title: Prediction of the parallel/antiparallel orientation of beta-strands using amino acid pairing preferences and support vector machines.
abstract: In principle, structural information of protein sequences with no detectable homology to a protein of known structure could be obtained by predicting the arrangement of their secondary structural elements. Although some ab initio methods for protein structure prediction have been reported, the long-range interactions required to accurately predict tertiary structures of beta-sheet containing proteins are still difficult to simulate. To remedy this problem and facilitate de novo prediction of beta-sheet containing protein structures, we developed a support vector machine (SVM) approach that classified parallel and antiparallel orientation of beta-strands by using the information of interstrand amino acid pairing preferences. Based on a second-order statistics on the relative frequencies of each possible interstrand amino acid pair, we defined an average amino acid pairing encoding matrix (APEM) for encoding beta-strands as input in the prediction model. As a result, a prediction accuracy of 86.89% and a Matthew's correlation coefficient value of 0.71 have been achieved through 7-fold cross-validation on a non-redundant protein dataset from PISCES. Although several issues still remain to be studied, the method presented here to some extent could indicate the important contribution of the amino acid pairs to the beta-strand orientation, and provide a possible way to further be combined with other algorithms making a full 'identification' of beta-strands.
authors.affiliation: Key Laboratory of Bioactive Materials, Ministry of Education and the College of Life Sciences, Nankai University, PR China.
authors.forename: Ning
authors.initials: N
authors.lastname: Zhang
authors.forename: Guangyou
authors.initials: G
authors.lastname: Duan
authors.forename: Shan
authors.initials: S
authors.lastname: Gao
authors.forename: Jishou
authors.initials: J
authors.lastname: Ruan
authors.forename: Tao
authors.initials: T
authors.lastname: Zhang
mesh_terms: D000596:Amino Acids / Q000737:chemistry*; D008962:Models, Theoretical; D011506:Proteins / Q000737:chemistry
chemical_list: D000596:Amino Acids; D011506:Proteins