Validation of macromolecular flexibility in solution by small-angle X-ray scattering (SAXS)

The dynamics of macromolecular conformations are critical to the action of cellular networks. Solution X-ray scattering studies, in combination with macromolecular X-ray crystallography (MX) and nuclear magnetic resonance (NMR), strive to determine complete and accurate states of macromolecules, providing novel insights describing allosteric mechanisms, supramolecular complexes, and dynamic molecular machines. This review addresses theoretical and practical concepts, concerns, and considerations for using these techniques in conjunction with computational methods to productively combine solution-scattering data with high-resolution structures. I discuss the principal means of direct identification of macromolecular flexibility from SAXS data followed by critical concerns about the methods used to calculate theoretical SAXS profiles from high-resolution structures. The SAXS profile is a direct interrogation of the thermodynamic ensemble and techniques such as, for example, minimal ensemble search (MES), enhance interpretation of SAXS experiments by describing the SAXS profiles as population-weighted thermodynamic ensembles. I discuss recent developments in computational techniques used for conformational sampling, and how these techniques provide a basis for assessing the level of the flexibility within a sample. Although these approaches sacrifice atomic detail, the knowledge gained from ensemble analysis is often appropriate for developing hypotheses and guiding biochemical experiments. Examples of the use of SAXS and combined approaches with X-ray crystallography, NMR, and computational methods to characterize dynamic assemblies are presented.     

Folding versus aggregation: polypeptide conformations on competing pathways

Protein aggregation has now become recognised as an important and generic aspect of protein energy landscapes. Since the discovery that numerous human diseases are caused by protein aggregation, the biophysical characterisation of misfolded states and their aggregation mechanisms has received increased attention. Utilising experimental techniques and computational approaches established for the analysis of protein folding reactions has ensured rapid advances in the study of pathways leading to amyloid fibrils and amyloid-related aggregates. Here we describe recent experimental and theoretical advances in the elucidation of the conformational properties of dynamic, heterogeneous and/or insoluble protein ensembles populated on complex, multidimensional protein energy landscapes. We discuss current understanding of aggregation mechanisms in this context and describe how the synergy between biochemical, biophysical and cell-biological experiments are beginning to provide detailed insights into the partitioning of non-native species between protein folding and aggregation pathways.     

Interpretation of chemical shifts and coupling constants in macromolecules

Recent developments in NMR spectroscopy, along with advances in computational techniques, have produced new approaches to the interpretation of chemical shifts and spin-spin coupling constants in biomolecules. Quantum chemical studies of useful accuracy are now becoming more routine and are increasingly being used in conjunction with experimental studies to map out expected structural patterns for peptides and oligonucleotides. Topics of recent special interest include spin couplings across hydrogen bonds and patterns of chemical shift anisotropies, in both diamagnetic and paramagnetic proteins.     

PPM: a side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles

The combination of the wide availability of protein backbone and side-chain NMR chemical shifts with advances in understanding of their relationship to protein structure makes these parameters useful for the assessment of structural-dynamic protein models. A new chemical shift predictor (PPM) is introduced, which is solely based on physical?Cchemical contributions to the chemical shifts for both the protein backbone and methyl-bearing amino-acid side chains. To explicitly account for the effects of protein dynamics on chemical shifts, PPM was directly refined against 100?ns long molecular dynamics (MD) simulations of 35 proteins with known experimental NMR chemical shifts. It is found that the prediction of methyl-proton chemical shifts by PPM from MD ensembles is improved over other methods, while backbone C??, C??, C??, N, and H^N chemical shifts are predicted at an accuracy comparable to the latest generation of chemical shift prediction programs. PPM is particularly suitable for the rapid evaluation of large protein conformational ensembles on their consistency with experimental NMR data and the possible improvement of protein force fields from chemical shifts.     

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.     

Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing

Although three-dimensional protein structure determination using nuclear magnetic resonance (NMR) spectroscopy is a computationally costly and tedious process that would benefit from advanced computational techniques, it has not garnered much research attention from specialists in bioinformatics and computational biology. In this paper, we review recent advances in computational methods for NMR protein structure determination. We summarize the advantages of and bottlenecks in the existing methods and outline some open problems in the field. We also discuss current trends in NMR technology development and suggest directions for research on future computational methods for NMR.     

核磁共振波谱应用于结构生物学的研究进展

综述了核磁共振波谱在结构生物学研究中的进展。在溶液中测定生物大分子的结构,分子大小的限制正被减少,尽管新结构的测定仍然需要付出比较大的努力。核磁共振是一个有效的手段,可用于研究在许多细胞过程中存在的弱的或者瞬态的蛋白质-蛋白质相互作用。结构的柔性在蛋白质分子功能中起了中心作用。由于最近方法学的发展,使NMR可以表征蛋白质的动力学,从而可以对分子机制有新的认识。核磁共振波谱可以在原子分辨率下表征无序的蛋白质系统,可以研究折叠路径。跨膜蛋白在细胞中起了关键作用,这使它们成为药物的靶标。应用液体和固体核磁共振技术已经成功测定了跨膜蛋白质的结构。     

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.     

Stochastic Simulation of Biomolecular Networks in Dynamic Environments

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.     

NMR and small-angle scattering-based structural analysis of protein complexes in solution

Structural analysis of multi-domain protein complexes is a key challenge in current biology and a prerequisite for understanding the molecular basis of essential cellular processes. The use of solution techniques is important for characterizing the quaternary arrangements and dynamics of domains and subunits of these complexes. In this respect solution NMR is the only technique that allows atomic- or residue-resolution structure determination and investigation of dynamic properties of multi-domain proteins and their complexes. As experimental NMR data for large protein complexes are sparse, it is advantageous to combine these data with additional information from other solution techniques. Here, the utility and computational approaches of combining solution state NMR with small-angle X-ray and Neutron scattering (SAXS/SANS) experiments for structural analysis of large protein complexes is reviewed. Recent progress in experimental and computational approaches of combining NMR and SAS are discussed and illustrated with recent examples from the literature. The complementary aspects of combining NMR and SAS data for studying multi-domain proteins, i.e. where weakly interacting domains are connected by flexible linkers, are illustrated with the structural analysis of the tandem RNA recognition motif (RRM) domains (RRM1-RRM2) of the human splicing factor U2AF65 bound to a nine-uridine (U9) RNA oligonucleotide.     

Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.     

COCO: a simple tool to enrich the representation of conformational variability in NMR structures

NMR structures are typically deposited in databases such as the PDB in the form of an ensemble of structures. Generally, each of the models in such an ensemble satisfies the experimental data and is equally valid. No unique solution can be calculated because the experimental NMR data is insufficient, in part because it reflects the conformational variability and dynamical behavior of the molecule in solution. Even for relatively rigid molecules, the limited number of structures that are typically deposited cannot completely encompass the structural diversity allowed by the observed NMR data, but they can be chosen to try and maximize its representation. We describe here the adaptation and application of techniques more commonly used to examine large ensembles from molecular dynamics simulations, to the analysis of NMR ensembles. The approach, which is based on principal component analysis, we call COCO ("Complementary Coordinates"). The COCO approach analyses the distribution of an NMR ensemble in conformational space, and generates a new ensemble that fills "gaps" in the distribution. The method is very rapid, and analysis of a 25-member ensemble and generation of a new 25 member ensemble typically takes 1-2 min on a conventional workstation. Applied to the 545 structures in the RECOORD database, we find that COCO generates new ensembles that are as structurally diverse-both from each other and from the original ensemble-as are the structures within the original ensemble. The COCO approach does not explicitly take into account the NMR restraint data, yet in tests on selected structures from the RECOORD database, the COCO ensembles are frequently good matches to this data, and certainly are structures that can be rapidly refined against the restraints to yield high-quality, novel solutions. COCO should therefore be a useful aid in NMR structure refinement and in other situations where a richer representation of conformational variability is desired-for example in docking studies. COCO is freely accessible via the website www.ccpb.ac.uk/COCO.     

Characterizing RNA ensembles from NMR data with kinematic models

Functional mechanisms of biomolecules often manifest themselves precisely in transient conformational substates. Researchers have long sought to structurally characterize dynamic processes in non-coding RNA, combining experimental data with computer algorithms. However, adequate exploration of conformational space for these highly dynamic molecules, starting from static crystal structures, remains challenging. Here, we report a new conformational sampling procedure, KGSrna, which can efficiently probe the native ensemble of RNA molecules in solution. We found that KGSrna ensembles accurately represent the conformational landscapes of 3D RNA encoded by NMR proton chemical shifts. KGSrna resolves motionally averaged NMR data into structural contributions; when coupled with residual dipolar coupling data, a KGSrna ensemble revealed a previously uncharacterized transient excited state of the HIV-1 trans-activation response element stem–loop. Ensemble-based interpretations of averaged data can aid in formulating and testing dynamic, motion-based hypotheses of functional mechanisms in RNAs with broad implications for RNA engineering and therapeutic intervention.     

Computational methods for protein identification from mass spectrometry data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology.     

Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1

The dynamic interactions between G protein-coupled receptors (GPCRs) and their cognate protein partners are central to several cell signaling pathways. For example, the association of CXC chemokine receptor 1 (CXCR1) with its cognate chemokine, interleukin-8 (IL8 or CXCL8) initiates pathways leading to neutrophil-mediated immune responses. The N-terminal domain of chemokine receptors confers ligand selectivity, but unfortunately the conformational dynamics of this intrinsically disordered region remains unresolved. In this work, we have explored the interaction of CXCR1 with IL8 by microsecond time scale coarse-grain simulations, complemented by atomistic models and NMR chemical shift predictions. We show that the conformational plasticity of the apo-receptor N-terminal domain is restricted upon ligand binding, driving it to an open C-shaped conformation. Importantly, we corroborated the dynamic complex sampled in our simulations against chemical shift perturbations reported by previous NMR studies and show that the trends are similar. Our results indicate that chemical shift perturbation is often not a reporter of residue contacts in such dynamic associations. We believe our results represent a step forward in devising a strategy to understand intrinsically disordered regions in GPCRs and how they acquire functionally important conformational ensembles in dynamic protein-protein interfaces.     

Protein dynamics from solution NMR

Solution nuclear magnetic resonance (NMR) spectroscopy is unique in its ability to elucidate the details of atomic-level structural and dynamical properties of biological macromolecules under native-like conditions. Recent advances in NMR techniques and protein sample preparation now allow comprehensive investigation of protein dynamics over timescales ranging 14 orders of magnitude at nearly every atomic site. Thus, solution NMR is poised to reveal aspects of the physico-chemical properties that govern the ensemble distribution of protein conformers and the dynamics of their interconversion. We review these advances as well as their recent application to the study of proteins.     

Protein dynamics from solution NMR: theory and applications

Solution nuclear magnetic resonance (NMR) spectroscopy is unique in its ability to elucidate the details of atomic-level structural and dynamical properties of biological macromolecules under native-like conditions. Recent advances in NMR techniques and protein sample preparation now allow comprehensive investigation of protein dynamics over timescales ranging 14 orders of magnitude at nearly every atomic site. Thus, solution NMR is poised to reveal aspects of the physico-chemical properties that govern the ensemble distribution of protein conformers and the dynamics of their interconversion. We review these advances as well as their recent application to the study of proteins.     

Determination of three-dimensional structures of proteins and nucleic acids in solution by nuclear magnetic resonance spectroscopy

Nuclear magnetic resonance (NMR) spectroscopy has evolved over the last decade into a powerful method for determining three-dimensional structures of biological macromolecules in solution. Key advances have been the introduction of two-dimensional experiments, high-field superconducting magnets, and computational procedures for converting the NMR-derived interproton distances and torsion angles into three-dimensional structures. This article outlines the methodology employed, describes the major NMR experiments necessary for the spectral analysis of macromolecules, and discusses the computational approaches employed to date. The present state of the art is illustrated using a variety of examples, and future developments are indicated.     

NMR illuminates intrinsic disorder

Nuclear magnetic resonance (NMR) has long been instrumental in the characterization of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs). This method continues to offer rich insights into the nature of IDPs in solution, especially in combination with other biophysical methods such as small-angle scattering, single-molecule fluorescence, electron paramagnetic resonance (EPR), and mass spectrometry. Substantial advances have been made in recent years in studies of proteins containing both ordered and disordered domains and in the characterization of problematic sequences containing repeated tracts of a single or a few amino acids. These sequences are relevant to disease states such as Alzheimer's, Parkinson's, and Huntington's diseases, where disordered proteins misfold into harmful amyloid. Innovative applications of NMR are providing novel insights into mechanisms of protein aggregation and the complexity of IDP interactions with their targets. As a basis for understanding the solution structural ensembles, dynamic behavior, and functional mechanisms of IDPs and IDRs, NMR continues to prove invaluable.     

Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models

Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient.