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Many proteins have small molecule-binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery, and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets, but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially “druggable” human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite web server is available at http://salilab.org/cryptosite.

Selecting peptides that bind strongly to the major histocompatibility complex (MHC) for inclusion in a vaccine has therapeutic potential for infections and tumors. Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding predictions. Here, we train support vector machine classifier (SVMC) models on physicochemical sequence-based and structure-based descriptor sets to predict peptide binding to a well-studied model mouse MHC I allele, H-2Db. Recursive feature elimination and two-way forward feature selection were also performed. Although low on sensitivity compared to the current state-of-the-art algorithms, models based on physicochemical descriptor sets achieve specificity and precision comparable to the most popular sequence-based algorithms. The best-performing model is a hybrid descriptor set containing both sequence-based and structure-based descriptors. Interestingly, close to half of the physicochemical sequence-based descriptors remaining in the hybrid model were properties of the anchor positions, residues 5 and 9 in the peptide sequence. In contrast, residues flanking position 5 make little to no residue-specific contribution to the binding affinity prediction. The results suggest that machine-learned models incorporating both sequence-based descriptors and structural data may provide information on specific physicochemical properties determining binding affinities.

Lactococcus lactis metabolizes glucose homofermentatively to lactate. However, after disruption of the gene coding for lactate dehydrogenase, LDH, a key enzyme in NAD^+ regeneration, the glycolytic flux shifts from homolactic to mixed-acid fermentation with the redirection of pyruvate towards production of formate, acetate, ethanol and CO"2. A mathematical model of the pyruvate metabolism pathway that enhances ethanol production was developed from in vivo Nuclear Magnetic Resonance (NMR) time-series measurements that describe the dynamics of the metabolites in L. lactis. Both Michaelis-Menten and S-system models capture the observed in vivo dynamics of the glycolysis pathway in L. lactis, while prior models describe only the in vitro dynamics. The models provide insight into the maximization of selectivity of ethanol with respect to acetate and CO"2 as undesired products in multiple reactions. High concentrations of NADH and acetyl-CoA and low concentrations of pyruvate and NAD appear to maximize ethanol selectivity.

Onconase (ONC), an amphibian member of the bovine pancreatic ribonuclease A (RNase A) superfamily, is in phase III clinical trials as a treatment for malignant mesothelioma. RNase A is a far more efficient catalyst of RNA cleavage than ONC but is not cytotoxic. The innate ability of ONC to evade the cytosolic ribonuclease inhibitor protein (RI) is likely to be a primary reason for its cytotoxicity. In contrast, the non-covalent interaction between RNase A and RI is one of the strongest known, with the RI.RNase A complex having a K(d) value in the femtomolar range. Here, we report on the use of the fast atomic density evaluation (FADE) algorithm to identify regions in the molecular interface of the RI.RNase A complex that exhibit a high degree of geometric complementarity. Guided by these "knobs" and "holes", we designed variants of RNase A that evade RI. The D38R/R39D/N67R/G88R substitution increased the K(d) value of the pRI.RNase A complex by 20 x 10(6)-fold (to 1.4 microM) with little change to catalytic activity or conformational stability. This and two related variants of RNase A were more toxic to human cancer cells than was ONC. Notably, these cytotoxic variants exerted their toxic activity on cancer cells selectively, and more selectively than did ONC. Substitutions that further diminish affinity for RI (which has a cytosolic concentration of 4 microM) are unlikely to produce a substantial increase in cytotoxic activity. These results demonstrate the utility of the FADE algorithm in the examination of protein-protein interfaces and represent a landmark towards the goal of developing chemotherapeutics based on mammalian ribonucleases.

Two methods for rapid characterization of molecular shape are presented. Both techniques are based on the density of atoms near the molecular surface. The Fast Atomic Density Evaluation (FADE) algorithm uses fast Fourier transforms to quickly estimate densities. The Pairwise Atomic Density Reverse Engineering (PADRE) method derives modified density measures from the relationship between atomic density and total potentials. While many shape-characterization techniques define shape relative to a surface, the descriptors returned by FADE and PADRE can measure local geometry from points within the three-dimensional space surrounding a molecule. The methods can be used to find crevices and protrusions near the surface of a molecule and to test shape complementarity at the interface between docking molecules.