Ruth Huey

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.

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.

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.

Computational protein–ligand docking and virtual drug screening with the AutoDock suite

Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ∼5 h.

Functional proteomic and structural insights into molecular recognition in the nitrilase family enzymes

Nitrilases are a large and diverse family of nonpeptidic C-N hydrolases. The mammalian genome encodes eight nitrilase enzymes, several of which remain poorly characterized. Prominent among these are nitrilase-1 (Nit1) and nitrilase-2 (Nit2), which, despite having been shown to exert effects on cell growth and possibly serving as tumor suppressor genes, are without known substrates or selective inhibitors. In previous studies, we identified several nitrilases, including Nit1 and Nit2, as targets for dipeptide-chloroacetamide activity-based proteomics probes. Here, we have used these probes, in combination with high-resolution crystallography and molecular modeling, to systematically map the active site of Nit2 and identify residues involved in molecular recognition. We report the 1.4 A crystal structure of mouse Nit2 and use this structure to identify residues that discriminate probe labeling between the Nit1 and Nit2 enzymes. Interestingly, some of these residues are conserved across all vertebrate Nit2 enzymes and, conversely, not found in any vertebrate Nit1 enzymes, suggesting that they are key discriminators of molecular recognition between these otherwise highly homologous enzymes. Our findings thus point to a limited set of active site residues that establish distinct patterns of molecular recognition among nitrilases and provide chemical probes to selectively perturb the function of these enzymes in biological systems.

Integrating biomolecular analysis and visual programming: flexibility and interactivity in the design of bioinformatics tools

One of the challenges in bio-computing is to enable the efficient use of a wide variety of rapidly evolving computational methods to simulate, analyze and understand complex interactions of molecular systems. Our laboratory is interested in the development of novel computational technologies and in the application of these technologies to the analysis and understanding of complex biological systems. We have been using the Python programming language as a platform to develop reusable and interoperable components dealing with different aspects of structural bioinformatics. These components are the basic building blocks from which several domain specific applications have been developed. In this paper we describe the integration of two applications developed in our laboratory: PMV and a visual-programming environment. PMV is a general purpose, command-driven molecular visualization and manipulation program built from reusable software components. The visual-programming environment enables a user to build interactively networks describing novel combinations of computational methods. We describe several applications demonstrating the synergy created by combining these two programs.