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Biomedical subjects

B B Braunheim

Publications and source records attributed to B B Braunheim.

4 recordsLinked to original sources
PubMed2000

Neural network methods for identification and optimization of quantum mechanical features needed for bioactivity.

This paper presents a new approach to the discovery and design of bioactive compounds. The focus of this application will be on the analysis of enzymatic inhibitors. At present the discovery of enzymatic inhibitors for therapeutic use is often accomplished through random searches. The first phase of discovery is a random search through a large pre-fabricated chemical library. Many molecules are tested with refined enzyme for signs of inhibition. Once a group of lead compounds have been discovered the chemical intuition of biochemists is used to find structurally related compounds that are more effective. This step requires new molecules to be conceived and synthesized, and it is the most time-consuming and expensive step. The development of computational and theoretical methods for prediction of the molecular structure that would bind most tightly prior to synthesis and testing, would facilitate the design of novel inhibitors. In the past, our work has focused on solving the problem of predicting the bioactivity of a molecule prior to synthesis. We used a neural network trained with the bioactivity of known compounds to predict the bioactivity of unknown compounds. In our current work, we use a separate neural network in conjunction with a trained neural network in an attempt to gain insight as to how to modify existing compounds and increase their bioactivity.

Enzyme Inhibitors↗
PubMed2000

Prokink: a protocol for numerical evaluation of helix distortions by proline.

Proline residues are known to perturb the structure of helices by introducing a kink between the segments preceding and following the proline residue. The distortion of the helical structure results from the avoided steric clash between the ring of the proline at position (i) and the backbone carbonyl at position (i - 4), as well as the elimination of helix backbone H-bonds for the carbonyls at positions (i - 3) and (i - 4). Both the departure from the ideal helical pattern and the reduction in H-bond stabilization contribute to the observed flexibility of a proline-containing alpha-helix. The special local flexibility of the proline kink can confer an important role on the proline-containing helix in the conformational changes related to the function of the protein. As a useful tool in determining and evaluating the role of proline-induced flexibility and distortions in protein function, we present here a protocol to quantify the geometry of the distortion introduced in helices by prolines both as a time-averaged value and for individual 'snapshots' along a molecular dynamics simulation.

Algorithms↗
PubMed1999

Prediction of inhibitor binding free energies by quantum neural networks. Nucleoside analogues binding to trypanosomal nucleoside hydrolase.

A computational method has been developed to predict inhibitor binding energy for untested inhibitor molecules. A neural network is trained from the electrostatic potential surfaces of known inhibitors and their binding energies. The algorithm is then able to predict, with high accuracy, the binding energy of unknown inhibitors. IU-nucleoside hydrolase from Crithidia fasciculata and the inhibitor molecules described previously [Miles, R. W. Tyler, P. C. Evans, G. Furneaux R. H., Parkin, D. W., and Schramm, V. L. (1999) Biochemistry 38, xxxx-xxxx] are used as the test system. Discrete points on the molecular electrostatic potential surface of inhibitor molecules are input to neural networks to identify the quantum mechanical features that contribute to binding. Feed-forward neural networks with back-propagation of error are trained to recognize the quantum mechanical electrostatic potential and geometry at the entire van der Waals surface of a group of training molecules and to predict the strength of interactions between the enzyme and novel inhibitors. The binding energies of unknown inhibitors were predicted, followed by experimental determination of K(i)() values. Predictions of K(i)() values using this theory are compared to other methods and are more robust in estimating inhibitory strength. The average deviation in estimating K(i)() values for 18 unknown inhibitor molecules, with 21 training molecules, is a factor of 5 x K(i)() over a range of 660 000 in K(i)() values for all molecules. The a posteriori accuracy of the predictions suggests the method will be effective as a guide for experimental inhibitor design.

Animals↗