Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:3.4.23.16 (HIV-1 protease)
 2,107 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Obtaining satisfactory results with neural networks depends on the availability of large data samples. The use of small training sets generally reduces performance. Most classical Quantitative Structure-Activity Relationship (QSAR) studies for a specific enzyme system have been performed on small data sets. We focus on the neuro-fuzzy prediction of biological activities of HIV-1 protease inhibitory compounds when inferring from small training sets. We propose two computational intelligence prediction techniques which are suitable for small training sets, at the expense of some computational overhead. Both techniques are based on the FAMR model. The FAMR is a Fuzzy ARTMAP (FAM) incremental learning system used for classification and probability estimation. During the learning phase, each sample pair is assigned a relevance factor proportional to the importance of that pair. The two proposed algorithms in this paper are: 1) The GA-FAMR algorithm, which is new, consists of two stages: a) During the first stage, we use a genetic algorithm (GA) to optimize the relevances assigned to the training data. This improves the generalization capability of the FAMR. b) In the second stage, we use the optimized relevances to train the FAMR. 2) The Ordered FAMR is derived from a known algorithm. Instead of optimizing relevances, it optimizes the order of data presentation using the algorithm of Dagher et al. In our experiments, we compare these two algorithms with an algorithm not based on the FAM, the FS-GA-FNN introduced in [4], [5]. We conclude that when inferring from small training sets, both techniques are efficient, in terms of generalization capability and execution time. The computational overhead introduced is compensated by better accuracy. Finally, the proposed techniques are used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.
IEEE/ACM Trans Comput Biol Bioinform
PMID:Fuzzy ARTMAP prediction of biological activities for potential HIV-1 protease inhibitors using a small molecular data set. 2107 99

Drug resistance is a major obstacle faced by therapist in treating HIV infected patients. The reason behind these phenomena is either protein mutation or the changes in gene expression level that induces resistance to drug treatments. These mutations affect the drug binding activity, hence resulting in failure of treatment. Therefore, it is necessary to conduct resistance testing in order to carry out HIV effective therapy. This study combines both sequence and structural features for predicting HIV resistance by applying SVM and Random Forests classifiers. The model was tested on the mutants of HIV-1 protease and reverse transcriptase. Taken together the features we have used in our method, total contact energies among multiple mutations have a strong impact in predicting resistance as they are crucial in understanding the interactions of HIV mutants. The combination of sequence-structure features offers high accuracy with support vector machines as compared to Random Forests classifier. Both single and acquisition of multiple mutations are important in predicting HIV resistance to certain drug treatments. We have discovered the practicality of these features; hence, these can be used in the future to predict resistance for other complex diseases.
IEEE/ACM Trans Comput Biol Bioinform
PMID:Prediction of HIV Drug Resistance by Combining Sequence and Structural Properties. 2799 46

Modeling the interface region of a protein complex paves the way for understanding its dynamics and functionalities. Existing works model the interface region of a complex by using different approaches, such as, the residue composition at the interface region, the geometry of the interface residues, or the structural alignment of interface regions. These approaches are useful for ranking a set of docked conformation or for building scoring function for protein-protein docking, but they do not provide a generic and scalable technique for the extraction of interface patterns leading to functional motif discovery. In this work, we model the interface region of a protein complex by graphs and extract interface patterns of the given complex in the form of frequent subgraphs. To achieve this, we develop a scalable algorithm for frequent subgraph mining. We show that a systematic review of the mined subgraphs provides an effective method for the discovery of functional motifs that exist along the interface region of a given protein complex. In our experiments, we use three PDB protein structure datasets. The first two datasets are composed of PDB structures from different conformations of two dimeric protein complexes: HIV-1 protease (329 structures), and triosephosphate isomerase (TIM) (86 structures). The third dataset is a collection of different enzyme structures protein structures from the six top-level enzyme classes, namely: Oxydoreductase, Transferase, Hydrolase, Lyase, Isomerase, and Ligase. We show that for the first two datasets, our method captures the locking mechanism at the dimeric interface by taking into account the spatial positioning of the interfacial residues through graphs. Indeed, our frequent subgraph mining based approach discovers the patterns representing the dimerization lock which is formed at the base of the structure in 323 of the 329 HIV-1 protease structures. Similarly, for 86 TIM structures, our approach discovers the dimerization lock formation in 50 structures. For the enzyme structures, we show that we are able to capture the functional motifs (active sites) that are specific to each of the six top-level classes of enzymes through frequent subgraphs.
IEEE/ACM Trans Comput Biol Bioinform
PMID:Discovery of Functional Motifs from the Interface Region of Oligomeric Proteins Using Frequent Subgraph Mining. 2896 Nov 23