Rigidity of protein structure revealed by incoherent neutron scattering

The rigidity and flexibility of a protein is reflected in its structural dynamics. Studies on protein dynamics often focus on flexibility and softness; this review focuses on protein structural rigidity. The extent of rigidity can be assessed experimentally with incoherent neutron scattering; a method that is complementary to molecular dynamics simulation. This experimental technique can provide information about protein dynamics in timescales of pico- to nanoseconds and at spatial scales of nanometers; these dynamics can help quantify the rigidity of a protein by indices such as force constant, Boson peak, dynamical transition, and dynamical heterogeneity. These indicators also reflect the rigidity of a protein's secondary and tertiary structures. In addition, the indices reveal how rigidity is influenced by different environmental parameters, such as hydration, temperature, pressure, and protein-protein interactions. Hydration affects both rigidity and softness more than other environmental factors. Interestingly, hydration affects harmonic and anharmonic motions in opposite ways. This difference is probably due to the protein's dynamic coupling with water molecules via hydrogen bonding.     

Structural similarity and classification of protein interaction interfaces

Interactions between proteins play a key role in many cellular processes. Studying protein-protein interactions that share similar interaction interfaces may shed light on their evolution and could be helpful in elucidating the mechanisms behind stability and dynamics of the protein complexes. When two complexes share structurally similar subunits, the similarity of the interaction interfaces can be found through a structural superposition of the subunits. However, an accurate detection of similarity between the protein complexes containing subunits of unrelated structure remains an open problem. Here, we present an alignment-free machine learning approach to measure interface similarity. The approach relies on the feature-based representation of protein interfaces and does not depend on the superposition of the interacting subunit pairs. Specifically, we develop an SVM classifier of similar and dissimilar interfaces and derive a feature-based interface similarity measure. Next, the similarity measure is applied to a set of 2,806×2,806 binary complex pairs to build a hierarchical classification of protein-protein interactions. Finally, we explore case studies of similar interfaces from each level of the hierarchy, considering cases when the subunits forming interactions are either homologous or structurally unrelated. The analysis has suggested that the positions of charged residues in the homologous interfaces are not necessarily conserved and may exhibit more complex conservation patterns.     

Protein Flexibility Facilitates Quaternary Structure Assembly and Evolution

The intrinsic flexibility of proteins allows them to undergo large conformational fluctuations in solution or upon interaction with other molecules. Proteins also commonly assemble into complexes with diverse quaternary structure arrangements. Here we investigate how the flexibility of individual protein chains influences the assembly and evolution of protein complexes. We find that flexibility appears to be particularly conducive to the formation of heterologous (i.e., asymmetric) intersubunit interfaces. This leads to a strong association between subunit flexibility and homomeric complexes with cyclic and asymmetric quaternary structure topologies. Similarly, we also observe that the more nonhomologous subunits that assemble together within a complex, the more flexible those subunits tend to be. Importantly, these findings suggest that subunit flexibility should be closely related to the evolutionary history of a complex. We confirm this by showing that evolutionarily more recent subunits are generally more flexible than evolutionarily older subunits. Finally, we investigate the very different explorations of quaternary structure space that have occurred in different evolutionary lineages. In particular, the increased flexibility of eukaryotic proteins appears to enable the assembly of heteromeric complexes with more unique components.     

Energy-based graph convolutional networks for scoring protein docking models

Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study, the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent protein and complex structures as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that aggregates interacting nodes’ features through edges so that generalized interaction energies can be learned directly from 3D data. The resulting energy-based graph convolutional networks (EGCN) with multihead attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state-of-the-art scoring function for model ranking, EGCN significantly improves ranking for a critical assessment of predicted interactions (CAPRI) test set involving homology docking; and is comparable or slightly better for Score_set, a CAPRI benchmark set generated by diverse community-wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.     

Multicolor BiFC analysis of competition among G protein beta and gamma subunit interactions

We have applied multicolor BiFC to study the association preferences of G protein beta and gamma subunits in living cells. Cells co-express multiple isoforms of beta and gamma subunits, most of which can form complexes. Although many betagamma complexes exhibit similar properties when assayed in reconstituted systems, knockout experiments in vivo suggest that individual isoforms have unique functions. BiFC makes it possible to correlate betagamma complex formation with functionality in intact cells by comparing the amounts of fluorescent betagamma complexes with their abilities to modulate effector proteins. The relative predominance of specific betagamma complexes in vivo is not known. To address this issue, multicolor BiFC can determine the association preferences of beta and gamma subunits by simultaneously visualizing the two fluorescent complexes formed when beta or gamma subunits fused to amino terminal fragments of yellow fluorescent protein (YFP-N) and cyan fluorescent protein (CFP-N) compete to interact with limiting amounts of a common gamma or beta subunit, respectively, fused to a carboxyl terminal fragment of CFP (CFP-C). Multicolor BiFC also makes it possible to determine the roles of interacting proteins in the subcellular targeting of complexes, study the formation of protein complexes that are unstable under isolation conditions, determine the roles of co-expressed proteins in regulating the association preferences of interacting proteins, and visualize dynamic events affecting multiple protein complexes. These approaches can be applied to studying the assembly and functions of a wide variety of protein complexes in the context of a living cell.     

Efficient comprehensive scoring of docked protein complexes using probabilistic support vector machines

Biological systems and processes rely on a complex network of molecular interactions. While the association of biological macromolecules is a fundamental biochemical phenomenon crucial for the understanding of complex living systems, protein-protein docking methods aim for the computational prediction of protein complexes from individual subunits. Docking algorithms generally produce large numbers of putative protein complexes with only few of these conformations resembling the native complex structure within an acceptable degree of structural similarity. A major challenge in the field of docking is to extract near-native structure(s) out of the large pool of solutions, the so called scoring or ranking problem. A series of structural, chemical, biological and physical properties are used in this work to classify docked protein-protein complexes. These properties include specialized energy functions, evolutionary relationship, class specific residue interface propensities, gap volume, buried surface area, empiric pair potentials on residue and atom level as well as measures for the tightness of fit. Efficient comprehensive scoring functions have been developed using probabilistic Support Vector Machines in combination with this array of properties on the largest currently available protein-protein docking benchmark. The established classifiers are shown to be specific for certain types of protein-protein complexes and are able to detect near-native complex conformations from large sets of decoys with high sensitivity. Using classification probabilities the ranking of near-native structures was drastically improved, leading to a significant enrichment of near-native complex conformations within the top ranks. It could be shown that the developed schemes outperform five other previously published scoring functions.     

Thermal adaptation of α-amylases: a review

The temperature adaptation of α-amylase can be gained by different adjustments in protein structure with consecutive effects on the stability and flexibility of the protein. In this review, meso, thermo and cold-active α-amylases have been compared with respect to their structure and intramolecular interactions. With decrease in temperature, the number of ionic interactions also decreases, leading to greater flexibility of proteins. It has also been observed that the proline and arginine content is higher in thermophilic amylases as compared to meso and psychrophilic amylases, increasing the rigidity and structural stability of protein molecule.     

Structure and reaction mechanisms of multifunctional mitochondrial cytochrome bc1 complex.

The cytochrome bc1 complex from bovine heart mitochondria is a multi-functional enzyme complex. In addition to electron and proton transfer activity, the complex also processes an activatable peptidase activity and a superoxide generating activity. The crystal structure of the complex exists as a closely interacting functional dimer. There are 13 transmembrane helices in each monomer, eight of which belong to cytochrome b, and five of which belong to cytochrome c1, Rieske iron-sulfur protein (ISP), subunits 7, 10 and 11, one each. The distances of 21 A between bL heme and bH heme and of 27 A between bL heme and the iron-sulfur cluster (FeS), accommodate well the observed fast electron transfers between the involved redox centers. However, the distance of 31 A between heme c1 and FeS, makes it difficult to explain the high electron transfer rate between them. 3D structural analyses of the bc1 complexes co-crystallized with the Qu site inhibitors suggest that the extramembrane domain of the ISP may undergo substantial movement during the catalytic cycle of the complex. This suggestion is further supported by the decreased in the cytochrome bc1 complex activity and the increased in activation energy for mutants with increased rigidity in the neck region of ISP.     

Heterologous Expression of Mycobacterial Esx Complexes in Escherichia coli for Structural Studies Is Facilitated by the Use of Maltose Binding Protein Fusions

The expression of heteroligomeric protein complexes for structural studies often requires a special coexpression strategy. The reason is that the solubility and proper folding of each subunit of the complex requires physical association with other subunits of the complex. The genomes of pathogenic mycobacteria encode many small protein complexes, implicated in bacterial fitness and pathogenicity, whose characterization may be further complicated by insolubility upon expression in Escherichia coli, the most common heterologous protein expression host. As protein fusions have been shown to dramatically affect the solubility of the proteins to which they are fused, we evaluated the ability of maltose binding protein fusions to produce mycobacterial Esx protein complexes. A single plasmid expression strategy using an N-terminal maltose binding protein fusion to the CFP-10 homolog proved effective in producing soluble Esx protein complexes, as determined by a small-scale expression and affinity purification screen, and coupled with intracellular proteolytic cleavage of the maltose binding protein moiety produced protein complexes of sufficient purity for structural studies. In comparison, the expression of complexes with hexahistidine affinity tags alone on the CFP-10 subunits failed to express in amounts sufficient for biochemical characterization. Using this strategy, six mycobacterial Esx complexes were expressed, purified to homogeneity, and subjected to crystallization screening and the crystal structures of the Mycobacterium abscessus EsxEF, M. smegmatis EsxGH, and M. tuberculosis EsxOP complexes were determined. Maltose binding protein fusions are thus an effective method for production of Esx complexes and this strategy may be applicable for production of other protein complexes.     

互作蛋白质与复合物亚基的基因表达相关性比较分析

利用在多种应激条件下酵母的基因表达谱数据 ,分别计算互作蛋白质及复合物亚基编码基因的表达相关性。结果发现 ,相对于随机对照组 ,互作蛋白质的编码基因与蛋白质复合物的编码基因表达相关性均显著 (P <0 .0 1) ,即互作蛋白质及复合物亚基有共表达的倾向。通过比较 ,进一步发现蛋白质复合物亚基的基因表达相关性显著高于互作蛋白质的基因表达相关性 (P <0 .0 1) ,这与复合物亚基之间功能联系强于定义不甚确切的互作蛋白之间功能联系现象吻合。     

Physical and functional modularity of the protein network in yeast

While protein-protein interactions have been studied largely as a network graph without physicality, here we analyze two protein complex data sets of Saccharomyces cerevisiae to relate physical and functional modularity to the network topology. We study for the first time the number of different protein complexes as a function of the protein complex size and find that it follows an exponential decay with a characteristic number of about 7. This reflects the dynamics of complex formation and dissociation in the cell. The analysis of the protein usage by complexes shows an extensive sharing of subunits that is due to the particular organization of the proteome into physical complexes and functional modules. This promiscuity accounts for the high clustering in the protein net-work graph. Our results underscore the need to include the information contained in observed protein complexes into protein network analyses.     

Nested allosteric interactions in extracellular hemoglobin of the leech Macrobdella decora

Hemoglobin from the leech Macrobdella decora belongs to the class of giant extracellular hexagonal bilayer globin structures found in annelid and vestimentiferan worms. These complexes consist of 144 heme-bearing subunits, exhibit a characteristic quaternary structure (2 x (6 x (3 x 4))), and contain tetramers as basic substructures that express cooperative oxygen binding and thus provide a structural basis for a hierarchy in allosteric interactions. A thorough analysis of the isolated tetramer indicates that it functions as a trimer of cooperatively interacting subunits and a non-cooperative monomer rather than as four interacting subunits. A thermodynamic analysis of the whole molecule favors the application of a nested Monod-Wyman-Changeux model with six cooperatively interacting 12-mer allosteric units. In contrast to the isolated tetramers, all subunits of the tetramers seem to be coupled cooperatively within the oligomerized 144-mer. Thus, besides hemocyanins and GroEL, the hexagonal bilayer hemoglobins represent another class of proteins in which the hierarchical quaternary structure provides the basis for nested interaction in their functional properties.     

Residue conservation information for generating near-native structures in protein-protein docking

MOTIVATION: Protein-protein docking algorithms typically generate large numbers of possible complex structures with only a few of them resembling the native structure. Recently (Duan et al., Protein Sci, 14:316-218, 2005), it was observed that the surface density of conserved residue positions is high at the interface regions of interacting protein surfaces, except for antibody-antigen complexes, where a lesser number of conserved positions than average is observed at the interface regions. Using this observation, we identified putative interacting regions on the surface of interacting partners and significantly improved docking results by assigning top ranks to near-native complex structures. In this paper, we combine the residue conservation information with a widely used shape complementarity algorithm to generate candidate complex structures with a higher percentage of near-native structures (hits). What is new in this work is that the conservation information is used early in the generation stage and not only in the ranking stage of the docking algorithm. This results in a significantly larger number of generated hits and an improved predictive ability in identifying the native structure of protein-protein complexes. RESULTS: We report on results from 48 well-characterized protein complexes, which have enough residue conservation information from the same 59 benchmark complexes used in our previous work. We compute conservation indices of residue positions on the surfaces of interacting proteins using available homologous sequences from UNIPROT and calculate the solvent accessible surface area. We combine this information with shape-complementarity scores to generate candidate protein-protein complex structures. When compared with pure shape-complementarity algorithms, performed by FTDock, our method results in significantly more hits, with the improvement being over 100% in many instances. We demonstrate that residue conservation information is useful not only in refinement and scoring of docking solutions, but also helpful in enrichment of near-native-structures during the generation of candidate geometries of complex structures.     

Predicting interacting and interfacial residues using continuous sequence segments

Development of sequence-based methods for predicting putative interfacial residues is an extremely important task in modeling 3D structures of protein–protein complexes. In the present paper we used non-gapped sequence segments to predict both interacting and interfacial residues. We demonstrated that continuous sequence segments do occur at the protein–protein interfaces and showed that continuous interacting interfacial segments (CIIS) of length nine are presented on average, in 37% of the complexes in our dataset. Our results indicate that CIIS consist mostly of interacting strands and/or loops, while the CIIS involving the helixes are scarce. We performed scoring of CIIS using four different scoring mechanisms and found that scores of CIIS differ significantly from the scores calculated for random stretches of residues. We argue that such statistical difference inferred thought the corresponding Z-scores could be used for detecting putative interfacial residue segments without using any structural information. This hypothesis was tested on our dataset and benchmarking resulted to 10–60% prediction accuracy depending on type of benchmarking and scoring scheme used in calculations. Such predictions that do not depend on the availability of the 3D structures of monomers can be quite valuable in modeling 3D structures of obligatory complexes, for which structures of separated monomers do not exist.     

Conformational changes in DNA‐binding proteins: Relationships with precomplex features and contributions to specificity and stability

Both Proteins and DNA undergo conformational changes in order to form functional complexes and also to facilitate interactions with other molecules. These changes have direct implications for the stability and specificity of the complex, as well as the cooperativity of interactions between multiple entities. In this work, we have extensively analyzed conformational changes in DNA‐binding proteins by superimposing DNA‐bound and unbound pairs of protein structures in a curated database of 90 proteins. We manually examined each of these pairs, unified the authors' annotations, and summarized our observations by classifying conformational changes into six structural categories. We explored a relationship between conformational changes and functional classes, binding motifs, target specificity, biophysical features of unbound proteins, and stability of the complex. In addition, we have also investigated the degree to which the intrinsic flexibility can explain conformational changes in a subset of 52 proteins with high quality coordinate data. Our results indicate that conformational changes in DNA‐binding proteins contribute significantly to both the stability of the complex and the specificity of targets recognized by them. We also conclude that most conformational changes occur in proteins interacting with specific DNA targets, even though unbound protein structures may have sufficient information to interact with DNA in a nonspecific manner. Proteins 2014; 82:841–857. © 2013 Wiley Periodicals, Inc.     

Scoring a diverse set of high-quality docked conformations: a metascore based on electrostatic and desolvation interactions

Predicting protein-protein interactions involves sampling and scoring docked conformations. Barring some large structural rearrangement, rapidly sampling the space of docked conformations is now a real possibility, and the limiting step for the successful prediction of protein interactions is the scoring function used to reduce the space of conformations from billions to a few, and eventually one high affinity complex. An atomic level free-energy scoring function that estimates in units of kcal/mol both electrostatic and desolvation interactions (plus van der Waals if appropriate) of protein-protein docked conformations is used to rerank the blind predictions (860 in total) submitted for six targets to the community-wide Critical Assessment of PRediction of Interactions (CAPRI; http://capri.ebi.ac.uk). We found that native-like models often have varying intermolecular contacts and atom clashes, making unlikely that one can construct a universal function that would rank all these models as native-like. Nevertheless, our scoring function is able to consistently identify the native-like complexes as those with the lowest free energy for the individual models of 16 (out of 17) human predictors for five of the targets, while at the same time the modelers failed to do so in more than half of the cases. The scoring of high-quality models developed by a wide variety of methods and force fields confirms that electrostatic and desolvation forces are the dominant interactions determining the bound structure. The CAPRI experiment has shown that modelers can predict valuable models of protein-protein complexes, and improvements in scoring functions should soon solve the docking problem for complexes whose backbones do not change much upon binding. A scoring server and programs are available at http://structure.pitt.edu.     

Relationship of protein flexibility to thermostability

Thermostability of proteins arises from the simultaneous effect of several forces, which in fact lead to decreased flexibility of the polypeptide chain. This is verified by flexibility indices, which are derived from normalized B-values of individual amino acids in several refined three-dimensional structures. Flexibility indices show that overall flexibility is reduced when thermostability is increased. Protein molecules require both flexibility and rigidity to function, but the higher the temperature optimum and stability the more rigid is the structure needed to compensate for increased thermal fluctuations. Flexibilities of proteins performing the same catalytic activity seem to be about the same at their temperature optima, but the more rigid thermostable proteins reach the flexibility of thermolabile proteins at higher temperatures. In several proteins such as allosteric enzymes, some local sites of flexibility are highly conserved. The relevance of reduced flexibility to overall stability of proteins is also discussed. Flexibility indices and profiles can be used in the design of more stable proteins by site-directed mutagenesis.     

Effects of bidentate coordination on the molecular properties rapta-C based complex using theoretical approach

In this work several quantum properties including the NEDA and QTAIM are computed on three models of rapta-C complexes using DFT with hybrid functional and basis set with ECP and without ECP. Several interesting correlations within the observed properties and also with the reported experimental behaviors of these complexes including their biological activities are presented. The study shows that the stability of the two complexes with bidentate ligands is associated with their high hydrogen bonding stability and existence of stronger non-covalent metal-ligand bonds. The energy decomposition analysis indicated that inter-atomic interactions in the three forms of rapta-C complexes and their stability are governed by the charge transfer term with significant contributions from polarization and electrostatic terms. The higher stability of complex 1 and 2 over 3 comes from the lower exchange repulsion and higher polarization contributions to their stability which agrees perfectly with the experimental observation. Our results provide insight into the nature of intramolecular forces that influence the structural stability of the three complexes.