Chopra, G. | Search bioRxiv

bioRxiv2018

CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials

Small molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations, such as, improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g. cofactors, metal-ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions and cofactors interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind and Astex proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions, such that, the statistical score of best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best docked pose with biological activity.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=28 SRC=\"FIGDIR/small/442897v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (16K):\norg.highwire.dtl.DTLVardef@1c1b2b0org.highwire.dtl.DTLVardef@1ba66a4org.highwire.dtl.DTLVardef@a8bfd3org.highwire.dtl.DTLVardef@c9d3bb_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2018

Lemon: a modern C++ tool for the rapid development of structural benchmarking datasets

MotivationThe protein data bank (PDB) currently holds over 140,000 biomolecular structures and continues to release new structures on a weekly basis. The PDB is an essential resource to the structural bioinformatics community to develop software that mine, use, categorize, and analyze such data. New computational biology methods are evaluated using custom benchmarking sets derived as subsets of 3D experimentally determined structures and structural features from the PDB. Currently, such benchmarking features are manually curated with custom scripts in a non-standardized manner that results in slow distribution and updates with new experimental structures. Finally, there is a scarcity of standardized tools to rapidly query 3D descriptors of the entire PDB. ApproachOur solution is the Lemon framework, a C++11 library with Python bindings, which provides a consistent workflow methodology for selecting biomolecular interactions based on user criterion and computing desired 3D structural features. This framework can parse and characterize the entire PDB in less than ten minutes on modern, multithreaded hardware. The speed in parsing is obtained by using the recently developed MacroMolecule Transmission Format (MMTF) to reduce the computational cost of reading text-based PDB files. The use of C++ lambda functions and Python binds provide extensive flexibility for analysis and categorization of the PDB by allowing the user to write custom functions to suite their objective. We think Lemon will become a one-stop-shop to quickly mine the entire PDB to generate desired structural biology features. The Lemon software is available as a C++ header library along with example functions at https://github.com/chopralab/lemon.

bioinformatics↗