LBPI: A WEB INTERFACE FOR THE IDENTIFICATION OF ALLOSTERIC LIGAND BINDING SITES

The development of efficient tools for allosteric ligand binding site identification in potential drug targets is an important step for computational drug design. Ligand binding specificity analysis (LIBSA) is one of the protocols that utilize filtering algorithms to assess the propensity of a site on a target structure or structures to bind a ligand. However, LIBSA requires expert skills to be properly executed. Thus, a Web interface, LBPI (Ligand Binding Pocket Identification) has been developed using Django, a Python-based web framework. A Python Wrapper has also been developed to streamline pre-existing algorithms of LIBSA. The Wrapper helps in the preparation of files, execution of individual programs and generation of appropriate results. LBPI provides an ideal platform for making complex binding site identification protocols readily available for non-expert users to submit jobs and monitor the results. The goal of LBPI is to integrate available algorithms in a systematic way and make it easily available for both experts and non-experts

Advances in Molecular Quantum Chemistry Contained in the Q-Chem 4 Program Package

A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller–Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube

Development and Implementation of (Q)SAR Modeling Within the CHARMMing Web-user Interface

Recent availability of large publicly accessible databases of chemical compounds and their biological activities (PubChem, ChEMBL) has inspired us to develop a web-based tool for structure activity relationship and quantitative structure activity relationship modeling to add to the services provided by CHARMMing (www.charmming.org). This new module implements some of the most recent advances in modern machine learning algorithms—Random Forest, Support Vector Machine, Stochastic Gradient Descent, Gradient Tree Boosting, so forth. A user can import training data from Pubchem Bioassay data collections directly from our interface or upload his or her own SD files which contain structures and activity information to create new models (either categorical or numerical). A user can then track the model generation process and run models on new data to predict activity. © 2014 Wiley Periodicals, Inc