Normal-modes-based prediction of protein conformational changes guided by distance constraints

Based on the elastic network model, we develop a novel method that predicts the conformational change of a protein complex given its initial-state crystal structure together with a small set of pairwise distance constraints for the end state. The predicted conformational change, which is a linear combination of multiple low-frequency normal modes that are solved from the elastic network model, is computed as a response displacement induced by a perturbation to the system Hamiltonian that incorporates the given distance constraints. For a list of test cases, we find that the computed response displacement overlaps significantly with the measured conformational changes, when only a handful of pairwise constraints are used (相似文献   

Protein structural alignment for detection of maximally conserved regions

An algorithm for comparison of homologous protein structures and for study of conformational changes in proteins, has been developed. The method is based on identification of pieces of the two molecules that have similar shapes, as determined by the local conformation of the polypeptide chain. Pieces that superpose within a specified tolerance are assembled into domains based on similar transformations for superposition. The result is sets of pieces that represent conserved structural elements and conserved spatial relationships between structural elements within the proteins being compared. A similarity criterion based on maximum distance rather than on root mean square deviation reduces bias by outliers. The utility of the method is demonstrated by using examples from the protein kinase family.     

Estimation of evolutionary distances from protein spatial structures

New equations are derived to estimate the number of amino acid substitutions per site between two homologous proteins from the root mean square (RMS) deviation between two spatial structures and from the fraction of identical residues between two sequences. The equations are based on evolutionary models, analyzing predominantly structural changes and not sequence changes. Evolution of spatial structure is treated as a diffusion in an elastic force field. Diffusion accounts for structural changes caused by amino acid substitutions, and elastic force reflects selection, which preserves protein fold. Obtained equations are supported by analysis of protein spatial structures. Received: 21 September 1995 / Accepted: 19 May 1997     

Multi-LZerD: multiple protein docking for asymmetric complexes

The tertiary structures of protein complexes provide a crucial insight about the molecular mechanisms that regulate their functions and assembly. However, solving protein complex structures by experimental methods is often more difficult than single protein structures. Here, we have developed a novel computational multiple protein docking algorithm, Multi‐LZerD, that builds models of multimeric complexes by effectively reusing pairwise docking predictions of component proteins. A genetic algorithm is applied to explore the conformational space followed by a structure refinement procedure. Benchmark on eleven hetero‐multimeric complexes resulted in near‐native conformations for all but one of them (a root mean square deviation smaller than 2.5Å). We also show that our method copes with unbound docking cases well, outperforming the methodology that can be directly compared with our approach. Multi‐LZerD was able to predict near‐native structures for multimeric complexes of various topologies.Proteins 2012; © 2012 Wiley Periodicals, Inc.     

Use of residual dipolar couplings as restraints in ab initio protein structure prediction

NMR residual dipolar couplings (RDCs), in the form of the projection angles between the respective internuclear bond vectors, are used as structural restraints in the ab initio structure prediction of a test set of six proteins. The restraints are applied using a recently developed SICHO (SIde-CHain-Only) lattice protein model that employs a replica exchange Monte Carlo (MC) algorithm to search conformational space. Using a small number of RDC restraints, the quality of the predicted structures is improved as reflected by lower RMSD/dRMSD (root mean square deviation/distance root mean square deviation) values from the corresponding native structures and by the higher correlation of the most cooperative mode of motion of each predicted structure with that of the native structure. The latter, in particular, has possible implications for the structure-based functional analysis of predicted structures.     

NMR snapshots of a fluctuating protein structure: ubiquitin at 30 bar-3 kbar

Conformational fluctuation plays a key role in protein function, but we know little about the associated structural changes. Here we present a general method for elucidating, at the atomic level, a large-scale shape change of a protein molecule in solution undergoing conformational fluctuation. The method utilizes the intimate relationship between conformation and partial molar volume and determines three-dimensional structures of a protein at different pressures using variable pressure NMR technique, whereby NOE distance and torsion angle constraints are used to create average coordinates. Ubiquitin (pH 4.6 at 20 degrees C) was chosen as the first target, for which structures were determined at 30 bar and at 3 kbar, giving "NMR snapshots" of a fluctuating protein structure at atomic resolution. The result reveals that the helix swings in and out by >3 angstroms with a simultaneous reorientation of the C-terminal segment, providing an "open" conformer suitable for enzyme recognition. Spin relaxation analysis indicates that this fluctuation occurs in the ten microsecond time range with activation volumes -4.2(+/-3.2) and 18.5(+/-3.0) ml/mol for the "closed-to-open" and the "open-to-closed" transitions, respectively.     

Human epidermal growth factor. High resolution solution structure and comparison with human transforming growth factor alpha.

The solution structure of the 53 amino acid peptide hormone, human epidermal growth factor (hEGF), has been determined to high resolution from nuclear magnetic resonance (n.m.r.) data. A large number of internuclear distance and dihedral restraints was obtained, including data from uniformly 15N-labelled hEGF. Dynamical simulated annealing methods using the program XPLOR were used for structure calculation. An improved protocol was developed combining efficient conformational searching at a reduced computational cost. The general fold of the calculated structures compared well with that of a derivative of the carboxy-terminally truncated hEGF determined previously. A group of 44 structures were calculated with no violations greater than 0.3 A and 3 degrees for distance and dihedral restraints, respectively. The average pairwise root mean square (r.m.s.) deviation of all backbone atoms for these structures was 2.25 A for all 53 residues, 0.92 A for the bulk of the protein, and 0.23 A for the functionally important carboxy-terminal domain. Two new helical segments containing highly conserved amino acids have been identified; one between cysteines 6 and 14 and a second at the end of the carboxy-terminal domain. New insight into the molecular architecture of the site of putative receptor binding was provided by comparing the structure of hEGF with its biologically equipotent analogue, human transforming growth factor alpha. This comparison revealed a close structural relationship between the two growth factors and provides an improved understanding of the structure/function relationships in EGF.     

Optimal bundling of transmembrane helices using sparse distance constraints

We present a two-step approach to modeling the transmembrane spanning helical bundles of integral membrane proteins using only sparse distance constraints, such as those derived from chemical cross-linking, dipolar EPR and FRET experiments. In Step 1, using an algorithm, we developed, the conformational space of membrane protein folds matching a set of distance constraints is explored to provide initial structures for local conformational searches. In Step 2, these structures refined against a custom penalty function that incorporates both measures derived from statistical analysis of solved membrane protein structures and distance constraints obtained from experiments. We begin by describing the statistical analysis of the solved membrane protein structures from which the theoretical portion of the penalty function was derived. We then describe the penalty function, and, using a set of six test cases, demonstrate that it is capable of distinguishing helical bundles that are close to the native bundle from those that are far from the native bundle. Finally, using a set of only 27 distance constraints extracted from the literature, we show that our method successfully recovers the structure of dark-adapted rhodopsin to within 3.2 A of the crystal structure.     

Structure and stability of an acidic fibroblast growth factor from Notophthalmus viridescens

The three-dimensional solution structure of an acidic fibroblast growth factor (nFGF-1) from the newt (Notophthalmus viridescens) is determined using multidimensional NMR techniques. Complete assignment of all the atoms ((1)H, (15)N, and (13)C) has been achieved using a variety of triple resonance experiments. 50 structures were calculated using hybrid distance geometry-dynamical simulated annealing technique with a total of 1359 constraints. The atomic root mean square distribution for the backbone atoms in the structured region is 0.60 A. The secondary structural elements include 12 beta-strands arranged antiparallely into a beta-barrel structure. The protein (nFGF-1) exists in a monomeric state upon binding to the ligand, sucrose octa sulfate (SOS), in a stoichiometric ratio of 1:1. The SOS binding site consists of a dense cluster of positively charged residues located at the C-terminal end of the molecule. The conformational stabilities of nFGF-1 and its structural and functional homologue from the human source (hFGF-1) are drastically different. The differential stabilities of nFGF-1 and hFGF-1 are attributed to the differences in the number of hydrogen bonds and the presence of solvent inaccessible cavities in the two proteins.     

Assembly of protein tertiary structures from secondary structures using optimized potentials

We present a simulated annealing-based method for the prediction of the tertiary structures of proteins given knowledge of the secondary structure associated with each amino acid in the sequence. The backbone is represented in a detailed fashion whereas the sidechains and pairwise interactions are modeled in a simplified way, following the LINUS model of Srinivasan and Rose. A perceptron-based technique is used to optimize the interaction potentials for a training set of three proteins. For these proteins, the procedure is able to reproduce the tertiary structures to below 3 A in root mean square deviation (rmsd) from the PDB targets. We present the results of tests on twelve other proteins. For half of these, the lowest energy decoy has a rmsd from the native state below 6 A and, in 9 out of 12 cases, we obtain decoys whose rmsd from the native states are also well below 5 A.     

Near native structure in an RNA collapsed state

Many large RNAs form conformationally collapsed, but non-native, states prior to folding to the native state or assembling with protein cofactors. Although RNA collapsed states play fundamental roles in RNA folding and ribonucleoprotein assembly processes, their structures have been poorly understood. We obtained 12 high-quality structural constraints for the collapsed state formed by the catalytic core of the bI5 intron RNA using site-specific cross-linking mediated by a short-lived reactant. RNA tertiary structures in the collapsed and native states are indistinguishable, even though only the native state forms a solvent-inaccessible core. Thus, structural neighbors in the collapsed state, including several long-range tertiary interactions, are approximately as close in space as in the native state, but RNA packing is sufficiently loose or dynamic to allow access by solvent. Binding by the obligate CBP2 protein cofactor has almost no effect on structural neighbors reported by cross-linking, even though protein binding chases the RNA from the collapsed state to the native state. Protein binding thus appears to promote only the final few angstroms of RNA folding rather than mediate global conformational rearrangements in the catalytic core. The bI5 RNA collapsed state functions to self-chaperone ribonucleoprotein assembly because this conformationally restrained structure lies very near that of the native state and excludes structures that otherwise misassemble efficiently.     

Similarity Measures for Protein Ensembles

Analyses of similarities and changes in protein conformation can provide important information regarding protein function and evolution. Many scores, including the commonly used root mean square deviation, have therefore been developed to quantify the similarities of different protein conformations. However, instead of examining individual conformations it is in many cases more relevant to analyse ensembles of conformations that have been obtained either through experiments or from methods such as molecular dynamics simulations. We here present three approaches that can be used to compare conformational ensembles in the same way as the root mean square deviation is used to compare individual pairs of structures. The methods are based on the estimation of the probability distributions underlying the ensembles and subsequent comparison of these distributions. We first validate the methods using a synthetic example from molecular dynamics simulations. We then apply the algorithms to revisit the problem of ensemble averaging during structure determination of proteins, and find that an ensemble refinement method is able to recover the correct distribution of conformations better than standard single-molecule refinement.     

Protein conformational transitions explored by mixed elastic network models

We develop a mixed elastic network model (MENM) to study large-scale conformational transitions of proteins between two (or more) known structures. Elastic network potentials for the beginning and end states of a transition are combined, in effect, by adding their respective partition functions. The resulting effective MENM energy function smoothly interpolates between the original surfaces, and retains the beginning and end structures as local minima. Saddle points, transition paths, potentials of mean force, and partition functions can be found efficiently by largely analytic methods. To characterize the protein motions during a conformational transition, we follow "transition paths" on the MENM surface that connect the beginning and end structures and are invariant to parameterizations of the model and the mathematical form of the mixing scheme. As illustrations of the general formalism, we study large-scale conformation changes of the motor proteins KIF1A kinesin and myosin II. We generate possible transition paths for these two proteins that reveal details of their conformational motions. The MENM formalism is computationally efficient and generally applicable even for large protein systems that undergo highly collective structural changes.     

Determination of a high-quality nuclear magnetic resonance solution structure of the bovine pancreatic trypsin inhibitor and comparison with three crystal structures.

A high-quality three-dimensional structure of the bovine pancreatic trypsin inhibitor (BPTI) in aqueous solution was determined by 1H nuclear magnetic resonance (n.m.r.) spectroscopy and compared to the three available high-resolution X-ray crystal structures. A newly collected input of 642 distance constraints derived from nuclear Overhauser effects and 115 dihedral angle constraints was used for the structure calculations with the program DIANA, followed by restrained energy minimization with the program AMBER. The BPTI solution structure is represented by a group of 20 conformers with an average root-mean-square deviation (RMSD) relative to the mean solution structure of 0.43 A for backbone atoms and 0.92 A for all heavy atoms of residues 2 to 56. The pairwise RMSD values of the three crystal structures relative to the mean solution structure are 0.76 to 0.85 A for the backbone atoms and 1.24 to 1.33 A for all heavy atoms of residues 2 to 56. Small local differences in backbone atom positions between the solution structure and the X-ray structures near residues 9, 25 to 27, 46 to 48 and 52 to 58, and conformational differences for individual amino acid side-chains were analyzed for possible correlations with intermolecular protein-protein contacts in the crystal lattices, using the pairwise RMSD values among the three crystal structures as a reference.     

A Self-Organizing Algorithm for Modeling Protein Loops

Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies.     

Accurate flexible fitting of high-resolution protein structures into cryo-electron microscopy maps using coarse-grained pseudo-energy minimization

Cryo-electron microscopy (cryo-EM) has been widely used to explore conformational states of large biomolecular assemblies. The detailed interpretation of cryo-EM data requires the flexible fitting of a known high-resolution protein structure into a low-resolution cryo-EM map. To this end, we have developed what we believe is a new method based on a two-bead-per-residue protein representation, and a modified form of the elastic network model that allows large-scale conformational changes while maintaining pseudobonds and secondary structures. Our method minimizes a pseudo-energy which linearly combines various terms of the modified elastic network model energy with a cryo-EM-fitting score and a collision energy that penalizes steric collisions. Unlike previous flexible fitting efforts using the lowest few normal modes, our method effectively utilizes all normal modes so that both global and local structural changes can be fully modeled. We have validated our method for a diverse set of 10 pairs of protein structures using simulated cryo-EM maps with a range of resolutions and in the absence/presence of random noise. We have shown that our method is both accurate and efficient compared with alternative techniques, and its performance is robust to the addition of random noise. Our method is also shown to be useful for the flexible fitting of three experimental cryo-EM maps.     

Release factors eRF1 and RF2: a universal mechanism controls the large conformational changes

Class I release factors 1 and 2 (RF1 and RF2) terminate protein synthesis by recognizing stop codons on the mRNA via their conserved amino acid motifs (NIKS in eRF1 and SPF in RF2) and by the conserved tripeptide (GGQ) interactions with the ribosomal peptidyltransferase center. Crystal structures of eRF1 and RF2 do not fit their ribosomal binding pocket (approximately 73 angstroms). Cryoelectron microscopy indicates large conformational changes in the ribosome-bound RF2. Here, we investigate the conformational dynamics of the eRF1 and RF2 using molecular dynamics simulation, structural alignment, and electrostatic analysis of domain interactions. We show that relaxed eRF1 has a shape remarkably similar to the ribosome-bound RF2 observed by cryoelectron microscopy. The similarity between the two release factors is as good as between elongation factor G and elongation factor Tu-guanosine-5'(beta,gamma-imido)triphosphate-tRNA. Further, the conformational transitions and dynamics of eRF1 and RF2 between the free and ribosome-bound states are most likely controlled by protonation of conserved histidines. For eRF1, the distance between the NIKS and GGQ motifs shrinks from 97.5 angstroms in the crystal to 70-80 angstroms. For RF2, the separation between SPF and GGQ elongates from 32 angstroms in the crystal to 50 angstroms. Coulombic interaction strongly favors the open conformation of eRF1; however, solvation and histidine protonation modulate the domain interactions, making the closed conformation of eRF1 more accessible. Thus, RF1 and RF2 function like molecular machines, most likely fueled by histidine protonation. The unified conformational control and the shapes of eRF1 and RF2 support the proposition that the termination of protein synthesis involves similar mechanisms across species.     

Accurate flexible fitting of high-resolution protein structures to small-angle x-ray scattering data using a coarse-grained model with implicit hydration shell

Small-angle x-ray scattering (SAXS) is a powerful technique widely used to explore conformational states and transitions of biomolecular assemblies in solution. For accurate model reconstruction from SAXS data, one promising approach is to flexibly fit a known high-resolution protein structure to low-resolution SAXS data by computer simulations. This is a highly challenging task due to low information content in SAXS data. To meet this challenge, we have developed what we believe to be a novel method based on a coarse-grained (one-bead-per-residue) protein representation and a modified form of the elastic network model that allows large-scale conformational changes while maintaining pseudobonds and secondary structures. Our method optimizes a pseudoenergy that combines the modified elastic-network model energy with a SAXS-fitting score and a collision energy that penalizes steric collisions. Our method uses what we consider a new implicit hydration shell model that accounts for the contribution of hydration shell to SAXS data accurately without explicitly adding waters to the system. We have rigorously validated our method using five test cases with simulated SAXS data and three test cases with experimental SAXS data. Our method has successfully generated high-quality structural models with root mean-squared deviation of 1 ∼ 3 Å from the target structures.     

Molecular Dynamics Simulations and Principal Component Analysis on Human Laforin Mutation W32G and W32G/K87A

Mutations in human laforin lead to an autosomal neurodegenerative disorder Lafora disease. In N-terminal carbohydrate binding domain of laforin, two mutations W32G and K87A are reported as highly disease causing laforin mutants. Experimental studies reported that mutations are responsible for the abolishment of glycogen binding which is a critical function of laforin. Our current computational study focused on the role of conformational changes in human laforin structure due to existing single mutation W32G and prepared double mutation W32G/K87A related to loss of glycogen binding. We performed 10 ns molecular dynamics (MD) simulation studies in the Gromacs package for both mutations and analyzed the trajectories. From the results, the global properties like root mean square deviation, root mean square fluctuation, radius of gyration, solvent accessible surface area and hydrogen bonds showed structural changes in atomic level observed in W32G and W32G/K87A laforin mutants. The conformational change induced by mutants influenced the loss of the overall stability of the native laforin. Moreover, the change in overall motion of protein was analyzed by principal component analysis and results showed protein clusters expanded more than native and also change in direction in case of double mutant in conformational space. Overall, our report provides theoretical information on loss of structure–function relationship due to flexible nature of laforin mutants. In conclusion, comparative MD simulation studies support the experimental data on W32G and W32G/K87A related to the lafora disease mechanism on glycogen binding.