Automated flexible ligand docking method and its application for database search

Makino, Shingo; Kuntz, Irwin D.

doi:10.1002/(sici)1096-987x(19971115)18:14<1812::aid-jcc10>3.0.co;2-h

Cited by 126 publications

(107 citation statements)

References 37 publications

(23 reference statements)

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“…Such procedures make theoretical predictions regarding the association of a flexible ligand with a protein receptor and require an efficient sampling of the conformational space of the ligand, as well as a way to arrive at the correct "pose" of the molecule in the receptor site. While several docking methodologies are currently available (Morris et al 1998;Rarey et al 1996;Jones et al 1997;Makino and Kuntz 1997), we have extended the MOLS algorithm as a novel "rigid receptor flexible ligand" docking method to simultaneously sample both the "docking space" and the conformational space of the ligand. In extending the method to the docking problem, the search space was expanded to include the docking space.…”

Section: Docking Of Peptides and Small Molecules To Proteinsmentioning

confidence: 99%

MOLS sampling and its applications in structural biophysics

et al. 2010

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This review describes the MOLS method and its applications. This computational method has been developed in our laboratory primarily to explore the conformational space of small peptides and identify features of interest, particularly the minima, i.e., the low energy conformations. A systematic "brute-force" search through the vast conformational space for such features faces the insurmountable problem of combinatorial explosion, whilst other techniques, e.g., Monte Carlo searches, are somewhat limited in their region of exploration and may be considered inexhaustive. The MOLS method, on the other hand, uses a sampling technique commonly employed in experimental design theory to identify a small sample of the conformational space that nevertheless retains information about the entire space. The information is extracted using a technique that is a variant of the self-consistent mean field technique, which has been used to identify, for example, the optimal set of side-chain conformations in a protein. Applications of the MOLS method to understand peptide structure, predict the structures of loops in proteins, predict three-dimensional structures of small proteins, and arrive at the best conformation, orientation, and positions of a small molecule ligand in a protein receptor site have all yielded satisfactory results.

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Section: Docking Of Peptides and Small Molecules To Proteinsmentioning

confidence: 99%

MOLS sampling and its applications in structural biophysics

et al. 2010

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“…Incremental construction algorithms place ligand fragments one at a time at the binding sites of the binding protein [18,22]. They require the knowledge of binding sites to place ligand fragments at the sites.…”

Section: Related Workmentioning

confidence: 99%

“…Docking is framed as a rigid alignment problem of two rigid objects with complementary shapes. Flexible docking algorithms solve the general protein docking problem termed unbound or predictive docking by prediction of binding of two proteins in their free or unbound states [7,9,12,16,18,20,22,26]. This problem regards one or both proteins as flexible objects to account for significant conformational shape changes which occur during protein interactions.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Guided Docking of WW Domain Proteins and Flexible Ligands

Haiyun

Rashid

et al. 2009

Pattern Recognition in Bioinformatics

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Abstract. Studies of interactions between protein domains and ligands are important in many aspects such as cellular signaling. We present a knowledge-guided approach for docking protein domains and flexible ligands. The approach is applied to the WW domain, a small protein module mediating signaling complexes which have been implicated in diseases such as muscular dystrophy and Liddle's syndrome. The first stage of the approach employs a substring search for two binding grooves of WW domains and possible binding motifs of peptide ligands based on known features. The second stage aligns the ligand's peptide backbone to the two binding grooves using a quasi-Newton constrained optimization algorithm. The backbone-aligned ligands produced serve as good starting points to the third stage which uses any flexible docking algorithm to perform the docking. The experimental results demonstrate that the backbone alignment method in the second stage performs better than conventional rigid superposition given two binding constraints. It is also shown that using the backbone-aligned ligands as initial configurations improves the flexible docking in the third stage. The presented approach can also be applied to other protein domains that involve binding of flexible ligand to two or more binding sites.

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“…In this method, electrostatic and Van der Waals forces are calculated to predict the approximate molecular mechanics interaction energies of each compound with the target molecule [29]. Specifically, DOCK uses an "anchor-and-grow" algorithm that first places a rigid portion of the compound, the anchor, into the binding site and calculates scores as it gradually builds the molecule piece-by-piece [4].…”

Section: Screening With Dockmentioning

confidence: 99%

“…This has been addressed with flexible docking, where a number of possible conformations of the compound are oriented into the binding site of the target molecule [4], and calculating the change in solvation energies for the molecular pairing [3]. Both of these strategies require a significant increase in computational cost.…”

Section: Introductionmentioning

confidence: 99%