Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

Vreven, Thom; Moal, Iain H.; Vangone, Anna; Pierce, Brian G.; Kastritis, Panagiotis L.; Torchala, Mieczyslaw; Chaleil, Raphaël A. G.; Jiménez‐García, Brian; Bates, Paul A.; Fernández‐Recio, Juan; Bonvin, Alexandre M. J. J.; Weng, Zhiping

doi:10.1016/j.jmb.2015.07.016

Cited by 369 publications

(530 citation statements)

References 69 publications

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“…In this work, we considered 212 protein complexes from the protein-protein docking benchmark 5.0 to form a benchmark for the training of the parameters of our presented scoring potentials [34, 35]. Between 6,000 and 60,000 decoys (depending on the size of the complex) were sampled and scored by ATTRACT's rigid body docking for each complex from its unbound constituents.…”

Section: Methodsmentioning

confidence: 99%

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking

et al. 2017

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Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scoring step is one of the bottlenecks in the performance of many state-of-the-art protocols. The performance of scoring functions depends on the quality of the generated structures and its coupling to the sampling algorithm. A tool kit, GRADSCOPT (GRid Accelerated Directly SCoring OPTimizing), was designed to allow rapid development and optimization of different knowledge-based scoring potentials for specific objectives in protein-protein docking. Different atomistic and coarse-grained potentials can be created by a grid-accelerated directly scoring dependent Monte-Carlo annealing or by a linear regression optimization. We demonstrate that the scoring functions generated by our approach are similar to or even outperform state-of-the-art scoring functions for predicting near-native solutions. Of additional importance, we find that potentials specifically trained to identify the native bound complex perform rather poorly on identifying acceptable or medium quality (near-native) solutions. In contrast, atomistic long-range contact potentials can increase the average fraction of near-native poses by up to a factor 2.5 in the best scored 1% decoys (compared to existing scoring), emphasizing the need of specific docking potentials for different steps in the docking protocol.

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Section: Methodsmentioning

confidence: 99%

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking

et al. 2017

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“…Nevertheless, the bound state of proteins still poses an unsolved problem due to wrong sampling (Kuroda & Gray, 2016). For this reason, comprehensive computational studies need to be conducted to successfully distinguish realistic complexes from unrealistic predictions (Vreven et al, 2015).…”

Section: Protein-protein Dockingmentioning

confidence: 99%

Acoplamiento Molecular: Avances Recientes y Retos

2018

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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.

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“…However, in general, for the atomic resolution unbound docking, some form of conformational sampling is required. For most crystallographically determined complexes, the unbound to bound conformational change is largely restricted to the surface side chains [77], thus drastically limiting the combinatorics of the conformational search. Protein docking approaches are extensively evaluated in the community-wide experiment on Critical Assessment of Predicted Interactions (CAPRI) [2], and in numerous studies based on benchmarking sets (e.g.…”

Section: Structural Modeling Of Protein Complexesmentioning

confidence: 99%

“…Protein docking approaches are extensively evaluated in the community-wide experiment on Critical Assessment of Predicted Interactions (CAPRI) [2], and in numerous studies based on benchmarking sets (e.g. [77, 78]). Protein docking procedures were also shown to be successful in packing protein structural motifs [79] and predicting complexes of membrane proteins [80].…”

Section: Structural Modeling Of Protein Complexesmentioning

confidence: 99%

Challenges in structural approaches to cell modeling

Liang

Olson

et al. 2016

Journal of Molecular Biology

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Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field.

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Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

Cited by 369 publications

References 69 publications

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking

Acoplamiento Molecular: Avances Recientes y Retos

Challenges in structural approaches to cell modeling

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