Identification of Substrate Binding Sites in Enzymes by Computational Solvent Mapping

Silberstein, Michael; Dennis, Sheldon; Brown, L. M.; Körtvélyesi, Tamás; Clodfelter, Karl H.; Vajda, Sándor

doi:10.1016/j.jmb.2003.08.019

Cited by 67 publications

(99 citation statements)

References 69 publications

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“…Another method uses purely geometric features of the protein structure (Ben-Shimon and Eisenstein 2005). Ligand binding sites can also be detected with the mapping of small solvent-like molecules onto the protein surface, either experimentally (Mattos and Ringe 1996) or with the corresponding computational docking method (Silberstein et al 2003). The method of Laurie and Jackson (2005) is of this type, but it uses only a single van der Waals probe.…”

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confidence: 99%

Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines

et al. 2008

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Theoretical microscopic titration curves (THEMATICS) is a computational method for the identification of active sites in proteins through deviations in computed titration behavior of ionizable residues. While the sensitivity to catalytic sites is high, the previously reported sensitivity to catalytic residues was not as high, about 50%. Here THEMATICS is combined with support vector machines (SVM) to improve sensitivity for catalytic residue prediction from protein 3D structure alone. For a test set of 64 proteins taken from the Catalytic Site Atlas (CSA), the average recall rate for annotated catalytic residues is 61%; good precision is maintained selecting only 4% of all residues. The average false positive rate, using the CSA annotations is only 3.2%, far lower than other 3D-structure-based methods. THEMATICS-SVM returns higher precision, lower false positive rate, and better overall performance, compared with other 3D-structure-based methods. Comparison is also made with the latest machine learning methods that are based on both sequence alignments and 3D structures. For annotated sets of well-characterized enzymes, THEMATICS-SVM performance compares very favorably with methods that utilize sequence homology. However, since THEMATICS depends only on the 3D structure of the query protein, no decline in performance is expected when applied to novel folds, proteins with few sequence homologues, or even orphan sequences. An extension of the method to predict non-ionizable catalytic residues is also presented. THEMATICS-SVM predicts a local network of ionizable residues with strong interactions between protonation events; this appears to be a special feature of enzyme active sites.

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confidence: 99%

Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines

et al. 2008

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“…Thus, computational solvent mapping can provide detailed and reliable information on substrate-binding sites. [44,45,46,47].…”

Section: Methodsmentioning

confidence: 99%

Location of Binding Sites on Proteins by the Multiple Solvent Crystal Structure Method

Ringe

Mattos

2006

Fragment‐based Approaches in Drug Discovery

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IntroductionOne of the greater challenges of structural biology is to characterize proteins in enough detail so that the structures can be used for computational prediction of function, and for the design of compounds that can be used to modulate or to interfere with that function. In general, the function of a protein is associated with its interaction with other molecules, either small molecules or other large biological molecules, such as nucleic acids or proteins. Invariably, the function can be disrupted when that interaction is prevented. A complete industry exists solely for the purpose of finding such disruptors -they are called drugs. The discovery of drugs had been a serendipitous process, in an age when very little was known about the structures of the small molecules or their targets. The process of discovery has become more sophisticated as such structures became available.The success of this process relies on knowing where an interaction site is located on a protein and characterizing the properties of that site. The first step is therefore the location of such a site. The properties of the site can then be used to design a molecule that fits it perfectly in terms of size, shape and chemical properties. One method to obtain such properties is to map a site with chemical probe molecules. Ideally these probe molecules can be connected to form a larger molecule that takes advantage of as many of the potential interaction partners in the site as possible. Finally, when the process of finding and characterizing becomes robust enough, the process can be accomplished computationally.At this moment in time, locating a binding site on a protein without any extraneous information, such as a fortuitously bound ligand in the crystal structure, is only marginally successful. The characterization of a binding site is still very laborious when done experimentally and is not robust when done computationally. Thus, mapping such a site with chemical probes could help characterize the site, the map then being used to design a specific ligand unique for the site. In addition, such a map can provide the data from which computational approaches can be calibrated. One way in which mapping can be achieved is to use a variety of small probe molecules and observe where they bind to the surface of the protein. 67 Fragment-based Approaches in Drug Discovery. Edited

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“…A complementary computational method to both MSCS and MCSS is computational solvent mapping, or CSmapping [33]. This method determines ligand binding sites computationally by mapping the binding of small organic molecules as probes on the surface of a protein, resulting in consensus sites that bind a number of different probes with the lowest average free energy [34,35].…”

Section: Reproduction Of Functionality Maps Of Known Inhibitorsmentioning

confidence: 99%

The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design

Schubert

Stultz

2009

J Comput Aided Mol Des

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Fragment-based ligand design approaches, such as the multi-copy simultaneous search (MCSS) methodology, have proven to be useful tools in the search for novel therapeutic compounds that bind pre-specified targets of known structure. MCSS offers a variety of advantages over more traditional high-throughput screening methods, and has been applied successfully to challenging targets. The methodology is quite general and can be used to construct functionality maps for proteins, DNA, and RNA. In this review, we describe the main aspects of the MCSS method and outline the general use of the methodology as a fundamental tool to guide the design of de novo lead compounds. We focus our discussion on the evaluation of MCSS results and the incorporation of protein flexibility into the methodology. In addition, we demonstrate on several specific examples how the information arising from the MCSS functionality maps has been successfully used to predict ligand binding to protein targets and RNA.

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Identification of Substrate Binding Sites in Enzymes by Computational Solvent Mapping

Cited by 67 publications

References 69 publications

Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines

Enhanced performance in prediction of protein active sites with THEMATICS and support vector machines

Location of Binding Sites on Proteins by the Multiple Solvent Crystal Structure Method

The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design

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