Including residual contact information into replica-exchange MD simulations significantly enriches native-like conformations

Abstract

Proteins are complex biomolecules which perform critical tasks in living organisms. Knowledge of a protein’s structure is essential for understanding its physiological function in detail. Despite the incredible progress in experimental techniques, protein structure determination is still expensive, time-consuming, and arduous. That is why computer simulations are often used to complement or interpret experimental data. Here, we explore how in silico protein structure determination based on replica-exchange molecular dynamics (REMD) can benefit from including contact information derived from theoretical and experimental sources, such as direct coupling analysis or NMR spectroscopy. To reflect the influence from erroneous and noisy data we probe how false-positive contacts influence the simulated ensemble. Specifically, we integrate varying numbers of randomly selected native and non-native contacts and explore how such a bias can guide simulations towards the native state. We investigate the number of contacts needed for a significant enrichment of native-like conformations and show the capabilities and limitations of this method. Adhering to a threshold of approximately 75% true-positive contacts within a simulation, we obtain an ensemble with native-like conformations of high quality. We find that contact-guided REMD is capable of delivering physically reasonable models of a protein’s structure.

pyrexMD: Workflow-Orientated Python Package for Replica Exchange Molecular Dynamics

“…pyrexMD was initially developed in the course of (Voronin et al, 2020). Currently, it is successfully applied in ongoing REX studies on protein and RNA structure refinement.…”

“…Currently, it is successfully applied in ongoing REX studies on protein and RNA structure refinement. (Voronin et al, 2020), such as a minimal TPR threshold of 75% (red) and a suggested optimal number of contacts between L/2 and L (orange), where L denotes the biomolecular sequence length.…”

“…Figure 2: Analysis of the true positive rate (TPR) for bias contacts with pyrexMD. The figure exemplarily shows the TPR of the considered bias contacts together with other relevant value guidelines for contact-guided REX(Voronin et al, 2020), such as a minimal TPR threshold of 75% (red) and a suggested optimal number of contacts between L/2 and L (orange), where L denotes the biomolecular sequence length.…”

Calculations of Absolute Free Energies, Enthalpies, and Entropies for Drug Binding

“…In principle, simulations at different temperatures can be used to improve sampling using replica exchange (RE), − and the combination of temperature RE with free energy methods can provide computational efficiency. − The number of replicas required to span a given temperature difference goes as √ f , where f is the number of degrees of freedom . This scaling means that for protein systems, temperature gaps between replicas can only be 1–3 K, − so at least 10 replicas will be required to span a temperature range wide enough (20–30 K) to determine the entropy change. Modified RE methods have, to date, been used successfully in free energy calculations. − Those methods scale better with system size by modifying the energy function so that sampling is enhanced along a limited subset of coordinates. , The modified replicas help generate data for low temperatures but do not generate data for higher temperatures.…”