NOXclass: prediction of protein-protein interaction types

Protein-protein interaction and interference of carcinogenesis by supramolecular modifications

Protein-protein interactions (PPIs) are essential in normal biological processes, but they can become disrupted or imbalanced in cancer. Various technological advancements have led to an increase in the number of PPI inhibitors, which target hubs in cancer cell's protein networks. However, it remains difficult to develop PPI inhibitors with desired potency and specificity. Supramolecular chemistry has only lately become recognized as a promising method to modify protein activities. In this review, we highlight recent advances in the use of supramolecular modification approaches in cancer therapy. We make special note of efforts to apply supramolecular modifications, such as molecular tweezers, to targeting the nuclear export signal (NES), which can be used to attenuate signaling processes in carcinogenesis. Finally, we discuss the strengths and weaknesses of using supramolecular approaches to targeting PPIs.

The protein common assembly database (ProtCAD)-a comprehensive structural resource of protein complexes

Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10-15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site (http://dunbrack2.fccc.edu/protcad).

Protein-protein interaction prediction with deep learning: A comprehensive review

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

Integrated structure-based protein interface prediction

Background

Identifying protein interfaces can inform how proteins interact with their binding partners, uncover the regulatory mechanisms that control biological functions and guide the development of novel therapeutic agents. A variety of computational approaches have been developed for predicting a protein’s interfacial residues from its known sequence and structure. Methods using the known three-dimensional structures of proteins can be template-based or template-free. Template-based methods have limited success in predicting interfaces when homologues with known complex structures are not available to use as templates. The prediction performance of template-free methods that only rely only upon proteins’ intrinsic properties is limited by the amount of biologically relevant features that can be included in an interface prediction model.

Results

We describe the development of an integrated method for protein interface prediction (ISPIP) to explore the hypothesis that the efficacy of a computational prediction method of protein binding sites can be enhanced by using a combination of methods that rely on orthogonal structure-based properties of a query protein, combining and balancing both template-free and template-based features. ISPIP is a method that integrates these approaches through simple linear or logistic regression models and more complex decision tree models. On a diverse test set of 156 query proteins, ISPIP outperforms each of its individual classifiers in identifying protein binding interfaces.

Conclusions

The integrated method captures the best performance of individual classifiers and delivers an improved interface prediction. The method is robust and performs well even when one of the individual classifiers performs poorly on a particular query protein. This work demonstrates that integrating orthogonal methods that depend on different structural properties of proteins performs better at interface prediction than any individual classifier alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04852-2.

QSalignWeb: A Server to Predict and Analyze Protein Quaternary Structure

The identification of physiologically relevant quaternary structures (QSs) in crystal lattices is challenging. To predict the physiological relevance of a particular QS, QSalign searches for homologous structures in which subunits interact in the same geometry. This approach proved accurate but was limited to structures already present in the Protein Data Bank (PDB). Here, we introduce a webserver (www.QSalign.org) allowing users to submit homo-oligomeric structures of their choice to the QSalign pipeline. Given a user-uploaded structure, the sequence is extracted and used to search homologs based on sequence similarity and PFAM domain architecture. If structural conservation is detected between a homolog and the user-uploaded QS, physiological relevance is inferred. The web server also generates alternative QSs with PISA and processes them the same way as the query submitted to widen the predictions. The result page also shows representative QSs in the protein family of the query, which is informative if no QS conservation was detected or if the protein appears monomeric. These representative QSs can also serve as a starting point for homology modeling.

PDB-wide identification of physiological hetero-oligomeric assemblies based on conserved quaternary structure geometry

An accurate understanding of biomolecular mechanisms and diseases requires information on protein quaternary structure (QS). A critical challenge in inferring QS information from crystallography data is distinguishing biological interfaces from fortuitous crystal-packing contacts. Here, we employ QS conservation across homologs to infer the biological relevance of hetero-oligomers. We compare the structures and compositions of hetero-oligomers, which allow us to annotate 7,810 complexes as physiologically relevant, 1,060 as likely errors, and 1,432 with comparative information on subunit stoichiometry and composition. Excluding immunoglobulins, these annotations encompass over 51% of hetero-oligomers in the PDB. We curate a dataset of 577 hetero-oligomeric complexes to benchmark these annotations, which reveals an accuracy >94%. When homology information is not available, we compare QS across repositories (PDB, PISA, and EPPIC) to derive confidence estimates. This work provides high-quality annotations along with a large benchmark dataset of hetero-assemblies.

INTERCAAT: identifying interface residues between macromolecules

SUMMARY

The Interface Contact definition with Adaptable Atom Types (INTERCAAT) was developed to determine the atomic interactions between molecules that form a known three dimensional structure. First, INTERCAAT creates a Voronoi tessellation where each atom acts as a seed. Interactions are defined by atoms that share a hyperplane and whose distance is less than the sum of each atoms' Van der Waals radii plus the diameter of a solvent molecule. Interacting atoms are then classified and interactions are filtered based on compatibility. INTERCAAT implements an adaptive atom classification method; therefore, it can explore interfaces between a variety macromolecules.

AVAILABILITY AND IMPLEMENTATION

Source code is freely available at: https://gitlab.com/fiserlab.org/intercaat.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Allosteric Inhibition of Adenylyl Cyclase Type 5 by G-Protein: A Molecular Dynamics Study

Adenylyl cyclases (ACs) have a crucial role in many signal transduction pathways, in particular in the intricate control of cyclic AMP (cAMP) generation from adenosine triphosphate (ATP). Using homology models developed from existing structural data and docking experiments, we have carried out all-atom, microsecond-scale molecular dynamics simulations on the AC5 isoform of adenylyl cyclase bound to the inhibitory G-protein subunit Gαi in the presence and in the absence of ATP. The results show that Gαi has significant effects on the structure and flexibility of adenylyl cyclase, as observed earlier for the binding of ATP and Gsα. New data on Gαi bound to the C1 domain of AC5 help explain how Gαi inhibits enzyme activity and obtain insight on its regulation. Simulations also suggest a crucial role of ATP in the regulation of the stimulation and inhibition of AC5.

Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge

Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.

Biological vs. Crystallographic Protein Interfaces: An Overview of Computational Approaches for Their Classification

Complexes between proteins are at the basis of almost every process in cells. Their study, from a structural perspective, has a pivotal role in understanding biological functions and, importantly, in drug development. X-ray crystallography represents the broadest source for the experimental structural characterization of protein-protein complexes. Correctly identifying the biologically relevant interface from the crystallographic ones is, however, not trivial and can be prone to errors. Over the past two decades, computational methodologies have been developed to study the differences of those interfaces and automatically classify them as biological or crystallographic. Overall, protein-protein interfaces show differences in terms of composition, energetics and evolutionary conservation between biological and crystallographic ones. Based on those observations, a number of computational methods have been developed for this classification problem, which can be grouped into three main categories: Energy-, empirical knowledge- and machine learning-based approaches. In this review, we give a comprehensive overview of the training datasets and methods so far implemented, providing useful links and a brief description of each method.

ProtCID: a data resource for structural information on protein interactions

Structural information on the interactions of proteins with other molecules is plentiful, and for some proteins and protein families, there may be 100s of available structures. It can be very difficult for a scientist who is not trained in structural bioinformatics to access this information comprehensively. Previously, we developed the Protein Common Interface Database (ProtCID), which provided clusters of the interfaces of full-length protein chains as a means of identifying biological assemblies. Because proteins consist of domains that act as modular functional units, we have extended the analysis in ProtCID to the individual domain level. This has greatly increased the number of large protein-protein clusters in ProtCID, enabling the generation of hypotheses on the structures of biological assemblies of many systems. The analysis of domain families allows us to extend ProtCID to the interactions of domains with peptides, nucleic acids, and ligands. ProtCID provides complete annotations and coordinate sets for every cluster.

The authors previously developed the Protein Common Interface Database (ProtCID), which compares and clusters the interfaces of pairs of full-length protein chains with defined Pfam domain architectures in different PDB entries to identify biological assemblies. Here the authors extend ProtCID to the clustering of domain-domain interactions that also allows analyzing domain interactions with peptides, nucleic acids, and ligands.

Dockground Tool for Development and Benchmarking of Protein Docking Procedures

Databases of protein-protein complexes are essential for the development of protein modeling/docking techniques. Such databases provide a knowledge base for docking algorithms, intermolecular potentials, search procedures, scoring functions, and refinement protocols. Development of docking techniques requires systematic validation of the modeling protocols on carefully curated benchmark sets of complexes. We present a description and a guide to the DOCKGROUND resource ( http://dockground.compbio.ku.edu ) for structural modeling of protein interactions. The resource integrates various datasets of protein complexes and other data for the development and testing of protein docking techniques. The sets include bound complexes, experimentally determined unbound, simulated unbound, model-model complexes, and docking decoys. The datasets are available to the user community through a Web interface.

Computational Analysis of Non-covalent Interactions in Phycocyanin Subunit Interfaces

Protein-protein interactions are an important phenomenon in biological processes and functions. We used the manually curated non-redundant dataset of 118 phycocyanin interfaces to gain additional insight into this phenomenon using a robust inter-atomic non-covalent interaction analyzing tool PPCheck. Our observations indicate that there is a relatively high composition of hydrophobic residues at the interfaces. Most of the interface residues are clustered at the middle of the range which we call "standard-size" interfaces. Furthermore, the multiple interaction patterns founded in the present study indicate that more than half of the residues involved in these interactions participate in multiple and water-bridged hydrogen bonds. Thus, hydrogen bonds contribute maximally towards the stability of protein-protein complexes. The analysis shows that hydrogen bond energies contribute to about 88 % to the total energy and it also increases with interface size. Van der Waals (vdW) energy contributes to 9.3 %±1.7 % on average in these complexes. Moreover, there is about 1.9 %±1.5 % contribution by electrostatic energy. Nevertheless, the role by vdW and electrostatic energy could not be ignored in interface binding. Results show that the total binding energy is more for large phycocyanin interfaces. The normalized energy per residue was less than -16 kJ mol^-1 , while most of them have energy in the range from -6 to -14 kJ mol^-1 . The non-covalent interacting residues in these proteins were found to be highly conserved. Obtained results might contribute to the understanding of structural stability of this class of evolutionary essential proteins with increased practical application and future designs of novel protein-bioactive compound interactions.

Machine-learning techniques for the prediction of protein-protein interactions

Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gap in identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms has been used in conjunction with experimental methods for discovery of novel protein interactions. The two most popular supervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forest classifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI dataset confidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Several clustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for the prediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have been widely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.

Accurate Classification of Biological and non-Biological Interfaces in Protein Crystal Structures using Subtle Covariation Signals

Proteins often work as oligomers or multimers in vivo. Therefore, elucidating their oligomeric or multimeric form (quaternary structure) is crucially important to ascertain their function. X-ray crystal structures of numerous proteins have been accumulated, providing information related to their biological units. Extracting information of biological units from protein crystal structures represents a meaningful task for modern biology. Nevertheless, although many methods have been proposed for identifying biological units appearing in protein crystal structures, it is difficult to distinguish biological protein-protein interfaces from crystallographic ones. Therefore, our simple but highly accurate classifier was developed to infer biological units in protein crystal structures using large amounts of protein sequence information and a modern contact prediction method to exploit covariation signals (CSs) in proteins. We demonstrate that our proposed method is promising even for weak signals of biological interfaces. We also discuss the relation between classification accuracy and conservation of biological units, and illustrate how the selection of sequences included in multiple sequence alignments as sources for obtaining CSs affects the results. With increased amounts of sequence data, the proposed method is expected to become increasingly useful.

Multifaceted protein-protein interaction prediction based on Siamese residual RCNN

MOTIVATION

Sequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.

RESULTS

We present an end-to-end framework, PIPR (Protein-Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.

AVAILABILITY AND IMPLEMENTATION

The implementation is available at https://github.com/muhaochen/seq_ppi.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ProteinsPlus: a web portal for structure analysis of macromolecules

With currently more than 126 000 publicly available structures and an increasing growth rate, the Protein Data Bank constitutes a rich data source for structure-driven research in fields like drug discovery, crop science and biotechnology in general. Typical workflows in these areas involve manifold computational tools for the analysis and prediction of molecular functions. Here, we present the ProteinsPlus web server that offers a unified easy-to-use interface to a broad range of tools for the early phase of structure-based molecular modeling. This includes solutions for commonly required pre-processing tasks like structure quality assessment (EDIA), hydrogen placement (Protoss) and the search for alternative conformations (SIENA). Beyond that, it also addresses frequent problems as the generation of 2D-interaction diagrams (PoseView), protein-protein interface classification (HyPPI) as well as automatic pocket detection and druggablity assessment (DoGSiteScorer). The unified ProteinsPlus interface covering all featured approaches provides various facilities for intuitive input and result visualization, case-specific parameterization and download options for further processing. Moreover, its generalized workflow allows the user a quick familiarization with the different tools. ProteinsPlus also stores the calculated results temporarily for future request and thus facilitates convenient result communication and re-access. The server is freely available at http://proteins.plus.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

Inhibition of Plasmodium falciparum cysteine protease falcipain-2 by a human cross-class inhibitor serpinB3: A mechanistic insight

Falcipain-2(FP2), a cysteine protease from Plasmodium falciparum, cleaves host erythrocyte hemoglobin and specific membrane skeleton components during the parasite life cycle. Therefore its inhibition has been considered as an attractive approach to combat the disease. SerpinB3 (SPB3) belongs to the ovalbumin-serpin family and is a potent cross-class inhibitor of cysteine cathepsins L, K, S and papain. This study explored the possibility of inhibition of FP2 by SPB3. It turned out that general proteolytic activities as well as specific hemoglobinolytic activity of FP2 have been inhibited by SPB3. Furthermore, studies have been designed to investigate and characterize the mechanism of inhibition in comparison with proteases Cathepsin L (CTSL) and papain. The Ki value of inhibition for FP2, measured against its specific substrate (VLK-pNA), is 338.11 nM and stoichiometry (I/E ratio) of inhibition is 1. These values are comparable to CTSL and papain. Analytical gel filtration profile and CD spectroscopy data confirm FP2-SPB3 complex formation. Our studies revealed that interaction of SPB3 with FP2 is non-covalent type like that of CTSL and papain but unlike other serine protease-inhibiting serpins. An in-silico docking and simulation study have been performed with FP2 as well as CTSL and results suggest different binding mode for FP2 and CTSL, though both the complexes are stable with significant contribution from electrostatic energy of interaction. We further showed a disease state mutant SPB3-Gly351Ala performed better anti-protease activity against FP2. This study, for the first time, has shown a serpin family inhibitor from human could efficiently inhibit activity of FP2.

Principles and characteristics of biological assemblies in experimentally determined protein structures

More than half of all structures in the PDB are assemblies of two or more proteins, including both homooligomers and heterooligomers. Structural information on these assemblies comes from X-ray crystallography, NMR, and cryo-EM spectroscopy. The correct assembly in an X-ray structure is often ambiguous, and computational methods have been developed to identify the most likely biologically relevant assembly based on physical properties of assemblies and sequence conservation in interfaces. Taking advantage of the large number of structures now available, some of the most recent methods have relied on similarity of interfaces and assemblies across structures of homologous proteins.

Protein-protein binding supersites

The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.

Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification

BACKGROUND

Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Due to the complexity of the biological assemblies currently tackled, classifying those interfaces, i.e. distinguishing biological from crystal lattice interfaces, is not trivial and often prone to errors. In this context, analyzing the physico-chemical characteristics of biological/crystal interfaces can help researchers identify possible features that distinguish them and gain a better understanding of the systems.

RESULTS

In this work, we are providing new insights into the differences between biological and crystallographic complexes by focusing on "pair-properties" of interfaces that have not yet been fully investigated. We investigated properties such intermolecular residue-residue contacts (already successfully applied to the prediction of binding affinities) and interaction energies (electrostatic, Van der Waals and desolvation). By using the XtalMany and BioMany interface datasets, we show that interfacial residue contacts, classified as a function of their physico-chemical properties, can distinguish between biological and crystallographic interfaces. The energetic terms show, on average, higher values for crystal interfaces, reflecting a less stable interface due to crystal packing compared to biological interfaces. By using a variety of machine learning approaches, we trained a new interface classification predictor based on contacts and interaction energetic features. Our predictor reaches an accuracy in classifying biological vs crystal interfaces of 0.92, compared to 0.88 for EPPIC (one of the main state-of-the-art classifiers reporting same performance as PISA).

CONCLUSION

In this work we have gained insights into the nature of intermolecular contacts and energetics terms distinguishing biological from crystallographic interfaces. Our findings might have a broader applicability in structural biology, for example for the identification of near native poses in docking. We implemented our classification approach into an easy-to-use and fast software, freely available to the scientific community from http://github.com/haddocking/interface-classifier .

Integrating co-evolutionary signals and other properties of residue pairs to distinguish biological interfaces from crystal contacts

It remains challenging to accurately discriminate between biological and crystal interfaces. Most existing analyses and algorithms focused on the features derived from a single side of the interface. However, less attention has been paid to the properties of residue pairs across protein interfaces. To address this problem, we defined a novel co-evolutionary feature for homodimers through integrating direct coupling analysis and image processing techniques. The residue pairs across biological homodimeric interfaces were significantly enriched in co-evolving residues compared to those across crystal contacts, resulting in a promising classification accuracy with area under the curves (AUCs) of >0.85. Considering the availability of co-evolutionary feature, we also designed other residue pair based features that were useful for both homodimers and heterodimers. The most informative residue pairs were identified to reflect the interaction preferences across protein interfaces. Regarding the other extant properties, we designed the new descriptors at the interface residue level as well as at the pairwise contact level. Extensive validation showed that these single properties can be used to identify biological interfaces with AUCs ranging from 0.60 to 0.88. By integrating co-evolutionary feature with other residue pair based properties, our final prediction model output excellent performance with AUCs of >0.91 on different datasets. Compared to existing methods, our algorithm not only yielded better or comparable results but also provided complementary information. An easy-to-use web server is freely accessible at http://liulab.hzau.edu.cn/RPAIAnalyst.

Prediction of spacer-α6 complex: a novel insight into binding of ADAMTS13 with A2 domain of von Willebrand factor under forces

Force-regulated cleavage of A2 domain of von Willebrand factor (vWF) by ADAMTS13 is a key event in preventing thrombotic thrombocytopenic purpura (TTP). Recognition and cleavage depend on cooperative and modular contacts between several ADAMTS13 subdomains and discrete segments of vWF A2 domain. Spacer domain of ADAMTS13 contains an important exosite interacting with α6 helix of unfold A2 domain, but it remains unclear whether stretching of α6 regulates binding to spacer. To understand the molecular mechanism underlying the interactions between spacer and α6 under stretching, we successfully predicted spacer-α6 complex by a novel computer strategy combined the steered molecular dynamics (SMD) and flexible docking techniques. This strategy included three steps: (1) constant-velocity SMD simulation of α6; (2) zero-velocity SMD simulations of α6, and (3) flexible dockings of α6 to spacer. In our spacer-α6 complex model, 13 key residues, six in α6 and seven in spacer, were identified. Our data demonstrated a biphasic extension-regulated binding of α6 to spacer. The binding strength of the complex increased with α6 extension until it reaches its optimum of 0.25 nm, and then decreased as α6 extension further increased, meaning that spacer is in favor to binding with a partially extended α6, which may contribute to the optimal contact and proteolysis. Changes of interface area and intermolecular salt bridge may serve as the molecular basis for this characteristic. These findings provide a novel insight into mechano-chemical regulation on interaction between ADAMTS13 and vWF A2 domain under forces.

Partner-Specific Prediction of Protein-Dimer Stability from Unbound Structure of Monomer

Protein complexes play deterministic roles in live entities in sensing, compiling, controlling, and responding to external and internal stimuli. Thermodynamic stability is an important property of protein complexes; having knowledge about complex stability helps us to understand the basics of protein assembly-related diseases and the mechanism of protein assembly clearly. Enormous protein-protein interactions, detected by high-throughput methods, necessitate finding fast methods for predicting the stability of protein assemblies in a quantitative and qualitative manner. The existing methods of predicting complex stability need knowledge about the three-dimensional (3D) structure of the intended protein complex. Here, we introduce a new method for predicting dissociation free energy of subunits by analyzing the structural and topological properties of a protein binding patch on a single subunit of the desired protein complex. The method needs the 3D structure of just one subunit and the information about the position of the intended binding site on the surface of that subunit to predict dimer stability in a classwise manner. The patterns of structural and topological properties of a protein binding patch are decoded by recurrence quantification analysis. Nonparametric discrimination is then utilized to predict the stability class of the intended dimer with accuracy greater than 85%.

Dimerization of the Pragmin Pseudo-Kinase Regulates Protein Tyrosine Phosphorylation

The pseudo-kinase and signaling protein Pragmin has been linked to cancer by regulating protein tyrosine phosphorylation via unknown mechanisms. Here we present the crystal structure of the Pragmin 906-1,368 amino acid C terminus, which encompasses its kinase domain. We show that Pragmin contains a classical protein-kinase fold devoid of catalytic activity, despite a conserved catalytic lysine (K997). By proteomics, we discovered that this pseudo-kinase uses the tyrosine kinase CSK to induce protein tyrosine phosphorylation in human cells. Interestingly, the protein-kinase domain is flanked by N- and C-terminal extensions forming an original dimerization domain that regulates Pragmin self-association and stimulates CSK activity. A1329E mutation in the C-terminal extension destabilizes Pragmin dimerization and reduces CSK activation. These results reveal a dimerization mechanism by which a pseudo-kinase can induce protein tyrosine phosphorylation. Further sequence-structure analysis identified an additional member (C19orf35) of the superfamily of dimeric Pragmin/SgK269/PEAK1 pseudo-kinases.

A structural dissection of protein–RNA interactions based on different RNA base areas of interfaces

Protein–RNA interactions are very common cellular processes, but the mechanisms of interactions are not fully understood, mainly due to the complicated RNA structures. By the elaborate investigation on RNA structures of protein–RNA complexes, it was firstly found in this paper that RNAs in these complexes could be clearly classified into three classes (high, medium and low) based on the different levels of P_base (the percentage of base area buried in the RNA interface). In view of the three RNA classes, more detailed analyses on protein–RNA interactions were comprehensively performed from various aspects, including interface area, structure, composition and interaction force, so as to achieve a deeper understanding of the recognition specificity for the three classes of protein–RNA interactions. According to our classification strategy, the three complex classes have significant differences in terms of almost all properties. Complexes in the high class have short and extended RNA structures and behave like protein–ssDNA interactions. Their hydrogen bonds and hydrophobic interactions are strong. For complexes in low class, their RNA structures are mainly double-stranded, like protein–dsDNA interactions, and electrostatic interactions frequently occur. The complexes in medium class have the longest RNA chains and largest average interface area. Meanwhile, they do not show any preference for the interaction force. On average, in terms of composition, secondary structures and intermolecular physicochemical properties, significant feature preferences can be observed in high and low complexes, but no highly specific features are found for medium complexes. We found that our proposed P_base is an important parameter which can be used as a new determinant to distinguish protein–RNA complexes. For high and low complexes, we can more easily understand the specificity of the recognition process from the interface features than for medium complexes. In the future, medium complexes should be our research focus to further structurally analyze from more feature aspects. Overall, this study may contribute to further understanding of the mechanism of protein–RNA interactions on a more detailed level.

Qualitative and quantitative measurements of the influence of structure and composition of RNA interfaces on protein–RNA interactions.

Inferring and Using Protein Quaternary Structure Information from Crystallographic Data

A precise knowledge of the quaternary structure of proteins is essential to illuminate both their function and their evolution. The major part of our knowledge on quaternary structure is inferred from X-ray crystallography data, but this inference process is hard and error-prone. The difficulty lies in discriminating fortuitous protein contacts, which make up the lattice of protein crystals, from biological protein contacts that exist in the native cellular environment. Here, we review methods devised to discriminate between both types of contacts and describe resources for downloading protein quaternary structure information and identifying high-confidence quaternary structures. The use of high-confidence datasets of quaternary structures will be critical for the analysis of structural, functional, and evolutionary properties of proteins.

Dockground: A comprehensive data resource for modeling of protein complexes

Characterization of life processes at the molecular level requires structural details of protein interactions. The number of experimentally determined structures of protein-protein complexes accounts only for a fraction of known protein interactions. This gap in structural description of the interactome has to be bridged by modeling. An essential part of the development of structural modeling/docking techniques for protein interactions is databases of protein-protein complexes. They are necessary for studying protein interfaces, providing a knowledge base for docking algorithms, and developing intermolecular potentials, search procedures, and scoring functions. Development of protein-protein docking techniques requires thorough benchmarking of different parts of the docking protocols on carefully curated sets of protein-protein complexes. We present a comprehensive description of the Dockground resource (http://dockground.compbio.ku.edu) for structural modeling of protein interactions, including previously unpublished unbound docking benchmark set 4, and the X-ray docking decoy set 2. The resource offers a variety of interconnected datasets of protein-protein complexes and other data for the development and testing of different aspects of protein docking methodologies. Based on protein-protein complexes extracted from the PDB biounit files, Dockground offers sets of X-ray unbound, simulated unbound, model, and docking decoy structures. All datasets are freely available for download, as a whole or selecting specific structures, through a user-friendly interface on one integrated website.

IntPred: a structure-based predictor of protein-protein interaction sites

Motivation

Protein–protein interactions are vital for protein function with the average protein having between three and ten interacting partners. Knowledge of precise protein–protein interfaces comes from crystal structures deposited in the Protein Data Bank (PDB), but only 50% of structures in the PDB are complexes. There is therefore a need to predict protein–protein interfaces in silico and various methods for this purpose. Here we explore the use of a predictor based on structural features and which exploits random forest machine learning, comparing its performance with a number of popular established methods.

Results

On an independent test set of obligate and transient complexes, our IntPred predictor performs well (MCC = 0.370, ACC = 0.811, SPEC = 0.916, SENS = 0.411) and compares favourably with other methods. Overall, IntPred ranks second of six methods tested with SPPIDER having slightly better overall performance (MCC = 0.410, ACC = 0.759, SPEC = 0.783, SENS = 0.676), but considerably worse specificity than IntPred. As with SPPIDER, using an independent test set of obligate complexes enhanced performance (MCC = 0.381) while performance is somewhat reduced on a dataset of transient complexes (MCC = 0.303). The trade-off between sensitivity and specificity compared with SPPIDER suggests that the choice of the appropriate tool is application-dependent.

Availability and implementation

IntPred is implemented in Perl and may be downloaded for local use or run via a web server at www.bioinf.org.uk/intpred/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Structural Insights into the Broad Substrate Specificity of a Novel Endoglycoceramidase I Belonging to a New Subfamily of GH5 Glycosidases

Endoglycoceramidases (EGCases) specifically hydrolyze the glycosidic linkage between the oligosaccharide and the ceramide moieties of various glycosphingolipids, and they have received substantial attention in the emerging field of glycosphingolipidology. However, the mechanism regulating the strict substrate specificity of these GH5 glycosidases has not been identified. In this study, we report a novel EGCase I from Rhodococcus equi 103S (103S_EGCase I) with remarkably broad substrate specificity. Based on phylogenetic analyses, the enzyme may represent a new subfamily of GH5 glycosidases. The X-ray crystal structures of 103S_EGCase I alone and in complex with its substrates monosialodihexosylganglioside (GM3) and monosialotetrahexosylganglioside (GM1) enabled us to identify several structural features that may account for its broad specificity. Compared with EGCase II from Rhodococcus sp. M-777 (M777_EGCase II), which possesses strict substrate specificity, 103S_EGCase I possesses a longer α7-helix and a shorter loop 4, which forms a larger substrate-binding pocket that could accommodate more extended oligosaccharides. In addition, loop 2 and loop 8 of the enzyme adopt a more open conformation, which also enlarges the oligosaccharide-binding cavity. Based on this knowledge, a rationally designed experiment was performed to examine the substrate specificity of EGCase II. The truncation of loop 4 in M777_EGCase II increased its activity toward GM1 (163%). Remarkably, the S63G mutant of M777_EGCase II showed a broader substrate spectra and significantly increased activity toward bulky substrates (up to >1370-fold for fucosyl-GM1). Collectively, the results presented here reveal the exquisite substrate recognition mechanism of EGCases and provide an opportunity for further engineering of these enzymes.

Structural determinants of adhesion by Protocadherin-19 and implications for its role in epilepsy

Non-clustered δ-protocadherins are homophilic cell adhesion molecules essential for the development of the vertebrate nervous system, as several are closely linked to neurodevelopmental disorders. Mutations in protocadherin-19 (PCDH19) result in a female-limited, infant-onset form of epilepsy (PCDH19-FE). Over 100 mutations in PCDH19 have been identified in patients with PCDH19-FE, about half of which are missense mutations in the adhesive extracellular domain. Neither the mechanism of homophilic adhesion by PCDH19, nor the biochemical effects of missense mutations are understood. Here we present a crystallographic structure of the minimal adhesive fragment of the zebrafish Pcdh19 extracellular domain. This structure reveals the adhesive interface for Pcdh19, which is broadly relevant to both non-clustered δ and clustered protocadherin subfamilies. In addition, we show that several PCDH19-FE missense mutations localize to the adhesive interface and abolish Pcdh19 adhesion in in vitro assays, thus revealing the biochemical basis of their pathogenic effects during brain development.

DOI:http://dx.doi.org/10.7554/eLife.18529.001

As the brain develops, its basic building blocks – cells called neurons – need to form the correct connections with one another in order to give rise to neural circuits. A mistake that leads to the formation of incorrect connections can result in a number of disorders or brain abnormalities.

Proteins called cadherins that are present on the surface of neurons enable them to stick to their correct partners like Velcro. One of these proteins is called Protocadherin-19. However, it was not fully understood how this protein forms an adhesive bond with other Protocadherin-19 molecules, or how some of the proteins within the cadherin family are able to distinguish between one another.

Cooper et al. used X-ray crystallography to visualize the molecular structure of Protocadherin-19 taken from zebrafish in order to better understand the adhesive bond that these proteins form with each other. In addition, the new structure showed the sites of the mutations that cause a form of epilepsy in infant females. From this, Cooper et al. could predict how the mutations would disrupt Protocadherin-19’s shape and function.

The structures revealed that Protocadherin-19 molecules from adjacent cells engage in a “forearm handshake” to form the bond that connects neurons. Some of the mutations that cause epilepsy occur in the region responsible for this Protocadherin-19 forearm handshake. Laboratory experiments confirmed that these mutations impair the formation of the adhesive bond, revealing the molecular basis for some of the mutations that underlie Protocadherin-19-female-limited epilepsy.

Other cadherin molecules may interact via a similar forearm handshake; this could be investigated in future experiments. It also remains to be discovered how brain wiring depends on Protocadherin-19 adhesion in animal development, and how altering these proteins can rewire developing brain circuits.

DOI:http://dx.doi.org/10.7554/eLife.18529.002

Implication of Terminal Residues at Protein-Protein and Protein-DNA Interfaces

Terminal residues of protein chains are charged and more flexible than other residues since they are constrained only on one side. Do they play a particular role in protein-protein and protein-DNA interfaces? To answer this question, we considered large sets of non-redundant protein-protein and protein-DNA complexes and analyzed the status of terminal residues and their involvement in interfaces. In protein-protein complexes, we found that more than half of terminal residues (62%) are either modified by attachment of a tag peptide (10%) or have missing coordinates in the analyzed structures (52%). Terminal residues are almost exclusively located at the surface of proteins (94%). Contrary to charged residues, they are not over or under-represented in protein-protein interfaces, but strongly prefer the peripheral region of interfaces when present at the interface (83% of terminal residues). The almost exclusive location of terminal residues at the surface of the proteins or in the rim regions of interfaces explains that experimental methods relying on tail hybridization can be successfully applied without disrupting the complexes under study. Concerning conformational rearrangement in protein-protein complexes, despite their expected flexibility, terminal residues adopt similar locations between the free and bound forms of the docking benchmark. In protein-DNA complexes, N-terminal residues are twice more frequent than C-terminal residues at interfaces. Both N-terminal and C-terminal residues are under-represented in interfaces, in contrast to positively charged residues, which are strongly favored. When located in protein-DNA interfaces, terminal residues prefer the periphery. N-terminal and C-terminal residues thus have particular properties with regard to interfaces, which cannot be reduced to their charged nature.

PPI4DOCK: large scale assessment of the use of homology models in free docking over more than 1000 realistic targets

MOTIVATION

Protein-protein docking methods are of great importance for understanding interactomes at the structural level. It has become increasingly appealing to use not only experimental structures but also homology models of unbound subunits as input for docking simulations. So far we are missing a large scale assessment of the success of rigid-body free docking methods on homology models.

RESULTS

We explored how we could benefit from comparative modelling of unbound subunits to expand docking benchmark datasets. Starting from a collection of 3157 non-redundant, high X-ray resolution heterodimers, we developed the PPI4DOCK benchmark containing 1417 docking targets based on unbound homology models. Rigid-body docking by Zdock showed that for 1208 cases (85.2%), at least one correct decoy was generated, emphasizing the efficiency of rigid-body docking in generating correct assemblies. Overall, the PPI4DOCK benchmark contains a large set of realistic cases and provides new ground for assessing docking and scoring methodologies.

AVAILABILITY AND IMPLEMENTATION

Benchmark sets can be downloaded from http://biodev.cea.fr/interevol/ppi4dock/ CONTACT: guerois@cea.frSupplementary information: Supplementary data are available at Bioinformatics online.

In silico construction of HK2-VDAC1 complex and investigating the HK2 binding-induced molecular gating mechanism of VDAC1

Hexokinase 2 (HK2) binds to Voltage-Dependent Anion Channel 1 (VDAC1) on mitochondrial outer membrane (MOM) to facilitate a preferential access of ATP to HK2 for glycolysis, in order to maintain a constant energy source for cell proliferation in cancer especially. While previous studies have discovered that the VDAC1 N-terminal helix is responsible for regulating molecules from within mitochondria to cytoplasm, the molecular mechanism of how HK2 is able to regulate the ATP access remains elusive. We hereby propose a model for the HK2-VDAC1 association. The model is then subjected to molecular dynamics (MD) simulations, where we probe the effect of HK2 binding on the mobility of the VDAC1 N-terminal helix. Results from the simulations show that HK2 binding restricts the movement of the VDAC1 N-terminal helix. As a result, VDAC1 is kept in the open state most of the time and probably allows a constant supply of ATP to HK2 for glycolysis.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Structure and Sequence Analyses of Clustered Protocadherins Reveal Antiparallel Interactions that Mediate Homophilic Specificity

Clustered protocadherin (Pcdh) proteins mediate dendritic self-avoidance in neurons via specific homophilic interactions in their extracellular cadherin (EC) domains. We determined crystal structures of EC1-EC3, containing the homophilic specificity-determining region, of two mouse clustered Pcdh isoforms (PcdhγA1 and PcdhγC3) to investigate the nature of the homophilic interaction. Within the crystal lattices, we observe antiparallel interfaces consistent with a role in trans cell-cell contact. Antiparallel dimerization is supported by evolutionary correlations. Two interfaces, located primarily on EC2-EC3, involve distinctive clustered Pcdh structure and sequence motifs, lack predicted glycosylation sites, and contain residues highly conserved in orthologs but not paralogs, pointing toward their biological significance as homophilic interaction interfaces. These two interfaces are similar yet distinct, reflecting a possible difference in interaction architecture between clustered Pcdh subfamilies. These structures initiate a molecular understanding of clustered Pcdh assemblies that are required to produce functional neuronal networks.

Understanding the fabric of protein crystals: computational classification of biological interfaces and crystal contacts

Modern structural biology still draws the vast majority of information from crystallography, a technique where the objects being investigated are embedded in a crystal lattice. Given the complexity and variety of those objects, it becomes fundamental to computationally assess which of the interfaces in the lattice are biologically relevant and which are simply crystal contacts. Since the mid-1990s, several approaches have been applied to obtain high-accuracy classification of crystal contacts and biological protein–protein interfaces. This review provides an overview of the concepts and main approaches to protein interface classification: thermodynamic estimation of interface stability, evolutionary approaches based on conservation of interface residues, and co-occurrence of the interface across different crystal forms. Among the three categories, evolutionary approaches offer the strongest promise for improvement, thanks to the incessant growth in sequence knowledge. Importantly, protein interface classification algorithms can also be used on multimeric structures obtained using other high-resolution techniques or for protein assembly design or validation purposes. A key issue linked to protein interface classification is the identification of the biological assembly of a crystal structure and the analysis of its symmetry. Here, we highlight the most important concepts and problems to be overcome in assembly prediction. Over the next few years, tools and concepts of interface classification will probably become more frequently used and integrated in several areas of structural biology and structural bioinformatics. Among the main challenges for the future are better addressing of weak interfaces and the application of interface classification concepts to prediction problems like protein–protein docking.

Supplementary information: Supplementary data are available at Bioinformatics online.

Contact:guido.capitani@psi.ch

DynaFace: Discrimination between Obligatory and Non-obligatory Protein-Protein Interactions Based on the Complex's Dynamics

Protein-protein interfaces have been evolutionarily-designed to enable transduction between the interacting proteins. Thus, we hypothesize that analysis of the dynamics of the complex can reveal details about the nature of the interaction, and in particular whether it is obligatory, i.e., persists throughout the entire lifetime of the proteins, or not. Indeed, normal mode analysis, using the Gaussian network model, shows that for the most part obligatory and non-obligatory complexes differ in their decomposition into dynamic domains, i.e., the mobile elements of the protein complex. The dynamic domains of obligatory complexes often mix segments from the interacting chains, and the hinges between them do not overlap with the interface between the chains. In contrast, in non-obligatory complexes the interface often hinges between dynamic domains, held together through few anchor residues on one side of the interface that interact with their counterpart grooves in the other end. In automatic analysis, 117 of 139 obligatory (84.2%) and 203 of 246 non-obligatory (82.5%) complexes are correctly classified by our method: DynaFace. We further use DynaFace to predict obligatory and non-obligatory interactions among a set of 300 putative protein complexes. DynaFace is available at: http://safir.prc.boun.edu.tr/dynaface.

Applying Side-chain Flexibility in Motifs for Protein Docking

Conventional rigid docking algorithms have been unsatisfactory in their computational results, largely due to the fact that protein structures are flexible in live environments. In response, we propose to introduce the side-chain flexibility in protein motif into the docking. First, the Morse theory is applied to curvature labeling and surface region growing, for segmentation of the protein surface into smaller patches. Then, the protein is described by an ensemble of conformations that incorporate the flexibility of interface side chains and are sampled using rotamers. Next, a 3D rotation invariant shape descriptor is proposed to deal with the flexible motifs and surface patches; thus, pairwise complementarity matching is needed only between the convex patches of ligand and the concave patches of receptor. The iterative closest point (ICP) algorithm is implemented for geometric alignment of the two 3D protein surface patches. Compared with the fast Fourier transform-based global geometric matching algorithm and other methods, our FlexDock system generates much less false-positive docking results, which benefits identification of the complementary candidates. Our computational experiments show the advantages of the proposed flexible docking algorithm over its counterparts.

A structural dissection of large protein-protein crystal packing contacts

With the rapid increase in crystal structures of protein-protein complexes deposited in the Protein Data Bank (PDB), more and more crystal contacts have been shown to have similar or even larger interface areas than biological interfaces. However, little attention has been paid to these large crystal packing contacts and their structural principles remain unknown. To address this issue, we used a comparative feature analysis to analyze the geometric and physicochemical properties of large crystal packing contacts by comparing two types of specific protein-protein interactions (PPIs), weak transient complexes and permanent homodimers. Our results show that although large crystal packing contacts have a similar interface area and contact size as permanent homodimers, they tend to be more planar, loosely packed and less hydrophobic than permanent homodimers and cannot form a central core region that is fully buried during interaction. However, the properties of large crystal packing contacts, except for the interface area and contact size, more closely resemble those of weak transient complexes. The large overlap between biological and large crystal packing contacts indicates that interface properties are not efficient indicators for classification of biological interfaces from large crystal packing contacts and finding other specific features urgently needed.

Structural Modeling of GR Interactions with the SWI/SNF Chromatin Remodeling Complex and C/EBP

The glucocorticoid receptor (GR) is a steroid-hormone-activated transcription factor that modulates gene expression. Transcriptional regulation by the GR requires dynamic receptor binding to specific target sites located across the genome. This binding remodels the chromatin structure to allow interaction with other transcription factors. Thus, chromatin remodeling is an essential component of GR-mediated transcriptional regulation, and understanding the interactions between these molecules at the structural level provides insights into the mechanisms of how GR and chromatin remodeling cooperate to regulate gene expression. This study suggests models for the assembly of the SWI/SNF-A (SWItch/Sucrose-NonFermentable) complex and its interaction with the GR. We used the PRISM algorithm (PRotein Interactions by Structural Matching) to predict the three-dimensional complex structures of the target proteins. The structural models indicate that BAF57 and/or BAF250 mediate the interaction between the GR and the SWI/SNF-A complex, corroborating experimental data. They further suggest that a BAF60a/BAF155 and/or BAF60a/BAF170 interaction is critical for association between the core and variant subunits. Further, we model the interaction between GR and CCAAT-enhancer-binding proteins (C/EBPs), since the GR can regulate gene expression indirectly by interacting with other transcription factors like C/EBPs. We observe that GR can bind to bZip domains of the C/EBPα homodimer as both a monomer and dimer of the DNA-binding domain. In silico mutagenesis of the predicted interface residues confirm the importance of these residues in binding. In vivo analysis of the computationally suggested mutations reveals that double mutations of the leucine residues (L317D+L335D) may disrupt the interaction between GR and C/EBPα. Determination of the complex structures of the GR is of fundamental relevance to understanding its interactions and functions, since the function of a protein or a complex is dictated by its structure. In addition, it may help us estimate the effects of mutations on GR interactions and signaling.