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.     

A host-guest optical sensor for aliphatic amines based on lipophilic cyclodextrin

A host-guest optical sensor for the determination of aliphatic amines as exemplified by octylamine is proposed. It is based on the reversible fluorescence enhancement of heptakis(2,6-di-O-isobutyl)-β-cyclodextrin(DOB-β-CD) hosting tetraphenylporphyrin (TPP) immobilized in poly(vinyl chloride) (PVC) membrane by aliphatic amine extracted from aqueous phase into membrane phase. The optimum membrane contained 1.15 wt % TPP, 6.15 wt % DOB-β-CD as sensing reagent and other membrane materials. The fluorescence enhancement of the membrane resulted from the formation of a stable three-component complex among DOB-β-CD, TPP, and aliphatic amines. With the optimum conditions described, the fluorescence response of the sensor to octylamine shows a good correlation with the theoretically derived equation in the range 1.0 × 10^–6 to 8.0 × 10^–4 mol/L. The response characteristics including reversibility, response time, reproducibility and lifetime and selectivity of this optical device are also discussed in detail. This sensor has also been applied for the determination of octylamine in water samples containing interferents with satisfactory recovery.     

Synthetic magnetic opals

We present studies of novel nanocomposites of BiNi impregnated into the structure of opals as well as inverse opals. Atomic force microscopy and high resolution elemental analyses show a highly ordered structure and uniform distribution of the BiNi filler in the matrix. These BiNi-based nanocomposites are found to exhibit distinct ferromagnetic-like ordering with transition temperature of about 675 K. As far as we know there exists no report in literature on any BiNi compound which is magnetic.     

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.     

A host-guest optical sensor for aliphatic amines based on lipophilic cyclodextrin

A host-guest optical sensor for the determination of aliphatic amines as exemplified by octylamine is proposed. It is based on the reversible fluorescence enhancement of heptakis(2,6-di-O-isobutyl)-β-cyclodextrin(DOB-β-CD) hosting tetraphenylporphyrin (TPP) immobilized in poly(vinyl chloride) (PVC) membrane by aliphatic amine extracted from aqueous phase into membrane phase. The optimum membrane contained 1.15 wt % TPP, 6.15 wt % DOB-β-CD as sensing reagent and other membrane materials. The fluorescence enhancement of the membrane resulted from the formation of a stable three-component complex among DOB-β-CD, TPP, and aliphatic amines. With the optimum conditions described, the fluorescence response of the sensor to octylamine shows a good correlation with the theoretically derived equation in the range 1.0 × 10^–6 to 8.0 × 10^–4 mol/L. The response characteristics including reversibility, response time, reproducibility and lifetime and selectivity of this optical device are also discussed in detail. This sensor has also been applied for the determination of octylamine in water samples containing interferents with satisfactory recovery. Received: 21 November 1999 / Revised: 10 January 2000 / Accepted: 15 January 2000     

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.     

Restricted dead‐end elimination: Protein redesign with a bounded number of residue mutations

Dead‐end elimination (DEE) has emerged as a powerful structure‐based, conformational search technique enabling computational protein redesign. Given a protein with n mutable residues, the DEE criteria guide the search toward identifying the sequence of amino acids with the global minimum energy conformation (GMEC). This approach does not restrict the number of permitted mutations and allows the identified GMEC to differ from the original sequence in up to n residues. In practice, redesigns containing a large number of mutations are often problematic when taken into the wet‐lab for creation via site‐directed mutagenesis. The large number of point mutations required for the redesigns makes the process difficult, and increases the risk of major unpredicted and undesirable conformational changes. Preselecting a limited subset of mutable residues is not a satisfactory solution because it is unclear how to select this set before the search has been performed. Therefore, the ideal approach is what we define as the κ‐restricted redesign problem in which any κ of the n residues are allowed to mutate. We introduce restricted dead‐end elimination (rDEE) as a solution of choice to efficiently identify the GMEC of the restricted redesign (the κGMEC). Whereas existing approaches require n‐choose‐κ individual runs to identify the κGMEC, the rDEE criteria can perform the redesign in a single search. We derive a number of extensions to rDEE and present a restricted form of the A* conformation search. We also demonstrate a 10‐fold speed‐up of rDEE over traditional DEE approaches on three different experimental systems. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010