Dataset Information

Three dimensional (3D) structure prediction and substrate-protein interaction study of the chitin binding protein CBP24 from B. thuringiensis.

ABSTRACT: Bacillus thuringiensis is an insecticidal bacterium whose chitinolytic system has been exploited to improve insect resistance in crops. In the present study, we studied the CBP24 from B. thuringiensis using homology modeling and molecular docking. The primary and secondary structure analyses showed CBP24 is a positively charged protein and contains single domain that belongs to family CBM33. The 3D model after refinement was used to explore the chitin binding characteristics of CBP24 using AUTODOCK. The docking analyses have shown that the surface exposed hydrophilic amino acid residues Thr-103, Lys-112 and Ser-162 interact with substrate through H-bonding. While, the amino acids resides Glu-39, Tyr-46, Ser-104 and Asn-109 were shown to have polar interactions with the substrate. The binding energy values evaluation of docking depicts a stable intermolecular conformation of the docked complex. The functional characterization of the CBP24 will elucidate the substrate-interaction pathway of the protein in specific and the carbohydrate binding proteins in general leading towards the exploration and exploitation of the prokaryotic substrate utilization pathways.

SUBMITTER: Sehar U

PROVIDER: S-EPMC3746096 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Three dimensional (3D) structure prediction and substrate-protein interaction study of the chitin binding protein CBP24 from B. thuringiensis.

Sehar Ujala U Mehmood Muhammad Aamer MA Nawaz Salman S Nadeem Shahid S Hussain Khadim K Sohail Iqra I Tabassum Muhammad Rizwan MR Gill Saba Shahid SS Saqib Anam A

Bioinformation 20130807 14

Bacillus thuringiensis is an insecticidal bacterium whose chitinolytic system has been exploited to improve insect resistance in crops. In the present study, we studied the CBP24 from B. thuringiensis using homology modeling and molecular docking. The primary and secondary structure analyses showed CBP24 is a positively charged protein and contains single domain that belongs to family CBM33. The 3D model after refinement was used to explore the chitin binding characteristics of CBP24 using AUTOD ...[more]

PMID: 23976829

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Project description:BackgroundThe fatty-acid profile of the vegetable oils determines its properties and nutritional value. Palm-oil obtained from the African oil-palm [Elaeis guineensis Jacq. (Tenera)] contains 44% palmitic acid (C16:0), but, palm-oil obtained from the American oilpalm [Elaeis oleifera] contains only 25% C16:0. In part, the b-ketoacyl-[ACP] synthase II (KASII) [EC: 2.3.1.179] protein is responsible for the high level of C16:0 in palm-oil derived from the African oil-palm. To understand more about E. guineensis KASII (EgKASII) and E. oleifera KASII (EoKASII) proteins, it is essential to know its structures. Hence, this study was undertaken.ObjectiveThe objective of this study was to predict three-dimensional (3D) structure of EgKASII and EoKASII proteins using molecular modelling tools.Materials and methodsThe amino-acid sequences for KASII proteins were retrieved from the protein database of National Center for Biotechnology Information (NCBI), USA. The 3D structures were predicted for both proteins using homology modelling and ab-initio technique approach of protein structure prediction. The molecular dynamics (MD) simulation was performed to refine the predicted structures. The predicted structure models were evaluated and root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values were calculated.ResultsThe homology modelling showed that EgKASII and EoKASII proteins are 78% and 74% similar with Streptococcus pneumonia KASII and Brucella melitensis KASII, respectively. The EgKASII and EoKASII structures predicted by using ab-initio technique approach shows 6% and 9% deviation to its structures predicted by homology modelling, respectively. The structure refinement and validation confirmed that the predicted structures are accurate.ConclusionThe 3D structures for EgKASII and EoKASII proteins were predicted. However, further research is essential to understand the interaction of EgKASII and EoKASII proteins with its substrates.

Project description:Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs occurring in coding regions result in single amino acid polymorphisms (SAPs) that may affect protein function and lead to pathology. Several methods attempt to estimate the impact of SAPs using different sources of information. Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures.In this paper, we present a structure-based machine learning approach for predicting disease-related SAPs. We have trained a Support Vector Machine (SVM) on a set of 3,342 disease-related mutations and 1,644 neutral polymorphisms from 784 protein chains. We use SVM input features derived from the protein's sequence, structure, and function. After dataset balancing, the structure-based method (SVM-3D) reaches an overall accuracy of 85%, a correlation coefficient of 0.70, and an area under the receiving operating characteristic curve (AUC) of 0.92. When compared with a similar sequence-based predictor, SVM-3D results in an increase of the overall accuracy and AUC by 3%, and correlation coefficient by 0.06. The robustness of this improvement has been tested on different datasets and in all the cases SVM-3D performs better than previously developed methods even when compared with PolyPhen2, which explicitly considers in input protein structure information.This work demonstrates that structural information can increase the accuracy of disease-related SAPs identification. Our results also quantify the magnitude of improvement on a large dataset. This improvement is in agreement with previously observed results, where structure information enhanced the prediction of protein stability changes upon mutation. Although the structural information contained in the Protein Data Bank is limiting the application and the performance of our structure-based method, we expect that SVM-3D will result in higher accuracy when more structural date become available.

Dataset Information

Three dimensional (3D) structure prediction and substrate-protein interaction study of the chitin binding protein CBP24 from B. thuringiensis.

Publications

Three dimensional (3D) structure prediction and substrate-protein interaction study of the chitin binding protein CBP24 from B. thuringiensis.

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