Principles of docking: An overview of search algorithms and a guide to scoring functions
By Inbal Halperin, Buyong Ma, Haim J. Wolfson, Ruth Nussinov. 2002
1,304 citations recorded by the source corpus.
The docking field has come of age. The time is ripe to present the principles of docking, reviewing the current state of the field. Two reasons are largely responsible for the maturity of the computational docking area. First, the early optimism that the very presence of the “correct” native conformation within the list of predicted docked conformations signals a near solution to the docking problem, has been replaced by the stark realization of the extreme difficulty of the next scoring/ranking step. Second, in the last couple of years more realistic approaches to handling molecular flexibility in docking schemes have emerged. As in folding, these derive from concepts abstracted from statistical mechanics, namely, populations. Docking and folding are interrelated. From the purely physical standpoint, binding and folding are analogous processes, with similar underlying principles. Computationally, the tools developed for docking will be tremendously useful for folding. For large, multidomain proteins, domain docking is probably the only rational way, mimicking the hierarchical nature of protein folding. The complexity of the problem is huge. Here we divide the computational docking problem into its two separate components. As in folding, solving the docking problem involves efficient search (and matching) algorithms, which cover the relevant conformational space, and selective scoring functions, which are both efficient and effectively discriminate between native and non‐native solutions. It is universally recognized that docking of drugs is immensely important. However, protein–protein docking is equally so, relating to recognition, cellular pathways, and macromolecular assemblies. Proteins function when they are bound to other molecules. Consequently, we present the review from both the computational and the biological points of view. Although large, it covers only partially the extensive body of literature, relating to small (drug) and to large protein–protein molecule docking, to rigid and to flexible. Unfortunately, when reviewing these, a major difficulty in assessing the results is the non‐uniformity in the formats in which they are presented in the literature. Consequently, we further propose a way to rectify it here. Proteins 2002;47:409–443. © 2002 Wiley‐Liss, Inc.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
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In Computational Theory and Mathematics
- Alexandre M. J. J. Bonvin
- Shengyou Huang
- Xiaoqin Zou
- David Baker
- Jeffrey J. Gray
- Brian G. Pierce
- M. V. van Dijk
- Stefano Moro
In Materials Chemistry
- Alexandre M. J. J. Bonvin
- Jeffrey J. Gray
- David Baker
- Jens Meiler
- Yang Zhang
- Shengyou Huang
- Tanja Kortemme
- M. V. van Dijk
In Molecular Biology
- Alexandre M. J. J. Bonvin
- David Baker
- Shengyou Huang
- Jeffrey J. Gray
- Xiaoqin Zou
- Jens Meiler
- Brian G. Pierce
- Yang Zhang
Citations in the corpus
- Improved prediction of protein-protein interactions using AlphaFold2 (2021, methodology): “Shape complementary approaches rely on protein structures or models of the monomers 8,9 , while template-based docking needs suitable templates.”
- Potential functions for hydrogen bonds in protein structure prediction and design. (2005, background): “…000012964.3D Proof by:Shankari QC by:Alakesan ProjectAcronym:bs:AIPC Volume:72002 U N C O R R E C T E D P R O O F reviews give a detailed account of the state of the art in this area (Camacho and Vajda, 2002; Halperin et al., 2002; Smith and Sternberg, 2002; Vajda and Camacho, 2004).”
- Potential of quantum computing for drug discovery (2018, background): “Different docking approaches employ either atomic, surface, or grid-based representation of the ligand and target structures [49].”
- FireDock: a web server for fast interaction refinement in molecular docking (2008, background): “In addition, state of the art scoring functions are not tolerant enough to the inaccuracies in the predicted structures (7).”
- Computational Methods in Drug Discovery (2014, methodology): “Docking methods can be classified as rigid-body docking and flexible docking applications depending on the degree to which they consider ligand and protein flexibility during the docking process (Halperin et al., 2002; Dias and de Azevedo, 2008).”
- Molecular docking: a powerful approach for structure-based drug discovery. (2011, background): “Various excellent reviews on docking have been published in the past [5, 11-14], and many comparison studies were conducted to evaluate the relative performance of the programs [15-18].”
- Ensemble docking of multiple protein structures: Considering protein structural variations in molecular docking (2006, methodology): “Given a target protein structure molecular docking samples hundreds or thousands of orientations or conformations of a ligand at the putative binding site, evaluates the energy score of each orientation/conformation, and ranks the orientations and conformations according to their scores.(1,2) The li”
- Efficient Unbound Docking of Rigid Molecules (2002, background): “Most of the docking algorithms encounter difficulties with this case, since shape complementarity is affected [14].”
- GEMDOCK: A generic evolutionary method for molecular docking (2004, methodology): “2,3 To address the above questions, we developed a molecular docking approach termed GEMDOCK (Generic Evolu-Grant tionary Method for molecular DOCKing).”