Reconciling Membrane Protein Simulations with Experimental DEER Spectroscopy Data

Spectroscopy experiments are crucial to study membrane proteins for which traditional structure determination methods still prove challenging. Double electron-electron resonance (DEER) spectroscopy experiments provide protein residue-pair distance distributions that are indicative of their conformational heterogeneity. Atomistic molecular dynamics (MD) simulations are another tool that have proved vital to study the structural dynamics of membrane proteins such as to identify inward-open, occluded, and outward-open conformations of transporter membrane proteins, among other partially open or closed states of the protein. Yet, studies have reported that there is no direct consensus between distributional data from DEER experiments and MD simulations, which has challenged validation of structures obtained from long-timescale simulations and using simulations to design experiments. Current coping strategies for comparisons rely on heuristics, such as mapping nearest matching peaks between two ensembles or biased simulations. Here we examine the differences in residue-pair distance distributions arising due to choice of membrane around the protein and covalent modification of a pair of residues to nitroxide spin labels in DEER experiments. Through comparing MD simulations of two proteins, PepTSo and LeuT - both of which have been characterized using DEER experiments previously - we show that the proteins’ dynamics are similar despite the choice of the detergent micelle as a membrane mimetic in DEER experiments. On the other hand, covalently modified residues show slight local differences in their dynamics and a huge divergence when the spin labels’ anointed oxygen atom pair distances are measured rather than protein backbone distances. Given the computational expense associated with pairwise MTSSL labeled MD simulations, we examine the use of biased simulations to explore the conformational dynamics of the spin labels only to reveal that such simulations alter the underlying protein dynamics. Our study identifies the main cause for the mismatch between DEER experiments and MD simulations and will accelerate developing potential mitigation strategies to improve simulation observables match with DEER spectroscopy experiments.

Co-evolutionary Distance Prediction for Flexibility Prediction

ABSTRACTCo-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predicting distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. Here we examine the potential of these residue-residue distance predictions to predict protein flexibility rather than static structure. We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were considered and classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. The average number of local maxima per residue pair was found to be different between the sets of rigid and flexible residue pairs. Flexible residue pairs more often had multiple local maxima in their predicted distance distribution than rigid residue pairs suggesting that the shape of predicted distance distributions is predictive of rigidity or flexibility of residue pairs.

Piece of the Puzzle: Remdesivir disassemble the multimeric SARS-CoV-2 RNA-dependent RNA Polymerase Non-Structural Proteins (RdRp-NSPs) complex

The recently emerged SARS-like coronavirus (SARS-CoV-2) has continued to spread rapidly among humans with alarming upsurges in global mortality rates. A major key to tackling this virus is to disrupt its RNA replication process as previously reported for Remdesivir (Rem-P3). For the first time, we modeled the binding of Rem-P3 to SARS-CoV-2 RdRp-NSPs complex, a multimeric assembly that drives viral RNA replication in human hosts. Findings revealed that while ATP-binding stabilized the replicative tripartite, Rem-P3 disintegrated the RdRp-NSP complex, starting with the detachment of the NSP7-NSP8 heterodimer followed by minimal displacement of the second NSP8 subunit (NSP8II). More so, Rem-P3 interacted with a relatively higher affinity (ΔGbind) while inducing high perturbations across the RdRp-NSP domains. D452, T556, V557, S682, and D760 were identified for their crucial roles in stacking the cyano-adenosine and 3,4-dihydroxyoxolan rings of Rem-P3 while its flexible P3 tail extended towards the palm domain blocking D618 and K798; a residue-pair identified for essential roles in RNA replication. However, ATP folded away from D618 indicative of a more coordinated binding favorable for nucleotide polymerization. We believe findings from this study will significantly contribute to the structure-based design of novel disruptors of the SARS-CoV-2 RNA replicative machinery.

Dissecting C−H∙∙∙π and N−H∙∙∙π Interactions in Two Proteins Using a Combined Experimental and Computational Approach

C−H∙∙∙π and N−H∙∙∙π interactions can have an important contribution for protein stability. However, direct measurements of these interactions in proteins are rarely reported. In this work, we combined the mutant cycle experiments and molecular dynamics (MD) simulations to characterize C−H∙∙∙π and N−H∙∙∙π interactions and their cooperativity in two model proteins. It is shown that the average C−H∙∙∙π interaction per residue pair is ~ −0.5 kcal/mol while the N−H∙∙∙π interaction is slightly stronger. The triple mutant box measurement indicates that N−H∙∙∙π∙∙∙C−H∙∙∙π and C−H∙∙∙π∙∙∙C−H∙∙∙π can have a positive or negative cooperativity. MD simulations suggest that the cooperativity, depending on the local environment of the interactions, mainly arises from the geometric rearrangement when the nearby interaction is perturbed.

Structure-based prediction of post-translational modification cross-talk within proteins using complementary residue- and residue pair-based features

Post-translational modification (PTM)-based regulation can be mediated not only by the modification of a single residue but also by the interplay of different modifications. Accurate prediction of PTM cross-talk is a highly challenging issue and is in its infant stage. Especially, less attention has been paid to the structural preferences (except intrinsic disorder and spatial proximity) of cross-talk pairs and the characteristics of individual residues involved in cross-talk, which may restrict the improvement of the prediction accuracy. Here we report a structure-based algorithm called PCTpred to improve the PTM cross-talk prediction. The comprehensive residue- and residue pair-based features were designed for paired PTM sites at the sequence and structural levels. Through feature selection, we reserved 23 newly introduced descriptors and 3 traditional descriptors to develop a sequence-based predictor PCTseq and a structure-based predictor PCTstr, both of which were integrated to construct our final prediction model. According to pair- and protein-based evaluations, PCTpred yielded area under the curve values of approximately 0.9 and 0.8, respectively. Even when removing the distance preference of samples or using the input of modeled structures, our prediction performance was maintained or moderately reduced. PCTpred displayed stable and reliable improvements over the state-of-the-art methods based on various evaluations. The source code and data set are freely available at https://github.com/Liulab-HZAU/PCTpred or http://liulab.hzau.edu.cn/PCTpred/.