Problems In Structural Biology Research Articles

Structure of a photosynthetic reaction centre determined by serial femtosecond crystallography

Serial femtosecond crystallography is an X-ray free-electron-laser-based method with considerable potential to have an impact on challenging problems in structural biology. Here we present X-ray diffraction data recorded from microcrystals of the Blastochloris viridis photosynthetic reaction centre to 2.8 Å resolution and determine its serial femtosecond crystallography structure to 3.5 Å resolution. Although every microcrystal is exposed to a dose of 33 MGy, no signs of X-ray-induced radiation damage are visible in this integral membrane protein structure.

Mass spectrometry supported determination of protein complex structure

Virtually all the biological processes are controlled and catalyzed by proteins which are, in many cases, in complexes with other proteins. Therefore, understanding the architecture and structure of protein complexes is critical to understanding their biological role and function. Traditionally, high-resolution data for structural analysis of proteins or protein complexes have been generated by the powerful methods of X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. More recently, mass spectrometry (MS)-based methods have been developed that provide low-resolution structural information, which contributes to the determination of the native structure of protein complexes that have remained refractory to the high-resolution methods. Native MS and affinity purification coupled with MS (AP-MS) have been used to characterize the composition, stoichiometry and connectivity of protein complexes. Chemical cross-linking MS (CX-MS) provides protein-protein interaction data supplemented with distance information that indicates residues that are in close spatial proximity in the native protein structure. Hydrogen-deuterium exchange combined with MS has been used to map protein-protein binding sites. Here, we focus on recent developments in CX-MS and native MS and their application to challenging problems in structural biology.

Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models

Spatially proximate amino acids in a protein tend to coevolve. A protein's three-dimensional (3D) structure hence leaves an echo of correlations in the evolutionary record. Reverse engineering 3D structures from such correlations is an open problem in structural biology, pursued with increasing vigor as more and more protein sequences continue to fill the data banks. Within this task lies a statistical inference problem, rooted in the following: correlation between two sites in a protein sequence can arise from firsthand interaction but can also be network-propagated via intermediate sites; observed correlation is not enough to guarantee proximity. To separate direct from indirect interactions is an instance of the general problem of inverse statistical mechanics, where the task is to learn model parameters (fields, couplings) from observables (magnetizations, correlations, samples) in large systems. In the context of protein sequences, the approach has been referred to as direct-coupling analysis. Here we show that the pseudolikelihood method, applied to 21-state Potts models describing the statistical properties of families of evolutionarily related proteins, significantly outperforms existing approaches to the direct-coupling analysis, the latter being based on standard mean-field techniques. This improved performance also relies on a modified score for the coupling strength. The results are verified using known crystal structures of specific sequence instances of various protein families. Code implementing the new method can be found at http://plmdca.csc.kth.se/.

Enhanced Sampling Assisted Flexible Fitting of Atomic Structures into Electron Microscopy Maps

For many problems in structural biology, flexible-fitting computational algorithms are often useful in interpreting low-resolution electron microscopy (EM) maps of macromolecular assemblies. A widely used atomistic simulation technique is molecular dynamics flexible fitting (MDFF), which has been applied to generate structural models of large complexes. All-atom explicit-solvent MDFF simulations are not only computationally demanding, but also can be sensitive to the resolution of the target EM map. Moreover, functional movements of many biomolecules require large-scale conformational reorganization elicited via domain translations/rotations, where methods such as MDFF may be limited in capturing the rotations of structural elements. To decrease the computational cost and alleviate the limitations stemming from domain orientations, one can combine MDFF with an enhanced sampling technique to accelerate the conformational search in a single atomistic simulation. In this work, we judiciously combine MDFF with temperature-accelerated molecular dynamics (TAMD), an enhanced sampling method, and carry out TAMD-assisted MDFF (TAMDFF) simulations of proteins and nucleic acids. We find that TAMDFF simulations can achieve target structures of similar quality as MDFF on short timescales. In some cases, only TAMDFF simulations are able to capture conformational changes likely because MDFF simulations are unable to overcome the underlying free-energy barriers. We suggest that TAMDFF may be a viable strategy for structural refinement of large ribonucleoprotein complexes such as the ribosome.

Protein interface classification by evolutionary analysis

BackgroundDistinguishing biologically relevant interfaces from lattice contacts in protein crystals is a fundamental problem in structural biology. Despite efforts towards the computational prediction of interface character, many issues are still unresolved.ResultsWe present here a protein-protein interface classifier that relies on evolutionary data to detect the biological character of interfaces. The classifier uses a simple geometric measure, number of core residues, and two evolutionary indicators based on the sequence entropy of homolog sequences. Both aim at detecting differential selection pressure between interface core and rim or rest of surface. The core residues, defined as fully buried residues (>95% burial), appear to be fundamental determinants of biological interfaces: their number is in itself a powerful discriminator of interface character and together with the evolutionary measures it is able to clearly distinguish evolved biological contacts from crystal ones. We demonstrate that this definition of core residues leads to distinctively better results than earlier definitions from the literature. The stringent selection and quality filtering of structural and sequence data was key to the success of the method. Most importantly we demonstrate that a more conservative selection of homolog sequences - with relatively high sequence identities to the query - is able to produce a clearer signal than previous attempts.ConclusionsAn evolutionary approach like the one presented here is key to the advancement of the field, which so far was missing an effective method exploiting the evolutionary character of protein interfaces. Its coverage and performance will only improve over time thanks to the incessant growth of sequence databases. Currently our method reaches an accuracy of 89% in classifying interfaces of the Ponstingl 2003 datasets and it lends itself to a variety of useful applications in structural biology and bioinformatics. We made the corresponding software implementation available to the community as an easy-to-use graphical web interface at http://www.eppic-web.org.

Eigenvector synchronization, graph rigidity and the molecule problem.

The graph realization problem has received a great deal of attention in recent years, due to its importance in applications such as wireless sensor networks and structural biology. In this paper, we extend the previous work and propose the 3D-As-Synchronized-As-Possible (3D-ASAP) algorithm, for the graph realization problem in ℝ3, given a sparse and noisy set of distance measurements. 3D-ASAP is a divide and conquer, non-incremental and non-iterative algorithm, which integrates local distance information into a global structure determination. Our approach starts with identifying, for every node, a subgraph of its 1-hop neighborhood graph, which can be accurately embedded in its own coordinate system. In the noise-free case, the computed coordinates of the sensors in each patch must agree with their global positioning up to some unknown rigid motion, that is, up to translation, rotation and possibly reflection. In other words, to every patch, there corresponds an element of the Euclidean group, Euc(3), of rigid transformations in ℝ3, and the goal was to estimate the group elements that will properly align all the patches in a globally consistent way. Furthermore, 3D-ASAP successfully incorporates information specific to the molecule problem in structural biology, in particular information on known substructures and their orientation. In addition, we also propose 3D-spectral-partitioning (SP)-ASAP, a faster version of 3D-ASAP, which uses a spectral partitioning algorithm as a pre-processing step for dividing the initial graph into smaller subgraphs. Our extensive numerical simulations show that 3D-ASAP and 3D-SP-ASAP are very robust to high levels of noise in the measured distances and to sparse connectivity in the measurement graph, and compare favorably with similar state-of-the-art localization algorithms.

An expert protein loop refinement protocol by molecular dynamics simulations with restraints

Protein structure prediction (PSP) is a long standing problem in structural biology and bioinformatics. Within the PSP problem loop refinement is a major bottleneck. In this article we report the latest version of the CReF expert predictor system for the PSP problem with emphasis on loop refinement of the approximate 3-D structure 1ZDD_P of the Z34C mini protein predicted by CReF. We designed a loop refinement protocol based on seven molecular dynamics (MD) simulations runs at different temperatures. We found that, by letting the loop residues move freely during dynamics at 325K and restraining the internal coordinates of the correctly predicted helical structures, while allowing them to move relative to each other, the refinement protocol was very effective in predicting an accurate loop conformation in the first 100ps of a 1000ps MD simulation. The quality of the predictions was confirmed by the RMSD between refined and experimental structures which varied from 0.6 to 1.3Å. In addition, stereochemical analyses showed that 100% of all residues of the refined 1ZDD_P, including those in the loop, populates the most favorable core regions of the Ramachandran plot. Our study suggests that the proposed protocol may be suitable to refine more complex mini proteins with different classes and architectures.

Using Enhanced Sampling and Structural Restraints to Refine Atomic Structures into Low-Resolution Electron Microscopy Maps

For a variety of problems in structural biology, low-resolution maps generated by electron microscopy imaging are often interpreted with the help of various flexible-fitting computational algorithms. In this work, we systematically analyze the quality of final models of various proteins obtained via molecular dynamics flexible fitting (MDFF) by varying the map-resolution, strength of structural restraints, and the steering forces. We find that MDFF can be extended to understand conformational changes in lower-resolution maps if larger structural restraints and lower steering forces are used to prevent overfitting. We further show that the capabilities of MDFF can be extended by combining it with an enhanced conformational sampling method, temperature-accelerated molecular dynamics (TAMD). Specifically, either TAMD can be used to generate better starting configurations for MDFF fitting or TAMD-assisted MDFF (TAMDFF) can be performed to accelerate conformational search in atomistic simulations.

Self-Assembly of Thin Plates from Micrococcal Nuclease-Digested Chromatin of Metaphase Chromosomes

The three-dimensional organization of the enormously long DNA molecules packaged within metaphase chromosomes has been one of the most elusive problems in structural biology. Chromosomal DNA is associated with histones and different structural models consider that the resulting long chromatin fibers are folded forming loops or more irregular three-dimensional networks. Here, we report that fragments of chromatin fibers obtained from human metaphase chromosomes digested with micrococcal nuclease associate spontaneously forming multilaminar platelike structures. These self-assembled structures are identical to the thin plates found previously in partially denatured chromosomes. Under metaphase ionic conditions, the fragments that are initially folded forming the typical 30-nm chromatin fibers are untwisted and incorporated into growing plates. Large plates can be self-assembled from very short chromatin fragments, indicating that metaphase chromatin has a high tendency to generate plates even when there are many discontinuities in the DNA chain. Self-assembly at 37°C favors the formation of thick plates having many layers. All these results demonstrate conclusively that metaphase chromatin has the intrinsic capacity to self-organize as a multilayered planar structure. A chromosome structure consistent of many stacked layers of planar chromatin avoids random entanglement of DNA, and gives compactness and a high physical consistency to chromatids.

Accurate Prediction of Protein Structural Class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods.

Disentangling the Coil: Modulation of Conformational and Dynamic Properties by Site-Directed Mutation in the Non-Native State of Hen Egg White Lysozyme

The conformational analysis of non-native states of proteins remains one of the most difficult problems in structural biology, because such states are represented by a superimposition of several states that are rapidly interconverting. Hence, model building of the conformational ensemble remains challenging, although many different biophysical observables can be determined in non-native states of proteins. Here, we present a comprehensive analysis of non-native states of wild-type and mutant forms of the model protein lysozyme by nuclear magnetic resonance spectroscopy. Relaxation rates, chemical shifts, backbone and side chain coupling constants, residual dipolar couplings, diffusion rate constants, and small-angle scattering data merged with computational approaches, such as flexible meccano and ASTEROIDS, allow the description of the non-native state of hen egg white lysozyme in unprecedented detail.

Automated sample-scanning methods for radiation damage mitigation and diffraction-based centering of macromolecular crystals

Automated scanning capabilities have been added to the data acquisition software, JBluIce-EPICS, at the National Institute of General Medical Sciences and the National Cancer Institute Collaborative Access Team (GM/CA CAT) at the Advanced Photon Source. A `raster' feature enables sample centering via diffraction scanning over two-dimensional grids of simple rectangular or complex polygonal shape. The feature is used to locate crystals that are optically invisible owing to their small size or are visually obfuscated owing to properties of the sample mount. The raster feature is also used to identify the best-diffracting regions of large inhomogeneous crystals. Low-dose diffraction images taken at grid positions are automatically processed in real time to provide a quick quality ranking of potential data-collection sites. A `vector collect' feature mitigates the effects of radiation damage by scanning the sample along a user-defined three-dimensional vector during data collection to maximize the use of the crystal volume and the quality of the collected data. These features are integrated into the JBluIce-EPICS data acquisition software developed at GM/CA CAT where they are used in combination with a robust mini-beam of rapidly changeable diameter from 5 µm to 20 µm. The powerful software-hardware combination is being applied to challenging problems in structural biology.

Guest Editors' Introduction to the Special Section on Bioinformatics Research and Applications

This special section includes a selection of papers presented at the Eighth International Symposium on Bioinformatics Research and Application (ISBRA), which was held in Dallas, Texas, on 21-23 May 2012. The ISBRA symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications. In 2012, 66 papers were submitted in response to the call for papers, out of which 26 papers appeared in the ISBRA proceedings published as volume 7292 of Springer Verlag's Lecture Notes in Bioinformatics series. Extended versions of nine symposium papers were invited and accepted for publication in this special section following a rigorous review process. The selected papers cover a broad range of bioinformatics topics, including biological networks, computational complexity of problems in structural biology and genomics, and phylogenetic inference and analysis. Herein, we briefly introduce each of them.

Determinants of Gas-Phase Disassembly Behavior in Homodimeric Protein Complexes with Related Yet Divergent Structures

The overall structure of a protein-protein complex reflects an intricate arrangement of noncovalent interactions. Whereas intramolecular interactions confer secondary and tertiary structure to individual subunits, intermolecular interactions lead to quaternary structure--the ordered aggregation of separate polypeptide chains into multisubunit assemblies. The specific ensemble of noncovalent contacts dictates the stability of subunit folds, enforces protein-protein binding specificity, and determines multimer stability. Consequently, noncovalent architecture is likely to play a role in the gas-phase dissociation of these assemblies during tandem mass spectrometry (MS/MS). To further advance the applicability of MS/MS to analytical problems in structural biology, a better understanding of the interplay between the structures and fragmentation behaviors of noncovalent protein complexes is essential. The present work constitutes a systematic study of model protein homodimers (bacteriophage N15 Cro, bacteriophage λ Cro, and bacteriophage P22 Arc) with related but divergent structures, both in terms of subunit folds and protein-protein interfaces. Because each of these dimers has a well-characterized structure (solution and/or crystal structure), specific noncovalent features could be correlated with gas-phase disassembly patterns as studied by collision-induced dissociation, surface-induced dissociation, and ion mobility. Of the several respects in which the dimers differed in structure, the presence or absence of intermolecular electrostatic contacts exerted the most significant influence on the gas-phase dissociation behavior. This is attributed to the well-known enhancement of ionic interactions in the absence of bulk solvent. Because salt bridges are general contributors to both intermolecular and intramolecular stability in protein complexes, these observations are broadly applicable to aid in the interpretation or prediction of dissociation spectra for noncovalent protein assemblies.

A new approach for structure analysis of two‐dimensional membrane protein crystals using X‐ray powder diffraction data

The application of powder diffraction methods to problems in structural biology is generally regarded as intractable because of the large number of unresolved, overlapping X-ray reflections. Here, we use information about unit cell lattice parameters, space group transformations, and chemical composition as a priori information in a bootstrap process that resolves the ambiguities associated with overlapping reflections. The measured ratios of reflections that can be resolved experimentally are used to refine the position, the shape, and the orientation of low-resolution molecular structures within the unit cell, in leading to the resolution of the overlapping reflections. The molecular model is then made progressively more sophisticated as additional diffraction information is included in the analysis. We apply our method to the recovery of the structure of the bacteriorhodopsin molecule (bR) to a resolution of 7 Å using experimental data obtained from two-dimensional purple membrane crystals. The approach can be used to determine the structure factors directly or to provide reliable low-resolution phase information that can be refined further by the conventional methods of protein crystallography.

Sub-Nanoscale Surface Ruggedness Provides a Water-Tight Seal for Exposed Regions in Soluble Protein Structure

Soluble proteins must maintain backbone hydrogen bonds (BHBs) water-tight to ensure structural integrity. This protection is often achieved by burying the BHBs or wrapping them through intermolecular associations. On the other hand, water has low coordination resilience, with loss of hydrogen-bonding partnerships carrying significant thermodynamic cost. Thus, a core problem in structural biology is whether natural design actually exploits the water coordination stiffness to seal the backbone in regions that are exposed to the solvent. This work explores the molecular design features that make this type of seal operative, focusing on the side-chain arrangements that shield the protein backbone. We show that an efficient sealing is achieved by adapting the sub-nanoscale surface topography to the stringency of water coordination: an exposed BHB may be kept dry if the local concave curvature is small enough to impede formation of the coordination shell of a penetrating water molecule. Examination of an exhaustive database of uncomplexed proteins reveals that exposed BHBs invariably occur within such sub-nanoscale cavities in native folds, while this level of local ruggedness is absent in other regions. By contrast, BHB exposure in misfolded proteins occurs with larger local curvature promoting backbone hydration and consequently, structure disruption. These findings unravel physical constraints fitting a spatially dependent least-action for water coordination, introduce a molecular design concept, and herald the advent of water-tight peptide-based materials with sufficient backbone exposure to remain flexible.

MUSTANG-MR Structural Sieving Server: Applications in Protein Structural Analysis and Crystallography

BackgroundA central tenet of structural biology is that related proteins of common function share structural similarity. This has key practical consequences for the derivation and analysis of protein structures, and is exploited by the process of “molecular sieving” whereby a common core is progressively distilled from a comparison of two or more protein structures. This paper reports a novel web server for “sieving” of protein structures, based on the multiple structural alignment program MUSTANG.Methodology/Principal Findings“Sieved” models are generated from MUSTANG-generated multiple alignment and superpositions by iteratively filtering out noisy residue-residue correspondences, until the resultant correspondences in the models are optimally “superposable” under a threshold of RMSD. This residue-level sieving is also accompanied by iterative elimination of the poorly fitting structures from the input ensemble. Therefore, by varying the thresholds of RMSD and the cardinality of the ensemble, multiple sieved models are generated for a given multiple alignment and superposition from MUSTANG. To aid the identification of structurally conserved regions of functional importance in an ensemble of protein structures, Lesk-Hubbard graphs are generated, plotting the number of residue correspondences in a superposition as a function of its corresponding RMSD. The conserved “core” (or typically active site) shows a linear trend, which becomes exponential as divergent parts of the structure are included into the superposition.ConclusionsThe application addresses two fundamental problems in structural biology: First, the identification of common substructures among structurally related proteins—an important problem in characterization and prediction of function; second, generation of sieved models with demonstrated uses in protein crystallographic structure determination using the technique of Molecular Replacement.

English

Protein folding is useful and challenging problem in structural biology. Molecular dynamics (MD) and simulation has proved to be a paramount tool and was widely used to study protein structures, folding kinetics and thermodynamics and structure-stability-function relationship. It was also used to help engineering and designing new proteins, and to answer even more general questions for target validation, such as the evolution principle of protein families. MD simulation is still undergoing rapid developments. The first trend is to toward developing new coarse-grained models and studying larger and more complex molecular systems such as protein-protein complex and their assembling process, and structure; the second trend is toward building high resolution protein models and explore more detailed and accurate pictures of protein folding and the associated processes. Keywords- Protein folding, Molecular dynamics, Protein Quality Control, protein-protein interactions

KING (Kinemage, Next Generation): A versatile interactive molecular and scientific visualization program

Proper visualization of scientific data is important for understanding spatial relationships. Particularly in the field of structural biology, where researchers seek to gain an understanding of the structure and function of biological macromolecules, it is important to have access to visualization programs which are fast, flexible, and customizable. We present KiNG, a Java program for visualizing scientific data, with a focus on macromolecular visualization. KiNG uses the kinemage graphics format, which is tuned for macromolecular structures, but is also ideal for many other kinds of spatially embedded information. KiNG is written in cross-platform, open-source Java code, and can be extended by end users through simple or elaborate "plug-in" modules. Here, we present three such applications of KiNG to problems in structural biology (protein backbone rebuilding), bioinformatics of high-dimensional data (e.g., protein sidechain chi angles), and classroom education (molecular illustration). KiNG is a mature platform for rapidly creating and capitalizing on scientific visualizations. As a research tool, it is invaluable as a test bed for new methods of visualizing scientific data and information. It is also a powerful presentation tool, whether for structure browsing, teaching, direct 3D display on the web, or as a method for creating pictures and videos for publications. KiNG is freely available for download at http://kinemage.biochem.duke.edu.

Intra and Inter-Molecular Communications Through Protein Structure Network

Communication within and across proteins is crucial for the biological functioning of proteins. Experiments such as mutational studies on proteins provide important information on the amino acids, which are crucial for their function. However, the protein structures are complex and it is unlikely that the entire responsibility of the function rests on only a few amino acids. A large fraction of the protein is expected to participate in its function at some level or other. Thus, it is relevant to consider the protein structures as a completely connected network and then deduce the properties, which are related to the global network features. In this direction, our laboratory has been engaged in representing the protein structure as a network of non-covalent connections and we have investigated a variety of problems in structural biology, such as the identification of functional and folding clusters, determinants of quaternary association and characterization of the network properties of protein structures. We have also addressed a few important issues related to protein dynamics, such as the process of oligomerization in multimers, mechanism of protein folding, and ligand induced communications (allosteric effect). In this review we highlight some of the investigations which we have carried out in the recent past. A review on protein structure graphs was presented earlier, in which the focus was on the graphs and graph spectral properties and their implementation in the study of protein structure graphs/networks (PSN). In this article, we briefly summarize the relevant parts of the methodology and the focus is on the advancement brought out in the understanding of protein structure-function relationships through structure networks. The investigations of structural/biological problems are divided into two parts, in which the first part deals with the analysis of PSNs based on static structures obtained from x-ray crystallography. The second part highlights the changes in the network, associated with biological functions, which are deduced from the network analysis on the structures obtained from molecular dynamics simulations.