Molecular mechanics methods for predicting protein–ligand binding

Huang, Niu; Kalyanaraman, Chakrapani; Bernacki, Katarzyna; Jacobson, Matthew P.

doi:10.1039/b608269f

Cited by 208 publications

(171 citation statements)

References 74 publications

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“…1 A few scoring functions include an explicit or implicit estimate of the desolvation free energy of the receptor and ligand. 2 Receptor flexibility, strain energies, 3,1 and various entropies are usually neglected, as is any reference to the unbound protein and ligand states. These approximations put the estimation of binding affinities well out of the reach of docking methods, although these methods can often correctly rank-order candidate molecules for testing.…”

Section: Introductionmentioning

confidence: 99%

Predicting Absolute Ligand Binding Free Energies to a Simple Model Site

Mobley

Graves

Chodera

et al. 2007

Journal of Molecular Biology

305

510

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A central challenge in structure-based ligand design is the accurate prediction of binding free energies. Here we apply alchemical free energy calculations in explicit solvent to predict ligand binding in a model cavity in T4 lysozyme. Even in this simple site, there are challenges. We made systematic improvements, beginning with single poses from docking, then including multiple poses, additional protein conformational changes, and using an improved charge model. Computed absolute binding free energies had an RMS error of 1.9 kcal/mol relative to previously determined experimental values. In blind prospective tests, the methods correctly discriminated between several true ligands and decoys in a set of putative binders identified by docking. In these prospective tests, the RMS error in predicted binding free energies relative to those subsequently determined experimentally was only 0.6 kcal/mol. X-ray crystal structures of the new ligands bound in the cavity corresponded closely to predictions from the free energy calculations, but sometimes differed from those predicted by docking. Finally, we examined the impact of holding the protein rigid, as in docking, with a view to learning how approximations made in docking affect accuracy and how they may be improved.

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Section: Introductionmentioning

confidence: 99%

Predicting Absolute Ligand Binding Free Energies to a Simple Model Site

Mobley

Graves

Chodera

et al. 2007

Journal of Molecular Biology

305

510

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show abstract

“…Molecular dynamics simulations take on a vital role in calculation of binding free energy [2,[198][199][200]. Recent advances in coarse-graining techniques have allowed the probing of dynamics of large membrane-bound protein systems on the microsecond time scale [201].…”

Section: Free Energy Calculation By Molecular Mechanicsmentioning

confidence: 99%

“…The transformation of ligands is accomplished by (DOCK and Glide) to the free energy calculation, followed by energy minimization in GB implicit solvent, instead of commonly used MD conformer sampling. The speed has reached as fast as 1 min per structure [199]. Yet, this method may appear to be inaccurate because it bypassed the timeconsuming entropy losses calculation and fully relied on GB semi-analytical approximation which contained documented drawbacks [221].…”

Section: Free Energy Calculation By Molecular Mechanicsmentioning

confidence: 99%

“…Protein-ligand binding energy is now perceived as a nonadditive effect, which is protein context-related, solvent-sensitive and involves protein-ligand cooperative dynamic processes [195,197]. Therefore, a more sophisticated and context-flexible approach is essential in structure-based drug design.Molecular dynamics simulations take on a vital role in calculation of binding free energy [2,[198][199][200]. Recent advances in coarse-graining techniques have allowed the probing of dynamics of large membrane-bound protein systems on the microsecond time scale [201].…”

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confidence: 99%

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From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

Chen¹,

Morrow²,

Tran³

et al. 2012

curr drug metab

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As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. KeywordsStructure-based drug design; protein modeling; focused library design; pharmacophore; flexible docking; high-throughput virtual screening; de novo design; protein-protein interaction; polypharmacology Modern computational-aided drug design established a novel platform by which researchers perform in-depth in silico simulation prior to labor-extensive wet-lab validation [1]. It comprises of two major parts corresponding to the information of molecular source it utilizes: structure-based (or receptor-based) drug design and ligand-based drug design. Structure-based drug design, which relies on the knowledge of biological target structures, aims to discover small molecules/peptides leads with desired chemistry properties, and orchestrate the following experimental validation and lead optimization. Structure-based approach provides mechanism-based basis, where potential ligands are excavated using receptor-dependent parameters, while ligand-based approaches bypass the consideration of complex biomolecular "black box" in a living cell. This in silico method permeates all aspects of drug discovery today [2], and we expect it will draw more attentions with the unprecedented advances of computational power and modeling accuracy in this decade.* Corresponding author: Shuxing Zhang, Ph.D., The University of Texas M. D. Anderson Cancer Center, Department of Experimental Therapeutics, Unit 1950, 1901 East Rd., Houston, TX 77054, Telephone: (713)-745-2958 794-5577, shuzhang@mdanderson.org. Conflict of interestThe authors declare that they do not have competing interests. NIH Public Access Author ManuscriptCurr Pharm Des. Author manuscript; available in PMC 2013 November 07.Published in final edited form as:Curr Pharm Des. 2012 ; 18(9): 1217-1239. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptIn this review, we will overview the state-of-the-art structure-based drug discovery techniques ranging from receptor modeling to lead identification and optimization. In each topic, we will also highlight the fruitful successes as well as ch...

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“…Recent work studying enzyme-substrate specificity in silico for metabolic processes suggests that protein docking methods can successfully identify preferred substrates for the diverse enzymes that comprise the glycolysis pathway in E. coli [36]. While substrate binding is not a sufficient condition for catalytic conversion, it is a necessary one, and thus computer-aided, structure-based approaches to investigate substrate binding that have been used successfully in drug design and other arenas to study enzyme promiscuity [37][38][39][40][41] offer a reasonable first-pass screen. Further insight into the catalytic potential of enzymes requires predicting conformational changes due to ligand binding [42].…”

Section: Introductionmentioning

confidence: 99%

Computational screening of novel thiamine-catalyzed decarboxylation reactions of 2-keto acids

Assary¹,

Broadbelt

2010

Bioprocess Biosyst Eng

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A molecular modeling strategy to screen the capacity of known enzymes to catalyze the reactions of non-native substrates is presented. The binding of pyruvic acid and non-native ketoacids in the active site of pyruvate ferredoxin oxidoreductase was examined using docking analysis, and our results suggest that enzyme-non-native ketoacid-bound species are feasible. Quantum mechanics/molecular mechanics methods were then used to study the geometry of the covalent intermediate formed from the enzyme and the various ketoacids. Finally, quantum mechanical methods were used to study the decarboxylation reaction of 2-keto acids at the mechanistic level. This hierarchical screening ranked the substrates from those that cannot be accommodated by the enzyme (phenyl pyruvate) to those whose conversion rate would most closely approach that of the native substrate (2-ketobutanoic acid and 2-ketovaleric acid). Most notably, our investigation suggests that novel pathways generated using generalized enzyme actions may be screened using the hierarchical approach employed here.

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Molecular mechanics methods for predicting protein–ligand binding

Cited by 208 publications

References 74 publications

Predicting Absolute Ligand Binding Free Energies to a Simple Model Site

Predicting Absolute Ligand Binding Free Energies to a Simple Model Site

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

Computational screening of novel thiamine-catalyzed decarboxylation reactions of 2-keto acids

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