Abstract
Rational-design methods have proven to be a valuable toolkit in the field of protein design. Numerical approaches such as free-energy calculations or QM/MM methods are fit to widen the understanding of a protein-sequence space but require large amounts of computational time and power. Here, we apply an efficient method for free-energy calculations that combines the one-step perturbation (OSP) with the third-power-fitting (TPF) approach. It is fit to calculate full free energies of binding from three different end states only. The nonpolar contribution to the free energies are calculated for a set of chosen amino acids from a single simulation of a judiciously chosen reference state. The electrostatic contributions, on the other hand, are predicted from simulations of the neutral and charged end states of the individual amino acids. We used this method to perform in silico saturation mutagenesis of two sites in human Caspase-2. We calculated relative binding free energies toward two different substrates that differ in their P1′ site and in their affinity toward the unmutated protease. Although being approximate, our approach showed very good agreement upon validation against experimental data. 76% of the predicted relative free energies of amino acid mutations was found to be true positives or true negatives. We observed that this method is fit to discriminate amino acid mutations because the rate of false negatives is very low (<1.5%). The approach works better for a substrate with medium/low affinity with a Matthews correlation coefficient (MCC) of 0.63, whereas for a substrate with very low affinity, the MCC was 0.38. In all cases, the combined TPF + OSP approach outperformed the linear interaction energy method.
Highlights
Traditional directed-evolution methods utilize a two-step protocol with an initial generation of a rich library by random mutagenesis[1] and identifying those library members that show improvements in the desired functions.[2]
We recently provided a successful example of such a rational design procedure.[7]
An efficient method for in silico saturation mutagenesis was qualitatively validated against experimental data
Summary
Traditional directed-evolution methods utilize a two-step protocol with an initial generation of a rich library by random mutagenesis[1] and identifying those library members that show improvements in the desired functions.[2]. Novel methods have advanced in recent years to substitute the random mutagenesis by knowledge-based design.[3] These modern design methods are fit to allow smaller libraries but preserve or even enhance their relevance. They do so by replacing the random components by information about the structure and function of protein sequences, usually supported by computational algorithms such as QM or MD calculations or machine-learning methods.[4−6] Rational methods can improve the productivity toward the engineered protein in two, usually in sequential steps: (1) locating potential target sites for mutation and (2) narrowing the list of possible amino acids for substitution. These alchemical methods−if done correctly−have been proven to be very accurate and reliable.[9−13] The drawback of these calculations is their high cost in terms of computational power
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
