Reordering Hydrogen Bonds Using Hamiltonian Replica Exchange Enhances Sampling of Conformational Changes in Biomolecular Systems

Computational methods for exploring protein conformations

Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.

Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: A molecular dynamics and enhanced sampling simulations study

The RNA recognition motif (RRM) is the most common RNA binding domain across eukaryotic proteins. It is therefore of great value to engineer its specificity to target RNAs of arbitrary sequence. This was recently achieved for the RRM in Rbfox protein, where four mutations R118D, E147R, N151S, and E152T were designed to target the precursor to the oncogenic miRNA 21. Here, we used a variety of molecular dynamics-based approaches to predict specific interactions at the binding interface. Overall, we have run approximately 50 microseconds of enhanced sampling and plain molecular dynamics simulations on the engineered complex as well as on the wild-type Rbfox·pre-miRNA 20b from which the mutated systems were designed. Comparison with the available NMR data on the wild type molecules (protein, RNA, and their complex) served to establish the accuracy of the calculations.

Free energy calculations suggest that further improvements in affinity and selectivity are achieved by the S151T replacement.

RNA is an outstanding target for oncological intervention. Engineering the most common RNA binding motif in human proteins (called RRM) so as to bind to a specific RNA has an enormous pharmacological potential. Yet, it is highly non trivial to design RRM-bearing protein variants with RNA selectivity and affinity sufficiently high for clinical applications. Here we present an extensive molecular simulation study which shed light on the exquisite molecular recognition of the empirically-engineered complex between the RRM-bearing protein Rbfox and its RNA target pre-miR21. The simulations allow predicting a variant, the S151T, which may lead to further enhancement of selectivity and affinity for pre-miR21.

Conformational properties of α- or β-(1→6)-linked oligosaccharides: Hamiltonian replica exchange MD simulations and NMR experiments

Conformational sampling for a set of 10 α- or β-(1→6)-linked oligosaccharides has been studied using explicit solvent Hamiltonian replica exchange (HREX) simulations and NMR spectroscopy techniques. Validation of the force field and simulation methodology is done by comparing calculated transglycosidic J coupling constants and proton-proton distances with the corresponding NMR data. Initial calculations showed poor agreement, for example, with >3 Hz deviation of the calculated (3)J(H5,H6R) values from the experimental data, prompting optimization of the ω torsion angle parameters associated with (1→6)-linkages. The resulting force field is in overall good agreement (i.e., within ∼0.5 Hz deviation) from experimental (3)J(H5,H6R) values, although some small limitations are evident. Detailed hydrogen bonding analysis indicates that most of the compounds lack direct intramolecular H-bonds between the two monosaccharides; however, minor sampling of the O6···HO2' hydrogen bond is present in three compounds. The results verify the role of the gauche effect between O5 and O6 atoms in gluco- and manno-configured pyranosides causing the ω torsion angle to sample an equilibrium between the gt and gg rotamers. Conversely, galacto-configured pyranosides sample a population distribution in equilibrium between gt and tg rotamers, while the gg rotamer populations are minor. Water radial distribution functions suggest decreased accessibility to the O6 atom in the (1→6)-linkage as compared to the O6' atom in the nonreducing sugar. The role of bridging water molecules between two sugar moieties on the distributions of ω torsion angles in oligosaccharides is also explored.

Hamiltonian replica-exchange simulations with adaptive biasing of peptide backbone and side chain dihedral angles

A Hamiltonian Replica-Exchange Molecular Dynamics (REMD) simulation method has been developed that employs a two-dimensional backbone and one-dimensional side chain biasing potential specifically to promote conformational transitions in peptides. To exploit the replica framework optimally, the level of the biasing potential in each replica was appropriately adapted during the simulations. This resulted in both high exchange rates between neighboring replicas and improved occupancy/flow of all conformers in each replica. The performance of the approach was tested on several peptide and protein systems and compared with regular MD simulations and previous REMD studies. Improved sampling of relevant conformational states was observed for unrestrained protein and peptide folding simulations as well as for refinement of a loop structure with restricted mobility of loop flanking protein regions.

Influence of solvent and intramolecular hydrogen bonding on the conformational properties of o-linked glycopeptides

A detailed investigation of the conformational properties of all the biologically relevant O-glycosidic linkages using the Hamiltonian replica exchange (HREX) simulation methodology and the recently developed CHARMM carbohydrate force field parameters is presented. Fourteen biologically relevant O-linkages between the five sugars N-acetylgalactosamine (GalNAc), N-acetylglucosamine (GlcNAc), D-glucose (Glc), D-mannose (Man), and L-fucose (Fuc) and the amino acids serine and threonine were studied. The force field was tested by comparing the simulation results of the model glycopeptides to various NMR (3)J coupling constants, NOE distances, and data from molecular dynamics with time-averaged restraints (tar-MD). The results show the force field to be in overall agreement with experimental and previous tar-MD simulations, although some small limitations are identified. An in-depth hydrogen bond and bridging water analysis revealed an interplay of hydrogen bonding and bridge water interactions influencing the geometry of the underlying peptide backbone, with the O-linkages favoring extended β-sheet and polyproline type II (PPII) conformations over the compact α(R)-helical conformation. The newly developed parameters were also able to identify hydrogen bonding and water mediated interactions between O-linked sugars and proteins. These results indicate that the newly developed parameters in tandem with HREX conformational sampling provide the means to study glycoproteins in the absence of targeted NMR restraint data.

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward.