A geometric approach to macromolecule-ligand interactions

Kuntz, Irwin D.; Blaney, Jeffrey M.; Oatley, Stuart J.; Langridge, Robert; Ferrin, Thomas E.

doi:10.1016/0022-2836(82)90153-x

Cited by 2,022 publications

(1,516 citation statements)

References 21 publications

Supporting

Mentioning

1,496

Contrasting

Unclassified

Order By: Relevance

“…Research toward this end includes analytical area and volume calculation (Connolly, 1983;Richmond, 1984;Gibson & Scheraga, 1987); cavity identification and measurement (Rashin et al, 1986;Voorintholt et al, 1989;Ho & Marshall, 1990;Alard & Wodak, 1991;Nicholls et al, 1993;Smart et al, 1993;Hubbard et al, 1994;Kleywegt & Jones, 1994;Williams et al, 1994); pocket or cleft computation (Kuntz et al, 1982;Delaney, 1992;Levitt & Banaszak, 1992;Laskowski, 1995;Peters et al, 1996); and molecular shape representation (Lin et al, 1994). Although useful, their application to pocket calculations is limited by lack of fully automatic computations, lack of analytical measurements of area and volume with real physical meaning, and/or use of arbitrarily adjusted parameters.…”

Section: Pocket and Cavity Analysismentioning

confidence: 99%

Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design

1998

View full text Add to dashboard Cite

Identification and size characterization of surface pockets and occluded cavities are initial steps in protein structurebased ligand design. A new program, CAST, for automatically locating and measuring protein pockets and cavities, is based on precise computational geometry methods, including alpha shape and discrete flow theory. CAST identifies and measures pockets and pocket mouth openings, as well as cavities. The program specifies the atoms lining pockets, pocket openings, and buried cavities; the volume and area of pockets and cavities; and the area and circumference of mouth openings. CAST analysis of over 100 proteins has been carried out; proteins examined include a set of SI monomeric enzyme-ligand structures, several elastase-inhibitor complexes, the pockets and cavities in protein crystal structures and quantifying their size. The method is a computational geometry treatment of complex shapes, based on alpha shape and discrete flow theory, and a related suite of programs,

show abstract

Section: Pocket and Cavity Analysismentioning

confidence: 99%

Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design

1998

View full text Add to dashboard Cite

show abstract

“…The number of search algorithms used in docking is exploding, with methods ranging from the simple but very successful geometric search in the DOCK program [5] to various combinatorial [6][7][8], Monte Carlo [9][10][11][12][13], and genetic [14,15] mapping-fragment assembly strategy [16] used in a variety of programs [17][18][19][20][21][22][23]. The basic idea is to construct maps of the binding site by placing individual functional groups on a grid, and determining the most favorable positions.…”

Section: Introductionmentioning

confidence: 99%

Empirical free energy as a target function in docking and design: application to HIV‐1 protease inhibitors

1996

View full text Add to dashboard Cite

Structure-based drug design requires the development of efficient computer programs for exploring the structural compatibility of various flexible ligands with a given receptor. While various algorithms are available for finding docked conformations, selecting a target function that can reliably score the conformations remains a serious problem. We show that the use of an empirical free energy evaluation method, originally developed to characterize protein-protein interactions, can substantially improve the efficacy of search algorithms. In addition to the molecular mechanics interaction energy, the function takes account of solvation and side chain conformational entropy, while remaining simple enough to replace the incomplete target functions used in many drug docking and design procedures. The free energy function is used here in conjunction with a simple site mapping-fragment assembly algorithm, for docking the MVT-101 non-peptide inhibitor to HIV-1 protease. In particular, we predict the bound structure with an all atom RMSD of 1.21 A, compared to 1.69 A using an energy target function, and also accurately predict the free energy shifts obtained with a series of five trimeric hydroxyethylene isostere analogs.

show abstract

“…Docking of potential inhibitors of GRK2 and cAPK was performed using the DOCK 3.5 software package [16][17][18][19] . Briefly, a molecular surface of the target site was created using the MS algorithm 27 .…”

Section: Docking Of Potential Inhibitors Of Grk2mentioning

confidence: 99%

“…Knowledge of the 3D structure of the enzyme can be useful in the design of potent and selective inhibitors. The docking of compounds in large chemical databases into a molecular model of a target protein using the DOCK software package developed by Kuntz and collegues [16][17][18][19] has proven valuable as a screening procedure, providing insight into ligand-receptor interactions and suggesting possible lead compounds 20 . However, the crystal structure of GRK2 has yet to be reported.…”

Section: Introductionmentioning

confidence: 99%

Molecular modeling of G-protein coupled receptor kinase 2: Docking and biochemical evaluation of inhibitors

et al. 2000

View full text Add to dashboard Cite

G-protein coupled receptor kinase 2 (GRK2) regulates the activity of many receptors. Because potent inhibitors of GRK2 are thus far limited to polyanionic compounds like heparin, we searched for new inhibitors with the aid of a molecular model of GRK2. We used the available crystal structure of cAMP dependent protein kinase (cAPK) as a template to construct a 3D homology model of GRK2. Known cAPK and GRK2 inhibitors were docked into the active sites of GRK2 and cAPK using DOCK v3.5. H8 docked into the hydrophobic pocket of the adenosine 5'-triphosphate (ATP) binding site of cAPK, consistent with its known competitive cAPK inhibition relative to ATP. Similarly, 3 of 4 known GRK2 inhibitors docked into the ATP binding pocket of GRK2 with good scores. Screening the Fine Chemicals Directory (FCD, containing the 3D structures of 13,000 compounds) for docking into the active sites of GRK2 identified H8 and the known GRK2 inhibitor trifluoperazine as candidates. Whereas H8 indeed inhibited light-dependent phosphorylation of rhodopsin by GRK2, but with low potency, 3 additional FCD compounds with promising GRK2 scores failed to inhibit GRK2. This result demonstrates limitations of the GRK2 model in predicting activity among diverse chemical structures. Docking suramin, an inhibitor of protein kinase C (not present in FCD) yielded a good fit into the ATP binding site of GRK2 over cAPK. Suramin did inhibit GRK2 with IC50 32 μM (pA 2 6.39 for competitive inhibition of ATP). Suramin congeners with fewer sulfonic acid residues (NF062, NF503 [IC50 14 μM]) or representing half of the suramin molecule (NF520) also inhibited GRK2 as predicted by docking. In conclusion, suramin and analogues are lead compounds in the development of more potent and selective inhibitors of GRK2.

show abstract

A geometric approach to macromolecule-ligand interactions

Cited by 2,022 publications

References 21 publications

Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design

Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design

Empirical free energy as a target function in docking and design: application to HIV‐1 protease inhibitors

Molecular modeling of G-protein coupled receptor kinase 2: Docking and biochemical evaluation of inhibitors

Contact Info

Product

Resources

About