Search

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p < 0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L.

A conserved arginine and threonine at respective sites in leucyl- (LeuRS) and isoleucyl- (IleRS) has diverged to confer amino acid substrate recognition. The site has complemented other sites in the amino acid binding pocket of the editing active site of Escherichia coli LeuRS, including [Thr.sup.252] and [Val.sup.338], which have fine-tuned amino acid specificity to confer fidelity.

The rates of the amide proton exchange (HX) were explained through the determination of the electrostatic structural effects on base-catalyzed HX rates and the calculation of the electrostatic shifts in the pKa values of amide protons. Determination of HX rates provides information on the local exposure of amide protons to solvent and on rates of structural openings. The extensive variations in the HX rates were generally attributed to systematic deviations from steric and inductive effects.

Acetylcholinesterase (AChE) is an efficient serine hydrolase containing Glu-His-Ser, a catalytic triad, mirroring those in other serin hydrolases. Studies have shown the existence of a three-pronged oxyanion hole formed by peptidic NH groups. Also quaternary ammonium binding locus components Trp84, Phe330 and Glu199 have a critical role, in the efficiency of AChE. Brownian dynamics simulations of the wild-type and Glu199 mutant of Torpedo californica acetylcholinesterase kinetics have been undertaken.

The relative configuration of the diastereoisomeric bromochlorofluoromethane (CHF-ClBr) complex was analyzed via molecular dynamics (MD) simulations, multiconfiguration thermodynamic integration (MCTI) methods. MD and MCTI analysis of the free energy difference for the binding of CHF-ClBr to (-)-crytophane-C indicated the high stability of the resulting diastereoisomeric complex. Furthermore, CHFClBr exhibited an absolute configuration of (R)-(-) or (S)-(+).

Monte Carlo simulations help investigate the effect of solvation on the free energies of the Z and E isomers of an ester. These studies indicate a preference for the stabilization of the E isomer and its enolate anion in water and acetonitrile. There is a reduction in the increase in acidity of the E ester in polar solvents. Ab initio calculations aid the development of potential functions for fluid simulations.