Computational Analysis Finds Protein Stability–Related Cancer Mutations

Hereditary spastic paraplegias (HSPs) are a genetically heterogeneous collection of neurodegenerative disorders categorized by progressive lower-limb spasticity and frailty. The complex HSP forms are characterized by various neurological features including progressive spastic weakness, urinary sphincter dysfunction, extra pyramidal signs and intellectual disability (ID). The kinesin superfamily proteins (KIFs) are microtubule-dependent molecular motors involved in intracellular transport. Kinesins directionally transport membrane vesicles, protein complexes, and mRNAs along neurites, thus playing important roles in neuronal development and function. Recent genetic studies have identified kinesin mutations in patients with HSPs. In this study, we used the computational approaches to investigate the 40 missense mutations associated with HSP and ID in KIF1A and KIF5A. We performed homology modeling to construct the structures of kinesin–microtubule binding domain and kinesin–tubulin complex. We applied structure-based energy calculation methods to determine the effects of missense mutations on protein stability and protein–protein interaction. The results revealed that the most of disease-causing mutations could change the folding free energy of kinesin motor domain and the binding free energy of kinesin–tubulin complex. We found that E253K associated with ID in KIF1A decrease the protein stability of kinesin motor domains. We showed that the HSP mutations located in kinesin–tubulin complex interface, such as K253N and R280C in KIF5A, can destabilize the kinesin–tubulin complex. The computational analysis provides useful information for understanding the roles of kinesin mutations in the development of ID and HSPs.

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Surface Salt Bridges, Double-Mutant Cycles, and Protein Stability: an Experimental and Computational Analysis of the Interaction of the Asp 23 Side Chain with the N-Terminus of the N-Terminal Domain of the Ribosomal Protein L9†

Biochemistry ◽

10.1021/bi027202n ◽

2003 ◽

Vol 42 (23) ◽

pp. 7050-7060 ◽

Cited By ~ 50

Author(s):

Donna L. Luisi ◽

Christopher D. Snow ◽

Jo-Jin Lin ◽

Zachary S. Hendsch ◽

Bruce Tidor ◽

...

Keyword(s):

Protein Stability ◽

Ribosomal Protein ◽

Double Mutant ◽

Computational Analysis ◽

Side Chain ◽

Salt Bridges ◽

Terminal Domain ◽

N Terminus ◽

Ribosomal Protein L9

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PREDICTION OF FUNCTIONAL, STRUCTURAL AND STABILITY CHANGES IN PMM2 GENE ASSOCIATED WITH NEPHROTIC SYNDROME USING COMPUTATIONAL ANALYSIS

International Journal of Pharmacy and Pharmaceutical Sciences ◽

10.22159/ijpps.2021v13i7.41802 ◽

2021 ◽

pp. 87-93

Author(s):

JINAL M. THAKOR ◽

KINNARI N. MISTRY ◽

SISHIR GANG ◽

DHARAMSHIBHAI N. RANK ◽

CHAITANYA G. JOSHI

Keyword(s):

Nephrotic Syndrome ◽

Protein Structure ◽

Genetic Polymorphism ◽

Protein Stability ◽

Computational Analysis ◽

Structure And Function ◽

Protein Structure And Function ◽

Computational Tools ◽

And Function ◽

Protein Enzyme

Objective: Nephrotic syndrome defines as a disorder with a group of symptoms like proteinuria, hypoalbuminemia, hyperlipidemia, and edema. PMM2 encodes phosphomannosemutase protein enzyme involved in the synthesis of N-glycan. Methods: Different Insilico analysis tools: SIFT, PolyPhen, PROVEAN, SNPandGO, MetaSNP, PhDSNP, MutPred, I-Mutant, STRUM, PROCHECK-Ramachandran, COACH and ConSurf, were used to check the effect of nsSNP on protein structure and function. Results: The genetic polymorphism in the PMM2 gene was retrieved from NCBI ClinVar and UniProtKB. Total 20 SNPs were predicted most significant and responsible for disease-causing and decrease protein stability. Conclusion: This study helps to discover disease-causing deleterious SNPs with different computational tools and gives information about potent SNPs.

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Computational analysis of protein stability and allosteric interaction networks in distinct conformational forms of the SARS-CoV-2 spike D614G mutant: reconciling functional mechanisms through allosteric model of spike regulation

Journal of Biomolecular Structure and Dynamics ◽

10.1080/07391102.2021.1933594 ◽

2021 ◽

pp. 1-18

Author(s):

Gennady M. Verkhivker ◽

Steve Agajanian ◽

Deniz Oztas ◽

Grace Gupta

Keyword(s):

Protein Stability ◽

Computational Analysis ◽

Interaction Networks ◽

Allosteric Interaction ◽

Functional Mechanisms ◽

Allosteric Model

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Higher temperatures worsen the effects of mutations on protein stability

10.1101/2020.10.13.337972 ◽

2020 ◽

Author(s):

Dimitrios - Georgios Kontopoulos ◽

Ilias Patmanidis ◽

Timothy G. Barraclough ◽

Samraat Pawar

Keyword(s):

Protein Stability ◽

Adenylate Kinase ◽

Computational Analysis ◽

Thermal Adaptation ◽

Moderately Thermophilic ◽

Rate Of Molecular Evolution ◽

Essential Enzyme ◽

The Stability ◽

Life On Earth ◽

First Time

AbstractUnderstanding whether and how temperature increases alter the effects of mutations on protein stability is crucial for understanding the limits to thermal adaptation by organisms. Currently, it is generally assumed that the stability effects of mutations are independent of temperature. Yet, mutations should become increasingly destabilizing as temperature rises due to the increase in the energy of atoms. Here, by performing an extensive computational analysis on the essential enzyme adenylate kinase in prokaryotes, we show, for the first time, that mutations become more destabilizing with temperature both across and within species. Consistent with these findings, we find that substitution rates of prokaryotes decrease nonlinearly with temperature. Our results suggest that life on Earth likely originated in a moderately thermophilic and thermally fluctuating environment, and indicate that global warming should decrease the per-generation rate of molecular evolution of prokaryotes.

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