Refinement of protein structure via contact based potentials in replica exchange simulations

Abstract

Proteins are complex biomolecules which perform critical tasks in living organisms. Knowledge of a proteins 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 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 REX MD is capable of delivering physically reasonable models of a proteins structure. Author summaryProtein structure prediction, that is obtaining a protein structure starting from a sequence using any computational method, is a great challenge. Over the past years a broad variety of methods evolved, ranging from algorithms for "blind" or de novo predictions using Monte-Carlo or physics-based biomolecular simulation methods to algorithms transferring structure information obtained from known homologous proteins. Recently, purely data-driven approaches using neural networks have shown to be capable of predicting high-quality structures. However, some local structural motifs are only poorly resolved and need further refinement. Here, we explore to what extent contact information helps guiding replica exchange molecular dynamics towards the native fold. By adding a contact pair bias potential to the energy function, we effectively guide the search towards the target structure by narrowing the conformational space to be sampled. We find that such an energetic bias, even if containing false-positive contacts to a certain extent, greatly enhances the refinement process and improves the chance of finding the native state in a single run.