An effective approach for ab initio calculations of activation free energies of enzymatic reactions is developed and examined. This approach uses an empirical valence bond (EVB) potential surface as a reference potential for evaluating the free energies of a hybrid ab initio quantum mechanics/molecular mechanics (QM(ai)/MM) potential surface. This procedure involves an automated calibration of the EVB potential using gas-phase ab initio calculations. In addition, strategies for treating the contact region of QM and MM atoms as well as enzyme and solvent environments are developed. Two levels of ab initio calculations are used in studying the QM atoms:  the HF/4-31G method, which allows calculations on a large number of points while still giving accurate results, and the MP2/6-31+G* approach. The QM(ai)/MM method is implemented and examined by simulating the nucleophilic attack step in the catalytic reaction of subtilisin. It is found that the use of the EVB potential as a reference allows one to obtain the actual ab initio activation free energies of enzymatic reactions. Possible powerful simplifications such as the use of the ab initio intermolecular electrostatic energy are discussed, and the advantage of focusing on the difference between the reaction in protein and solution is demonstrated.

We present a combined computational and experimental method for the rapid optimization of proteins. Using ␤-lactamase as a test case, we redesigned the active site region using our Protein Design Automation technology as a computational screen to search the entire sequence space. By eliminating sequences incompatible with the protein fold, Protein Design Automation rapidly reduced the number of sequences to a size amenable to experimental screening, resulting in a library of Ϸ200,000 mutants. These were then constructed and experimentally screened to select for variants with improved resistance to the antibiotic cefotaxime. In a single round, we obtained variants exhibiting a 1,280-fold increase in resistance. To our knowledge, all of the mutations were novel, i.e., they have not been identified as beneficial by random mutagenesis or DNA shuffling or seen in any of the naturally occurring TEM ␤-lactamases, the most prevalent type of Gram-negative ␤-lactamases. This combined approach allows for the rapid improvement of any property that can be screened experimentally and provides a powerful broadly applicable tool for protein engineering.computational protein design ͉ protein engineering ͉ mutagenesis ͉ directed evolution ͉ ␤-lactamase

In this paper, we describe an in silico first principal approach to predict the mutagenic potential of primary aromatic amines. This approach is based on the so-called "nitrenium hypothesis", which was developed by Ford et al. in the early 1990s. This hypothesis asserts that the mutagenic effect for this class of molecules is mediated through the transient formation of a nitrenium ion and that the stability of this cation is correlated with the mutagenic potential. Here we use quantum mechanical calculations at different levels of theory (semiempirical AM1, ab initio HF/3-21G, HF/6-311G(d,p), and DFT/B3LYP/6-311G(d,p)) to compute the stability of nitrenium ions. When applied to a test set of 257 primary aromatic amines, we show that this method can correctly differentiate between Ames active and inactive compounds, and furthermore that it is able to rationalize and predict SAR trends within structurally related chemical series. For this test set, the AM1 nitrenium stability calculations are found to provide a good balance between speed and accuracy, resulting in an overall accuracy of 85%, and sensitivity and specificity of 91% and 72%, respectively. The nitrenium-based predictions are also compared to the commercial software packages DEREK, MULTICASE, and the MOE-Toxicophore descriptor. One advantage of the approach presented here is that the calculation of relative stabilities results in a continuous spectrum of activities and not a simple yes/no answer. This allows us to observe and rationalize subtle trends due to the different electrostatic properties of the organic molecules. Our results strongly indicate that nitrenium ion stability calculations should be used as a complementary approach to assist the medicinal chemist in prioritizing and selecting nonmutagenic primary aromatic amines during preclinical drug discovery programs.