Essential considerations for using protein–ligand structures in drug discovery

Warren, Gregory L.; Thanh, D.; Kelley, Brian P.; Nicholls, Anthony; Warren, Stephen D.

doi:10.1016/j.drudis.2012.06.011

Cited by 150 publications

(195 citation statements)

References 43 publications

Supporting

Mentioning

191

Contrasting

Unclassified

Order By: Relevance

“…33) Furthermore, we may sometimes need to check the quality of the X-ray crystal structure. 34) As we have demonstrated for several spectroscopic results, [35][36][37] many experiments face difficulties for interpreting their results. Because computer simulation can help to correctly interpret the experiments, MD simulation will be essential in making the drug design procedure more logical and efficient.…”

Section: Resultsmentioning

confidence: 99%

The Feasibility of an Efficient Drug Design Method with High-Performance Computers

et al. 2015

View full text Add to dashboard Cite

In this study, we propose a supercomputer-assisted drug design approach involving all-atom molecular dynamics (MD)-based binding free energy prediction after the traditional design/selection step. Because this prediction is more accurate than the empirical binding affinity scoring of the traditional approach, the compounds selected by the MD-based prediction should be better drug candidates. In this study, we discuss the applicability of the new approach using two examples. Although the MD-based binding free energy prediction has a huge computational cost, it is feasible with the latest 10 petaflop-scale computer. The supercomputer-assisted drug design approach also involves two important feedback procedures: The first feedback is generated from the MD-based binding free energy prediction step to the drug design step. While the experimental feedback usually provides binding affinities of tens of compounds at one time, the supercomputer allows us to simultaneously obtain the binding free energies of hundreds of compounds. Because the number of calculated binding free energies is sufficiently large, the compounds can be classified into different categories whose properties will aid in the design of the next generation of drug candidates. The second feedback, which occurs from the experiments to the MD simulations, is important to validate the simulation parameters. To demonstrate this, we compare the binding free energies calculated with various force fields to the experimental ones. The results indicate that the prediction will not be very successful, if we use an inaccurate force field. By improving/validating such simulation parameters, the next prediction can be made more accurate.Key words computational drug design; molecular dynamics; binding free energy; high-performance computing; force field parameter As predicted by the Moore's law, 1) computational power progresses exponentially each year. The K computer (RIKEN, Japan) was the first one to reach the computational speed of 10 petaflops (PF).2) Subsequently, the United States and China released 10 PF-scale supercomputers. Thus, such huge computational power is expected to be utilized effectively for progress in a wide variety of science and technology fields. The ultimate purpose of this study is to develop and demonstrate an efficient drug design method with the help of such a stateof-the-art supercomputer.To overcome the difficulty of drug development, many computational structure-based drug design (SBDD) methods have been proposed in the last two decades. One important advantage of these computational methods is that they are free of experimental difficulty; thus, the computational methods are expected to reduce the experimental effort involved in the SBDD. The in silico SBDD methods can be categorized into two groups: virtual screening [3][4][5][6][7][8] and de novo drug design. 9-11)In virtual screening method, drug candidates are selected from libraries of chemical compounds by predicting their binding free energies approximately. In de novo drug de...

show abstract

Section: Resultsmentioning

confidence: 99%

The Feasibility of an Efficient Drug Design Method with High-Performance Computers

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Testing of the implementation of AFITT in PHENIX was performed using a set of 189 protein-ligand PDB structures taken from the Iridium data set (Warren et al, 2012). This set contains a chemically varied and largely drug-like set of ligands.…”

Section: Resultsmentioning

confidence: 99%

Improved ligand geometries in crystallographic refinement usingAFITTinPHENIX

Janowski

Moriarty

Kelley

et al. 2016

Acta Crystallogr D Struct Biol

Self Cite

View full text Add to dashboard Cite

Modern crystal structure refinement programs rely on geometry restraints to overcome the challenge of a low data-to-parameter ratio. While the classical Engh and Huber restraints work well for standard amino-acid residues, the chemical complexity of small-molecule ligands presents a particular challenge. Most current approaches either limit ligand restraints to those that can be readily described in the Crystallographic Information File (CIF) format, thus sacrificing chemical flexibility and energetic accuracy, or they employ protocols that substantially lengthen the refinement time, potentially hindering rapid automated refinement workflows. PHENIX-AFITT refinement uses a full molecular-mechanics force field for user-selected small-molecule ligands during refinement, eliminating the potentially difficult problem of finding or generating high-quality geometry restraints. It is fully integrated with a standard refinement protocol and requires practically no additional steps from the user, making it ideal for high-throughput workflows. PHENIX-AFITT refinements also handle multiple ligands in a single model, alternate conformations and covalently bound ligands. Here, the results of combining AFITT and the PHENIX software suite on a data set of 189 protein-ligand PDB structures are presented. Refinements using PHENIX-AFITT significantly reduce ligand conformational energy and lead to improved geometries without detriment to the fit to the experimental data. For the data presented, PHENIX-AFITT refinements result in more chemically accurate models for small-molecule ligands.

show abstract

“…Its weaknesses are unexpected results with of them can correct common problems in PDB files. However, it has been shown that some structural aspects are often overlooked (Warren, Do, Kelley, Nicholls & Warren, 2012).…”

Section: Ligand and Protein Preparationmentioning

confidence: 99%

Acoplamiento Molecular: Avances Recientes y Retos

2018

View full text Add to dashboard Cite

Nota: Artículo recibido el 18 de octubre de 2017 y aceptado el 02 de mayo de 2018. ARTÍCULO DE REVISIÓN abstractAutomated molecular docking aims at predicting the possible interactions between two molecules. This method has proven useful in medicinal chemistry and drug discovery providing atomistic insights into molecular recognition. Over the last 20 years methods for molecular docking have been improved, yielding accurate results on pose prediction. Nonetheless, several aspects of molecular docking need revision due to changes in the paradigm of drug discovery. In the present article, we review the principles, techniques, and algorithms for docking with emphasis on protein-ligand docking for drug discovery. We also discuss current approaches to address major challenges of docking.Key Words: chemoinformatics, computer-aided drug design, drug discovery, structure-activity relationships. Acoplamiento Molecular: Avances Recientes y Retos resuMenEl acoplamiento molecular automatizado tiene como objetivo proponer un modelo de unión entre dos moléculas. Este método ha sido útil en química farmacéutica y en el descubrimiento de nuevos fármacos por medio del entendimiento de las fuerzas de interacción involucradas en el reconocimiento molecular. Durante los últimos 20 años se ha modificado extensamente la técnica de acoplamiento molecular dando resultados precisos en la predicción de los modos de unión. Sin embargo, hay algunas áreas que requieren ser mejoradas substancialmente. En este trabajo se revisan principios, técnicas y algoritmos usados en los programas computacionales del acoplamiento molecular con enfoque en la interacción proteína-ligando aplicado al descubrimiento de nuevos fármacos. También se discuten las estrategias dirigidas a solucionar los principales retos de esta técnica computacional.Palabras Clave: descubrimiento de nuevos fármacos, diseño de fármacos asistido por computadora, quimioinformática, relaciones estructura-actividad.

show abstract

Essential considerations for using protein–ligand structures in drug discovery

Cited by 150 publications

References 43 publications

The Feasibility of an Efficient Drug Design Method with High-Performance Computers

The Feasibility of an Efficient Drug Design Method with High-Performance Computers

Improved ligand geometries in crystallographic refinement usingAFITTinPHENIX

Acoplamiento Molecular: Avances Recientes y Retos

Contact Info

Product

Resources

About