Journal ArticleDOI
Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments
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TLDR
It is shown that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets.Abstract:
Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.read more
Citations
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Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs)
TL;DR: This review will highlight recent computational strategies that incorporate protein flexibility into SBDD of GPCR-targeted ligands, thus enhancing the accuracy of rationally designed ligands.
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High-throughput virtual screening with e-pharmacophore and molecular simulations study in the designing of pancreatic lipase inhibitors.
Ganesh Kumar Veeramachaneni,Krishna Raj,Leela Madhuri Chalasani,Jayakumar Singh Bondili,Venkateswara Rao Talluri +4 more
TL;DR: Zinc 85893731 is specified as a lead molecule with higher binding score and energetically stable complex with pancreatic lipase, which can be further tested as a novel inhibitor against pancreaticlipase using in vitro protocols.
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Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.
Salma Jamal,Mohd Khubaib,Rishabh Gangwar,Sonam Grover,Abhinav Grover,Seyed Ehtesham Hasnain,Seyed Ehtesham Hasnain +6 more
TL;DR: A computational framework that uses artificial intelligence (AI) based machine learning (ML) approaches for predicting resistance in the genes rpoB, inhA, katG, pncA, gyrA and gyrB for the drugs rifampicin, isoniazid, pyrazinamide and fluoroquinolones is presented.
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Benchmark assessment of molecular geometries and energies from small molecule force fields.
TL;DR: It is shown that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules.
Journal ArticleDOI
Tribolium castaneum defensins are primarily active against Gram-positive bacteria.
Miray Tonk,Eileen Knorr,Alejandro Cabezas-Cruz,James J. Valdés,Christian Kollewe,Andreas Vilcinskas,Andreas Vilcinskas +6 more
TL;DR: The red flour beetle Tribolium castaneum, mealworms, Udo longhorn beetle and houseflies cluster within a well-defined clade of insect defensins, and it is concluded that T.Castaneumdefensins are primarily active against Gram-positive bacteria and that other AMPs may play a more prominent role against Gram -negative species.
References
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Journal ArticleDOI
The Protein Data Bank
Helen M. Berman,John D. Westbrook,Zukang Feng,Gary L. Gilliland,Talapady N. Bhat,Helge Weissig,Ilya N. Shindyalov,Philip E. Bourne +7 more
TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.
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Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
Garrett M. Morris,David S. Goodsell,Robert Scott Halliday,Ruth Huey,William E. Hart,Richard K. Belew,Arthur J. Olson +6 more
TL;DR: It is shown that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckia genetic algorithm is the most efficient, reliable, and successful of the three.
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TL;DR: The Protein Data Bank is a computer-based archival file for macromolecular structures that stores in a uniform format atomic co-ordinates and partial bond connectivities, as derived from crystallographic studies.
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Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.
Richard A. Friesner,Jay L. Banks,Robert B. Murphy,Thomas A. Halgren,Jasna Klicic,Daniel T. Mainz,Matthew P. Repasky,Eric H. Knoll,Mee Shelley,Jason K. Perry,David E. Shaw,Perry Francis,Peter S Shenkin +12 more
TL;DR: Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand to find the best docked pose using a model energy function that combines empirical and force-field-based terms.
Journal ArticleDOI
Development and validation of a genetic algorithm for flexible docking.
TL;DR: GOLD (Genetic Optimisation for Ligand Docking) is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein, and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding.