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Dr. Marc Nicklaus

Dr. Marc Nicklaus
Computer-Aided Drug Design

Research Summary

Computer-Aided Drug Design

The Computer-Aided Drug Design (CADD) Group is a research unit within the Chemical Biology Laboratory (CBL) that employs, analyzes, and develops computer-based methods to aid in the drug discovery, design, and development projects of the CBL and other researchers at the NIH. We split our efforts about evenly between support-type projects and research projects initiated and conducted by CADD staff members.

Increasingly, we are implementing projects, and making available resources developed by the CADD Group, in a Web-based manner. This innovation offers three advantages: (1) it frees all users, including the group members themselves, from platform restraints and the concomitant expenses for specific software/hardware, (2) it makes resources and results immediately available for sharing among all collaborators regardless of their location, and (3) at the same time, without additional effort, it helps further the mission of the NCI as a publicly funded institution by providing data and services to the public in the most economical way for both users and developers.

Enhanced NCI Database Browser. One such project is the Enhanced NCI Database Browser used to search the 250,000-compound Open NCI Database. This dataset is the publicly available part of the half-million structure collection assembled by the NCI's Developmental Therapeutics Program during the program's 45 years of screening compounds against cancer and, more recently, AIDS. In collaboration with researchers at the Computer Chemistry Center of the University of Erlangen-Nuremberg, we have implemented a Web-based graphical user interface for searching the structure and data in the Open NCI Database. This interface offers the user powerful tools for searching, analyzing, and displaying search results. With this interface in place, it is now easier to add large amounts of additional, mostly calculated, data to the pool of searchable information. In collaboration with a group at the Russian Academy of Medical Sciences in Moscow, predictions are now included for more than 500 different types of biological activities for most of the quarter-million structures in the database. A three-dimensional (3D) pharmacophore search feature has also been implemented. Furthermore, hyperlinks to additional services allow users immediate access to further processing of individual structures or hit sets in a wide variety of ways. Visit the CADD Group's home page or the Enhanced NCI Database Browser service for more information. We hope this tool will be useful in drug design for researchers both inside and outside the NCI.

HIV Integrase. The second main interest of our group is HIV integrase (IN) as a drug development target. This enzyme catalyzes the integration of the viral DNA into the human DNA, which is an essential step in the viral replication cycle. HIV has been shown to develop rapid resistance to current inhibitors of protease and reverse transcriptase. Because there is no known analog for IN in human cells, IN is thought to be an ideal target for anti-AIDS drug discovery. NMR and/or x-ray crystallography techniques have determined the structures for the separate domains of HIV-1 IN. However, an experimental structure of the full-length protein remains unavailable. We have therefore initiated a project that aims to utilize all the currently available experimental results, structural, mechanistic, and otherwise, to build a model of the full-length HIV IN protein complexed with (the ends of) the viral DNA and, possibly, also with model stretches of host DNA. Using this model as a starting point, we plan to conduct 3D pharmacophore searches and docking studies with the goal of finding compounds that are potent in an assay using preintegration complexes of IN and DNA.

Among our collaborators are Bernard Brooks and Yves Pommier, NIH; Wolf-Dietrich Ihlenfeldt, Xemistry, Germany; Neamati Nouri, University of Southern California; Vladimir Poroikov, Russian Academy of Medical Sciences, Moscow; and Anders Wallqvist, BHSAI, USAMRMC.
1 - 5 of 119 results

1)  Weidlich IE, Filippov IV, Brown J, Kaushik-Basu N, Krishnan R, Nicklaus MC, Thorpe IF.
Inhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methods.
Bioorg. Med. Chem. [Epub ahead of print], 2013. [Journal]

2)  Lagunin AA, Filimonov DA, Gloriozova TA, Tarasova OA, Zakharov AV, Guasch-Pamies L, Nicklaus MC, Poroikov VV.
Virtual Screening of Potential Anti-HIV Agents in Libraries of Commercially Available Organic Compounds (Russ.).
Chem. Pharm. J. (Moscow). 47: 3-21, 2013. [Journal]

3)  Peach ML, Zakharov AV, Liu R, Pugliese A, Tawa G, Wallqvist A, Nicklaus MC.
Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software.
Future Med Chem. 4: 1907-32, 2012. [Journal]

4)  Zakharov AV, Peach ML, Sitzmann M, Filippov IV, McCartney HJ, Smith LH, Pugliese A, Nicklaus MC.
Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes.
Future Med Chem. 4: 1933-44, 2012. [Journal]

5)  Muresan S, Sitzmann M, Southan C.
Mapping between databases of compounds and protein targets.
Methods Mol. Biol. 910: 145-64, 2012. [Journal]

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