scholarly journals 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

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

Computational analysis of hereditary spastic paraplegia mutations in the kinesin motor domains of KIF1A and KIF5A

2020 ◽  
Vol 19 (06) ◽  
pp. 2041003
Author(s):  
Vidhyanand Mahase ◽  
Adebiyi Sobitan ◽  
Christina Johnson ◽  
Farion Cooper ◽  
Yixin Xie ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Stability ◽  
Molecular Motors ◽  
Intracellular Transport ◽  
Computational Analysis ◽  
Binding Free Energy ◽  
Membrane Vesicles ◽  
Energy Calculation ◽  
Missense Mutations ◽  
Kinesin Motor

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.

Unravelling Nature's networks

2003 ◽  
Vol 31 (6) ◽  
pp. 1457-1461 ◽  
Author(s):  
N.A.M. Monk
Keyword(s):  
Dynamical System ◽  
Mathematical Modelling ◽  
Computational Analysis ◽  
Spatiotemporal Dynamics ◽  
Interaction Networks ◽  
Living Systems ◽  
Cellular Interaction ◽  
Cellular Mechanisms ◽  
Complex Dynamical System ◽  
Insight Into

Dramatic progress has been made recently in determining the genetic and molecular composition of cells. This has prompted the development of new approaches to the challenge of understanding how basic cellular mechanisms are coordinated to produce the dazzling complexity of living systems. To face this challenge fully, it is critical not only to know what genes and proteins are expressed in cells, but also to understand the spatiotemporal dynamics of their networks of interactions. The sheer scale and complexity of cellular interaction networks necessitates a multi-disciplinary effort in which sophisticated experimental techniques are employed in combination with computational analysis and mathematical modelling. Such approaches are beginning to provide insight into basic structures and mechanisms, and promise to become critical to the post-genomic mission of understanding the cell as a complex dynamical system.

scholarly journals NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation

BMC Genomics ◽  
2014 ◽  
Vol 15 (Suppl 4) ◽  
pp. S7 ◽  
Author(s):  
Manuel Giollo ◽  
Alberto JM Martin ◽  
Ian Walsh ◽  
Carlo Ferrari ◽  
Silvio CE Tosatto
Keyword(s):  
Protein Stability ◽  
Interaction Networks ◽  
Residue Interaction

scholarly journals Specificity in PDZ-peptide interaction networks: Computational analysis and review

2020 ◽  
Vol 4 ◽  
pp. 100022 ◽  
Author(s):  
Jeanine F. Amacher ◽  
Lionel Brooks ◽  
Thomas H. Hampton ◽  
Dean R. Madden
Keyword(s):  
Computational Analysis ◽  
Interaction Networks ◽  
Peptide Interaction

scholarly journals Computational analysis of protein interaction networks for infectious diseases

2015 ◽  
Vol 17 (3) ◽  
pp. 517-526 ◽  
Author(s):  
Archana Pan ◽  
Chandrajit Lahiri ◽  
Anjana Rajendiran ◽  
Buvaneswari Shanmugham
Keyword(s):  
Infectious Diseases ◽  
Protein Interaction ◽  
Computational Analysis ◽  
Protein Interaction Networks ◽  
Interaction Networks

scholarly journals A computational analysis of protein-protein interaction networks in neurodegenerative diseases

2008 ◽  
Vol 2 (1) ◽  
pp. 52 ◽  
Author(s):  
Joaquín Goñi ◽  
Francisco J Esteban ◽  
Nieves de Mendizábal ◽  
Jorge Sepulcre ◽  
Sergio Ardanza-Trevijano ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Protein Interaction ◽  
Computational Analysis ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

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

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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