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.

A hallmark of Huntington's disease (HD) is a prolonged polyglutamine sequence in the huntingtin protein and, correspondingly, an expanded cytosine, adenine, and guanine (CAG) triplet repeat region in the mRNA. A majority of studies investigating disease pathology were concerned with toxic huntingtin protein, but the mRNA moved into focus due to its recruitment to RNA foci and emerging novel therapeutic approaches targeting the mRNA. A hallmark of CAG-RNA is that it forms a stable hairpin in vitro which seems to be crucial for specific protein interactions. Using in-cell folding experiments, we show that the CAG-RNA is largely destabilized in cells compared to dilute buffer solutions but remains folded in the cytoplasm and nucleus. Surprisingly, we found the same folding stability in the nucleoplasm and in nuclear speckles under physiological conditions suggesting that CAG-RNA does not undergo a conformational transition upon recruitment to the nuclear speckles. We found that the metabolite adenosine triphosphate (ATP) plays a crucial role in promoting unfolding, enabling its recruitment to nuclear speckles and preserving its mobility. Using in vitro experiments and molecular dynamics simulations, we found that the ATP effects can be attributed to a direct interaction of ATP with the nucleobases of the CAG-RNA rather than ATP acting as "a fuel" for helicase activity. ATP-driven changes in CAG-RNA homeostasis could be disease-relevant since mitochondrial function is affected in HD disease progression leading to a decline in cellular ATP levels.

Despite the incredible progress of experimental techniques, protein structure determination still remains a challenging task. Due to the rapid improvements of computer technology, simulations are often used to complement or interpret experimental data, in particular for sparse or low-resolution data. Many such in silico methods allow to obtain highly accurate models of a protein structure either de novo or via refinement of a physical model with experimental restraints. One crucial question is how to select a representative member or ensemble out of vast number of computationally generated structures. Here, we introduce such a method. As a representative task, we add co-evolutionary contact pairs as distance restraints to a physical force field and want to select a good characterization of the resulting native-like ensemble. To generate large ensembles, we run replica-exchange molecular dynamics (REMD) on five mid-sized test proteins and over a wide temperature range. High temperatures allow overcoming energetic barriers while low temperatures perform local searches of native-like conformations. The integrated bias is based on co-evolutionary contact pairs derived from a deep residual neural network to guide the simulation towards native-like conformations. We shortly compare and discuss the achieved model precision of contact-guided REMD for mid-sized proteins. Lastly, we discuss four robust ensemble-selection algorithms in great detail which are capable to extract the representative structure models with a high certainty. To assess the performance of the selection algorithms we exemplarily mimic a "blind scenario', i.e. where the target structure is unknown, and select a representative structural ensemble of native-like folds.

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