scholarly journals Computational and Molecular Dynamics Simulation Approach To Analyze the Impact of XPD Gene Mutation on Protein Stability and Function

2020 ◽  
Author(s):  
Nagesh Kishan Panchal ◽  
Aishwarya Bhale ◽  
Vinod Kumar Verma ◽  
Syed Sultan Beevi
Keyword(s):  
Dna Repair ◽  
Protein Stability ◽  
Double Helix ◽  
Dynamics Simulation ◽  
Missense Mutations ◽  
Simulated Environment ◽  
Dna Repair Deficiency ◽  
And Function ◽  
The Impact ◽  
Damaged Dna

AbstractXPD acts as a functional helicase and aids in unwinding double helix around damaged DNA, leading to efficient DNA repair. Mutations of XPD give rise to DNA-repair deficiency diseases and cancer proneness. In this study, cancer-causing missense mutation that could inactivate helicase function and hinder its binding with other complexes were analysed using bioinformatics approach. Rigorous computational methods were employed to understand the molecular pathogenic profile of mutation. The mutant model with the desired mutation was built with I-TASSER. GROMACS 5.0.1 was used to evaluate the effect of a mutation on protein stability and function. Of the 276 missense mutations, 64 were found to be disease-causing. Out of these 64, seven were of cancer-causing mutations. Among these, we evaluated K48R mutation in a computational simulated environment to determine its impact on protein stability and function since K48 position was ascertained to be highly conserved and substitution with arginine could impair the XPD activity. Molecular Dynamic Simulation and Essential Dynamics analysis showed that K48R mutation altered protein structural stability and produced conformational drift. Our predictions thus revealed that K48R mutation could impair the XPD helicase activity and affect its ability to repair the damaged DNA, thus augmenting the risk for cancer.

scholarly journals The conformational and mutational landscape of the ubiquitin-like marker for the autophagosome formation in cancer

2019 ◽  
Author(s):  
Burcu Aykac Fas ◽  
Mukesh Kumar ◽  
Valentina Sora ◽  
Maliha Mashkoor ◽  
Matteo Lambrughi ◽  
...  
Keyword(s):  
Physical Models ◽  
Missense Mutations ◽  
Cancer Types ◽  
Neutral Mutations ◽  
And Function ◽  
Cellular Components ◽  
The Impact ◽  
Pan Cancer ◽  
Structural Ensemble

AbstractAutophagy is a cellular process to recycle damaged cellular components and its modulation can be exploited for disease treatments. A key autophagy player is a ubiquitin-like protein, LC3B. Compelling evidence attests the role of autophagy and LC3B in different cancer types. Many LC3B structures have been solved, but a comprehensive study, including dynamics, has not been yet undertaken. To address this knowledge gap, we assessed ten physical models for molecular dynamics for their capabilities to describe the structural ensemble of LC3B in solution using different metrics and comparison with NMR data. With the resulting LC3B ensembles, we characterized the impact of 26 missense mutations from Pan-Cancer studies with different approaches. Our findings shed light on driver or neutral mutations in LC3B, providing an atlas of its modifications in cancer. Our framework could be used to assess the pathogenicity of mutations by accounting for the different aspects of protein structure and function altered by mutational events.

scholarly journals Prediction of the stability of proteins by computational approaches: a case report

2018 ◽  
Author(s):  
Bernardina Scafuri ◽  
Angelo Facchiano ◽  
Anna Marabotti
Keyword(s):  
Case Report ◽  
Protein Stability ◽  
Protein Sequence ◽  
Genotypic Variation ◽  
Missense Mutations ◽  
Computational Approaches ◽  
The Past ◽  
The Stability ◽  
The Impact

The prediction of the stability of a protein is a very important issue in computational biology. Indeed, missense mutations are frequently associated to a change in protein stability, leading usually to destabilization, unfolding and aggregation. However, the direct measurement of the effect of mutations on proteins' stability is often impaired by the large number of mutations that can affect a protein sequence. Therefore, predicting the impact of a mutation on this feature is of remarkable interest to infer the phenotypic effects associated to a genotypic variation. For this reason, many different predictors of the effects of mutations on protein stability have been developed during the past years, and they are available online as Web servers. In the present work, we applied several tools based on different approaches to predict the stability of three proteins involved in the different forms of the rare disease galactosemia, and we compare their different results, describing also the problems that we had to face, the solutions that we have adopted and the lessons learnt from this case study.

scholarly journals Using AlphaFold to predict the impact of single mutations on protein stability and function

2021 ◽  
Author(s):  
Marina A Pak ◽  
Karina A Markhieva ◽  
Mariia S Novikova ◽  
Dmitry S Petrov ◽  
Ilya S Vorobyev ◽  
...  
Keyword(s):  
Protein Folding ◽  
Protein Stability ◽  
Structure Prediction ◽  
Large Scale ◽  
3D Structure ◽  
Three Dimensional ◽  
Single Mutation ◽  
Before And After ◽  
And Function ◽  
The Impact

AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the AlphaFold predictions on the impact of a single mutation on structure with a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold cannot be immediately applied to other problems or applications in protein folding.

scholarly journals PremPS: Predicting the impact of missense mutations on protein stability

2020 ◽  
Vol 16 (12) ◽  
pp. e1008543
Author(s):  
Yuting Chen ◽  
Haoyu Lu ◽  
Ning Zhang ◽  
Zefeng Zhu ◽  
Shuqin Wang ◽  
...  
Keyword(s):  
Protein Stability ◽  
Protein Design ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Predictive Performance ◽  
Computational Method ◽  
Single Mutation ◽  
Missense Mutations ◽  
Benchmark Datasets ◽  
The Impact

Computational methods that predict protein stability changes induced by missense mutations have made a lot of progress over the past decades. Most of the available methods however have very limited accuracy in predicting stabilizing mutations because existing experimental sets are dominated by mutations reducing protein stability. Moreover, few approaches could consistently perform well across different test cases. To address these issues, we developed a new computational method PremPS to more accurately evaluate the effects of missense mutations on protein stability. The PremPS method is composed of only ten evolutionary- and structure-based features and parameterized on a balanced dataset with an equal number of stabilizing and destabilizing mutations. A comprehensive comparison of the predictive performance of PremPS with other available methods on nine benchmark datasets confirms that our approach consistently outperforms other methods and shows considerable improvement in estimating the impacts of stabilizing mutations. A protein could have multiple structures available, and if another structure of the same protein is used, the predicted change in stability for structure-based methods might be different. Thus, we further estimated the impact of using different structures on prediction accuracy, and demonstrate that our method performs well across different types of structures except for low-resolution structures and models built based on templates with low sequence identity. PremPS can be used for finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. PremPS is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/, which allows to do large-scale mutational scanning and takes about four minutes to perform calculations for a single mutation per protein with ~ 300 residues and requires ~ 0.4 seconds for each additional mutation.

scholarly journals A comprehensive in silico investigation into the nsSNPs of Drd2 gene predicts significant functional consequences in dopamine signaling and pharmacotherapy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Samia Sultana Lira ◽  
Ishtiaque Ahammad
Keyword(s):  
Neuronal Cell ◽  
Surface Protein ◽  
Dynamics Simulation ◽  
Atomic Interaction ◽  
Nucleotide Polymorphisms ◽  
Ligand Complex ◽  
Depth Analysis ◽  
Dopamine Signaling ◽  
And Function ◽  
The Impact

AbstractDRD2 is a neuronal cell surface protein involved in brain development and function. Variations in the Drd2 gene have clinical significance since DRD2 is a pharmacotherapeutic target for treating psychiatric disorders like ADHD and schizophrenia. Despite numerous studies on the disease association of single nucleotide polymorphisms (SNPs) in the intronic regions, investigation into the coding regions is surprisingly limited. In this study, we aimed at identifying potential functionally and pharmaco-therapeutically deleterious non-synonymous SNPs of Drd2. A wide array of bioinformatics tools was used to evaluate the impact of nsSNPs on protein structure and functionality. Out of 260 nsSNPs retrieved from the dbSNP database, initially 9 were predicted as deleterious by 15 tools. Upon further assessment of their domain association, conservation profile, homology models and inter-atomic interaction, the mutant F389V was considered as the most impactful. In-depth analysis of F389V through Molecular Docking and Dynamics Simulation revealed a decline in affinity for its native agonist dopamine and an increase in affinity for the antipsychotic drug risperidone. Remarkable alterations in binding interactions and stability of the protein–ligand complex in simulated physiological conditions were also noted. These findings will improve our understanding of the consequence of nsSNPs in disease-susceptibility and therapeutic efficacy.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
C. George Priya Doss ◽  
Chiranjib Chakraborty ◽  
Luonan Chen ◽  
Hailong Zhu
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Molecular Dynamics Simulation ◽  
In Silico ◽  
Drug Response ◽  
Genomic Medicine ◽  
Dynamics Simulation ◽  
Missense Mutations ◽  
Anaplastic Lymphoma ◽  
The Impact

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.

Protein stability versus function: effects of destabilizing missense mutations on BRCA1 DNA repair activity

2015 ◽  
Vol 466 (3) ◽  
pp. 613-624 ◽  
Author(s):  
David C.A. Gaboriau ◽  
Pamela J.E. Rowling ◽  
Ciaran G. Morrison ◽  
Laura S. Itzhaki
Keyword(s):  
Dna Repair ◽  
Protein Stability ◽  
Structural Stability ◽  
Cancer Susceptibility ◽  
Cellular Function ◽  
Brct Domain ◽  
Missense Mutations ◽  
Brca1 Mutations ◽  
Repair Activity

The majority of the unstable BRCA1 BRCT domain missense mutations we studied disrupted DNA repair in vivo, but reduced cellular function only weakly correlated with reduced structural stability. The findings have an impact on assessing cancer susceptibility in patients with BRCA1 mutations.

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