Biomaterial Database

Artificial neural network method for predicting HIV protease cleavage sites in protein. 1998

Y D Cai, and H Yu, and K C Chou

Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences.

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen's self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63 = 92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.

UI	MeSH Term	Description	Entries
D008956	Models, Chemical	Theoretical representations that simulate the behavior or activity of chemical processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.	Chemical Models,Chemical Model,Model, Chemical
D008969	Molecular Sequence Data	Descriptions of specific amino acid, carbohydrate, or nucleotide sequences which have appeared in the published literature and/or are deposited in and maintained by databanks such as GENBANK, European Molecular Biology Laboratory (EMBL), National Biomedical Research Foundation (NBRF), or other sequence repositories.	Sequence Data, Molecular,Molecular Sequencing Data,Data, Molecular Sequence,Data, Molecular Sequencing,Sequencing Data, Molecular
D009842	Oligopeptides	Peptides composed of between two and twelve amino acids.	Oligopeptide
D000595	Amino Acid Sequence	The order of amino acids as they occur in a polypeptide chain. This is referred to as the primary structure of proteins. It is of fundamental importance in determining PROTEIN CONFORMATION.	Protein Structure, Primary,Amino Acid Sequences,Sequence, Amino Acid,Sequences, Amino Acid,Primary Protein Structure,Primary Protein Structures,Protein Structures, Primary,Structure, Primary Protein,Structures, Primary Protein
D001665	Binding Sites	The parts of a macromolecule that directly participate in its specific combination with another molecule.	Combining Site,Binding Site,Combining Sites,Site, Binding,Site, Combining,Sites, Binding,Sites, Combining
D012680	Sensitivity and Specificity	Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)	Specificity,Sensitivity,Specificity and Sensitivity
D013329	Structure-Activity Relationship	The relationship between the chemical structure of a compound and its biological or pharmacological activity. Compounds are often classed together because they have structural characteristics in common including shape, size, stereochemical arrangement, and distribution of functional groups.	Relationship, Structure-Activity,Relationships, Structure-Activity,Structure Activity Relationship,Structure-Activity Relationships
D015195	Drug Design	The molecular designing of drugs for specific purposes (such as DNA-binding, enzyme inhibition, anti-cancer efficacy, etc.) based on knowledge of molecular properties such as activity of functional groups, molecular geometry, and electronic structure, and also on information cataloged on analogous molecules. Drug design is generally computer-assisted molecular modeling and does not include PHARMACOKINETICS, dosage analysis, or drug administration analysis.	Computer-Aided Drug Design,Computerized Drug Design,Drug Modeling,Pharmaceutical Design,Computer Aided Drug Design,Computer-Aided Drug Designs,Computerized Drug Designs,Design, Pharmaceutical,Drug Design, Computer-Aided,Drug Design, Computerized,Drug Designs,Drug Modelings,Pharmaceutical Designs
D016333	HIV Protease	Enzyme of the human immunodeficiency virus that is required for post-translational cleavage of gag and gag-pol precursor polyproteins into functional products needed for viral assembly. HIV protease is an aspartic protease encoded by the amino terminus of the pol gene.	HIV Proteinase,HTLV-III Protease,p16 pol gene product, HIV,p16 protease, HIV,HIV p16 protease,HTLV III Protease,Protease, HIV,Protease, HTLV-III
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron