The fully automated docking and discrimination server ClusPro can be found at http://structure.bu.edu

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.

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. © 2000 Wiley‐Liss, Inc.

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. © 2000 Wiley‐Liss, Inc.

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: first, we perform rigid-body docking using the program DOT, keeping the top 20,000 structures as ranked by surface complementarity; second, we re-rank these structures according to a free energy estimate that includes both desolvation and electrostatics and retain the top 2,000 complexes; third, we cluster the filtered complexes using a pairwise RMS deviation criterion; finally, the twenty-five 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 RMS deviations 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 h ighest 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 4 out of 7 targets, the top models predicted by our automated procedure were among the best community-wide predictions.

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 atomlevel extension of the Miyazawa-Jernigan potential parameterized on protein structures, whereas RPScore (residue 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 higher). As a general solution to the proteindocking 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; 52:000 -000.

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.

is a professor of Biomedical Engineering in the Robert R. McCormick School of Engineering and Applied Science, and of Neurobiology in the Weinberg College of Arts and Sciences, with an additional appointment in Ophthalmology. His research interests are in the role of retinal oxygen transport and metabolism in both normal conditions and diseases such as diabetic retinopathy and retinal detachment, and in bioengineering and physiology education. His teaching is largely in the area of human and animal physiology. He is the Director of the Northwestern Center for Engineering Education Research. Formerly, he was the Associate Director of the VaNTH Engineering Research Center in Bioengineering Educational Technologies, and chair of the Biomedical Engineering Department at Northwestern. He is a fellow of the American Institute of Medical and Biological Engineering, the Biomedical Engineering Society, and the Association for Research in Vision and Ophthalmology.