4)-cyclin D1 complex plays a crucial role in the transition from the G1 phase to S phase of the cell cycle. Among the CDKs, CDK4 is one of the genes most frequently affected by somatic genetic variations that are associated with various forms of cancer. Thus, because the abnormal function of the CDK4-cyclin D1 protein complex might play a vital role in causing cancer, CDK4 can be considered a genetically validated therapeutic target. In this study, we used a systematic, integrated computational approach to identify deleterious nsSNPs and predict their effects on protein-protein (CDK4-cyclin D1) and protein-ligand (CDK4-flavopiridol) interactions. This analysis resulted in the identification of possible inhibitors of mutant CDK4 proteins that bind the conformations induced by deleterious nsSNPs. Using computational prediction methods, we identified five nsSNPs as highly deleterious: R24C, Y180H, A205T, R210P, and R246C. From molecular docking and molecular dynamic studies, we observed that these deleterious nsSNPs affected CDK4-cyclin D1 and CDK4-flavopiridol interactions. Furthermore, in a virtual screening approach, the drug 5-7-DIHYDROXY-2-(3-4-5-TRI HYDROXYPHENYL)-4H-CHROMEN-4-ONE displayed good binding affinity for proteins with the mutations R24C or R246C, the drug diosmin displayed good binding affinity for the protein with the mutation Y180H, and the drug rutin displayed good binding affinity for proteins with the mutations A205T and R210P. Overall, this computational investigation of the CDK4 gene highlights the link between genetic variation and biological phenomena in human cancer and aids in the discovery of molecularly targeted therapies for personalized treatment. © 2015 N et al.

Fulltext

PLOS ONE

Analysing the Effect of Mutation on Protein Function and Discovering Potential Inhibitors of CDK4: Molecular Modelling and Dynamics Studies

Fanconi anemia (FA) is an autosomal recessive human disease characterized by genomic instability and a marked increase in cancer risk. The importance of FANCD1 gene is manifested by the fact that deleterious amino acid substitutions were found to confer susceptibility to hereditary breast and ovarian cancers. Attaining experimental knowledge about the possible disease-associated substitutions is laborious and time consuming. The recent introduction of genome variation analyzing in silico tools have the capability to identify the deleterious variants in an efficient manner. In this study, we conducted in silico variation analysis of deleterious non-synonymous SNPs at both functional and structural level in the breast cancer and FA susceptibility gene BRCA2/FANCD1. To identify and characterize deleterious mutations in this study, five in silico tools based on two different prediction methods namely pathogenicity prediction (SIFT, PolyPhen, and PANTHER), and protein stability prediction (I-Mutant 2.0 and MuStab) were analyzed. Based on the deleterious scores that overlap in these in silico approaches, and the availability of three-dimensional structures, structure analysis was carried out with the major mutations that occurred in the native protein coded by FANCD1/BRCA2 gene. In this work, we report the results of the first molecular dynamics (MD) simulation study performed to analyze the structural level changes in time scale level with respect to the native and mutated protein complexes (G25R, W31C,W31R in FANCD1/BRCA2-PALB2, and F1524V, V1532F in FANCD1/BRCA2-RAD51). Analysis of the MD trajectories indicated that predicted deleterious variants alter the structural behavior of BRCA2-PALB2 and BRCA2-RAD51 protein complexes. In addition, statistical analysis was employed to test the significance of these in silico tool predictions. Based on these predictions, we conclude that the identification of disease-related SNPs by in silico methods, in combination with MD approach has the potential to create personalized tools for the diagnosis, prognosis, and treatment of diseases. The methods reviewed here generated a considerable amount of valuable data, but also the need for further validation. © Springer Science+Business Media New York 2014

An Integrated in Silico Approach to Analyze the Involvement of Single Amino Acid Polymorphisms in FANCD1/BRCA2-PALB2 and FANCD1/BRCA2-RAD51 Complex

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

In this work, we computationally identified the most detrimental missense mutations of ABL tyrosine kinase in Chronic Myelogenous Leukemia (CML) and analyzed the drug resistance from these detrimental missense mutations. Out of 45 missense mutations, 20 variants were commonly found less stable, deleterious and damaging by I-Mutant2.0, SIFT and PolyPhen programs respectively. Subsequently, we performed modeling of these 20 variants to understand their change in conformations with respect to native ABL tyrosine kinase by computing their RMSD (Root Mean Square Deviation). Further, the native and 20 mutants were docked with the drug 'imatinib' to find out the drug resistance of these detrimental missense mutations. Among the 20 mutants, we found by docking studies that 12 mutants, namely M244V, Q252H, Y253H, K285N, T315I, F317L, F317I, S349L, M351T, E355G, S417Y and W430L had less binding affinity with imatinib than the native type. Finally, we analyzed that the loss of binding affinity of these 12 mutants were due to altered flexibility in their binding amino acids with imatinib as compared with native type by normal mode analysis. In our work, we found the novel data that majority of the drug binding amino acids in those 12 mutants had encountered the loss of flexibility which could be the theoretical basis for the cause of drug resistance. © 2011 Bentham Science Publishers.

Current Signal Transduction Therapy

In Silico Identification and Analysis of Drug Resistant Mutants of ABL Tyrosine Kinase Based on Detrimental Missense Mutations

Single nucleotide polymorphism (SNP) serve as frequent genetic markers along the chromosome. They can, however, have important consequences for individual susceptibility to disease and reactions to medical treatment. Also, genetics of the human phenotype variation could be understood by knowing the functions of these SNPs. Currently, a vast literature exists reporting possible associations between SNPs and diseases. It is still a major challenge to identify the functional SNPs in a disease related gene. In this work, we have analyzed the genetic variation that can alter the expression and the function in chronic myeloid leukemia (CML) by ABL1 gene through computational methods. Out of the total 827 SNPs, 18 were found to be non-synonymous (nsSNPs). Among the 30 SNPs in the untranslated region, 3 SNPs were found in 5' and 27 SNPs were found in 3' untranslated regions (UTR). It was found that 16.7% nsSNPs were found to be damaging by both SIFT and PolyPhen server. UTR resource tool suggested that 6 out of 27 SNPs in the 3' UTR region were functionally significant. The two major mutations that occurred in the native protein (1OPL) coded by ABL1 gene were at positions 159 (L--&gt;P) and 178 (G--&gt;S). Val (6), Ala (7) and Trp (344) were found to be stabilizing residues in the native protein (1OPL) coded by ABL1 gene. Even though all the three residues were found in the mutant protein 178 (G--&gt;S), only two of them Val (6) and Ala (7) were acting as stabilizing residue in another mutant 159 (L--&gt;P). We propose from the overall results obtained in this work that, both the mutations 159 (L--&gt;P) and 178 (G--&gt;S) should be considered important in the chronic myeloid leukemia caused by ABL1 gene. Our results on this computational study will find good application with the cancer biologist working on experimental protocols.

Journal of Biomedical Informatics

Identification and structural comparison of deleterious mutations in nsSNPs of ABL1 gene in chronic myeloid leukemia: A bio-informatics study

In this study, we identified the most deleterious non-synonymous SNP of ERBB2 (HER2) receptors by its stability and investigated its binding affinity with herceptin. Out of 135 SNPs, 10 are nsSNPs in the coding region, in which one of the nsSNP (SNPid rs4252633) is commonly found to be damaged by I-Mutant 2.0, SIFT and PolyPhen servers. With this effort, we modelled the mutant HER2 protein based on this deleterious nsSNP (rs4252633). The modeled mutant showed less stability than native HER 2 protein, based on both total energy of the mutant and stabilizing residues in the mutant protein. This is due to a deviation between the mutant and the native HER2, having an RMSD of about 2.81 Å. Furthermore, we compared the binding efficiency of herceptin with native and mutant HER2 receptors. We found that herceptin has a high binding affinity with mutant HER2 receptor, with a binding energy of -24.40 kcal/mol, as compared to the native type, which has a binding energy of -15.26 kcal/mol due to six-hydrogen bonding and two salt bridges exist between herceptin and the mutant type, whereas the native type establishes four hydrogen bonds and two salt bridges with herceptin. This analysis portrays that mutant type has two additional hydrogen bonds with herceptin compared with the native type. Normal mode analysis also showed that the two amino acids, namely Asp596 and Glu598 of mutant HER2, forming additional hydrogen bonding with herceptin, had a slightly higher flexibility than the native type. Based on our investigations, we propose that SNPid rs4252633 could be the most deleterious nsSNP for HER2 receptor, and that herceptin could be the best drug for mutant compared to the native HER2 target. To cite this article: R. Rajasekaran et al., C. R. Biologies 331 (2008). © 2008 Académie des sciences.

Journal	Data powered by TypesetCell Biochemistry and Biophysics
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	1085-9195
Open Access	No