Solvent interaction analysis as a proteomic approach to structure-based biomarker discovery and clinical diagnostics

Proteins have several measurable features in biological fluids that may change under pathological conditions. The current disease biomarker discovery is mostly based on protein concentration in the sample as the measurable feature. Changes in protein structures, such as post-translational modifications and in protein–partner interactions are known to accompany pathological processes. Changes in glycosylation profiles are well-established for many plasma proteins in various types of cancer and other diseases. The solvent interaction analysis method is based on protein partitioning in aqueous two-phase systems and is highly sensitive to changes in protein structure and protein–protein- and protein–partner interactions while independent of the protein concentration in the biological sample. It provides quantitative index: partition coefficient representing changes in protein structure and interactions with partners. The fundamentals of the method are presented with multiple examples of applications of the method to discover and monitor structural protein biomarkers as disease-specific diagnostic indicators.     

Proteins

Proteins continue to surprise and amaze us in the myriad of ways in which they achieve biological function. The Proteins section in this issue of Current Opinion in Structural Biology highlights several proteins in which large conformational changes and evolutionary divergence in structure and function, play essential roles in their adaptation to a variety of biological functions. In addition, fundamental advances have been made in research, spurred on by industrial interest in the use of proteins as drug targets or as catalysts. All of the reviews in this section document the fact that multiple crystal structures of a protein in different functional states, and of different members of protein families, are necessary for the composition of a complete structural picture.     

Deep learning methods for 3D structural proteome and interactome modeling

Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in tasks ranging from protein contact map prediction to protein folding, prediction of protein–protein interaction interfaces, and characterization of protein–drug binding pockets. In particular, emergence of ab initio protein structure prediction methods including AlphaFold2 has revolutionized protein structural modeling. From a protein function perspective, numerous deep learning methods have facilitated deconvolution of the exact amino acid residues and protein surface regions responsible for binding other proteins or small molecule drugs. In this review, we provide a comprehensive overview of recent deep learning methods applied in structural proteomics.     

DisCanVis: Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in a wide range of biological processes and have been associated with various diseases, including cancer. In the last few years, cancer genome projects have systematically collected genetic variations underlying multiple cancer types. In parallel, the number and different types of disordered proteins characterized by experimental methods have also significantly increased. Nevertheless, the role of IDPs in various types of cancer is still not well understood. In this work, we present DisCanVis, a novel visualization tool for cancer mutations with a special focus on IDPs. In order to aid the interpretation of observed mutations, genome level information is combined with information about the structural and functional properties of proteins. The web server enables users to inspect individual proteins, collect examples with existing annotations of protein disorder and associated function or to discover currently uncharacterized examples with likely disease relevance. Through a REST API interface and precompiled tables the analysis can be extended to a group of proteins.     

Integrative Structural Biology in the Era of Accurate Structure Prediction

Characterizing the three-dimensional structure of macromolecules is central to understanding their function. Traditionally, structures of proteins and their complexes have been determined using experimental techniques such as X-ray crystallography, NMR, or cryo-electron microscopy—applied individually or in an integrative manner. Meanwhile, however, computational methods for protein structure prediction have been improving their accuracy, gradually, then suddenly, with the breakthrough advance by AlphaFold2, whose models of monomeric proteins are often as accurate as experimental structures. This breakthrough foreshadows a new era of computational methods that can build accurate models for most monomeric proteins. Here, we envision how such accurate modeling methods can combine with experimental structural biology techniques, enhancing integrative structural biology. We highlight the challenges that arise when considering multiple structural conformations, protein complexes, and polymorphic assemblies. These challenges will motivate further developments, both in modeling programs and in methods to solve experimental structures, towards better and quicker investigation of structure–function relationships.     

Alignment and structure prediction of divergent protein families: periplasmic and outer membrane proteins of bacterial efflux pumps

Broad-specificity efflux pumps have been implicated in multidrug-resistant strains of Pseudomonas aeruginosa and other Gram-negative bacteria. Most Gram-negative pumps of clinical relevance have three components, an inner membrane transporter, an outer membrane channel protein, and a periplasmic protein, which together coordinate efflux from the cytoplasmic membrane across the outer membrane through an unknown mechanism. The periplasmic efflux proteins (PEPs) and outer membrane efflux proteins (OEPs) are not obviously related to proteins of known structure, and understanding the structure and function of these proteins has been hindered by the difficulty of obtaining reasonable multiple alignments. We present a general strategy for the alignment and structure prediction of protein families with low mutual sequence similarity using the PEP and OEP families as detailed examples. Gibbs sampling, hidden Markov models, and other analysis techniques were used to locate motifs, generate multiple alignments, and assign PEP or OEP function to hypothetical proteins in several species. We also developed an automated procedure which combines multiple alignments with structure prediction algorithms in order to identify conserved structural features in protein families. This process was used to identify a probable alpha-helical hairpin in the PEP family and was applied to the detection of transmembrane beta-strands in OEPs. We also show that all OEPs contain a large tandem duplication, and demonstrate that the OEP family is unlikely to adopt a porin fold, in contrast to previous predictions.     

蛋白质结构与功能中的结构域

结构域是蛋白质亚基结构中的紧密球状区域.结构域作为蛋白质结构中介于二级与三级结构之间的又一结构层次,在蛋白质中起着独立的结构单位、功能单位与折叠单位的作用.在复杂蛋白质中,结构域具有结构与功能组件与遗传单位的作用.结构域层次的研究将会促进蛋白质结构与功能关系、蛋白质折叠机制以及蛋白质设计的研究.     

Comparing protein structures and inferring functions with a novel three-dimensional Yau–Hausdorff method

Abstract

Structures and functions of proteins play various essential roles in biological processes. The functions of newly discovered proteins can be predicted by comparing their structures with that of known-functional proteins. Many approaches have been proposed for measuring the protein structure similarity, such as the template-modeling (TM)-score method, GRaphlet (GR)-Align method as well as the commonly used root-mean-square deviation (RMSD) measures. However, the alignment comparisons between the similarity of protein structure cost much time on large dataset, and the accuracy still have room to improve. In this study, we introduce a new three-dimensional (3D) Yau–Hausdorff distance between any two 3D objects. The (3D) Yau–Hausdorff distance can be used in particular to measure the similarity/dissimilarity of two proteins of any size and does not need aligning and superimposing two structures. We apply structural similarity to study function similarity and perform phylogenetic analysis on several datasets. The results show that (3D) Yau–Hausdorff distance could serve as a more precise and effective method to discover biological relationships between proteins than other methods on structure comparison.

Communicated by Ramaswamy H. Sarma     

植物类金属硫蛋白半胱氨酸富含区结构的建模

详细了解蛋白质的三级结构信息有助于理解其生物学功能.随着植物基因组研究的进展,已发现了50多个植物类金属硫蛋白(Metallothionein-Like, MT-L)基因.但至今只有少数几个MT-L蛋白得到了纯化,而其结构尚无报道,因此有必要建立分析这类蛋白结构特征的方法.本研究根据已知的哺乳动物MT的结构数据,分析得出了CXC、CXXC模式和金属-硫络合簇结构原子间的距离限制条件,并用距离几何算法计算得出预测蛋白可能的构象;然后通过统计分析筛选出目标函数值显著较小、构象能低的结构作为这些蛋白半胱氨酸富含区的预测结构,由此建成了适合于植物类金属硫蛋白半胱氨酸富含区的结构预测方法.从应用该方法正确地预测出了已知结构的蓝蟹MT的结构来看,该方法是可行的.并用该方法预测了油菜MT-L蛋白的半胱氨酸富含区的结构.     

植物类金属硫蛋白半胱氨酸富含区结构的建模

详细了解蛋白质的三级结构信息有助于理解其生物学功能。随着植物基因组研究的进展 ,已发现了 50多个植物类金属硫蛋白 (Metallothionein_Like ,MT_L)基因。但至今只有少数几个MT_L蛋白得到了纯化 ,而其结构尚无报道 ,因此有必要建立分析这类蛋白结构特征的方法。本研究根据已知的哺乳动物MT的结构数据 ,分析得出了CXC、CXXC模式和金属 硫络合簇结构原子间的距离限制条件 ,并用距离几何算法计算得出预测蛋白可能的构象 ;然后通过统计分析筛选出目标函数值显著较小、构象能低的结构作为这些蛋白半胱氨酸富含区的预测结构 ,由此建成了适合于植物类金属硫蛋白半胱氨酸富含区的结构预测方法。从应用该方法正确地预测出了已知结构的蓝蟹MT的结构来看 ,该方法是可行的。并用该方法预测了油菜MT_L蛋白的半胱氨酸富含区的结构。     

Mechanics of proteins with a focus on atomic force microscopy

The capacity of proteins to function relies on a balance between molecular stability to maintain their folded state and structural flexibility allowing conformational changes related to biological function. Among many others, four different examples can be chosen. The giant protein titin is stretched and can unfold during muscle contraction providing passive elasticity to muscle tissue; myoglobin adsorbs and releases oxygen molecules thank to conformational changes in its structure; the outer membrane protein G (OmpG) is a bacterial porin with a long and flexible loop that modulates gating; and the proton pump bacteriorhodopsin adapts its cytosolic half to allow proton pumping. All these conformational changes triggered either by chemical or by physical cues, require mechanical flexibility or elasticity of certain protein domains. While the methods to determine protein structure, X-ray crystallography above all, have been dramatically improved over the last decades, the number of tools that directly measure the mechanical flexibility of proteins and protein domains is still limited. In this tutorial, after a brief introduction to protein structure, we present some of the available techniques to estimate protein flexibility, then focusing on atomic force microscopy (AFM). We describe the principles of the technique and its various imaging and force spectroscopy modes of operation that allow probing the elasticity of proteins, protein domains and their surrounding environment.     

Insights into the biology of Escherichia coli through structural proteomics

Escherichia coli has historically been an important organism for understanding a multitude of biological processes, and represents a model system as we attempt to simulate the workings of living cells. Many E. coli strains are also important human and animal pathogens for which new therapeutic strategies are required. For both reasons, a more complete and comprehensive understanding of the protein structure complement of E. coli is needed at the genome level. Here, we provide examples of insights into the mechanism and function of bacterial proteins that we have gained through the Bacterial Structural Genomics Initiative (BSGI), focused on medium-throughput structure determination of proteins from E. coli. We describe the structural characterization of several enzymes from the histidine biosynthetic pathway, the structures of three pseudouridine synthases, enzymes that synthesize one of the most abundant modified bases in RNA, as well as the combined use of protein structure and focused functional analysis to decipher functions for hypothetical proteins. Together, these results illustrate the power of structural genomics to contribute to a deeper biological understanding of bacterial processes.     

Computational approaches for protein function prediction: A combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein–ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.     

Visualizing transiently associated catalytic domains in assembly-line biosynthesis using cryo-electron microscopy

While cryo-electron microscopy (cryo-EM) has revolutionized the structure determination of supramolecular protein complexes that are refractory to structure determination by X-ray crystallography, structure determination by cryo-EM can nonetheless be complicated by excessive conformational flexibility or structural heterogeneity resulting from weak or transient protein–protein association. Since such transient complexes are often critical for function, specialized approaches must be employed for the determination of meaningful structure–function relationships. Here, we outline examples in which transient protein–protein interactions have been visualized successfully by cryo-EM in the biosynthesis of fatty acids, polyketides, and terpenes. These studies demonstrate the utility of chemical crosslinking to stabilize transient protein–protein complexes for cryo-EM structural analysis, as well as the use of partial signal subtraction and localized reconstruction to extract useful structural information out of cryo-EM data collected from inherently dynamic systems. While these approaches do not always yield atomic resolution insights on protein–protein interactions, they nonetheless enable direct experimental observation of complexes in assembly-line biosynthesis that would otherwise be too fleeting for structural analysis.     

Metamorphic protein folding as evolutionary adaptation

Metamorphic proteins switch reversibly between multiple distinct, stable structures, often with different functions. It was previously hypothesized that metamorphic proteins arose as intermediates in the evolution of a new fold – rare and transient exceptions to the ‘one sequence, one fold’ paradigm. However, as described herein, mounting evidence suggests that metamorphic folding is an adaptive feature, preserved and optimized over evolutionary time as exemplified by the NusG family and the chemokine XCL1. Analysis of extant protein families and resurrected protein ancestors demonstrates that large regions of sequence space are compatible with metamorphic folding. As a category that enhances biological fitness, metamorphic proteins are likely to employ fold switching to perform important biological functions and may be more common than previously thought.     

Analytical ultracentrifugation in structural biology

Researchers in the field of structural biology, especially X-ray crystallography and protein nuclear magnetic resonance, are interested in knowing as much as possible about the state of their target protein in solution. Not only is this knowledge relevant to studies of biological function, it also facilitates determination of a protein structure using homogeneous monodisperse protein samples. A researcher faced with a new protein to study will have many questions even after that protein has been purified. Analytical ultracentrifugation (AUC) can provide all of this information readily from a small sample in a non-destructive way, without the need for labeling, enabling structure determination experiments without any wasting time and material on uncharacterized samples. In this article, I use examples to illustrate how AUC can contribute to protein structural analysis. Integrating information from a variety of biophysical experimental methods, such as X-ray crystallography, small angle X-ray scattering, electrospray ionization-mass spectrometry, AUC allows a more complete understanding of the structure and function of biomacromolecules.     

Enzymatic activity and protein interactions in alpha/beta hydrolase fold proteins: moonlighting versus promiscuity

Genes coding for members of the alpha/beta hydrolase fold superfamily of proteins are present in all known genomes. Although there is no common and essential function performed by these proteins shared in all living organisms, this fold has been used for a number of diverse functions. The ancestry of both enzymatic and protein-protein interaction capability of this structural scaffold made it an important tinkering tool kit for protein function evolution. Recently, enzymes known since a long time have been found to have a second function in acting promiscuously on alternative substrates, or to be true moonlighting proteins acting also as transporters, receptors, chaperones… The reverse situation has been encountered for adhesion proteins shown to be enzymes. This review, while not exhaustive, surveys some of the best-known examples of multiple functions in alpha/beta hydrolase fold proteins.     

Extracting Hydrogen-Bond Signature Patterns from Protein Structure Data

Classification of protein sequences and structures into families is a fundamental task in biology, and it is often used as a basis for designing experiments for gaining further knowledge. Some relationships between proteins are detected by the similarities in their sequences, and many more by the similarities in their structures. Despite this, there are a number of examples of functionally similar molecules without any recognisable sequence or structure similarities, and there are also a number of protein molecules that share common structural scaffolds but exhibit different functions. Newer methods of comparing molecules are required in order to detect similarities and dissimilarities in protein molecules. In this article, it is proposed that the precise 3-dimensional disposition of key residues in a protein molecule is what matters for its function, or what conveys the "meaning" for a biological system, but not what means it uses to achieve this. The concept of comparing two molecules through their intramolecular interaction networks is explored, since these networks dictate the disposition of amino acids in a protein structure. First, signature patterns, or fingerprints, of interaction networks in pre-classified protein structural families are computed using an approach to find structural equivalences and consensus hydrogen bonds. Five examples from different structural classes are illustrated. These patterns are then used to search the entire Protein Data Bank, an approach through which new, unexpected similarities have been found. The potential for finding relationships through this approach is highlighted. The use of hydrogen-bond fingerprints as a new metric for measuring similarities in protein structures is also described.     

Cross-linking and mass spectrometry methodologies to facilitate structural biology: finding a path through the maze

Multiprotein complexes, rather than individual proteins, make up a large part of the biological macromolecular machinery of a cell. Understanding the structure and organization of these complexes is critical to understanding cellular function. Chemical cross-linking coupled with mass spectrometry is emerging as a complementary technique to traditional structural biology methods and can provide low-resolution structural information for a multitude of purposes, such as distance constraints in computational modeling of protein complexes. In this review, we discuss the experimental considerations for successful application of chemical cross-linking-mass spectrometry in biological studies and highlight three examples of such studies from the recent literature. These examples (as well as many others) illustrate the utility of a chemical cross-linking-mass spectrometry approach in facilitating structural analysis of large and challenging complexes.     

Progress and challenges in protein structure prediction

Depending on whether similar structures are found in the PDB library, the protein structure prediction can be categorized into template-based modeling and free modeling. Although threading is an efficient tool to detect the structural analogs, the advancements in methodology development have come to a steady state. Encouraging progress is observed in structure refinement which aims at drawing template structures closer to the native; this has been mainly driven by the use of multiple structure templates and the development of hybrid knowledge-based and physics-based force fields. For free modeling, exciting examples have been witnessed in folding small proteins to atomic resolutions. However, predicting structures for proteins larger than 150 residues still remains a challenge, with bottlenecks from both force field and conformational search.