title: Feature Selection Combined with Neural Network Structure Optimization for HIV-1 Protease Cleavage Site Prediction.
abstract: It is crucial to understand the specificity of HIV-1 protease for designing HIV-1 protease inhibitors. In this paper, a new feature selection method combined with neural network structure optimization is proposed to analyze the specificity of HIV-1 protease and find the important positions in an octapeptide that determined its cleavability. Two kinds of newly proposed features based on Amino Acid Index database plus traditional orthogonal encoding features are used in this paper, taking both physiochemical and sequence information into consideration. Results of feature selection prove that p2, p1, p1', and p2' are the most important positions. Two feature fusion methods are used in this paper: combination fusion and decision fusion aiming to get comprehensive feature representation and improve prediction performance. Decision fusion of subsets that getting after feature selection obtains excellent prediction performance, which proves feature selection combined with decision fusion is an effective and useful method for the task of HIV-1 protease cleavage site prediction. The results and analysis in this paper can provide useful instruction and help designing HIV-1 protease inhibitor in the future.
authors.affiliation: Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.
authors.forename: Hui
authors.identifier: 0000-0002-2594-7386
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.
authors.forename: Xiaomiao
authors.initials: X
authors.lastname: Shi
authors.affiliation: Second Affiliated Hospital, Dalian Medical University, Dalian 116027, China.
authors.forename: Dongmei
authors.initials: D
authors.lastname: Guo
authors.affiliation: Second Affiliated Hospital, Dalian Medical University, Dalian 116027, China.
authors.forename: Zuowei
authors.identifier: 0000-0002-3411-7026
authors.initials: Z
authors.lastname: Zhao
authors.affiliation: Department of Advanced Medicine, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido 060-8638, Japan.
authors.lastname: Yimin
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D015658:HIV Infections / Q000188:drug therapy / Q000821:virology*; D016333:HIV Protease / Q000737:chemistry* / Q000378:metabolism; D015497:HIV-1 / Q000737:chemistry* / Q000187:drug effects / Q000201:enzymology; D006801:Humans; D008956:Models, Chemical; D016571:Neural Networks, Computer; D043484:Proprotein Convertases / Q000737:chemistry* / Q000627:therapeutic use; D011480:Protease Inhibitors / Q000737:chemistry* / Q000627:therapeutic use; D011487:Protein Conformation / Q000187:drug effects; D012697:Serine Endopeptidases / Q000737:chemistry* / Q000627:therapeutic use; D013329:Structure-Activity Relationship
chemical_list: D011480:Protease Inhibitors; D043484:Proprotein Convertases; D012697:Serine Endopeptidases; C115575:membrane-bound transcription factor peptidase, site 1; D016333:HIV Protease; C514803:p16 protease, Human immunodeficiency virus 1