cyclinpred a svm-based method for predicting cyclin protein sequencescyclinpred基于svm方法预测细胞周期蛋白的蛋白质序列.pdfVIP

CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences 1 1 1 1 Mridul K. Kalita , Umesh K. Nandal , Ansuman Pattnaik , Anandhan Sivalingam , Gowthaman 1 2 2 1 Ramasamy , Manish Kumar , Gajendra P. S. Raghava , Dinesh Gupta * 1 Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Aruna Asaf Ali Marg, New Delhi, India, 2 Bioinformatics Centre, Institute of Microbial Technology, Chandigarh, India Abstract Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trai