Biomedical subjects

David W Gatchell

Publications and source records attributed to David W Gatchell.

4 recordsLinked to original sources

PubMed2004

ClusPro: a fully automated algorithm for protein-protein docking.

ClusPro (http://nrc.bu.edu/cluster) represents the first fully automated, web-based program for the computational docking of protein structures. Users may upload the coordinate files of two protein structures through ClusPro's web interface, or enter the PDB codes of the respective structures, which ClusPro will then download from the PDB server (http://www.rcsb.org/pdb/). The docking algorithms evaluate billions of putative complexes, retaining a preset number with favorable surface complementarities. A filtering method is then applied to this set of structures, selecting those with good electrostatic and desolvation free energies for further clustering. The program output is a short list of putative complexes ranked according to their clustering properties, which is automatically sent back to the user via email.

Algorithms↗

PubMed2004

ClusPro: an automated docking and discrimination method for the prediction of protein complexes.

MOTIVATION: Predicting protein interactions is one of the most challenging problems in functional genomics. Given two proteins known to interact, current docking methods evaluate billions of docked conformations by simple scoring functions, and in addition to near-native structures yield many false positives, i.e. structures with good surface complementarity but far from the native. RESULTS: We have developed a fast algorithm for filtering docked conformations with good surface complementarity, and ranking them based on their clustering properties. The free energy filters select complexes with lowest desolvation and electrostatic energies. Clustering is then used to smooth the local minima and to select the ones with the broadest energy wells-a property associated with the free energy at the binding site. The robustness of the method was tested on sets of 2000 docked conformations generated for 48 pairs of interacting proteins. In 31 of these cases, the top 10 predictions include at least one near-native complex, with an average RMSD of 5 A from the native structure. The docking and discrimination method also provides good results for a number of complexes that were used as targets in the Critical Assessment of PRedictions of Interactions experiment. AVAILABILITY: The fully automated docking and discrimination server ClusPro can be found at http://structure.bu.edu

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PubMed2003

Combination of scoring functions improves discrimination in protein-protein docking.

Two structure-based potentials are used for both filtering (i.e., selecting a subset of conformations generated by rigid-body docking), and rescoring and ranking the selected conformations. ACP (atomic contact potential) is an atom-level extension of the Miyazawa-Jernigan potential parameterized on protein structures, whereas RPScore (residue pair potential score) is a residue-level potential, based on interactions in protein-protein complexes. These potentials are combined with other energy terms and applied to 13 sets of protein decoys, as well as to the results of docking 10 pairs of unbound proteins. For both potentials, the ability to discriminate between near-native and non-native docked structures is substantially improved by refining the structures and by adding a van der Waals energy term. It is observed that ACP and RPScore complement each other in a number of ways (e.g., although RPScore yields more hits than ACP, mainly as a result of its better performance for charged complexes, ACP usually ranks the near-native complexes better). As a general solution to the protein-docking problem, we have found that the best discrimination strategies combine either an RPScore filter with an ACP-based scoring function, or an ACP-based filter with an RPScore-based scoring function. Thus, ACP and RPScore capture complementary structural information, and combining them in a multistage postprocessing protocol provides substantially better discrimination than the use of the same potential for both filtering and ranking the docked conformations.

Algorithms↗

PubMed2003

Successful discrimination of protein interactions.

We present results from the prediction of protein complexes associated with the first Critical Assessment of PRediction of Interactions (CAPRI) experiment. Our algorithm, SmoothDock, comprises four steps: (1) we perform rigid body docking using the program DOT, keeping the top 20,000 structures as ranked by surface complementarity; (2) we rerank these structures according to a free energy estimate that includes both desolvation and electrostatics and retain the top 2000 complexes; (3) we cluster the filtered complexes using a pairwise root-mean-square deviation (RMSD) criterion; (4) the 25 largest clusters are subject to a smooth docking discrimination algorithm where van der Waals forces are taken into account. We predicted targets 1, 6, and 7 with RMSDs of 9.5, 2.4, and 2.6 A, respectively. More importantly, from the perspective of biological applications, our approach consistently ranked the correct model first (i.e., with highest confidence). For target 5 we identified the binding region but not the correct orientation. Although we were able to find reasonable clusters for all targets, low-affinity complexes (K(d) < nM) were harder to discriminate. For four of seven targets, the top models predicted by our automated procedure were among the best communitywide predictions.

Algorithms↗