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An analysis of the growth inhibitory potency of 122 anticancer agents available from the National Cancer Institute anticancer drug screen is presented. Methods of singular value decomposition (SVD) were applied to determine the matrix of distances between all compounds. These SVD-derived dissimilarity distances were used to cluster compounds that exhibit similar tumor growth inhibitory activity patterns against 60 human cancer cell lines. Cluster analysis divides the 122 standard agents into 25 statistically distinct groups. The first eight groups include structurally diverse compounds with reactive functionalities that act as DNA-damaging agents while the remaining 17 groups include compounds that inhibit nucleic acid biosynthesis and mitosis. Examination of the average activity patterns across the 60 tumor cell lines reveals unique 'fingerprints' associated with each group. A diverse set of structural features are observed for compounds within these groups, with frequent occurrences of strong within-group structural similarities. Clustering of cell types by their response to the 122 anticancer agents divides the 60 cell types into 21 groups. The strongest within-panel groupings were found for the renal, leukemia and ovarian cell panels. These results contribute to the basis for comparisons between log(GI(50)) screening patterns of the 122 anticancer agents and additional tested compounds.

A number of recent studies called attention to the presence of kinetically important residues underlying the formation and stabilization of folding nuclei in proteins, and to the possible existence of a correlation between conserved residues and those participating in the folding nuclei. Here, we use the Gaussian network model (GNM), which recently proved useful in describing the dynamic characteristics of proteins for identifying the kinetically hot residues in folded structures. These are the residues involved in the highest frequency fluctuations near the native state coordinates. Their high frequency is a manifestation of the steepness of the energy landscape near their native state positions. The theory is applied to a series of proteins whose kinetically important residues have been extensively explored: chymotrypsin inhibitor 2, cytochrome c, and related C2 proteins. Most of the residues previously pointed out to underlie the folding process of these proteins, and to be critically important for the stabilization of the tertiary fold, are correctly identified, indicating a correlation between the kinetic hot spots and the early forming structural elements in proteins. Additionally, a strong correlation between kinetically hot residues and loci of conserved residues is observed. Finally, residues that may be important for the stability of the tertiary structure of CheY are proposed.

The role of hydrophobicity as a determinant of protein-protein interactions is examined. Surfaces of apo-protein targets comprising 9 classes of enzymes, 7 antibody fragments, hirudin, growth hormone, and retinol-binding protein, and their associated ligands with available X-ray structures for their complexed forms, are scanned to determine clusters of surface-accessible amino acids. Clusters of surface residues are ranked on the basis of the hydrophobicity of their constituent amino acids. The results indicate that the location of the co-crystallized ligand is commonly found to correspond with one of the strongest hydrophobic clusters on the surface of the target molecule. In 25 of 38 cases, the correspondence is exact, with the position of the most hydrophobic cluster coinciding with more than one-third of the surface buried by the bound ligand. The remaining 13 cases demonstrate this correspondence within the top 6 hydrophobic clusters. These results suggest that surface hydrophobicity can be used to identify regions of a protein's surface most likely to interact with a binding ligand. This fast and simple procedure may be useful for identifying small sets of well-defined loci for possible ligand attachment.