Garrett M. Morris

Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function

A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the free energy change upon binding. Interestingly, this method applies a Lamarckian model of genetics, in which environmental adaptations of an individuals phenotype are reverse transcribed into its genotype and become heritable traits (sic). We consider three search methods, Monte Carlo simulated annealing, a traditional genetic algorithm, and the Lamarckian genetic algorithm, and compare their performance in dockings of seven protein–ligand test systems having known three‐dimensional structure. We show that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckian genetic algorithm is the most efficient, reliable, and successful of the three. The empirical free energy function was calibrated using a set of 30 structurally known protein–ligand complexes with experimentally determined binding constants. Linear regression analysis of the observed binding constants in terms of a wide variety of structure‐derived molecular properties was performed. The final model had a residual standard error of 9.11 kJ mol−1 (2.177 kcal mol−1) and was chosen as the new energy function. The new search methods and empirical free energy function are available in AUTODOCK, version 3.0. © 1998 John Wiley & Sons, Inc. J Comput Chem 19: 1639–1662, 1998

AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility

We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand‐protein complexes and a cross‐docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid‐based docking method and a modification of the flexible sidechain technique.

A semiempirical free energy force field with charge‐based desolvation

The authors describe the development and testing of a semiempirical free energy force field for use in AutoDock4 and similar grid‐based docking methods. The force field is based on a comprehensive thermodynamic model that allows incorporation of intramolecular energies into the predicted free energy of binding. It also incorporates a charge‐based method for evaluation of desolvation designed to use a typical set of atom types. The method has been calibrated on a set of 188 diverse protein–ligand complexes of known structure and binding energy, and tested on a set of 100 complexes of ligands with retroviral proteases. The force field shows improvement in redocking simulations over the previous AutoDock3 force field.

Automated docking of flexible ligands: applications of AutoDock.

AutoDock is a suite of C programs used to predict the bound conformations of a small, flexible ligand to a macromolecular target of known structure. The technique combines simulated annealing for conformation searching with a rapid grid‐based method of energy evaluation. This paper reviews recent applications of the technique and describes the enhancements included in the current release.

Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4

SummaryAutoDock 2.4 predicts the bound conformations of a small, flexible ligand to a nonflexible macromolecular target of known structure. The technique combines simulated annealing for conformation searching with a rapid grid-based method of energy evaluation based on the AMBER force field. AutoDock has been optimized in performance without sacrificing accuracy; it incorporates many enhancements and additions, including an intuitive interface. We have developed a set of tools for launching and analyzing many independent docking jobs in parallel on a heterogeneous network of UNIX-based workstations. This paper describes the current release, and the results of a suite of diverse test systems. We also present the results of a systematic investigation into the effects of varying simulated-annealing parameters on molecular docking. We show that even for ligands with a large number of degrees of freedom, root-mean-square deviations of less than 1 Å from the crystallographic conformation are obtained for the lowest-energy dockings, although fewer dockings find the crystallographic conformation when there are more degrees of freedom.

Automated docking to multiple target structures: Incorporation of protein mobility and structural water heterogeneity in AutoDock

Protein motion and heterogeneity of structural waters are approximated in ligand‐docking simulations, using an ensemble of protein structures. Four methods of combining multiple target structures within a single grid‐based lookup table of interaction energies are tested. The method is evaluated using complexes of 21 peptidomimetic inhibitors with human immunodeficiency virus type 1 (HIV‐1) protease. Several of these structures show motion of an arginine residue, which is essential for binding of large inhibitors. A structural water is also present in 20 of the structures, but it must be absent in the remaining one for proper binding. Mean and minimum methods perform poorly, but two weighted average methods permit consistent and accurate ligand docking, using a single grid representation of the target protein structures. Proteins 2002;46:34–40.

Target Flexibility: An Emerging Consideration in Drug Discovery and Design†

Department of General and Inorganic Chemistry, UniVersity of Parma, Via G.P. Usberti 17/A 43100, Parma, Italy, National Institute for Biosystems and Biostructures, Rome, Italy, Department of Medicinal Chemistry and Institute for Structural Biology & Drug DiscoVery, Virginia Commonwealth UniVersity, Richmond, Virginia 23298-0540, Department of Pharmaceutics, UniVersity of Parma, Via GP Usberti 27/A, 43100 Parma, Italy, High Performance Systems, CINECA Supercomputing Centre, Casalecchio di Reno, Bologna, Italy, Dulbecco Telethon Institute, Department of Chemistry, UniVersity of Modena and Reggio Emilia, Via Campi 183, 41100 Modena, Italy, Department of Mathematics and Natural Sciences, Pharmaceutical Institute, Christian-Albrechts-UniVersity, Gutenbergstrasse 76, 24118 Kiel, Germany, Departments of Biochemistry & Molecular Biology, Computer Science & Engineering, and Physics & Astronomy, Michigan State UniVersity, East Lansing, Michigan 48824-1319, Department of Molecular Biology, MB-5, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037-1000, Molecular Modeling and Bioinformatics Unit, Institute of Biomedical Research, Scientific Park of Barcelona, Department of Biochemistry and Molecular Biology, UniVersity of Barcelona, Josep Samitier 1-5, Barcelona 08028, Spain, Department of Experimental Medicine, UniVersity of Parma, Via Volturno, 39, 43100, Parma, Italy, Department of Chemical, Food, Pharmaceutical and Pharmacological Sciences, UniVersity of Piemonte Orientale “Amedeo AVogadro”, Via BoVio 6, 28100 NoVara, Italy, Institute of Pharmacy and Food Chemistry, UniVersity of Wurzburg, Am Hubland, D-97074 Wurzburg, Germany

Using AutoDock for ligand-receptor docking.

This unit describes how to set up and analyze ligand‐protein docking calculations using AutoDock and the graphical user interface, AutoDockTools (ADT). The AutoDock scoring function is a subset of the AMBER force field that treats molecules using the United Atom model. The unit uses an X‐ray crystal structure of Indinavir bound to HIV‐1 protease taken from the Protein Data Bank (UNIT 1.9) and shows how to prepare the ligand and receptor for AutoGrid, which computes grid maps needed by AutoDock. Indinavir is prepared for AutoDock, adding the polar hydrogens, and partial charges, and defining the rotatable bonds that will be explored during the docking. The input files for AutoGrid and AutoDock are created, and then the grid map calculation run, followed by the docking calculation in AutoDock. Finally, this unit describes some of the ways the results can be analyzed using AutoDockTools. Curr. Protoc. Bioinform. 24:8.14.1‐8.14.40.

Freely Available Conformer Generation Methods: How Good Are They?

Conformer generation has important implications in cheminformatics, particularly in computational drug discovery where the quality of conformer generation software may affect the outcome of a virtual screening exercise. We examine the performance of four freely available small molecule conformer generation tools (Balloon, Confab, Frog2, and RDKit) alongside a commercial tool (MOE). The aim of this study is 3-fold: (i) to identify which tools most accurately reproduce experimentally determined structures; (ii) to examine the diversity of the generated conformational set; and (iii) to benchmark the computational time expended. These aspects were tested using a set of 708 drug-like molecules assembled from the OMEGA validation set and the Astex Diverse Set. These molecules have varying physicochemical properties and at least one known X-ray crystal structure. We found that RDKit and Confab are statistically better than other methods at generating low rmsd conformers to the known structure. RDKit is particularly suited for less flexible molecules while Confab, with its systematic approach, is able to generate conformers which are geometrically closer to the experimentally determined structure for molecules with a large number of rotatable bonds (≥10). In our tests RDKit also resulted as the second fastest method after Frog2. In order to enhance the performance of RDKit, we developed a postprocessing algorithm to build a diverse and representative set of conformers which also contains a close conformer to the known structure. Our analysis indicates that, with postprocessing, RDKit is a valid free alternative to commercial, proprietary software.

Structural mapping of CD134 residues critical for interaction with feline immunodeficiency virus.

CD134 is a primary binding receptor for feline immunodeficiency virus (FIV), and with CXCR4 facilitates infection of CD4+ T cells. Human CD134 fails to support FIV infection. To delineate the regions important for defining virus specificity of CD134, we exchanged domains between human and feline CD134. The binding site for FIV surface glycoprotein (SU) is located in domain 1, in a region distinct from the natural ligand (CD134L)-binding site. Mutagenesis showed that Asp60 and Asp62 are required for interaction with FIV, and modeling studies localized these two residues to the outer edge of domain 1. Substitutions S60D and N62D, in conjunction with H45S, R59G and V64K, imparted both FIV SU binding and receptor function to human CD134. Finally, we demonstrated that soluble CD134 facilitates infection of CD134− CXCR4+ target cells in a manner analogous to CD4 augmentation of HIV infection.