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- Gao, Chunxia, et al.
(författare)
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Impact of mutations on K-Ras-p120GAP interaction
- 2013
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Ingår i: Computational Molecular Biosciences. - : Scientific Research Publishing, Inc.. - 2165-3445 .- 2165-3453. ; 3:2, s. 9-17
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Tidskriftsartikel (refereegranskat)abstract
- The K-Ras protein plays a key role in the signal transduction cascade. Certain mutations in K-Ras lead to a permanent “on” state which results in tumorigenesis due to failed interaction with the GTPase activating protein (GAP). In this study, we examined the mutations E31N, D33N and D38N of K-Ras coupled and decoupled to wildtype GAP-334 and mutation K935N of GAP-334 coupled and decoupled to wildtype K-Ras, to illustrate the potential mechanism by which these mutants affect the interaction between the two proteins. We identify Tyr32 in the Ras Switch I region as a critical residue that acts as a gate to the GTP binding site and which needs to be “open” during Ras coupling with GAP to allow for insertion of GAP residue Arg789. This residue plays a vital role in stabilizing the transition state during GTP hydrolysis. The different mutations studied herein caused a reduced binding affinity, and the fluctuation of the Tyr32 side chain might hinder the insertion of Arg789. This may in turn be the cause of decreased GTP hydrolysis, and permanent “on” state of K-Ras, observed for these mutants.
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| 2. |
- Uzelac, Ivana, et al.
(författare)
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A Structural Comparison Approach for Identifying Small Variations in Binding Sites of Homologous Proteins
- 2015
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Ingår i: Computational Molecular Bioscience. - : Scientific Research Publishing, Inc.. - 2165-3453 .- 2165-3445. ; 5:3, s. 45-55
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Tidskriftsartikel (refereegranskat)abstract
- A method for analyzing the protein site similarity was devised aiming at understanding selectivity of homologous proteins and guiding the design of new drugs. The method is based on calculating Cα distances between selected pocket residues and subsequent analysis by multivariate methods. Five closely related serine proteases, the coagulation factors II, VII, IX, X, and XI, were studied and their pocket similarity was illustrated by PCA clustering. OPLS-DA was then applied to identify the residues responsible for the variation. By combining these two multivariate methods, we could successfully cluster the different proteases according to class and identify the important residues responsible for the observed variation.
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