Similarly, in atoms are decoupled from all of those other operational program

Similarly, in atoms are decoupled from all of those other operational program. aftereffect of mutations on both binding affinity as well as the structural balance. Importantly, we incorporate multiple ways of faithfully estimation the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design. Subject terms: Computational chemistry, Molecular dynamics Introduction Alchemical free energy perturbation (FEP) simulation1 is a rigorous physics-based method to calculate the free energy difference between distinct chemical systems. Due to recent technological advancement, FEP is Thalidomide Mouse monoclonal to CHK1 now capable of accurately predicting relative binding affinities2C4 and has found increasingly more applications in drug development. Antibodies are proteins produced by the immune system to bind specific antigenic proteins and thereby neutralize the antigen. Therapeutically, antibodies can be designed and manufactured as Thalidomide effective medicines against the infection. Recently, FEP has been successfully applied to predict the relative binding affinity between antibody and antigen5,6, thus potentially facilitating antibody design7C9 and optimization. In our ongoing battle against the current COVID19 pandemic10, we aim to develop antibodies to neutralize SARS-CoV-2 and other coronaviruses. Toward this goal, we introduced FEP simulations as one of the computational tools for our multidisciplinary team. Specifically, the basic task of FEP is to predict the change in the binding affinity due to proposed mutations on the antibody. In our workflow, batches of mutations are routinely proposed for evaluation, and quick turnarounds are necessary for further decision making. Such requirements necessitate automated processing of many FEP calculations. In this situation, it is not feasible Thalidomide to manually examine all the simulations individually and identify potential problems therein. Therefore, a faithful and automated estimation of the uncertainties in such calculations is especially important, as it could provide the level of confidence for the FEP results when data from many different sources are integrated to inform the decision making. With these requirements in mind, we implemented automated protocols for performing FEP calculations with Hamiltonian replica exchange11 using the Amber software package12, along with an uncertainty estimation that incorporates a number of factors in the analysis of the simulations. Whereas the binding affinity to antigen is a critical component for the efficacy of an antibody, the structural stability is important for the developability13, such as manufacture, storage, and distribution of the antibody. Therefore, computational stability evaluation14 is desired in our antibody design. Recent studies demonstrated that FEP15C18 and other computational approaches19C24 could provide reasonable predictions for the relative stability of protein mutants. In this project, we also incorporated stability prediction by FEP calculations, such that our FEP protocols evaluate the effect of proposed mutation on both the binding affinity and the conformational stability of the antibody. In this report, we take the m396 antibody25 as a case study to demonstrate our FEP protocols. m396 is known to neutralize the coronavirus SARS-CoV-1 by binding to the receptor binding domain (RBD) of the viral spike protein25, but does not bind or neutralize SARS-CoV-226. The RBD is a self-contained and stable domain, and isolated RBDs could independently bind m396 without other parts of the spike protein25. One of our objectives is to modify relevant residues of m396 such that the mutated antibody could bind the SARS-CoV-2 RBD. The focus of this article is only on the implementation of FEP calculations, and our much broader efforts in the antibody design will be reported in due course..