Flexible docking and refinement with a coarse‐grained protein model using ATTRACT

Vries, Sjoerd de; Zacharias, Martin

doi:10.1002/prot.24400

Cited by 59 publications

(56 citation statements)

References 25 publications

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“…The combination and weighing of the terms is what differentiates these methods from one another. Some methods like Attract [21] use VdW and electrostatic energy calculated on a coarse grained modeling of the protein to save computational time. While it is known that proteins often change their conformations upon binding, modeling flexibility is a challenging problem due to the additional computational cost to an already difficult problem.…”

Section: Scoring Functions For Protein-protein Dockingmentioning

confidence: 99%

AccuRMSD

Akbal-Delibas

Pomplun

Haspel

2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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Protein-protein docking methods aim to compute the correct bound form of two or more proteins. One of the major challenges for docking methods is to accurately discriminate native-like structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against a specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives.Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm and achieved RMSD prediction accuracy with less than 1Å error margin on 19,600 test samples.

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Section: Scoring Functions For Protein-protein Dockingmentioning

confidence: 99%

AccuRMSD

Akbal-Delibas

Pomplun

Haspel

2014

Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

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“…HAD-DOCK [31]) and/or use simplified representations of the system such as coarse-grained models (e.g. ATTRACT [38,39] and RosettaDock [37]). Finally, some methods have been developed to tackle the particular problem of dealing with large domain motions, such as the flexible multidomain approach of Karaca & Bonvin [15] that can describe extremely large conformational changes (up to 19.5 A r.m.s.d.…”

Section: Sampling: Exploring the Conformational And Interaction Landsmentioning

confidence: 99%

Integrative computational modeling of protein interactions

Rodrigues

Bonvin

2014

The FEBS Journal

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Protein interactions define the homeostatic state of the cell. Our ability to understand these interactions and their role in both health and disease is tied to our knowledge of the 3D atomic structure of the interacting partners and their complexes. Despite advances in experimental method of structure determination, the majority of known protein interactions are still missing an atomic structure. High‐resolution methods such as X‐ray crystallography and NMR spectroscopy struggle with the high‐throughput demand, while low‐resolution techniques such as cryo‐electron microscopy or small‐angle X‐ray scattering provide data that are too coarse. Computational structure prediction of protein complexes, or docking, was first developed to complement experimental research and has since blossomed into an independent and lively field of research. Its most successful products are hybrid approaches that combine powerful algorithms with experimental data from various sources to generate high‐resolution models of protein complexes. This minireview introduces the concept of docking and docking with the help of experimental data, compares and contrasts the available integrative docking methods, and provides a guide for the experimental researcher for what types of data and which particular software can be used to model a protein complex.

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“…However, some docking protocols model conformational adjustments during the binding process by introducing flexibilities during the sampling stage [11][12] and most approaches include a flexible refinement step after the initial rigid body search [13]. …”

Section: Introductionmentioning

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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Flexible docking and refinement with a coarse‐grained protein model using ATTRACT

Cited by 59 publications

References 25 publications

AccuRMSD

AccuRMSD

Integrative computational modeling of protein interactions

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

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