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John P Priestle

Publications and source records attributed to John P Priestle.

3 recordsLinked to original sources

Complementarity of hydrophobic properties in ATP-protein binding: a new criterion to rank docking solutions.

ATP is an important substrate of numerous biochemical reactions in living cells. Molecular recognition of this ligand by proteins is very important for understanding enzymatic mechanisms. Considerable insight into the problem may be gained via molecular docking simulations. At the same time, standard docking protocols are often insufficient to predict correct conformations for protein-ATP complexes. Thus, in most cases the native-like solutions can be found among the docking poses, but current scoring functions have only limited ability to discriminate them from false positives. To improve the selection of correct docking solutions obtained with the GOLD software, we developed a new ranking criterion specific for ATP-protein binding. The method is based on detailed analysis of the intermolecular interactions in 40 high-resolution 3D structures of ATP-protein complexes (the training set). We found that the most important factors governing this recognition are hydrogen-bonding, stacking between adenine and aromatic protein residues, and hydrophobic contacts between adenine and protein residues. To address the latter, we applied the formalism of 3D molecular hydrophobicity potential. The results obtained were used to construct an ATP-oriented scoring criterion as a linear combination of the terms describing these intermolecular interactions. The criterion was then validated using the test set of 10 additional ATP-protein complexes. As compared with the standard scoring functions, the new ranking criterion significantly improved the selection of correct docking solutions in both sets and allowed considerable enrichment at the top of the list containing docking poses with correct solutions.

Adenine↗

Functional profiling of recombinant NS3 proteases from all four serotypes of dengue virus using tetrapeptide and octapeptide substrate libraries.

Regulated proteolysis by the two-component NS2B/NS3 protease of dengue virus is essential for virus replication and the maturation of infectious virions. The functional similarity between the NS2B/NS3 proteases from the four genetically and antigenically distinct serotypes was addressed by characterizing the differences in their substrate specificity using tetrapeptide and octapeptide libraries in a positional scanning format, each containing 130,321 substrates. The proteases from different serotypes were shown to be functionally homologous based on the similarity of their substrate cleavage preferences. A strong preference for basic amino acid residues (Arg/Lys) at the P1 positions was observed, whereas the preferences for the P2-4 sites were in the order of Arg > Thr > Gln/Asn/Lys for P2, Lys > Arg > Asn for P3, and Nle > Leu > Lys > Xaa for P4. The prime site substrate specificity was for small and polar amino acids in P1' and P3'. In contrast, the P2' and P4' substrate positions showed minimal activity. The influence of the P2 and P3 amino acids on ground state binding and the P4 position for transition state stabilization was identified through single substrate kinetics with optimal and suboptimal substrate sequences. The specificities observed for dengue NS2B/NS3 have features in common with the physiological cleavage sites in the dengue polyprotein; however, all sites reveal previously unrecognized suboptimal sequences.

Amino Acid Sequence↗

Computational analysis of crystallization trials.

A system for the automatic categorization of the results of crystallization experiments generated by robotic screening is presented. Images from robotically generated crystallization screens are taken at preset time intervals and analyzed by the computer program Crystal Experiment Evaluation Program (CEEP). This program attempts to automatically categorize the individual crystal experiments into a number of simple classes ranging from clear drop to mountable crystal. The algorithm first selects features from the images via edge detection and texture analysis. Classification is achieved via a self-organizing neural net generated from a set of hand-classified images used as a training set. New images are then classified according to this neural net. It is demonstrated that incorporation of time-series information may enhance the accuracy of classification. Preliminary results from the screening of the proteome of Thermotoga maritima are presented showing the utility of the system.

Algorithms↗