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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.

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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.

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.

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.
Journal ArticleDOI

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.

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

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

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Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.

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.
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