Motivation: MIEC-SVM is a structure-based method for predicting protein recognition specificity. Here, we present an automated MIEC-SVM pipeline providing an integrated and user-friendly workflow for construction and application of the MIEC-SVM models. This pipeline can handle standard amino acids and those with post-translational modifications (PTMs) or small molecules. Moreover, multi-threading and support to Sun Grid Engine (SGE) are implemented to significantly boost the computational efficiency.
Availability And Implementation: The program is available at http://wanglab.ucsd.edu/MIEC-SVM CONTACT: : wei-wang@ucsd.edu
Supplementary Information: Supplementary data available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv666 | DOI Listing |
Acta Pharmacol Sin
September 2024
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Prostate cancer (PCa) is the second most prevalent malignancy among men worldwide. The aberrant activation of androgen receptor (AR) signaling has been recognized as a crucial oncogenic driver for PCa and AR antagonists are widely used in PCa therapy. To develop novel AR antagonist, a machine-learning MIEC-SVM model was established for the virtual screening and 51 candidates were selected and submitted for bioactivity evaluation.
View Article and Find Full Text PDFSci Rep
April 2016
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China.
The MIEC-SVM approach, which combines molecular interaction energy components (MIEC) derived from free energy decomposition and support vector machine (SVM), has been found effective in capturing the energetic patterns of protein-peptide recognition. However, the performance of this approach in identifying small molecule inhibitors of drug targets has not been well assessed and validated by experiments. Thereafter, by combining different model construction protocols, the issues related to developing best MIEC-SVM models were firstly discussed upon three kinase targets (ABL, ALK, and BRAF).
View Article and Find Full Text PDFBioinformatics
March 2016
Department of Chemistry and Biochemistry, UC, San Diego, La Jolla, CA 92093-0359 USA.
Motivation: MIEC-SVM is a structure-based method for predicting protein recognition specificity. Here, we present an automated MIEC-SVM pipeline providing an integrated and user-friendly workflow for construction and application of the MIEC-SVM models. This pipeline can handle standard amino acids and those with post-translational modifications (PTMs) or small molecules.
View Article and Find Full Text PDFJ Chem Inf Model
May 2013
Department of Chemistry and Biochemistry, UCSD, La Jolla, California 92093-0359, United States.
Drug resistance severely erodes the efficacy of therapeutic treatments for many diseases. Assessing the potency of a drug lead to combat resistance is no doubt critical for designing new drugs or new therapeutic combinations. Virtual screening is often the first step in drug discovery and a challenging problem is to accurately predict the resistant profile of an inhibitor based on the docking structures.
View Article and Find Full Text PDFJ Chem Inf Model
January 2013
Department of Chemistry and Biochemistry, UCSD, La Jolla, California 92093-0359, USA.
Accurately ranking docking poses remains a great challenge in computer-aided drug design. In this study, we present an integrated approach called MIEC-SVM that combines structure modeling and statistical learning to characterize protein-ligand binding based on the complex structure generated from docking. Using the HIV-1 protease as a model system, we showed that MIEC-SVM can successfully rank the docking poses and consistently outperformed the state-of-art scoring functions when the true positives only account for 1% or 0.
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