Integrative structural modeling with small angle X-ray scattering profiles

Recent technological advances enabled high-throughput collection of Small Angle X-ray Scattering (SAXS) profiles of biological macromolecules. Thus, computational methods for integrating SAXS profiles into structural modeling are needed more than ever. Here, we review specifically the use of SAXS profiles for the structural modeling of proteins, nucleic acids, and their complexes. First, the approaches for computing theoretical SAXS profiles from structures are presented. Second, computational methods for predicting protein structures, dynamics of proteins in solution, and assembly structures are covered. Third, we discuss the use of SAXS profiles in integrative structure modeling approaches that depend simultaneously on several data types.     

Structural analysis of intrinsically disordered proteins by small-angle X-ray scattering

Small-angle scattering of X-rays (SAXS) is an established method to study the overall structure and structural transitions of biological macromolecules in solution. For folded proteins, the technique provides three-dimensional low resolution structures ab initio or it can be used to drive rigid-body modeling. SAXS is also a powerful tool for the quantitative analysis of flexible systems, including intrinsically disordered proteins (IDPs), and is highly complementary to the high resolution methods of X-ray crystallography and NMR. Here we present the basic principles of SAXS and review the main approaches to the characterization of IDPs and flexible multidomain proteins using SAXS. Together with the standard approaches based on the analysis of overall parameters, a recently developed Ensemble Optimization Method (EOM) is now available. The latter method allows for the co-existence of multiple protein conformations in solution compatible with the scattering data. Analysis of the selected ensembles provides quantitative information about flexibility and also offers insights into structural features. Examples of the use of SAXS and combined approaches with NMR, X-ray crystallography, and computational methods to characterize completely or partially disordered proteins are presented.     

Thinking outside the crystal: complementary approaches for examining transporter conformational change

As the number of high-resolution structures of membrane proteins continues to rise, so has the necessity for techniques to link this structural information to protein function. In the case of transporters, function is achieved via coupling of conformational changes to substrate binding and release. Static structural data alone cannot convey information on these protein movements, but it can provide a high-resolution foundation on which to interpret lower resolution data obtained by complementary approaches. Here, we review selected biochemical and spectroscopic methods for assessing transporter conformational change. In addition to more traditional techniques, we present 1?F-NMR as an attractive method for characterizing conformational change in transporters of known structure. Using biosynthetic labeling, multiple, non-perturbing fluorine-labeled amino acids can be incorporated throughout a protein to serve as reporters of conformational change. Such flexibility in labeling allows characterization of movement in protein regions that may not be accessible via other methods.     

Computational Prediction of Protein–Protein Interactions

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.     

Computational characterization of proteins

Computational characterization of proteins is a necessary first step in understanding the biologic role of a protein. The composite architecture of mammalian proteins makes the prediction of the biologic role rather difficult. Nevertheless, integration of many different prediction methods allows for a more accurate representation. Information on the 3D structure of a protein improves the reliability of predictions of many features. This article reviews existing methods used to characterize proteins and several tools that provide an integrated access to different types of information. The authors point out the increasing importance of structural constraints and an increasing need to integrate different approaches.     

Computational characterization of proteins

Computational characterization of proteins is a necessary first step in understanding the biologic role of a protein. The composite architecture of mammalian proteins makes the prediction of the biologic role rather difficult. Nevertheless, integration of many different prediction methods allows for a more accurate representation. Information on the 3D structure of a protein improves the reliability of predictions of many features. This article reviews existing methods used to characterize proteins and several tools that provide an integrated access to different types of information. The authors point out the increasing importance of structural constraints and an increasing need to integrate different approaches.     

NMR of membrane proteins in micelles and bilayers: the FXYD family proteins

Determining the atomic resolution structures of membrane proteins is of particular interest in contemporary structural biology. Helical membrane proteins constitute one-third of the expressed proteins encoded in a genome, many drugs have membrane-bound proteins as their receptors, and mutations in membrane proteins result in human diseases. Although integral membrane proteins provide daunting technical challenges for all methods of protein structure determination, nuclear magnetic resonance (NMR) spectroscopy can be an extremely versatile and powerful method for determining their structures and characterizing their dynamics, in lipid environments that closely mimic the cell membranes. Once milligram amounts of isotopically labeled protein are expressed and purified, micelle samples can be prepared for solution NMR analysis, and lipid bilayer samples can be prepared for solid-state NMR analysis. The two approaches are complementary and can provide detailed structural and dynamic information. This paper describes the steps for membrane protein structure determination using solution and solid-state NMR. The methods for protein expression and purification, sample preparation and NMR experiments are described and illustrated with examples from the FXYD proteins, a family of regulatory subunits of the Na,K-ATPase.     

Thinking outside the crystal: Complementary approaches for examining transporter mechanism

As the number of high-resolution structures of membrane proteins continues to rise, so has the necessity for techniques to link this structural information to protein function. In the case of transporters, function is achieved via coupling of conformational changes to substrate binding and release. Static structural data alone cannot convey information on these protein movements, but it can provide a high-resolution foundation on which to interpret lower resolution data obtained by complementary approaches. Here, we review selected biochemical and spectroscopic methods for assessing transporter conformational change. In addition to more traditional techniques, we present ¹⁹F-NMR as an attractive method for characterizing conformational change in transporters of known structure. Using biosynthetic labeling, multiple, non-perturbing fluorine-labeled amino acids can be incorporated throughout a protein to serve as reporters of conformational change. Such flexibility in labeling allows characterization of movement in protein regions that may not be accessible via other methods.     

The dynamic duo: Combining NMR and small angle scattering in structural biology

Structural biology provides essential information for elucidating molecular mechanisms that underlie biological function. Advances in hardware, sample preparation, experimental methods, and computational approaches now enable structural analysis of protein complexes with increasing complexity that more closely represent biologically entities in the cellular environment. Integrated multidisciplinary approaches are required to overcome limitations of individual methods and take advantage of complementary aspects provided by different structural biology techniques. Although X‐ray crystallography remains the method of choice for structural analysis of large complexes, crystallization of flexible systems is often difficult and does typically not provide insights into conformational dynamics present in solution. Nuclear magnetic resonance spectroscopy (NMR) is well‐suited to study dynamics at picosecond to second time scales, and to map binding interfaces even of large systems at residue resolution but suffers from poor sensitivity with increasing molecular weight. Small angle scattering (SAS) methods provide low resolution information in solution and can characterize dynamics and conformational equilibria complementary to crystallography and NMR. The combination of NMR, crystallography, and SAS is, thus, very useful for analysis of the structure and conformational dynamics of (large) protein complexes in solution. In high molecular weight systems, where NMR data are often sparse, SAS provides additional structural information and can differentiate between NMR‐derived models. Scattering data can also validate the solution conformation of a crystal structure and indicate the presence of conformational equilibria. Here, we review current state‐of‐the‐art approaches for combining NMR, crystallography, and SAS data to characterize protein complexes in solution.     

Prediction of protein function from protein sequence and structure

The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.     

Resolving the micro-heterogeneity and structural integrity of monoclonal antibodies by hybrid mass spectrometric approaches

For therapeutic monoclonal antibodies (mAbs), detailed analysis of the structural integrity and heterogeneity, which results from multiple types of post-translational modifications (PTMs), is relevant to various processes, including product characterization, storage stability and quality control. Despite the recent rapid development of new bioanalytical techniques, it is still challenging to completely characterize the proteoform profile of a mAb. As a nearly indispensable tool in mAb analysis, mass spectrometry (MS) provides unique structural information at multiple levels. Here, we tested a hybrid strategy for the comprehensive characterization of micro-heterogeneity by integrating 2 state-of-the-art MS-based approaches, high-resolution native MS and targeted glycan profiling, to perform complementary analysis at the intact protein level and released glycan level, respectively. We compared the performance of these methods using samples of engineered half-body IgG4s and a panel of mAbs approved for human use. The glycosylation characterization data derived from these approaches were found to be mutually consistent in composition profiling, and complementary in identification and relative-quantitation of low-abundant uncommon glycoforms. In addition, multiple other sources of micro-heterogeneity, such as glycation, lack of glycosylation, and loss of light chains, could be detected by this approach, and the contribution of multiple types of modifications to the overall micro-heterogeneity could be assessed using our superposition algorithm. Our data demonstrate that the hybrid strategy allows reliable and thorough characterization of mAbs, revealing product characteristics that would easily be missed if only a single approach were used.     

Reweighting methods for elucidation of conformation ensembles of proteins

Proteins are inherently dynamic macromolecules that exist in equilibrium among multiple conformational states, and motions of protein backbone and side chains are fundamental to biological function. The ability to characterize the conformational landscape is particularly important for intrinsically disordered proteins, multidomain proteins, and weakly bound complexes, where single-structure representations are inadequate. As the focus of structural biology shifts from relatively rigid macromolecules toward larger and more complex systems and molecular assemblies, there is a need for structural approaches that can paint a more realistic picture of such conformationally heterogeneous systems. Here, we review reweighting methods for elucidation of structural ensembles based on experimental data, with the focus on applications to multidomain proteins.     

Proteomics of membrane receptors and signaling

Receptors represent an abundant class of integral membrane proteins that transmit information on various types of signals within the cell. Assemblages of receptors and their interacting proteins (receptor complexes) have emerged as important units of signal transduction for various types of receptors including G protein coupled, ligand-gated ion channel, and receptor tyrosine kinase. This review aims to summarize the major approaches and findings of receptor proteomics. Isolation and characterization of receptor complexes from cells has become common using the methods of immunoaffinity-, ligand-, and tag-based chromatography followed by MS for the analysis of enriched receptor preparations. In addition, tools such as stable isotope labeling have contributed to understanding quantitative properties and PTMs to receptors and their interacting proteins. As data from studies on receptor-protein interactions considerably expands, complementary approaches such as bioinformatics and computational biology will undoubtedly play a significant role in defining cellular and network functions for various types of receptor complexes. Findings from receptor proteomics may also shed light on the mechanism of action for pharmacological drugs and can be of value in understanding molecular pathologies of disease states.     

Investigation of ribosomes using molecular dynamics simulation methods

The ribosome as a complex molecular machine undergoes significant conformational changes while synthesizing a protein molecule. Molecular dynamics simulations have been used as complementary approaches to X-ray crystallography and cryoelectron microscopy, as well as biochemical methods, to answer many questions that modern structural methods leave unsolved. In this review, we demonstrate that all-atom modeling of ribosome molecular dynamics is particularly useful in describing the process of tRNA translocation, atomic details of behavior of nascent peptides, antibiotics, and other small molecules in the ribosomal tunnel, and the putative mechanism of allosteric signal transmission to functional sites of the ribosome.     

Assigning function to yeast proteins by integration of technologies

Interpreting genome sequences requires the functional analysis of thousands of predicted proteins, many of which are uncharacterized and without obvious homologs. To assess whether the roles of large sets of uncharacterized genes can be assigned by targeted application of a suite of technologies, we used four complementary protein-based methods to analyze a set of 100 uncharacterized but essential open reading frames (ORFs) of the yeast Saccharomyces cerevisiae. These proteins were subjected to affinity purification and mass spectrometry analysis to identify copurifying proteins, two-hybrid analysis to identify interacting proteins, fluorescence microscopy to localize the proteins, and structure prediction methodology to predict structural domains or identify remote homologies. Integration of the data assigned function to 48 ORFs using at least two of the Gene Ontology (GO) categories of biological process, molecular function, and cellular component; 77 ORFs were annotated by at least one method. This combination of technologies, coupled with annotation using GO, is a powerful approach to classifying genes.     

Domains,motifs and clusters in the protein universe

The rapid growth of bio-sequence information has resulted in an increasing demand for reliable methods that group proteins. A few databases with curated alignments of protein families have demonstrated that expert-driven repositories can keep up with the data deluge in the genome era. These original resources implicitly identify domain-like modules in proteins. An increasing number of automatic methods have sprouted over the past few years that cluster the protein universe. Many of these implicitly dissect proteins into structural domain-like fragments. In a very coarse-grained evaluation, some of the automatic methods appear to be on par with expert-driven approaches. However, neither automatic nor manual methods are currently entirely up to the challenges of tasks such as target selection in structural genomics. Thus, we urgently need refined and sustained automatic clustering tools.     

The use of soluble protein structures in modeling helical proteins in a layered membrane

Major advances have been made in the prediction of soluble protein structures, led by the knowledge-based modeling methods that extract useful structural trends from known protein structures and incorporate them into scoring functions. The same cannot be reported for the class of transmembrane proteins, primarily due to the lack of high-resolution structural data for transmembrane proteins, which render many of the knowledge-based method unreliable or invalid. We have developed a method that harnesses the vast structural knowledge available in soluble protein data for use in the modeling of transmembrane proteins. At the core of the method, a set of transmembrane protein decoy sets that allow us to filter and train features recognized from soluble proteins for transmembrane protein modeling into a set of scoring functions. We have demonstrated that structures of soluble proteins can provide significant insight into transmembrane protein structures. A complementary novel two-stage modeling/selection process that mimics the two-stage helical membrane protein folding was developed. Combined with the scoring function, the method was successfully applied to model 5 transmembrane proteins. The root mean square deviations of the predicted models ranged from 5.0 to 8.8?Å to the native structures.     

Membrane trafficking in higher plant cells: GFP and antibodies, partners for probing the secretory pathway.

Eukaryotic cells are characterised by the organised distribution of membrane bounded compartments in their cytoplasm. The endoplasmic reticulum (ER) and the Golgi apparatus (GA) are part of this endomembrane machinery. They are involved in protein flow, and are in charge of specific functions such as the assembly, sorting and transport of newly synthesised proteins, glycoproteins or polysaccharides to their final destination, where the macromolecules are recognised either for action, storage, deposition or degradation. The structural and functional relationship between the ER and GA in higher plants is still a matter of debate. Therefore, it was essential to develop probes that would specifically label proteins or glycoproteins of the endomembrane system in situ. Here we compare two complementary approaches to probe plant endomembranes; immunocytochemistry on fixed cells, and in vivo studies using the expression of GFP tagged chimeric proteins. The structural relationship between ER and GA as based on pharmacological approaches using the two systems is explored.     

The roles of contact residue disorder and domain composition in characterizing protein-ligand binding specificity and promiscuity

Most protein chains interact with only one ligand but a small number of protein chains can bind several ligands, and many examples are available in the protein-ligand complex database of PDB. Among these proteins, some show preferences for the ligands or types of ligands they bind; however, so far we have only poor understanding of what determines protein-ligand binding and its specificity. Here we investigate the structural and functional properties of proteins in protein-ligand complexes. Analysis of the protein-ligand complex dataset from the PDB structure database reveals that proteins with more interactions have more disordered contact residues. Those proteins containing few disordered contact residues that bind multiple ligands have a tendency to consist of several domains. Analysis of physicochemical properties of hub contact residues binding multiple ligands indicates that they are enriched for hydrophilic, charged, polar and His-Asp catalytic triad residues. Finally, in order to differentiate proteins binding different classes of ligands, we mapped the three most prominent classes of ligands onto different superfamily domains. Our results demonstrate that contact residue disorder and ordered multiple domains are complementary factors that play a crucial role in determining ligand binding specificity and promiscuity.