Dataset Information

STRIKE: evaluation of protein MSAs using a single 3D structure.

ABSTRACT:

Motivation

Evaluating alternative multiple protein sequence alignments is an important unsolved problem in Biology. The most accurate way of doing this is to use structural information. Unfortunately, most methods require at least two structures to be embedded in the alignment, a condition rarely met when dealing with standard datasets.

Result

We developed STRIKE, a method that determines the relative accuracy of two alternative alignments of the same sequences using a single structure. We validated our methodology on three commonly used reference datasets (BAliBASE, Homestrad and Prefab). Given two alignments, STRIKE manages to identify the most accurate one in 70% of the cases on average. This figure increases to 79% when considering very challenging datasets like the RV11 category of BAliBASE. This discrimination capacity is significantly higher than that reported for other metrics such as Contact Accepted mutation or Blosum. We show that this increased performance results both from a refined definition of the contacts and from the use of an improved contact substitution score.

Contact

cedric.notredame@crg.eu

Availability

STRIKE is an open source freeware available from www.tcoffee.org

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Kemena C

PROVIDER: S-EPMC3232373 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

STRIKE: evaluation of protein MSAs using a single 3D structure.

Kemena Carsten C Taly Jean-Francois JF Kleinjung Jens J Notredame Cedric C

Bioinformatics (Oxford, England) 20111028 24

<h4>Motivation</h4>Evaluating alternative multiple protein sequence alignments is an important unsolved problem in Biology. The most accurate way of doing this is to use structural information. Unfortunately, most methods require at least two structures to be embedded in the alignment, a condition rarely met when dealing with standard datasets.<h4>Result</h4>We developed STRIKE, a method that determines the relative accuracy of two alternative alignments of the same sequences using a single stru ...[more]

PMID: 22039207

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Project description:BackgroundIn the backdrop of challenge to obtain a protein structure under the known limitations of both experimental and theoretical techniques, the need of a fast as well as accurate protein structure evaluation method still exists to substantially reduce a huge gap between number of known sequences and structures. Among currently practiced theoretical techniques, homology modelling backed by molecular dynamics based optimization appears to be the most popular one. However it suffers from contradictory indications of different validation parameters generated from a set of protein models which are predicted against a particular target protein. For example, in one model Ramachandran Score may be quite high making it acceptable, whereas, its potential energy may not be very low making it unacceptable and vice versa. Towards resolving this problem, the main objective of this study was fixed as to utilize a simple experimentally derived output, Surface Roughness Index of concerned protein of unknown structure as an intervening agent that could be obtained using ordinary microscopic images of heat denatured aggregates of the same protein.ResultIt was intriguing to observe that direct experimental knowledge of the concerned protein, however simple it may be, might give insight on acceptability of its particular structural model out of a confusion set of models generated from database driven comparative technique for structure prediction. The result obtained from a widely varying structural class of proteins indicated that speed of protein structure evaluation can be further enhanced without compromising with accuracy by recruiting simple experimental output.ConclusionIn this work, a semi-empirical methodological approach was provided for improving protein structure evaluation. It showed that, once structure models of a protein were obtained through homology technique, the problem of selection of a best model out of a confusion set of Pareto-optimal structures could be resolved by employing a structure agent directly obtainable through experiment with the same protein as experimental ingredient. Overall, in the backdrop of getting a reasonably accurate protein structure of pathogens causing epidemics or biological warfare, such approach could be of use as a plausible solution for fast drug design.

Project description:The impact of somatic missense mutation on cancer etiology and progression is often difficult to interpret. One common approach for assessing the contribution of missense mutations in carcinogenesis is to identify genes mutated with statistically nonrandom frequencies. Even given the large number of sequenced cancer samples currently available, this approach remains underpowered to detect drivers, particularly in less studied cancer types. Alternative statistical and bioinformatic approaches are needed. One approach to increase power is to focus on localized regions of increased missense mutation density or hotspot regions, rather than a whole gene or protein domain. Detecting missense mutation hotspot regions in three-dimensional (3D) protein structure may also be beneficial because linear sequence alone does not fully describe the biologically relevant organization of codons. Here, we present a novel and statistically rigorous algorithm for detecting missense mutation hotspot regions in 3D protein structures. We analyzed approximately 3 × 10(5) mutations from The Cancer Genome Atlas (TCGA) and identified 216 tumor-type-specific hotspot regions. In addition to experimentally determined protein structures, we considered high-quality structural models, which increase genomic coverage from approximately 5,000 to more than 15,000 genes. We provide new evidence that 3D mutation analysis has unique advantages. It enables discovery of hotspot regions in many more genes than previously shown and increases sensitivity to hotspot regions in tumor suppressor genes (TSG). Although hotspot regions have long been known to exist in both TSGs and oncogenes, we provide the first report that they have different characteristic properties in the two types of driver genes. We show how cancer researchers can use our results to link 3D protein structure and the biologic functions of missense mutations in cancer, and to generate testable hypotheses about driver mechanisms. Our results are included in a new interactive website for visualizing protein structures with TCGA mutations and associated hotspot regions. Users can submit new sequence data, facilitating the visualization of mutations in a biologically relevant context. Cancer Res; 76(13); 3719-31. ©2016 AACR.

Dataset Information

STRIKE: evaluation of protein MSAs using a single 3D structure.

Motivation

Result

Contact

Availability

Supplementary information

Publications

STRIKE: evaluation of protein MSAs using a single 3D structure.

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