Towards Convergence in Folding Simulations of RNA Tetraloops: Comparison of Enhanced Sampling Techniques and Effects of Force Field Corrections

Atomistic molecular dynamics (MD) simulations represent established technique for investigation of RNA structural dynamics. Despite continuous development, contemporary RNA simulations still suffer from suboptimal accuracy of empirical potentials (force fields, ffs) and sampling limitations. Development of efficient enhanced sampling techniques is important for two reasons. First, they allow to overcome the sampling limitations and, second, they can be used to quantify ff imbalances provided they reach a sufficient convergence. Here, we study two RNA tetraloops (TLs), namely the GAGA and UUCG motifs. We perform extensive folding simulations and calculate folding free energies (ΔGfold) with the aim to compare different enhanced sampling techniques and to test several modifications of the nonbonded terms extending the AMBER OL3 RNA ff. We demonstrate that replica exchange solute tempering (REST2) simulations with 12-16 replicas do not show any sign of convergence even when extended to time scale of 120 μs per replica. However, combination of REST2 with well-tempered metadynamics (ST-MetaD) achieves good convergence on a time-scale of 5-10 μs per replica, improving the sampling efficiency by at least two orders of magnitude. Effects of ff modifications on ΔGfold energies were initially explored by the reweighting approach and then validated by new simulations. We tested several manually-prepared variants of gHBfix potential which improve stability of the native state of both TLs by up to ~2 kcal/mol. This is sufficient to conveniently stabilize the folded GAGA TL while the UUCG TL still remains under-stabilized. Appropriate adjustment of van der Waals parameters for C-H...O5' base-phosphate interaction are also shown to be capable of further stabilizing the native states of both TLs by ~0.6 kcal/mol.

MMpred: a distance-assisted multimodal conformation sampling for de novo protein structure prediction

MotivationThe mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations.ResultsA distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages: The first is a modal exploration stage, in which a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. The second is a modal maintaining stage, where an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on a large set of 320 non-redundant proteins, where MMpred obtains models with TM-score≥0.5 on 268 cases, which is 20.3% higher than that of Rosetta guided with the same set of distance constraints. The results showed that MMpred can help significantly improve the model accuracy of protein assembly simulations through the sampling of multiple promising energy basins with enhanced structural diversity.AvailabilityThe source code and executable versions are freely available at https://github.com/iobio-zjut/[email protected] or [email protected] or [email protected]

The fight against COVID-19: Running Folding@Home simulations on ATLAS resources

Following the outbreak of the COVID–19 pandemic, the ATLAS experiment considered how it could most efficiently contribute using its distributed computing resources. After considering many suggestions, examining several potential projects and following the advice of the CERN COVID Task Force, it was decided to engage in the Folding@Home initiative, which provides payloads that perform protein folding simulations. This paper describes how ATLAS made a significant contribution to this project over the summer of 2020.

Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP and CAMEO experiments, and outperformed other state-of-the-art methods by at least 58.4% for the CASP 11&12 and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.AvailabilityThe training and testing data, standalone package, and the online server for TripletRes are available at https://zhanglab.ccmb.med.umich.edu/TripletRes/.Author SummaryAb initio protein folding has been a major unsolved problem in computational biology for more than half a century. Recent community-wide Critical Assessment of Structure Prediction (CASP) experiments have witnessed exciting progress on ab initio structure prediction, which was mainly powered by the boosting of contact-map prediction as the latter can be used as constraints to guide ab initio folding simulations. In this work, we proposed a new open-source deep-learning architecture, TripletRes, built on the residual convolutional neural networks for high-accuracy contact prediction. The large-scale benchmark and blind test results demonstrate significant advancement of the proposed methods over other approaches in predicting medium- and long-range contact-maps that are critical for guiding protein folding simulations. Detailed data analyses showed that the major advantage of TripletRes lies in the unique protocol to fuse multiple evolutionary feature matrices which are directly extracted from whole-genome and metagenome databases and therefore minimize the information loss during the contact model training.

The C-terminal domain of transcription factor RfaH: Folding, fold switching and energy landscape

We study the folding and fold switching of the C-terminal domain (CTD) of the transcription factor RfaH using a hybrid sequence-structure based model. We show that this model captures the essential thermodynamic behavior of this metamorphic domain, i.e., a switch in the global free energy minimum from an α-helical hairpin to a 5-stranded β-barrel upon separating the CTD from the rest of the protein. Using this model and Monte Carlo sampling techniques, we analyze the energy landscape of the CTD in terms of progress variables for folding towards the two folds. We find that, below the folding temperature, the energy landscape is characterized by a single, dominant funnel to the native β-barrel structure. The absence of a deep funnel to the α-helical hairpin state reflects a negligible population of this fold for the isolated CTD. We observe, however, a significantly higher α-helix structure content in the unfolded state compared to results from a similar but fold switch-incompetent version of our model. Moreover, in folding simulations started from an extended chain conformation we find transient α-helix structure that disappears as the chain progresses to the thermally stable β-barrel state.

Assessing the Quality of Cotranscriptional Folding Simulations

Structural changes in RNAs are an important contributor to controlling gene expression not only at the post-transcriptional stage but also during transcription. A subclass of riboswitches and RNA thermometers located in the 5’ region of the primary transcript regulates the downstream functional unit – usually an ORF – through premature termination of transcription. Such elements not only occur naturally but they are also attractive devices in synthetic biology. The possibility to design such riboswitches or RNA thermometers is thus of considerable practical interest. Since these functional RNA elements act already during transcription, it is important to model and understand the dynamics of folding and, in particular, the formation of intermediate structures concurrently with transcription. Cotranscriptional folding simulations are therefore an important step to verify the functionality of design constructs before conducting expensive and labour-intensive wet lab experiments. For RNAs, full-fledged molecular dynamics simulations are far beyond practical reach both because of the size of the molecules and the time scales of interest. Even at the simplified level of secondary structures further approximations are necessary. The BarMap approach is based on representing the secondary structure landscape for each individual transcription step by a coarse-grained representation that only retains a small set of low-energy local minima and the energy barriers between them. The folding dynamics between two transcriptional elongation steps is modeled as a Markov process on this representation. Maps between pairs of consecutive coarse-grained landscapes make it possible to follow the folding process as it changes in response to transcription elongation.In its original implementation, the BarMap software provides a general framework to investigate RNA folding dynamics on temporally changing landscapes. It is, however, difficult to use in particular for specific scenarios such as cotranscriptional folding. To overcome this limitation, we developed the user-friendly BarMap-QA pipeline described in detail in this contribution. It is illustrated here by an elaborate example that emphasizes the careful monitoring of several quality measures. Using an iterative workflow, a reliable and complete kinetics simulation of a synthetic, transcription regulating riboswitch is obtained using minimal computational resources. All programs and scripts used in this contribution are free software and available for download as a source distribution for Linux®, or as a platform-independent Docker® image including support for Apple macOS® and Microsoft Windows®.

Fueling ab initio folding with marine metagenomics enables structure and function predictions of new protein families

Introduction The ocean microbiome represents one of the largest microbiomes and produces nearly half of the primary energy on the planet through photosynthesis or chemosynthesis. Using recent advances in marine genomics, we explore new applications of oceanic metagenomes for protein structure and function prediction. Results By processing 1.3 TB of high-quality reads from the Tara Oceans data, we obtain 97 million non-redundant genes. Of the 5721 Pfam families that lack experimental structures, 2801 have at least one member associated with the oceanic metagenomics dataset. We apply C-QUARK, a deep-learning contact-guided ab initio structure prediction pipeline, to model 27 families, where 20 are predicted to have a reliable fold with estimated template modeling score (TM-score) at least 0.5. Detailed analyses reveal that the abundance of microbial genera in the ocean is highly correlated to the frequency of occurrence in the modeled Pfam families, suggesting the significant role of the Tara Oceans genomes in the contact-map prediction and subsequent ab initio folding simulations. Of interesting note, PF15461, which has a majority of members coming from ocean-related bacteria, is identified as an important photosynthetic protein by structure-based function annotations. The pipeline is extended to a set of 417 Pfam families, built on the combination of Tara with other metagenomics datasets, which results in 235 families with an estimated TM-score over 0.5. Conclusions These results demonstrate a new avenue to improve the capacity of protein structure and function modeling through marine metagenomics, especially for difficult proteins with few homologous sequences.