We present a fast computational method to efficiently screen enzyme activity. In the presented method, the effect of mutations on the barrier height of an enzyme-catalysed reaction can be computed within 24 hours on roughly 10 processors. The methodology is based on the PM6 and MOZYME methods as implemented in MOPAC2009, and is tested on the first step of the amide hydrolysis reaction catalyzed by the Candida Antarctica lipase B (CalB) enzyme. The barrier heights are estimated using adiabatic mapping and shown to give barrier heights to within 3 kcal/mol of B3LYP/6-31G(d)//RHF/3-21G results for a small model system. Relatively strict convergence criteria (0.5 kcal/(molÅ)), long NDDO cutoff distances within the MOZYME method (15 Å) and single point evaluations using conventional PM6 are needed for reliable results. The generation of mutant structures and subsequent setup of the semiempirical calculations are automated so that the effect on barrier heights can be estimated for hundreds of mutants in a matter of weeks using high performance computing.
Our previously presented method for high throughput computational screening of mutant activity (Hediger et al., 2012) is benchmarked against experimentally measured amidase activity for 22 mutants of Candida antarctica lipase B (CalB). Using an appropriate cutoff criterion for the computed barriers, the qualitative activity of 15 out of 22 mutants is correctly predicted. The method identifies four of the six most active mutants with ≥3-fold wild type activity and seven out of the eight least active mutants with ≤0.5-fold wild type activity. The method is further used to screen all sterically possible (386) double-, triple- and quadruple-mutants constructed from the most active single mutants. Based on the benchmark test at least 20 new promising mutants are identified.
We present a web interface which allows us to conveniently set up calculations based on the BioFET-SIM model. With the interface, the signal of a BioFET sensor can be calculated depending on its parameters, as well as the signal dependence on pH. As an illustration, two case studies are presented. In the first case, a generic peptide with opposite charges on both ends is inverted in orientation on a semiconducting nanowire surface leading to a corresponding change in sign of the computed sensitivity of the device. In the second case, the binding of an antibody/antigen complex on the nanowire surface is studied in terms of orientation and analyte/nanowire surface distance. We demonstrate how the BioFET-SIM web interface can aid in the understanding of experimental data and postulate alternative ways of antibody/antigen orientation on the nanowire surface.
We present a semi-empirical (PM6-based) computational method for systematically estimating the effect of all possible single mutants, within a certain radius of the active site, on the barrier height of an enzymatic reaction. The intent of this method is not a quantitative prediction of the barrier heights, but rather to identify promising mutants for further computational or experimental study. The method is applied to identify promising single and double mutants of Bacillus circulans xylanase (BCX) with increased hydrolytic activity for the artificial substrate ortho-nitrophenyl β-xylobioside (ONPX2). The estimated reaction barrier for wild-type (WT) BCX is 18.5 kcal/mol, which is in good agreement with the experimental activation free energy value of 17.0 kcal/mol extracted from the observed kcat using transition state theory (Joshi et al., 2001). The PM6 reaction profiles for eight single point mutations are recomputed using FMO-MP2/PCM/6-31G(d) single points. PM6 predicts an increase in barrier height for all eight mutants while FMO predicts an increase for six of the eight mutants. Both methods predict that the largest change in barrier occurs for N35F, where PM6 and FMO predict a 9.0 and 15.8 kcal/mol increase, respectively. We thus conclude that PM6 is sufficiently accurate to identify promising mutants for further study. We prepared a set of all theoretically possible (342) single mutants in which every amino acid of the active site (except for the catalytically active residues E78 and E172) was mutated to every other amino acid. Based on results from the single mutants we construct a set of 111 double mutants consisting of all possible pairs of single mutants with the lowest barrier for a particular position and compute their reaction profile. None of the mutants have, to our knowledge, been prepared experimentally and therefore present experimentally testable predictions.
Biosensors based on nanowire field effect transistor (FET) have received much attention in recent years as a way to achieve ultra-sensitive and label-free sensing of molecules of biological interest. The BioFET-SIM computer model permits the analysis and interpretation of experimental sensor signals through its web-based interface www.biofetsim.org. The model also allows for redictions of the effects of changes in the experimental setup on the sensor signal. After an introduction to nanowire-based FET biosensors, this chapter reviews the theoretical basis of BioFET-SIM models describing both single and multiple charges on the analyte. Afterwards the usage of the interface and its relative command line version is briefly shown. Finally, possible applications of the BioFET-SIM model are presented. Among the possible uses of the interface, the effects on the predicted signal of pH, buffer ionic strength, analyte concentration, and analyte relative orientation on nanowire surface are illustrated. Wherever possible, a comparison to experimental data available in literature is given, displaying the potential of BioFETSIM for interpreting experimental results
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