Graphical abstract

The OPEP coarse-grained protein model has been applied to a wide range of applications since its first release 15 years ago. The model, which combines energetic and structural accuracy and chemical specificity, allows the study of single protein properties, DNA-RNA complexes, amyloid fibril formation and protein suspensions in a crowded environment. Here we first review the current state of the model and the most exciting applications using advanced conformational sampling methods. We then present the current limitations and a perspective on the ongoing developments.

Understanding the structural repertoire of RNA is crucial for RNA genomics research. Yet current methods for finding novel RNAs are limited to small or known RNA families. To expand known RNA structural motifs, we develop a two-dimensional graphical representation approach for describing and estimating the size of RNA's secondary structural repertoire, including naturally occurring and other possible RNA motifs. We employ tree graphs to describe RNA tree motifs and more general (dual) graphs to describe both RNA tree and pseudoknot motifs. Our estimates of RNA's structural space are vastly smaller than the nucleotide sequence space, suggesting a new avenue for finding novel RNAs. Specifically our survey shows that known RNA trees and pseudoknots represent only a small subset of all possible motifs, implying that some of the 'missing' motifs may represent novel RNAs. To help pinpoint RNA-like motifs, we show that the motifs of existing functional RNAs are clustered in a narrow range of topological characteristics. We also illustrate the applications of our approach to the design of novel RNAs and automated comparison of RNA structures; we report several occurrences of RNA motifs within larger RNAs. Thus, our graph theory approach to RNA structures has implications for RNA genomics, structure analysis and design.

Coarse-grained protein models with various levels of granularity and degrees of freedom offer the possibility to explore many phenomena including folding, assembly, and recognition in terms of dynamics and thermodynamics that are inaccessible to all-atom representations in explicit aqueous solution. Here, we present a refined version of the coarse-grained optimized potential for efficient protein structure prediction (OPEP) based on a six-bead representation. The OPEP version 4.0 parameter set, which uses a new analytical formulation for the nonbonded interactions and adds specific side-chain-side-chain interactions for α-helix, is subjected to three tests. First, we show that molecular dynamics simulations at 300 K preserve the experimental rigid conformations of 17 proteins with 37-152 amino acids within a root-mean-square deviation (RMSD) of 3.1 Å after 30 ns. Extending the simulation time to 100 ns for five proteins does not change the RMSDs. Second, replica exchange molecular dynamics (REMD) simulations recover the NMR structures of three prototypical β-hairpin and α-helix peptides and the NMR three-stranded β-sheet topology of a 37-residue WW domain, starting from randomly chosen states. Third, REMD simulations on the ccβ peptide show a temperature transition from a three-stranded coiled coil to amyloid-like aggregates consistent with experiments, while simulations on low molecular weight aggregates of the prion protein helix 1 do not. Overall, these studies indicate the effectiveness of our OPEP4 coarse-grained model for protein folding and aggregation, and report two future directions for improvement.

Although RNAs play many cellular functions, the gap between their sequences and 3D structures is increasing and our knowledge of RNA thermodynamics and long time scale dynamics is still limited at an atomic level of detail. In principle, all-atom molecular dynamics (MD) and replica exchange molecular dynamics (REMD) simulations can investigate these issues, but with current computer facilities, these simulations in explicit solvent have been limited to small RNAs and to short times. To move to larger systems, we can resort to coarse-graining. In this study, we present HiRE-RNA, a generic high resolution coarse-grained model for RNA, and report MD and REMD simulations on two RNAs of 22 and 36 nucleotides. Starting from unfolded structures, the 22-mer folds within 1.8 A rmsd from the NMR structure, while the 36-mer folds within 4.6 A rmsd. Current results suggest that further optimization of the HiRE-RNA force field should open the door to a relevant model for studying large RNAs, such as riboswitches, and for predicting 3D structures from secondary structure information.

Although RNAs play many cellular functions, little is known about the dynamics and thermodynamics of these molecules. In principle, all-atom molecular dynamics simulations can investigate these issues, but with current computer facilities, these simulations have been limited to small RNAs and to short times. HiRe-RNA, a recently proposed high-resolution coarse-grained RNA that captures many geometric details such as base pairing and stacking, is able to fold RNA molecules to near-native structures in a short computational time. So far, it had been applied to simple hairpins, and here we present its application to duplexes of a couple dozen nucleotides and show how with replica exchange molecular dynamics (REMD) we can easily predict the correct double helix from a completely random configuration and study the dissociation curve. To show the versatility of our model, we present an application to a double stranded DNA molecule as well. A reconstruction algorithm allows us to obtain full atom structures from the coarse-grained model. Through atomistic molecular dynamics (MD), we can compare the dynamics starting from a representative structure of a low temperature replica or from the experimental structure, and show how the two are statistically identical, highlighting the validity of a coarse-grained approach for structured RNAs and DNAs.

HiRE-RNA is a coarse-grained model for RNA structure prediction and the dynamical study of RNA folding. Using a reduced set of particles and detailed interactions accounting for base-pairing and stacking, we show that noncanonical and multiple base interactions are necessary to capture the full physical behavior of complex RNAs. In this paper, we give a full account of the model and present results on the folding, stability, and free energy surfaces of 16 systems with 12 to 76 nucleotides of increasingly complex architectures, ranging from monomers to dimers, using a total of 850 μs of simulation time.

Spider silk is a fascinating material combining mechanical properties such as maximum strength and high toughness comparable or better than man-made materials, with biocompatible degradability characteristics. Experimental measurements have shown that pH triggers the dimer formation of the N-terminal domain (NTD) of the major ampullate spidroin 1 (MaSp 1). A coarse-grained model accounting for electrostatics, van der Waals and pH-dependent charge-fluctuation interactions, by means of Monte Carlo simulations, gave us a more comprehensive view of the NTD dimerization process. A detailed analysis of the electrostatic properties and free energy derivatives for the NTD homoassociation was carried out at different pH values and salt concentrations for the protein wild type and for several mutants. We observed an enhancement of dipole-dipole interactions at pH 6 due to the ionization of key amino acids, a process identified as the main driving force for dimerization. Analytical estimates based on the DVLO theory framework corroborate our findings. Molecular dynamics simulations using the OPEP coarse-grained force field for proteins show that the mutant E17Q is subject to larger structural fluctuations when compared to the wild type. Estimates of the association rate constants for this mutant were evaluated by the Debye-Smoluchowski theory and are in agreement with the experimental data when thermally relaxed structures are used instead of the crystallographic data. Our results can contribute to the design of new mutants with specific association properties.