David W. Gatchell

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

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 ClusPros 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.

Scoring docked conformations generated by rigid-body protein-protein docking.

Rigid‐body methods, particularly Fourier correlation techniques, are very efficient for docking bound (co‐crystallized) protein conformations using measures of surface complementarity as the target function. However, when docking unbound (separately crystallized) conformations, the method generally yields hundreds of false positive structures with good scores but high root mean square deviations (RMSDs). This paper describes a two‐step scoring algorithm that can discriminate near‐native conformations (with less than 5 Å RMSD) from other structures. The first step includes two rigid‐body filters that use the desolvation free energy and the electrostatic energy to select a manageable number of conformations for further processing, but are unable to eliminate all false positives. Complete discrimination is achieved in the second step that minimizes the molecular mechanics energy of the retained structures, and re‐ranks them with a combined free‐energy function which includes electrostatic, solvation, and van der Waals energy terms. After minimization, the improved fit in near‐native complex conformations provides the free‐energy gap required for discrimination. The algorithm has been developed and tested using docking decoys, i.e., docked conformations generated by Fourier correlation techniques. The decoy sets are available on the web for testing other discrimination procedures. Proteins 2000;40:525–537.

Discrimination of Near-Native Protein Structures From Misfolded Models by Empirical Free Energy Functions

Free energy potentials, combining molecular mechanics with empirical solvation and entropic terms, are used to discriminate native and near‐native protein conformations from slightly misfolded decoys. Since the functional forms of these potentials vary within the field, it is of interest to determine the contributions of individual free energy terms and their combinations to the discriminative power of the potential. This is achieved in terms of quantitative measures of discrimination that include the correlation coefficient between RMSD and free energy, and a new measure labeled the minimum discriminatory slope (MDS). In terms of these criteria, the internal energy is shown to be a good discriminator on its own, which implies that even well‐constructed decoys are substantially more strained than the native protein structure. The discrimination improves if, in addition to the internal energy, the free energy expression includes the electrostatic energy, calculated by assuming non‐ionized side chains, and an empirical solvation term, with the classical atomic solvation parameter model providing slightly better discrimination than a structure‐based atomic contact potential. Finally, the inclusion of a term representing the side chain entropy change, and calculated by an established empirical scale, is so inaccurate that it makes the discrimination worse. It is shown that both the correlation coefficient and the MDS value (or its dimensionless form) are needed for an objective assessment of a potential, and that together they provide much more information on the origins of discrimination than simple inspection of the RMSD‐free energy plots. Proteins 2000;41:518–534.

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 Å, 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 (Kd < nM) were harder to discriminate. For four of seven targets, the top models predicted by our automated procedure were among the best communitywide predictions. Proteins 2003;52:92–97.

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. Proteins 2003.

Cytotoxic T cell recognition of allelic variants of HLA B35 bound to an Epstein-Barr virus epitope: influence of peptide conformation and TCR-peptide interaction

Fine specificity analysis of HLA B35‐restricted Epstein‐Barr virus (EBV)‐specific cytotoxic T lymphocyte (CTL) clones revealed a unique heterogeneity whereby one group of these clones cross‐recognized an EBV epitope (YPLHEQHGM) on virus‐infected cells expressing either HLA B*3501 or HLA B*3503, while another group cross‐recognized this epitope in association with either HLA B*3502 or HLA B*3503. Peptide binding and titration studies ruled out the possibility that these differences were due to variation in the efficiency of peptide presentation by the HLA B35 alleles. Sequence analysis of the TCR genetic elements showed that these clonotypes either expressed BV12/AV3 or BV14/ADV17S1 heterodimers. Interestingly, CTL analysis with monosubstituted alanine mutants of the YPLHEQHGM epitope indicated that the BV12/AV3+ clones preferentially recognized residues towards the C terminus of the peptide, while the BV14/ADV17S1+ clones interacted with residues towards N terminus of the peptide. Molecular modelling of the MHC‐peptide complexes suggests that the differences in two floor positions (114 and 116) of the HLA B35 alleles dictate different conformations of the peptide residues L3 and/or H7 and directly contribute in the discerning allele‐specific immune recognition by the CTL clonotypes. These results provide evidence for a critical role for the selective interaction of the TCR with specific residues within the peptide epitope in the fine specificity of CTL recognition of allelic variants of an HLA molecule.

Determination of the core undergraduate BME curriculum - the 1/sup st/ step in a Delphi study

The VaNTH Engineering Research Center for Bioengineering Education Technologies has completed the first round of a Delphi study to determine the key concepts that comprise the core curriculum of undergraduate programs in biomedical engineering. The study was conducted as a Web-based survey, consisting of eighty questions divided among nineteen topics, including eleven biomedical engineering domains, four biology domains, and mathematical and scientific prerequisites. Participants included representatives from academia, industry, and young alumni of undergraduate BME programs. Results from the survey will be available at: http://www.vanth.org/curriculum/.