The turnover of a tissue specific cell surface ligand which inhibits lectin induced capping

Ten-day-old embryonic chick neural retina release into the environment glycoprotein ligands which bind to homologous cells, inhibiting the lectin-induced redistribution of cell surface receptors. Material with identical activity is released from trypsin-dissociated neural retina cells that are allowed to repair in culture for 2 h and are then transferred to fresh medium. Release of ligand is inhibited by cytosine arabinoside, hydroxyurea, UDP, and EDTA, and is potentiated by MnCl2. These data suggest that a glycosyltransferase reaction plays a critical role in the turnover of the cell surface ligand. Reactivation of enzymatically deglycosylated ligand solutions by intact cells provides further support for this hypothesis. Release of ligand is also accompanied by a loss of the agglutinability of the cells by a tissue-specific component which accumulates in monolayer conditioned medium. Conditions which inhibit release maintain maximal agglutinability suggesting similar mechanisms mediate both processes.     

The minimized dead-end elimination criterion and its application to protein redesign in a hybrid scoring and search algorithm for computing partition functions over molecular ensembles

One of the main challenges for protein redesign is the efficient evaluation of a combinatorial number of candidate structures. The modeling of protein flexibility, typically by using a rotamer library of commonly-observed low-energy side-chain conformations, further increases the complexity of the redesign problem. A dominant algorithm for protein redesign is dead-end elimination (DEE), which prunes the majority of candidate conformations by eliminating rigid rotamers that provably are not part of the global minimum energy conformation (GMEC). The identified GMEC consists of rigid rotamers (i.e., rotamers that have not been energy-minimized) and is thus referred to as the rigid-GMEC. As a postprocessing step, the conformations that survive DEE may be energy-minimized. When energy minimization is performed after pruning with DEE, the combined protein design process becomes heuristic, and is no longer provably accurate: a conformation that is pruned using rigid-rotamer energies may subsequently minimize to a lower energy than the rigid-GMEC. That is, the rigid-GMEC and the conformation with the lowest energy among all energy-minimized conformations (the minimized-GMEC) are likely to be different. While the traditional DEE algorithm succeeds in not pruning rotamers that are part of the rigid-GMEC, it makes no guarantees regarding the identification of the minimized-GMEC. In this paper we derive a novel, provable, and efficient DEE-like algorithm, called minimized-DEE (MinDEE), that guarantees that rotamers belonging to the minimized-GMEC will not be pruned, while still pruning a combinatorial number of conformations. We show that MinDEE is useful not only in identifying the minimized-GMEC, but also as a filter in an ensemble-based scoring and search algorithm for protein redesign that exploits energy-minimized conformations. We compare our results both to our previous computational predictions of protein designs and to biological activity assays of predicted protein mutants. Our provable and efficient minimized-DEE algorithm is applicable in protein redesign, protein-ligand binding prediction, and computer-aided drug design.     

Efficient a priori identification of drug resistant mutations using Dead-End Elimination and MM-PBSA

Active site mutations that disrupt drug binding are an important mechanism of drug resistance. Computational methods capable of predicting resistance a priori are poised to become extremely useful tools in the fields of drug discovery and treatment design. In this paper, we describe an approach to predicting drug resistance on the basis of Dead-End Elimination and MM-PBSA that requires no prior knowledge of resistance. Our method utilizes a two-pass search to identify mutations that impair drug binding while maintaining affinity for the native substrate. We use our method to probe resistance in four drug-target systems: isoniazid-enoyl-ACP reductase (tuberculosis), ritonavir-HIV protease (HIV), methotrexate-dihydrofolate reductase (breast cancer and leukemia), and gleevec-ABL kinase (leukemia). We validate our model using clinically known resistance mutations for all four test systems. In all cases, the model correctly predicts the majority of known resistance mutations.     

Applying medicinal chemistry transformations and multiparameter optimization to guide the search for high-quality leads and candidates

In this article we describe a computational method that automatically generates chemically relevant compound ideas from an initial molecule, closely integrated with in silico models, and a probabilistic scoring algorithm to highlight the compound ideas most likely to satisfy a user-defined profile of required properties. The new compound ideas are generated using medicinal chemistry 'transformation rules' taken from examples in the literature. We demonstrate that the set of 206 transformations employed is generally applicable, produces a wide range of new compounds, and is representative of the types of modifications previously made to move from lead-like to drug-like compounds. Furthermore, we show that more than 94% of the compounds generated by transformation of typical drug-like molecules are acceptable to experienced medicinal chemists. Finally, we illustrate an application of our approach to the lead that ultimately led to the discovery of duloxetine, a marketed serotonin reuptake inhibitor.     

Virtual decoy sets for molecular docking benchmarks

Virtual docking algorithms are often evaluated on their ability to separate active ligands from decoy molecules. The current state-of-the-art benchmark, the Directory of Useful Decoys (DUD), minimizes bias by including decoys from a library of synthetically feasible molecules that are physically similar yet chemically dissimilar to the active ligands. We show that by ignoring synthetic feasibility, we can compile a benchmark that is comparable to the DUD and less biased with respect to physical similarity.     

Determinants of new industrial product performance: a strategicre-examination of the empirical literature

Research on the determinants of industrial innovation performance using a three-dimensional framework is examined. Those dimensions are: generality over innovations, decision focus, and managerial controllability. The major determinants identified are: strategic and organizational factors, including general management's support, business-project fit, and R&D-marketing interaction; R&D and production factors, including product superiority, experience and synergy effect, user benefit of the product, and patent protection; and market and environmental factors, including degree of competition and market growth. An empirical study of 112 industrial products confirms that dynamic interaction exists between these determinants and the launch time of the product     

Optimization and dynamics of protein-protein complexes using B-splines

A moving-grid approach for optimization and dynamics of protein-protein complexes is introduced, which utilizes cubic B-spline interpolation for rapid energy and force evaluation. The method allows for the efficient use of full electrostatic potentials joined smoothly to multipoles at long distance so that multiprotein simulation is possible. Using a recently published benchmark of 58 protein complexes, we examine the performance and quality of the grid approximation, refining cocrystallized complexes to within 0.68 A RMSD of interface atoms, close to the optimum 0.63 A produced by the underlying MMFF94 force field. We quantify the theoretical statistical advantage of using minimization in a stochastic search in the case of two rigid bodies, and contrast it with the underlying cost of conjugate gradient minimization using B-splines. The volumes of conjugate gradient minimization basins of attraction in cocrystallized systems are generally orders of magnitude larger than well volumes based on energy thresholds needed to discriminate native from nonnative states; nonetheless, computational cost is significant. Molecular dynamics using B-splines is doubly efficient due to the combined advantages of rapid force evaluation and large simulation step sizes. Large basins localized around the native state and other possible binding sites are identifiable during simulations of protein-protein motion. In addition to providing increased modeling detail, B-splines offer new algorithmic possibilities that should be valuable in refining docking candidates and studying global complex behavior.     

Pessimistic quasipartitioning protocols for distributed databasesystems

A communication link failure can result in a network partitioning that fragments a distributed database system into isolated parts. If a severed high-speed link (e.g. satellite link) between the partitions can be replaced by a much slower backup link (e.g. a dial-up telephone line), the partitioning becomes a quasipartitioning. Two protocols for transaction processing in quasipartitioned databases are proposed. The protocols are pessimistic in that they permit transactions to be updated in exactly one partition. The first protocol is defined for a fully partition-replicated database in which every partition contains a copy of every data object. The second protocol is defined for a partially partition-replicated database in which some objects have no copies in some partitions. Both protocols improve their major performance measures linearly with the backup link speed but are not visibly affected by duration of the partitioning or the database size     

Allosteric inhibition of the protein-protein interaction between the leukemia-associated proteins Runx1 and CBFbeta

The two subunits of core binding factor (Runx1 and CBFbeta) play critical roles in hematopoiesis and are frequent targets of chromosomal translocations found in leukemia. The binding of the CBFbeta-smooth muscle myosin heavy chain (SMMHC) fusion protein to Runx1 is essential for leukemogenesis, making this a viable target for treatment. We have developed inhibitors with low micromolar affinity which effectively block binding of Runx1 to CBFbeta. NMR-based docking shows that these compounds bind to CBFbeta at a site displaced from the binding interface for Runx1, that is, these compounds function as allosteric inhibitors of this protein-protein interaction, a potentially generalizable approach. Treatment of the human leukemia cell line ME-1 with these compounds shows decreased proliferation, indicating these are good candidates for further development.