Bridging the gap in RNA structure prediction

The field of RNA structure prediction has experienced significant advances in the past several years, thanks to the availability of new experimental data and improved computational methodologies. These methods determine RNA secondary structures and pseudoknots from sequence alignments, thermodynamics-based dynamic programming algorithms, genetic algorithms and combined approaches. Computational RNA three-dimensional modeling uses this information in conjunction with manual manipulation, constraint satisfaction methods, molecular mechanics and molecular dynamics. The ultimate goal of automatically producing RNA three-dimensional models from given secondary and tertiary structure data, however, is still not fully realized. Recent developments in the computational prediction of RNA structure have helped bridge the gap between RNA secondary structure prediction, including pseudoknots, and three-dimensional modeling of RNA.     

Simulation of the structure and dynamics of nonhelical RNA motifs

Computer simulation methods are increasingly being used to study possible conformations and dynamics of structural motifs in RNA. Recent results of molecular dynamics simulations and continum solvent studies of RNA structures and RNA-ligand complexes show promising agreement with experimental data. Combined with the ongoing progress in the experimental characterization of RNA structure and thermodynamics, these computational approaches can help to better understand the mechanism of RNA structure formation and the binding of ligands.     

Outer membrane phospholipase A in phospholipid bilayers: a model system for concerted computational and experimental investigations of amino acid side chain partitioning into lipid bilayers

Understanding the forces that stabilize membrane proteins in their native states is one of the contemporary challenges of biophysics. To date, estimates of side chain partitioning free energies from water to the lipid environment show disparate values between experimental and computational measures. Resolving the disparities is particularly important for understanding the energetic contributions of polar and charged side chains to membrane protein function because of the roles these residue types play in many cellular functions. In general, computational free energy estimates of charged side chain partitioning into bilayers are much larger than experimental measurements. However, the lack of a protein-based experimental system that uses bilayers against which to vet these computational predictions has traditionally been a significant drawback. Moon & Fleming recently published a novel hydrophobicity scale that was derived experimentally by using a host-guest strategy to measure the side chain energetic perturbation due to mutation in the context of a native membrane protein inserted into a phospholipid bilayer. These values are still approximately an order of magnitude smaller than computational estimates derived from molecular dynamics calculations from several independent groups. Here we address this discrepancy by showing that the free energy differences between experiment and computation become much smaller if the appropriate comparisons are drawn, which suggests that the two fields may in fact be converging. In addition, we present an initial computational characterization of the Moon & Fleming experimental system used for the hydrophobicity scale: OmpLA in DLPC bilayers. The hydrophobicity scale used OmpLA position 210 as the guest site, and our preliminary results demonstrate that this position is buried in the center of the DLPC membrane, validating its usage in the experimental studies. We further showed that the introduction of charged Arg at position 210 is well tolerated in OmpLA and that the DLPC bilayers accommodate this perturbation by creating a water dimple that allows the Arg side chain to remain hydrated. Lipid head groups visit the dimple and can hydrogen bond with Arg, but these interactions are transient. Overall, our study demonstrates the unique advantages of this molecular system because it can be interrogated by both computational and experimental practitioners, and it sets the stage for free energy calculations in a system for which there is unambiguous experimental data. This article is part of a Special Issue entitled: Membrane protein structure and function.     

Determination of protein oligomeric structure from small‐angle X‐ray scattering

Small‐angle X‐ray scattering (SAXS) is useful for determining the oligomeric states and quaternary structures of proteins in solution. The average molecular mass in solution can be calculated directly from a single SAXS curve collected on an arbitrary scale from a sample of unknown protein concentration without the need for beamline calibration or protein standards. The quaternary structure in solution can be deduced by comparing the experimental SAXS curve to theoretical curves calculated from proposed models of the oligomer. This approach is especially robust when the crystal structure of the target protein is known, and the candidate oligomer models are derived from the crystal lattice. When SAXS data are obtained at multiple protein concentrations, this analysis can provide insight into dynamic self‐association equilibria. Herein, we summarize the computational methods that are used to determine protein molecular mass and quaternary structure from SAXS data. These methods are organized into a workflow and demonstrated with four case studies using experimental SAXS data from the published literature.     

Recent developments in structural proteomics for protein structure determination

The major challenges in structural proteomics include identifying all the proteins on the genome-wide scale, determining their structure-function relationships, and outlining the precise three-dimensional structures of the proteins. Protein structures are typically determined by experimental approaches such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. However, the knowledge of three-dimensional space by these techniques is still limited. Thus, computational methods such as comparative and de novo approaches and molecular dynamic simulations are intensively used as alternative tools to predict the three-dimensional structures and dynamic behavior of proteins. This review summarizes recent developments in structural proteomics for protein structure determination; including instrumental methods such as X-ray crystallography and NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulations.     

Ab initio RNA folding by discrete molecular dynamics: from structure prediction to folding mechanisms

RNA molecules with novel functions have revived interest in the accurate prediction of RNA three-dimensional (3D) structure and folding dynamics. However, existing methods are inefficient in automated 3D structure prediction. Here, we report a robust computational approach for rapid folding of RNA molecules. We develop a simplified RNA model for discrete molecular dynamics (DMD) simulations, incorporating base-pairing and base-stacking interactions. We demonstrate correct folding of 150 structurally diverse RNA sequences. The majority of DMD-predicted 3D structures have <4 A deviations from experimental structures. The secondary structures corresponding to the predicted 3D structures consist of 94% native base-pair interactions. Folding thermodynamics and kinetics of tRNA(Phe), pseudoknots, and mRNA fragments in DMD simulations are in agreement with previous experimental findings. Folding of RNA molecules features transient, non-native conformations, suggesting non-hierarchical RNA folding. Our method allows rapid conformational sampling of RNA folding, with computational time increasing linearly with RNA length. We envision this approach as a promising tool for RNA structural and functional analyses.     

Water dynamics clue to key residues in protein folding

A computational method independent of experimental protein structure information is proposed to recognize key residues in protein folding, from the study of hydration water dynamics. Based on all-atom molecular dynamics simulation, two key residues are recognized with distinct water dynamical behavior in a folding process of the Trp-cage protein. The identified key residues are shown to play an essential role in both 3D structure and hydrophobic-induced collapse. With observations on hydration water dynamics around key residues, a dynamical pathway of folding can be interpreted.     

Biomolecular structure prediction at a low resolution using a neural network and the double-iterated kalman filter technique

We report the application of an integrated computational approach for biomolecular structure determination at a low resolution. In particular, a neural network is trained to predict the spatial proximity of C-alpha atoms that are less than a given threshold apart, whereas a Kalman filter algorithm is employed to outline the biomolecular fold, with a constraints set that includes these pairwise atomic distances, and the distances and angles that define the structure as it is known from the protein's sequence. The results for Crambin demonstrate that this integrated approach is useful for molecular structure prediction at a low resolution and may also complement existing experimental distance data for a protein structure determination. © 1996 John Wiley & Sons, Inc.     

Conformational investigation of two isomeric chiral porphyrins: a convergent approach with different techniques

The conformation in solution of two atropisomeric meso-tetrabinaphthyl porphyrins, used as catalytic precursors in asymmetric synthesis, was studied by means of experimental ((1)H-NMR ROESY, UV-Vis, and circular dichroism) and computational (semiempirical structure optimization, DeVoe's coupled oscillators calculations) methods. UV-Vis and CD spectra are calculated for several molecular models, with a systematic sampling of the conformational space, and compared to the experimental ones, leading to a structural hypothesis which is confirmed by NMR and PM3.     

Reconciling the solution and X-ray structures of the villin headpiece helical subdomain: molecular dynamics simulations and double mutant cycles reveal a stabilizing cation-pi interaction

The 36-residue helical subdomain of the villin headpiece, HP36, is one of the smallest cooperatively folded proteins, folding on the microsecond time scale. The domain is an extraordinarily popular model system for both experimental and computational studies of protein folding. The structure of HP36 has been determined using X-ray crystallography and NMR spectroscopy, with the resulting structures exhibiting differences in helix packing, van der Waals contacts, and hydrogen bonding. It is important to determine the solution structure of HP36 with as much accuracy as possible since this structure is widely used as a reference for simulations and experiments. We complement the existing data by using all-atom molecular dynamics simulations with explicit solvent to evaluate which of the experimental models is the better representation of HP36 in solution. After simulation for 50 ns initiated with the NMR structure, we observed that the protein spontaneously adopts structures with a backbone conformation, core packing, and C-capping motif on the third helix that are more consistent with the crystal structure. We also examined hydrogen bonding and side chain packing interactions between D44 and R55 and between F47 and R55, respectively, which were observed in the crystal structure but not in the NMR-based solution structure. Simulations showed large fluctuations in the distance between D44 and R55, while the distance between F47 and R55 remained stable, suggesting the formation of a cation-pi interaction between those residues. Experimental double mutant cycles confirmed that the F47-R55 pair has a larger energetic coupling than the D44-R55 interaction. Overall, these combined experimental and computational studies show that the X-ray crystal structure is the better reference structure for HP36 in solution at neutral pH. Our analysis also shows how detailed molecular dynamics simulations combined with experimental validation can help bridge the gap between NMR and crystallographic methods.     

Molecular mechanisms of membrane-associated amyloid aggregation: Computational perspective and challenges

Amyloidogenic proteins are related to a variety of amyloid diseases, such as type 2 diabetes (T2D), Alzheimer's disease (AD) and Parkinson's disease (PD). The amyloid proteins in which this review focuses include amylin, Aβ, tau and α-synuclein. Understanding the molecular mechanisms in which these amyloidogenic proteins interact with membranes is a challenging research to both experimental and computational studies. This review illustrates recent studies on amyloid-membrane interactions, but it mainly focuses on the challenge issues related to experimental techniques to investigate at the molecular level these interactions and provides thoughts and outlook for future computational studies.     

Computational prediction of actin–actin interaction

Actin is one of the most abundant proteins in eukaryotic cells, where it plays key roles in cell shape, motility, and regulation. Actin is found in globular (G) and filamentous (F) structure in the cell. The helix of actin occurs as a result of polymerization of monomeric G-actin molecules through sequential rowing, is called F-actin. Recently, the crystal structure of an actin dimer has been reported, which details molecular interface in F-actin. In this study, the computational prediction model of actin and actin complex has been constructed base on the atomic model structure of G-actin. To this end, a docking simulation was carried out using predictive docking tools to obtain modeled structures of the actin–actin complex. Following molecular dynamics refinement, hot spots interactions at the protein interface were identified, that were predicted to contribute substantially to the free energy of binding. These provided a detailed prediction of key amino acid interactions at the protein–protein interface. The obtained model can be used for future experimental and computational studies to draw biological and functional conclusions. Also, the identified interactions will be used for designing next studies to understand the occurrence of F-actin structure.     

Atomic-level characterization of disordered protein ensembles

The roles of unfolded states of proteins in normal folding and in diseases involving aggregation, as well as the prevalence and regulatory functions of intrinsically disordered proteins, have become increasingly recognized. The structural representation of these disordered states as ensembles of interconverting conformers can therefore provide critical insights. Experimental methods can be used to probe ensemble-averaged structural properties of disordered states and computational approaches generate representative ensembles of conformers using experimental restraints. In particular, NMR and small-angle X-ray scattering provide quantitative data that can readily be incorporated into calculations. These techniques have gleaned structural information about denatured, unfolded and intrinsically disordered proteins. The use of experimental data in different computational approaches, including ensemble molecular dynamics simulations and algorithms that assign populations to pregenerated conformers, has highlighted the presence of both local and long-range structure, and the occurrence of native-like and non-native interactions in unfolded and denatured states. Analysis of the resulting ensembles has suggested important implications of this fluctuating structure for folding, aggregation and binding.     

Acyl chain conformations in phospholipid bilayers: a comparative study of docosahexaenoic acid and saturated fatty acids

A variety of experimental methods indicate unique biophysical properties of membranes containing the highly polyunsaturated ω-3 fatty acid, docosahexaenoic acid (DHA). In the following we review the atomically detailed picture of DHA acyl chains structure and dynamics that has emerged from computational studies of this system in our lab. A comprehensive approach, beginning with ab-initio quantum chemical studies of model compounds representing segments of DHA and ending with large scale classical molecular dynamics simulations of DHA-containing bilayers, is described with particular attention paid to contrasting the properties of DHA with those of saturated fatty acids. Connection with experiment is made primarily through comparison with Nuclear Magnetic Resonance (NMR) studies, particularly those that probe details of the chain structure and dynamics. Our computational results suggest that low torsional energy barriers, comparable to kT at physiological conditions, for the rotatable bonds in the DHA chain are the key to the differences observed between polyunsaturated and saturated acyl chains.     

Molecular modelling and simulations in cancer research

The complexity of cancer and the vast amount of experimental data available have made computer-aided approaches necessary. Biomolecular modelling techniques are becoming increasingly easier to use, whereas hardware and software are becoming better and cheaper. Cross-talk between theoretical and experimental scientists dealing with cancer-research from a molecular approach, however, is still uncommon. This is in contrast to other fields, such as amyloid-related diseases, where molecular modelling studies are widely acknowledged. The aim of this review paper is therefore to expose some of the more common approaches in molecular modelling to cancer scientists in simple terms, illustrating success stories while also revealing the limitations of computational studies at the molecular level.     

Automatic procedure for using models of proteins in molecular replacement

In a crystallography experiment, a crystal is irradiated with X-rays whose diffracted waves are collected and measured. The reconstruction of the structure of the molecule in the crystal requires knowledge of the phase of the diffracted waves, information that is lost in the passage from the three-dimensional structure of the molecule to its diffraction pattern. It can be recovered using experimental methods such as heavy-atom isomorphous replacement and anomalous scattering or by molecular replacement, which relies on the availability of an atomic model of the target structure. This can be the structure of the target protein itself, if a previous structure determination is available, or a computational model or, in some cases, the structure of a homologous protein. It is not straightforward to predict beforehand whether or not a computational model will work in a molecular replacement experiment, although some rules of thumb exist. The consensus is that even minor differences in the quality of the model, which are rather difficult to estimate a priori, can have a significant effect on the outcome of the procedure. We describe here a method for quickly assessing whether a protein structure can be solved by molecular replacement. The procedure consists in submitting the sequence of the target protein to a selected list of freely available structure prediction servers, cluster the resulting models, select the representative structures of each cluster and use them as search models in an automatic phasing procedure. We tested the procedure using the structure factors of newly released proteins of known structure downloaded from the Protein Data Bank as soon as they were made available. Using our automatic procedure we were able to obtain an interpretable electron density map in more than half the cases.     

Studies of folding and misfolding using simplified models

Computer simulations are as vital to our studies of biological systems as experiments. They bridge and rationalize experimental observations, extend the experimental "field of view", which is often limited to a specific time or length scale, and, most importantly, provide novel insights into biological systems, offering hypotheses about yet-to-be uncovered phenomena. These hypotheses spur further experimental discoveries. Simplified molecular models have a special place in the field of computational biology. Branded as less accurate than all-atom protein models, they have offered what all-atom molecular dynamics simulations could not--the resolution of the length and time scales of biological phenomena. Not only have simplified models proven to be accurate in explaining or reproducing several biological phenomena, they have also offered a novel multiscale computational strategy for accessing a broad range of time and length scales upon integration with traditional all-atom simulations. Recent computer simulations of simplified models have shaken or advanced the established understanding of biological phenomena. It was demonstrated that simplified models can be as accurate as traditional molecular dynamics approaches in identifying native conformations of proteins. Their application to protein structure prediction yielded phenomenal accuracy in recapitulating native protein conformations. New studies that utilize the synergy of simplified protein models with all-atom models and experiments yielded novel insights into complex biological processes, such as protein folding, aggregation and the formation of large protein complexes.     

Novel computational biology methods and their applications to drug discovery

Computational biology methods are now firmly entrenched in the drug discovery process. These methods focus on modeling and simulations of biological systems to complement and direct conventional experimental approaches. Two important branches of computational biology include protein homology modeling and the computational biophysics method of molecular dynamics. Protein modeling methods attempt to accurately predict three-dimensional (3D) structures of uncrystallized proteins for subsequent structure-based drug design applications. Molecular dynamics methods aim to elucidate the molecular motions of the static representations of crystallized protein structures. In this review we highlight recent novel methodologies in the field of homology modeling and molecular dynamics. Selected drug discovery applications using these methods conclude the review.