A coherent framework is presented for determining the free energy change accompanying ligand binding to protein receptors. The most important new feature of the method is the contribution of the flexibility of the free ligand, and hence its conformational change on binding, to the free energy. Flexibility introduces two additional terms in the free energy difference: the internal energy difference between the ligand in the bound and free states and the backbone entropy loss. The former requires taking explicit account of the difference in solvation of the various forms of the free ligand. The solvation free energy change is estimated using an atomic solvation parameter model [Eisenberg & Mclachlan (1986) Nature 319, 199-203], with an improved parameter set. In order to evaluate the method, we applied it to three data sets for which increasingly general methods are required. The set to which the most restrictive theory can be applied consists of eight crystallized endopeptidase--protein inhibitor complexes which do not change conformation on binding and for which the major contribution to the solvation free energy is entropic. The results are in good agreement with the measured values and somewhat better than those previously reported in the literature. The second data set compares the relative binding free energies of biotin and its analogs for streptavidin. In this case the structures are also rigid, but solvation free energy must include both enthalpic and entropic components. We find that differential free energy predictions are approximately the same as those obtained by free energy perturbation techniques. The final application is an analysis of the measured stabilities of 13 different MHC receptor-peptide complexes. In this case we show that flexibility contributes 30-50% of the free energy change and find a correlation of 0.88 between our predicted free energies and peptide dissociation times.

Glycosylation is the most versatile and one of the most abundant protein modifications. It has a structural role as well as diverse functional roles in many specific biological functions, including cancer development, viral and bacterial infections, and autoimmunity. The diverse roles of glycosylation in biological processes are rapidly growing areas of research, however, Glycobiology research is limited by the lack of a technology for rapid analysis of glycan composition of glycoproteins. Currently used methods for glycoanalysis are complex, typically requiring high levels of expertise and days to provide answers, and are not readily available to all researcher.We have developed a lectin array-based method, Qproteome™ GlycoArray kits, for rapid analysis of glycosylation profiles of glycoproteins. Glycoanalysis is performed on intact glycoproteins, requiring only 4-6 h for most analysis types. The method, demonstrated in this manuscript by several examples, is based on binding of an intact glycoprotein to the arrayed lectins, resulting in a characteristic fingerprint that is highly sensitive to changes in the protein's glycan composition. The large number of lectins, each with its specific recognition pattern, ensures high sensitivity to changes in the glycosylation pattern. A set of proprietary algorithms automatically interpret the fingerprint signals to provide a comprehensive glycan profile output.

Docking and design are the major computational steps toward understanding and affecting receptor-ligand interactions. The flexibility of many ligands makes these calculations difficult and requires the development and use of special methods. The need for such tools is illustrated by two examples: the design of protease inhibitors and the analysis and design of peptide antigens binding to specific MHC receptors. We review the computational concepts that have been extended from rigid-body to flexible docking, as well as the following important strategies for flexible docking and design: (a) Monte Carlo/molecular dynamics docking, (b) in-site combinatorial search, (c) ligand build-up, and (d) site mapping and fragment assembly. The use of empirical free energy as a target function is discussed. Due to the rapid development of the methodology, most new methods have been tested on only a limited number of applications and are likely to improve results obtained by more traditional computational or graphic tools.

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We recently developed a rapid loop closure algorithm in which bond lengths are scaled to constrain the ends of a segment to match a known distance and then gradually relaxed to their standard values, with boundary constraints maintained. Although the algorithm predicted the Zif286 zinc-finger loop to within approximately 2 A, it had a serious limitation that made its more general use tentative: it omitted the atomic environment of the loop. Here we report an extension of the algorithm to take into account the protein environment surrounding a given loop from the outset of the conformational search and show that it predicts structure with an efficiency and accuracy that could not be achieved without continuous environmental inclusion. The algorithm should be widely applicable to structure determination when complete experimental information is unavailable.