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Structure-functional prediction and analysis of cancer mutation effects in protein kinases.

ABSTRACT: A central goal of cancer research is to discover and characterize the functional effects of mutated genes that contribute to tumorigenesis. In this study, we provide a detailed structural classification and analysis of functional dynamics for members of protein kinase families that are known to harbor cancer mutations. We also present a systematic computational analysis that combines sequence and structure-based prediction models to characterize the effect of cancer mutations in protein kinases. We focus on the differential effects of activating point mutations that increase protein kinase activity and kinase-inactivating mutations that decrease activity. Mapping of cancer mutations onto the conformational mobility profiles of known crystal structures demonstrated that activating mutations could reduce a steric barrier for the movement from the basal "low" activity state to the "active" state. According to our analysis, the mechanism of activating mutations reflects a combined effect of partial destabilization of the kinase in its inactive state and a concomitant stabilization of its active-like form, which is likely to drive tumorigenesis at some level. Ultimately, the analysis of the evolutionary and structural features of the major cancer-causing mutational hotspot in kinases can also aid in the correlation of kinase mutation effects with clinical outcomes.

SUBMITTER: Dixit A

PROVIDER: S-EPMC4000980 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Structure-functional prediction and analysis of cancer mutation effects in protein kinases.

Dixit Anshuman A Verkhivker Gennady M GM

Computational and mathematical methods in medicine 20140408

A central goal of cancer research is to discover and characterize the functional effects of mutated genes that contribute to tumorigenesis. In this study, we provide a detailed structural classification and analysis of functional dynamics for members of protein kinase families that are known to harbor cancer mutations. We also present a systematic computational analysis that combines sequence and structure-based prediction models to characterize the effect of cancer mutations in protein kinases. ...[more]

PMID: 24817905

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Project description:Aurora kinases play critical roles in regulating spindle assembly, chromosome segregation, and cytokinesis to ensure faithful segregation of chromosomes during mitotic cell division cycle. Molecular and cell biological studies have revealed that Aurora kinases, at physiological levels, orchestrate complex sequential cellular processes at distinct subcellular locations through functional interactions with its various substrates. Aberrant expression of Aurora kinases, on the other hand, cause defects in mitotic spindle assembly, checkpoint response activation, and chromosome segregation leading to chromosomal instability. Elevated expression of Aurora kinases correlating with chromosomal instability is frequently detected in human cancers. Recent genomic profiling of about 3000 human cancer tissue specimens to identify various oncogenic signatures in The Cancer Genome Atlas project has reported that recurrent amplification and overexpression of Aurora kinase-A characterize distinct subsets of human tumors across multiple cancer types. Besides the well-characterized canonical pathway interactions of Aurora kinases in regulating assembly of the mitotic apparatus and chromosome segregation, growing evidence also supports the notion that deregulated expression of Aurora kinases in non-canonical pathways drive transformation and genomic instability by antagonizing tumor suppressor and exacerbating oncogenic signaling through direct interactions with critical proteins. Aberrant expression of the Aurora kinases-p53 protein family signaling axes appears to be critical in the abrogation of p53 protein family mediated tumor suppressor pathways frequently deregulated during oncogenic transformation process. Recent findings reveal the existence of feedback regulatory loops in mRNA expression and protein stability of these protein families and their consequences on downstream effectors involved in diverse physiological functions, such as mitotic progression, checkpoint response pathways, as well as self-renewal and pluripotency in embryonic stem cells. While these investigations have focused on the functional consequences of Aurora kinase protein family interactions with wild-type p53 family proteins, those involving Aurora kinases and mutant p53 remain to be elucidated. This article presents a comprehensive review of studies on Aurora kinases-p53 protein family interactions along with a prospective view on the possible functional consequences of Aurora kinase-mutant p53 signaling pathways in tumor cells. Additionally, we also discuss therapeutic implications of these findings in Aurora kinases overexpressing subsets of human tumors.

Project description:MotivationMutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling.ResultsWe developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, ΔΔG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the ΔΔG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations.Availability and implementationhttp://zhanglab.ccmb.med.umich.edu/STRUM/ CONTACT: qiang@suda.edu.cn and zhng@umich.eduSupplementary informationSupplementary data are available at Bioinformatics online.

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Structure-functional prediction and analysis of cancer mutation effects in protein kinases.

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Structure-functional prediction and analysis of cancer mutation effects in protein kinases.

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