A wide variety of computational algorithms have been developed that strive to capture the chemical similarity between two compounds for use in virtual screening and lead discovery. One limitation of such approaches is that, while a returned similarity value reflects the perceived degree of relatedness between any two compounds, there is no direct correlation between this value and the expectation or confidence that any two molecules will in fact be equally active. A lack of a common framework for interpretation of similarity measures also confounds the reliable fusion of information from different algorithms. Here, we present a probabilistic framework for interpreting similarity measures that directly correlates the similarity value to a quantitative expectation that two molecules will in fact be equipotent. The approach is based on extensive benchmarking of 10 different similarity methods (MACCS keys, Daylight fingerprints, maximum common subgraphs, rapid overlay of chemical structures (ROCS) shape similarity, and six connectivity-based fingerprints) against a database of more than 150,000 compounds with activity data against 23 protein targets. Given this unified and probabilistic framework for interpreting chemical similarity, principles derived from decision theory can then be applied to combine the evidence from different similarity measures in such a way that both capitalizes on the strengths of the individual approaches and maintains a quantitative estimate of the likelihood that any two molecules will exhibit similar biological activity.

The temperature dependence of the unfolding kinetics of rubredoxins from the hyperthermophile Pyrococcus furiosus (RdPf) and the mesophile Clostridium pasteurianum (RdCp) has been studied. Results show that RdPf unfolds much more slowly, under all experimentally accessible temperature regimes, than RdCp and other typical mesophilic proteins. Rates of RdCp and RdPf unfolding decrease upon increasing the pH above 2 and diverge dramatically at pH 7. As shown by detailed electrostatic energy calculations, this is the result of a differential degree of protonation of the negatively charged amino acids, which causes distinct electrostatic configurations as a function of pH. We propose that ion pairs, particularly those that are placed in key surface positions, may play a kinetic role by mildly clamping the protein and thereby influencing the nature and the number of the vibrational normal modes that are thermally accessible upon unfolding. More generally, these modes are also likely to be affected by the favorable electrostatic configurations, which we have shown to be directly linked to the extremely slow unfolding rates of RdPf at neutral pH. Even at pH 2, in the absence of any salt bridges, the unfolding rates of RdPf are much smaller than those of RdCp. This is ascribed to presently unidentified structural elements of nonelectrostatic nature. Since electrostatic effects influence the unfolding kinetics of both mesophilic and thermophilic rubredoxins, these findings may be of general significance for proteins.

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