Quantitative Detection of Nucleoside Analogues by Multi-enzyme Biosensors using Time-Resolved Kinetic Measurements.

Fast, simple and cost-effective methods for detecting and quantifying pharmaceutical agents in patients are highly sought after to replace equipment and labor-intensive analytical procedures. The development of new diagnostic technology including portable detection devices also enables point-of-care by non-specialists in resource-limited environments. We have focused on the detection and dose monitoring of nucleoside analogues used in viral and cancer therapies. Using deoxyribonucleoside kinases (dNKs) as biosensors, our chemometric model compares observed time-resolved kinetics of unknown analytes to known substrate interactions across multiple enzymes. The resulting dataset can simultaneously identify and quantify multiple nucleosides and nucleoside analogues in complex sample mixtures.

Redesigning human 2'-deoxycytidine kinase enantioselectivity for L-nucleoside analogues as reporters in positron emission tomography.

Recent advances in nuclear medicine have allowed for positron emission tomography (PET) to track transgenes in cell-based therapies using PET reporter gene/probe pairs. A promising example for such reporter gene/probe pairs are engineered nucleoside kinases that effectively phosphorylate isotopically labeled nucleoside analogues. Upon expression in target cells, the kinase facilitates the intracellular accumulation of radionuclide monophosphate, which can be detected by PET imaging. We have employed computational design for the semi-rational engineering of human 2'-deoxycytidine kinase to create a reporter gene with selectivity for L-nucleosides including L-thymidine and 1-(2'-fluoro-5-methyl-ß-L-arabinofuranosyl) uracil. Our design strategy relied on a combination of preexisting data from kinetic and structural studies of native kinases, as well as two small, focused libraries of kinase variants to generate an in silico model for assessing the effects of single amino acid changes on favorable activation of L-nucleosides over their corresponding D-enantiomers. The approach identified multiple amino acid positions distal to the active site that conferred desired L-enantioselectivity. Recombination of individual amino acid substitutions yielded orthogonal kinase variants with significantly improved catalytic performance for unnatural L-nucleosides but reduced activity for natural D-nucleosides.

Blind prediction of interfacial water positions in CAPRI.

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27.

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.

Novel protease inhibitors via computational redesign of subtilisin BPN' propeptide.

The propeptide domain of subtilisin BPN' functions as a molecular chaperone for its cognate protease yet quickly assumes a predominantly unfolded structure following cleavage by the mature protease. In contrast, structural stabilization of the propeptide domain has been proposed to competitively inhibit protease self-cleavage, suggesting the possibility for the generation of novel proteinaceous subtilisin inhibitors. Using a Rosetta fixed backbone design, we have redesigned the subtilisin BPN' propeptide structure to generate synthetic peptide sequences with increased and tunable structural stability. Molecular dynamics simulations provide supporting evidence that the artificial sequences retain structure without its protease cognate unlike the inherently disordered wild-type propeptide. Experimental evaluation of two designer domains by spectroscopic methods verified their structural integrity. Furthermore, the novel propeptide domains were shown to possess significantly enhanced thermostability. Nevertheless, their modest functional performance as protease inhibitors raises doubt that propeptide stability alone is sufficient for effective inhibitor design.

Benchmarking and analysis of protein docking performance in Rosetta v3.2.

RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 'other' complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of 'other' targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction.