Improved prediction of accessible surface area results in efficient energy function application. (7th September 2015)

• Record Type: Journal Article
• Authors: Iqbal, Sumaiya; Mishra, Avdesh; Hoque, Md Tamjidul
• Is Part Of: Journal of theoretical biology. Volume 380(2015)
• Page Start: 380
• Page End: 391
• Publication Date: 2015-09-07
• Subjects: Accessible surface area -- Protein structure -- Energy function -- Regression -- Benchmark -- Decoysets; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2015.06.012
• Languages: English
• ISSNs: 0022-5193

Abstract

Abstract: An accurate prediction of real value accessible surface area (ASA) from protein sequence alone has wide application in the field of bioinformatics and computational biology. ASA has been helpful in understanding the 3-dimensional structure and function of a protein, acting as high impact feature in secondary structure prediction, disorder prediction, binding region identification and fold recognition applications. To enhance and support broad applications of ASA, we have made an attempt to improve the prediction accuracy of absolute accessible surface area by developing a new predictor paradigm, namely REGAd 3 p, for real value prediction through classical Exact Regression with Regularization and p olynomial kernel of d egree 3 which was further optimized using Genetic Algorithm . ASA assisting effective energy function, motivated us to enhance the accuracy of predicted ASA for better energy function application. Our ASA prediction paradigm was trained and tested using a new benchmark dataset, proposed in this work, consisting of 1001 and 298 protein chains, respectively. We achieved maximum Pearson Correlation Coefficient (PCC) of 0.76 and 1.45% improved PCC when compared with existing top performing predictor, SPINE-X, in ASA prediction on independent test set. Furthermore, we modeled the error between actual and predicted ASA in terms of energy and combined this energy linearly with the energy function 3DIGARS which resulted in an effective energy function, namely 3DIGARS2.0, outperforming all the state-of-the-art energy functions. Based on Rosetta and Tasser decoy-sets 3DIGARS2.0 resulted 80.78%, 73.77%, 141.24%, 16.52%, and 32.32% improvement over DFIRE, RWplus, dDFIRE, GOAP and 3DIGARS respectively. Graphical abstract: Accessible Surface Area (ASA) Prediction Framework (REGAd 3 p). Energy Function (3DIGARS-2.0) Development Framework. Highlights: Regularized exact regression with 3rd order polynomial kernel based predicted ASA. Kernel based prediction performance is further optimized by genetic algorithm (GA). ASA prediction error is modeled into the basic Energy function optimistically. Test results based on multiple benchmark datasets outperformed all others.