“Soft docking”: Matching of molecular surface cubes

Jiang, Fan; Kim, Sung‐Hou

doi:10.1016/0022-2836(91)90859-5

Cited by 376 publications

(238 citation statements)

References 43 publications

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“…The binding site is typically selected with human intervention (Miller et al, 1994), or defaults to the location of a known ligand (Clark et al, 1995;Gehlhaar et al, 1995;Rarey et al, 1995). An alternative is to exhaustively dock against the entire protein surface (Jiang & Kim, 1991;Lin et al, 1994), which increases the computational cost.…”

Section: Relation To Previous Workmentioning

confidence: 99%

Automatic identification and representation of protein binding sites for molecular docking

1997

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Molecular docking is a popular way to screen for novel drug compounds. The method involves aligning small molecules to a protein structure and estimating their binding affinity. To do this rapidly for tens of thousands of molecules requires an effective representation of the binding region of the target protein. This paper presents an algorithm for representing a protein's binding site in a way that is specifically suited to molecular docking applications. Initially, the protein's surface is coated with a collection of molecular fragments that could potentially interact with the protein. Each fragment, or probe, serves as a potential alignment point for atoms in a ligand, and is scored to represent that probe's affinity for the protein. Probes are then clustered by accumulating their affinities, where high affinity clusters are identified as being the "stickiest" portions of the protein surface. The stickiest cluster is used as a computational binding "pocket" for docking. This method of site identification was tested on a number of ligand-protein complexes; in each case the pocket constructed by the algorithm coincided with the known ligand binding site. Successful docking experiments demonstrated the effectiveness of the probe representation.

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Section: Relation To Previous Workmentioning

confidence: 99%

Automatic identification and representation of protein binding sites for molecular docking

1997

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“…Rigid body docking algorithms (Cherfils et al, 1991;Jiang & Kim, 1991;Shoichet & Kuntz, 1991;Bacon & Moult, 1992;Stoddard & Koshland, 1992;Walls & Sternberg, 1992;Helmer-Citterich & Tramontano, 1994;Judson et al, 1995) permit exploration of the orientations of a ligand in a receptor combining site, and effectively select those relatively few that meet prespecified criteria-including surface complementarity, interaction energy, and hydrophobic surface burial.…”

Section: Introductionmentioning

confidence: 99%

Prediction of protein complexes using empirical free energy functions

1996

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A long sought goal in the physical chemistry of macromolecular structure, and one directly relevant to understanding the molecular basis of biological recognition, is predicting the geometry of bimolecular complexes from the geometries of their free monomers. Even when the monomers remain relatively unchanged by complex formation, prediction has been difficult because the free energies of alternative conformations of the complex have been difficult to evaluate quickly and accurately. This has forced the use of incomplete target functions, which typically do no better than to provide tens of possible complexes with no way of choosing between them. Here we present a general framework for empirical free energy evaluation and report calculations, based on a relatively complete and easily executable free energy function, that indicate that the structures of complexes can be predicted accurately from the structures of monomers, including close sequence homologues. The calculations also suggest that the binding free energies themselves may be predicted with reasonable accuracy. The method is compared to an alternative formulation that has also been applied recently to the same data set. Both approaches promise to open new opportunities in macromolecular design and specificity modification.Keywords: desolvation free energy; electrostatic interaction energy; rigid body docking; side-chain conformational search Understanding biological function at its most fundamental level, and manipulating it for therapeutic purposes, requires understanding the molecular basis of specificity. The critical determinant of true understanding is the ability to make quantitative predictions. In the case of molecular recognition, this means predicting the geometry of a complex from the free structures of its constituents.Even when conformations remain relatively unperturbed by reaction, thereby restricting the search for potential molecular complexes to a manageable size, achieving the goal of true prediction has proved elusive. Rigid body docking algorithms (Cherfils et al., 1991;Jiang & Kim, 1991;Shoichet & Kuntz, 1991;Bacon & Moult, 1992;Stoddard & Koshland, 1992;Walls & Sternberg, 1992;Helmer-Citterich & Tramontano, 1994;Judson et al., 1995) permit exploration of the orientations of a ligand in a receptor combining site, and effectively select those relatively few that meet prespecified criteria-including surface complementarity, interaction energy, and hydrophobic surface burial.As shown by Shoichet and Kuntz (1991), the methods go far toward reducing hundreds of thousands of possibilities to tens ~

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“…Considerable further efforts have relied on a high match of shape to obtain good fitting solutions (Zielenkiewicz & Rabcenko, 1984;Connolly, 1986;Wodak et al, 1987;Jiang & Kim, 1991;Shiochet & Kuntz, 1991, 1993Bacon & Moult, 1992;Walls & Sternberg, 1992;Helmer-Citterich & Tramontano, 1994;Norel et al, 1994). Despite this profusion of methods, shape complementarity has only been quantified for a limited number of protein interfaces, using atomic packing density (Tulip et al, 1992;Walls & Sternberg, 1992) and a statistical measure (Lawrence & Colman, 1993).…”

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confidence: 99%

Cavities and packing at protein interfaces

1994

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An analysis of internal packing defects or "cavities" (both empty and water-containing) within protein structures has been undertaken and includes 3 cavity classes: within domains, between domains, and between protein subunits. We confirm several basic features common to all cavity types but also find a number of new characteristics, including those that distinguish the classes. The total cavity volume remains only a small fraction of the total protein volume and yet increases with protein size. Water-filled "cavities" possess a more polar surface and are typically larger. Their constituent waters are necessary to satisfy the local hydrogen bonding potential. Cavitysurrounding atoms are observed to be, on average, less flexible than their environments. Intersubunit and interdomain cavities are on average larger than the intradomain cavities, occupy a larger fraction of their resident surfaces, and are more frequently water-filled. We observe increased cavity volume at domain-domain interfaces involved with shear type domain motions. The significance of interfacial cavities upon subunit and domain shape complementarity and the protein docking problem, as well as in their structural and functional role in oligomeric proteins, will be discussed. The results concerning cavity size, polarity, solvation, general abundance, and residue type constituency should provide useful guidelines for protein modeling and design.

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“Soft docking”: Matching of molecular surface cubes

Cited by 376 publications

References 43 publications

Automatic identification and representation of protein binding sites for molecular docking

Automatic identification and representation of protein binding sites for molecular docking

Prediction of protein complexes using empirical free energy functions

Cavities and packing at protein interfaces

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