Extension of the stochastic CpHMD method to the CHARMM36m force field

Constant-pH Molecular Dynamics (CpHMD) methods are nowadays essential to describe pH and the pH effects on the conformational space of biological systems [1]. The stochastic CpHMD method [2] has shown excellent performance over the years [1–3]. Until recently, our implementation of this method only supported the GROMOS 54A7, a force field compatible with the Generalized Reaction-Field (GRF) formalism to treat long-range electrostatic interactions, hence allowing for non-neutral systems [3]. Despite GROMOS popularity, one of the most used force fields is CHARMM36m, which is all-atom and particularly suited for protein, nucleic acids, and lipids simulations [4]. However, it uses mainly PME to treat the long-range electrostatics, which requires a system charge neutralization, a major limitation in its CpHMD implementation [3].

In this work, we present an extension to the stochastic CpHMD to include the CHARMM36m force field. In this preliminary benchmark study, we simulated two well-known proteins - lysozyme and thioredoxin - for which there is a significant amount of experimental data available [5]. These systems were thoroughly studied (pH range 1-12) and the final pKa values were compared between force fields and with the experimental data [5]. Please visit our Poster to see the performance of both force fields, the details on how to circumvent the PME neutralization step, and the code efficiency (ns/day).

1. Vila-Viçosa D, Reis PBPS, Baptista AM, Oostenbrink C, Machuqueiro M. A pH Replica Exchange Scheme in the Stochastic Titration Constant-pH MD Method. J Chem Theory Comput. 2019;15: 3108–3116. doi:10.1021/acs.jctc.9b00030

2. Baptista AM, Teixeira VH, Soares CM. Constant-pH molecular dynamics using stochastic titration. J Chem Phys. 2002;117: 4184–4200. doi:10.1063/1.1497164

3. Silva TFD, Vila-Viçosa D, Reis PBPS, Victor BL, Diem M, Oostenbrink C, et al. The Impact of Using Single Atomistic Long-Range Cutoff Schemes with the GROMOS 54A7 Force Field. J Chem Theory Comput. 2018;14: 5823–5833. doi:10.1021/acs.jctc.8b00758

4. Huang J, Rauscher S, Nawrocki G, Ran T, Feig M, de Groot BL, et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods. 2017;14: 71–73. doi:10.1038/nmeth.4067

5. Pahari S, Sun L, Alexov E. PKAD: a database of experimentally measured pKa values of ionizable groups in proteins. Database. 2019;2019.