Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA.

Mucopolysaccharidosis (MPS) IIIA, also known as Sanfilippo syndrome type A, is a severe, progressive disease that affects the central nervous system (CNS). MPS IIIA is inherited in an autosomal recessive manner and is caused by a deficiency in the lysosomal enzyme sulfamidase, which is required for the degradation of heparan sulfate. The sulfamidase is produced by the N-sulphoglucosamine sulphohydrolase (SGSH) gene. In MPS IIIA patients, the excess of lysosomal storage of heparan sulfate often leads to mental retardation, hyperactive behavior, and connective tissue impairments, which occur due to various known missense mutations in the SGSH, leading to protein dysfunction. In this study, we focused on three mutations (R74C, S66W, and R245H) based on in silico pathogenic, conservation, and stability prediction tool studies. The three mutations were further subjected to molecular dynamic simulation (MDS) analysis using GROMACS simulation software to observe the structural changes they induced, and all the mutants exhibited maximum deviation patterns compared with the native protein. Conformational changes were observed in the mutants based on various geometrical parameters, such as conformational stability, fluctuation, and compactness, followed by hydrogen bonding, physicochemical properties, principal component analysis (PCA), and salt bridge analyses, which further validated the underlying cause of the protein instability. Additionally, secondary structure and surrounding amino acid analyses further confirmed the above results indicating the loss of protein function in the mutants compared with the native protein. The present results reveal the effects of three mutations on the enzymatic activity of sulfamidase, providing a molecular explanation for the cause of the disease. Thus, this study allows for a better understanding of the effect of SGSH mutations through the use of various computational approaches in terms of both structure and functions and provides a platform for the development of therapeutic drugs and potential disease treatments.

Computational Resources for Predicting Protein-Protein Interactions.

Proteins are the essential building blocks and functional components of a cell. They account for the vital functions of an organism. Proteins interact with each other and form protein interaction networks. These protein interactions play a major role in all the biological processes and pathways. The previous methods of predicting protein interactions were experimental which focused on a small set of proteins or a particular protein. However, these experimental approaches are low-throughput as they are time-consuming and require a significant amount of human effort. This led to the development of computational techniques that uses high-throughput experimental data for analyzing protein-protein interactions. The main purpose of this review is to provide an overview on the computational advancements and tools for the prediction of protein interactions. The major databases for the deposition of these interactions are also described. The advantages, as well as the specific limitations of these tools, are highlighted which will shed light on the computational aspects that can help the biologist and researchers in their research.

An Integrated Computational Framework to Assess the Mutational Landscape of α-L-Iduronidase IDUA Gene.

Mucopolysaccharidosis type I is a lysosomal genetic disorder caused due to the deficiency of the α-L-iduronidase enzyme (IDUA). Mutations associated with IDUA lead to mild to severe forms of diseases characterized by different clinical features. In the present study, we first performed a comprehensive analysis using various in silico prediction tools to screen and prioritize the missense mutations or nonsynonymous SNPs (nsSNPs) associated with IDUA. Subsequently, statistical analysis was empowered to examine the predictive ability and accuracy of the in silico prediction tool results supporting the disease phenotype ranging from mild to severe. Till date, no study has been carried out in IDUA in analyzing the impact of the nsSNPs at the structural level. In this context with the aid of pathogenic and stability prediction in silico tools, we identified nsSNPs R89Q, R89W, and P533R to be most deleterious and disease-causing having impact on the function of the protein. Extensive molecular dynamics analysis was performed using Gromacs to understand the deleterious nature of the mutants. Variations observed between the trajectory files of native and mutants R89Q, R89W, and P533R using Gromacs utilities enabled us to measure the adverse effects on the protein and could be the underlying reasons for the disease pathogenesis. These findings may be helpful in understanding the genotype-phenotype relationship and molecular basis of the disease to design drugs for better treatment. J. Cell. Biochem. 119: 555-565, 2018. © 2017 Wiley Periodicals, Inc.

A Computational Approach to Identify the Biophysical and Structural Aspects of Methylenetetrahydrofolate Reductase (MTHFR) Mutations (A222V, E429A, and R594Q) Leading to Schizophrenia.

The association between depression and methylenetetrahydrofolate reductase (MTHFR) has been continually demonstrated in clinical studies, yet there are sparse resources available to build a relationship between the mutations associated with MTHFR and depression. The common mutations found to be associated with schizophrenia and MTHFR are A222V, E429A, and R594Q. Although abundant research on structural and functional effects caused by A222V mutation is available, very less amount of studies have been done on the other two mutants (E429A and R594Q). Hence in this study, a comparative analysis was carried out between the most common A222V mutation, a prevalent E429A mutation, and a less prevalent and less deleterious R594Q mutation. To predict structural rearrangements upon mutation, we proposed a computational pipeline using in silico prediction tools, molecular docking, and molecular dynamics simulation analysis. Since the association of flavin adenine dinucleotide (FAD) is important for the functioning of the protein, binding analysis between protein and the coenzyme was performed. This would enable us to understand the interference level of each mutation over FAD-binding activity. Consequently, we found that two mutations (A222V and E429A) showed lesser binding activity and structural deviations when compared to the native molecule and mutant R594Q. Comparatively, higher structural changes were observed with A222V mutant complex in comparison to other mutant complexes. Computational studies like this could render better insights into the structural changes in the protein and their relationship with the disease condition.

Structural Analysis of G1691S Variant in the Human Filamin B Gene Responsible for Larsen Syndrome: A Comparative Computational Approach.

Larsen syndrome (LRS) is a rare genetic disease associated with variable manifestations including skeletal malformations, dislocations of the large joints, and notable changes in facial and limb features. Genetic variants in the Filamin B (FLNB) gene are associated with the development of LRS. We searched two literature databases (OMIM and PubMed) and three gene variant databases (HGMD, UniProt, & dbSNP) to capture all the possible variants associated with LRS phenotype, which may have an impact on the FLNB function. Our search yielded 77 variants that might impact the FLNB protein function in patients with LRS. We performed rigorous computational analysis such as conservational, biochemical, pathogenicity, and structural computational analyses to understand the deleterious effect of the G1691S variant. Further, the structural changes of the G1691S variant was compared with a null variant (G1691A) and the native protein through a molecular dynamic simulation study of 50 ns. We found that the variant G1691S was highly deleterious and destabilize the protein when compared to the native and variant G1691A. This might be due to the physicochemical changes in the variant G1691S when compared to the native and variant G1691A. The destabilization was further supported by transformation of bend to coil in variant G1691S whereas bend was retained in native and variant G1691A through molecular dynamics analysis. Our study shed light on the importance of computational methods to understand the molecular nature of genetic variants and structural insights on the function of the FLNB protein. J. Cell. Biochem. 118: 1900-1910, 2017. © 2017 Wiley Periodicals, Inc.

Predicting the impact of deleterious single point mutations in SMAD gene family using structural bioinformatics approach.

Functional alteration in SMAD proteins leads to dis-regulation of its mechanism results in possibilities of high risk diseases like fibrosis, cancer, juvenile polyposis etc. Studying single nucleotide polymorphism (SNP) in SMAD genes helps understand the malfunction of these proteins. In this study, we focused on deleterious effects of nsSNPs in both structural and functional level using publically available bioinformatics tools. We have mainly focused on identifying deleterious nsSNPs in both structural and functional level in SMAD genes by using SIFT, PolyPhen, SNPs&GO, I-Mutant 3.0, MUpro and PANTHER. Structure analysis was carried out with the major mutation that occurred in the native protein coded by SMAD genes and its amino acid positions (R358W, K306S, R310G, S433R and R361C). SRide was used to check the stability of the native and mutant modelled proteins. In addition, we used MAPPER to identify SNPs present in transcription factor binding sites. These findings demonstrate that the in silico approaches can be used efficiently to identify potential candidate SNPs in large scale analysis.