Structural and functional analysis of disease-associated mutations in GOT1 gene: An in silico study

Saxena, Sidharth; B, Sai Achyuth; Murthy, T P Krishna; Chandramohan, Vivek; Yadav, Arvind Kumar; Singh, Tiratha Raj

doi:10.1016/j.compbiomed.2021.104695

Cited by 14 publications

(3 citation statements)

References 65 publications

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“…Therefore, we indicate that the four most detrimental mutant proteins, R144C, I148N, S172W, and A297D might exhibit a significant effect on OX1R structure and function. This supposition is well-aligned with the outcomes derived from the investigations conducted by Saxena et al 55 , Agrahari et al 56 , and Shinwari et al 57 .…”

Section: Discussionsupporting

confidence: 81%

Unraveling the function and structure impact of deleterious missense SNPs in the human OX1R receptor by computational analysis

Farajzadeh-Dehkordi,

Mafakher,

Harifi

et al. 2024

Sci Rep

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The orexin/hypocretin receptor type 1 (OX1R) plays a crucial role in regulating various physiological functions, especially feeding behavior, addiction, and reward. Genetic variations in the OX1R have been associated with several neurological disorders. In this study, we utilized a combination of sequence and structure-based computational tools to identify the most deleterious missense single nucleotide polymorphisms (SNPs) in the OX1R gene. Our findings revealed four highly conserved and structurally destabilizing missense SNPs, namely R144C, I148N, S172W, and A297D, located in the GTP-binding domain. Molecular dynamics simulations analysis demonstrated that all four most detrimental mutant proteins altered the overall structural flexibility and dynamics of OX1R protein, resulting in significant changes in the structural organization and motion of the protein. These findings provide valuable insights into the impact of missense SNPs on OX1R function loss and their potential contribution to the development of neurological disorders, thereby guiding future research in this field.

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Section: Discussionsupporting

confidence: 81%

Unraveling the function and structure impact of deleterious missense SNPs in the human OX1R receptor by computational analysis

Farajzadeh-Dehkordi,

Mafakher,

Harifi

et al. 2024

Sci Rep

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“…Hence, while conservation analysis is good as a secondary form of validation of the deleterious nature of screened SNPs, it is not guaranteed that if an SNP is at a relatively less conserved residue it will be benign in nature. In fact, in a similar study on the GOT1 gene, an SNP (L345P) was predicted to be highly deleterious despite having a color score (predicted by ConSurf) of 1 and its deleterious nature was reflected in further in silico analyses of that study 93 .…”

Section: Resultsmentioning

confidence: 91%

A bioinformatics approach to the identification of novel deleterious mutations of human TPMT through validated screening and molecular dynamics

Saxena¹,

Murthy²,

Chandrashekhar³

et al. 2022

Sci Rep

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Polymorphisms of Thiopurine S-methyltransferase (TPMT) are known to be associated with leukemia, inflammatory bowel diseases, and more. The objective of the present study was to identify novel deleterious missense SNPs of TPMT through a comprehensive in silico protocol. The initial SNP screening protocol used to identify deleterious SNPs from the pool of all TPMT SNPs in the dbSNP database yielded an accuracy of 83.33% in identifying extremely dangerous variants. Five novel deleterious missense SNPs (W33G, W78R, V89E, W150G, and L182P) of TPMT were identified through the aforementioned screening protocol. These 5 SNPs were then subjected to conservation analysis, interaction analysis, oncogenic and phenotypic analysis, structural analysis, PTM analysis, and molecular dynamics simulations (MDS) analysis to further assess and analyze their deleterious nature. Oncogenic analysis revealed that all five SNPs are oncogenic. MDS analysis revealed that all SNPs are deleterious due to the alterations they cause in the binding energy of the wild-type protein. Plasticity-induced instability caused by most of the mutations as indicated by the MDS results has been hypothesized to be the reason for this alteration. While in vivo or in vitro protocols are more conclusive, they are often more challenging and expensive. Hence, future research endeavors targeted at TPMT polymorphisms and/or their consequences in relevant disease progressions or treatments, through in vitro or in vivo means can give a higher priority to these SNPs rather than considering the massive pool of all SNPs of TPMT.

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“…Similarly, another research work that uses in-silico based methods to identify a severe disease caused by nsSNP is (Quan et al 2019) where the authors make use of CONSURF web server to identify the most pathogenic variations of the STXBP1 gene, which is a gene that is associated with early infantile epileptic Encephalopathy (also known as Ohtahara Syndrome). Another research work is conducted by (Saxena et al 2021) where the authors make use of in-silico methods to identify the disease associated SNPs of human GOT1 (Glutamic-Oxaloacetic Transaminase 1). The human GOT1 is a gene that can be associated with several neurodegenerative diseases and also different types of cancer.…”

Section: Related Workmentioning

confidence: 99%

Identification of Diseases caused by non-Synonymous Single Nucleotide Polymorphism using Random Forest and Linear Regression Algorithms

Anjum

Tariq

Anjum

et al. 2023

Preprint

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The analysis of different types of diseases is an extremal vital task which would help in producing vaccines for that particular type of disease. However, this is a very costly process as to test every disease it would mean to analyze every gene related to that specific disease. This issue of genic analysis is further elevated when different variations of each disease is considered. As such the use of different computational methods is taken into consideration to tackle the task of genic variation identification. This research makes use of Machine Learning algorithms to help in the identification and prediction of Single Nucleotide Polymorphism or more specifically Single Amino Acid Polymorphism. Taking into consideration ten different types of diseases, this research makes use of Random Forest and Linear Regression algorithms to identify and predict different genic variations of these diseases. From the extensive research, this article concludes that Random Forest algorithm performs better in comparison to Linear regression in genic variation predictions.

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Structural and functional analysis of disease-associated mutations in GOT1 gene: An in silico study

Cited by 14 publications

References 65 publications

Unraveling the function and structure impact of deleterious missense SNPs in the human OX1R receptor by computational analysis

Unraveling the function and structure impact of deleterious missense SNPs in the human OX1R receptor by computational analysis

A bioinformatics approach to the identification of novel deleterious mutations of human TPMT through validated screening and molecular dynamics

Identification of Diseases caused by non-Synonymous Single Nucleotide Polymorphism using Random Forest and Linear Regression Algorithms

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