The Structural Pathway of Interleukin 1 (IL-1) Initiated Signaling Reveals Mechanisms of Oncogenic Mutations and SNPs in Inflammation and Cancer

Interleukin-1 (IL-1) is a large cytokine family closely related to innate immunity and inflammation. IL-1 proteins are key players in signaling pathways such as apoptosis, TLR, MAPK, NLR and NF-κB. The IL-1 pathway is also associated with cancer, and chronic inflammation increases the risk of tumor development via oncogenic mutations. Here we illustrate that the structures of interfaces between proteins in this pathway bearing the mutations may reveal how. Proteins are frequently regulated via their interactions, which can turn them ON or OFF. We show that oncogenic mutations are significantly at or adjoining interface regions, and can abolish (or enhance) the protein-protein interaction, making the protein constitutively active (or inactive, if it is a repressor). We combine known structures of protein-protein complexes and those that we have predicted for the IL-1 pathway, and integrate them with literature information. In the reconstructed pathway there are 104 interactions between proteins whose three dimensional structures are experimentally identified; only 15 have experimentally-determined structures of the interacting complexes. By predicting the protein-protein complexes throughout the pathway via the PRISM algorithm, the structural coverage increases from 15% to 71%. In silico mutagenesis and comparison of the predicted binding energies reveal the mechanisms of how oncogenic and single nucleotide polymorphism (SNP) mutations can abrogate the interactions or increase the binding affinity of the mutant to the native partner. Computational mapping of mutations on the interface of the predicted complexes may constitute a powerful strategy to explain the mechanisms of activation/inhibition. It can also help explain how an oncogenic mutation or SNP works.     

Constructing structural networks of signaling pathways on the proteome scale

Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces.     

Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional (3D) structure is reassessed by evaluating blind predictions, performed during 2003-2004 as part of Rounds 3-5 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Ten newly determined structures of protein-protein complexes were used as targets for these rounds. They comprised 2 enzyme-inhibitor complexes, 2 antigen-antibody complexes, 2 complexes involved in cellular signaling, 2 homo-oligomers, and a complex between 2 components of the bacterial cellulosome. For most targets, the predictors were given the experimental structures of 1 unbound and 1 bound component, with the latter in a random orientation. For some, the structure of the free component was derived from that of a related protein, requiring the use of homology modeling. In some of the targets, significant differences in conformation were displayed between the bound and unbound components, representing a major challenge for the docking procedures. For 1 target, predictions could not go to completion. In total, 1866 predictions submitted by 30 groups were evaluated. Over one-third of these groups applied completely novel docking algorithms and scoring functions, with several of them specifically addressing the challenge of dealing with side-chain and backbone flexibility. The quality of the predicted interactions was evaluated by comparison to the experimental structures of the targets, made available for the evaluation, using the well-agreed-upon criteria used previously. Twenty-four groups, which for the first time included an automatic Web server, produced predictions ranking from acceptable to highly accurate for all targets, including those where the structures of the bound and unbound forms differed substantially. These results and a brief survey of the methods used by participants of CAPRI Rounds 3-5 suggest that genuine progress in the performance of docking methods is being achieved, with CAPRI acting as the catalyst.     

Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.     

Target selection for complex structural genomics

Most cellular processes are carried out by macromolecular assemblies and regulated through a complex network of transient protein-protein interactions. Genome-wide interaction discovery experiments are already delivering the first drafts of whole organism interactomes and, thus, depicting the limits of the interaction space. However, a complete understanding of molecular interactions can only come from high-resolution three-dimensional structures, as they provide key atomic details about the binding interfaces. The launch of structural genomics initiatives focused on protein interactions and complexes could quickly fill up the interaction space with structural details, offering a new perspective on how cell networks operate at atomic level. Clear target selection strategies that rationally identify the key interactions and complexes that should be first tackled are fundamental to maximize the return, minimize the costs and prevent experimental difficulties.     

蛋白质复合体及蛋白质相互作用研究新策略—化学交联结合质谱分析法

近年来化学交联法结合质谱分析法被广泛用于蛋白质复合体结构及蛋白质相互作用的研究。研究表明这两种方法的有机结合为研究蛋白质复合体结构及蛋白质相互作用提供了一条新的途径。文章对不同类型的化学交联剂、质谱分析中的Bottom-up 与Top-down 两种研究策略,以及化学交联法结合质谱分析法在蛋白质复合体结构、蛋白质相互作用研究中的应用进行综述。这两种方法的不断发展与完善,将会极大促进生物大分子复合体结构及蛋白质相互作用的研究。     

Structural analyses of death domains and their interactions

The death domain (DD), which is a versatle protein interaction module, is the prime mediator of the interactions necessary for apoptosis, innate immunity and the necrosis signaling pathway. Because DD mediated signaling events are associated with critical human diseases, studies in these areas are of great biological importance. Accordingly, many biochemical and structural studies of DD have been conducted in the past decade to investigate apoptotic and innate immune signaling. Evaluation of the molecular structure of DD and their interactions with partners have shown the underlying molecular basis for the assembly of DD mediated complexes and for the regulation of apoptosis and innate immunity. This review summarizes the structure and function of various DDs and DD:DD complexes involved in those signaling pathways.     

Non-Redundant Unique Interface Structures as Templates for Modeling Protein Interactions

Improvements in experimental techniques increasingly provide structural data relating to protein-protein interactions. Classification of structural details of protein-protein interactions can provide valuable insights for modeling and abstracting design principles. Here, we aim to cluster protein-protein interactions by their interface structures, and to exploit these clusters to obtain and study shared and distinct protein binding sites. We find that there are 22604 unique interface structures in the PDB. These unique interfaces, which provide a rich resource of structural data of protein-protein interactions, can be used for template-based docking. We test the specificity of these non-redundant unique interface structures by finding protein pairs which have multiple binding sites. We suggest that residues with more than 40% relative accessible surface area should be considered as surface residues in template-based docking studies. This comprehensive study of protein interface structures can serve as a resource for the community. The dataset can be accessed at http://prism.ccbb.ku.edu.tr/piface.     

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.     

DOCKGROUND resource for studying protein-protein interfaces

MOTIVATION: Public resources for studying protein interfaces are necessary for better understanding of molecular recognition and developing intermolecular potentials, search procedures and scoring functions for the prediction of protein complexes. RESULTS: The first release of the DOCKGROUND resource implements a comprehensive database of co-crystallized (bound-bound) protein-protein complexes, providing foundation for the upcoming expansion to unbound (experimental and simulated) protein-protein complexes, modeled protein-protein complexes and systematic sets of docking decoys. The bound-bound part of DOCKGROUND is a relational database of annotated structures based on the Biological Unit file (Biounit) provided by the RCSB as a separated file containing probable biological molecule. DOCKGROUND is automatically updated to reflect the growth of PDB. It contains 67,220 pairwise complexes that rely on 14,913 Biounit entries from 34,778 PDB entries (January 30, 2006). The database includes a dynamic generation of non-redundant datasets of pairwise complexes based either on the structural similarity (SCOP classification) or on user-defined sequence identity. The growing DOCKGROUND resource is designed to become a comprehensive public environment for developing and validating new methodologies for modeling of protein interactions. AVAILABILITY: DOCKGROUND is available at http://dockground.bioinformatics.ku.edu. The current first release implements the bound-bound part.     

Application of hydrogen/deuterium exchange mass spectrometry to study protein tyrosine phosphatase dynamics, ligand binding, and substrate specificity

Protein tyrosine phosphatases (PTPs) are signaling enzymes that control a diverse array of cellular processes. Further insight into the specific functional roles of PTPs in cellular signaling requires detailed understanding of the molecular basis for substrate recognition by the PTPs. A central question is how PTPs discriminate between multiple structurally diverse substrates that they encounter in the cell. Although X-ray crystallography is capable of revealing the intimate structural details for molecular interaction, structures of higher order PTP.substrate complexes are often difficult to obtain. Hydrogen/deuterium exchange mass spectrometry (H/DX-MS) is a powerful tool for mapping protein-protein interfaces, as well as identifying conformational and dynamic perturbations in proteins. In addition, H/DX-MS enables analysis of large protein complexes at physiological concentrations and provides insight into the solution behavior of these complexes that can not be gleaned from crystal structures. We have utilized H/DX-MS to probe PTP dynamics, ligand binding, and the structural basis of substrate recognition. In this article, we review general principles of H/DX-MS technology as applied to study protein-protein interactions and dynamics. We also provide protocols for H/DX-MS successfully used in our laboratory to define the molecular basis of ERK2 substrate recognition by MKP3. Many of the aspects that we cover in detail should be applicable to the study of other PTPs with their specific targets.     

ISIS: interaction sites identified from sequence

MOTIVATION: Large-scale experiments reveal pairs of interacting proteins but leave the residues involved in the interactions unknown. These interface residues are essential for understanding the mechanism of interaction and are often desired drug targets. Reliable identification of residues that reside in protein-protein interface typically requires analysis of protein structure. Therefore, for the vast majority of proteins, for which there is no high-resolution structure, there is no effective way of identifying interface residues. RESULTS: Here we present a machine learning-based method that identifies interacting residues from sequence alone. Although the method is developed using transient protein-protein interfaces from complexes of experimentally known 3D structures, it never explicitly uses 3D information. Instead, we combine predicted structural features with evolutionary information. The strongest predictions of the method reached over 90% accuracy in a cross-validation experiment. Our results suggest that despite the significant diversity in the nature of protein-protein interactions, they all share common basic principles and that these principles are identifiable from sequence alone.     

Molecular dynamics-solvated interaction energy studies of protein-protein interactions: the MP1-p14 scaffolding complex

Using the MP1-p14 scaffolding complex from the mitogen-activated protein kinase signaling pathway as model system, we explored a structure-based computational protocol to probe and characterize binding affinity hot spots at protein-protein interfaces. Hot spots are located by virtual alanine-scanning consensus predictions over three different energy functions and two different single-structure representations of the complex. Refined binding affinity predictions for select hot-spot mutations are carried out by applying first-principle methods such as the molecular mechanics generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) to the molecular dynamics (MD) trajectories for mutated and wild-type complexes. Here, predicted hot-spot residues were actually mutated to alanine, and crystal structures of the mutated complexes were determined. Two mutated MP1-p14 complexes were investigated, the p14(Y56A)-mutated complex and the MP1(L63A,L65A)-mutated complex. Alternative ways to generate MD ensembles for mutant complexes, not relying on crystal structures for mutated complexes, were also investigated. The SIE function, fitted on protein-ligand binding affinities, gave absolute binding affinity predictions in excellent agreement with experiment and outperformed standard MM-GBSA predictions when tested on the MD ensembles of Ras-Raf and Ras-RalGDS protein-protein complexes. For wild-type and mutant MP1-p14 complexes, SIE predictions of relative binding affinities were supported by a yeast two-hybrid assay that provided semiquantitative relative interaction strengths. Results on the MP1-mutated complex suggested that SIE predictions deteriorate if mutant MD ensembles are approximated by just mutating the wild-type MD trajectory. The SIE data on the p14-mutated complex indicated feasibility for generating mutant MD ensembles from mutated wild-type crystal structure, despite local structural differences observed upon mutation. For energetic considerations, this would circumvent costly needs to produce and crystallize mutated complexes. The sensitized protein-protein interface afforded by the p14(Y56A) mutation identified here has practical applications in screening-based discovery of first-generation small-molecule hits for further development into specific modulators of the mitogen-activated protein kinase signaling pathway.     

PYRIN domains and their interactions in the apoptosis and inflammation signaling pathway

The PYRIN domain (PYD) is a well known protein interaction module and a prime mediator of the protein interactions necessary for apoptosis, inflammation and innate immune signaling pathway. Because PYD-mediated apoptosis, inflammation and innate immune processes are associated with many human diseases, studies in these areas are of great biological importance. Intensive biochemical and structural studies of PYD have been conducted in the past decade to elucidate PYD-mediated signaling events, and evaluations of the molecular structure of PYDs have shown the underlying molecular basis for the assembly of PYD-mediated complexes and for the regulation of inflammation and innate immunity. This review summarizes the structure and function of various PYDs and proposes a PYD:PYD interaction for assembly of the complexes involved in those signaling pathways.     

A homologous mapping method for three-dimensional reconstruction of protein networks reveals disease-associated mutations

Background

One of the crucial steps toward understanding the associations among molecular interactions, pathways, and diseases in a cell is to investigate detailed atomic protein-protein interactions (PPIs) in the structural interactome. Despite the availability of large-scale methods for analyzing PPI networks, these methods often focused on PPI networks using genome-scale data and/or known experimental PPIs. However, these methods are unable to provide structurally resolved interaction residues and their conservations in PPI networks.

Results

Here, we reconstructed a human three-dimensional (3D) structural PPI network (hDiSNet) with the detailed atomic binding models and disease-associated mutations by enhancing our PPI families and 3D–domain interologs from 60,618 structural complexes and complete genome database with 6,352,363 protein sequences across 2274 species. hDiSNet is a scale-free network (γ?=?2.05), which consists of 5177 proteins and 19,239 PPIs with 5843 mutations. These 19,239 structurally resolved PPIs not only expanded the number of PPIs compared to present structural PPI network, but also achieved higher agreement with gene ontology similarities and higher co-expression correlation than the ones of 181,868 experimental PPIs recorded in public databases. Among 5843 mutations, 1653 and 790 mutations involved in interacting domains and contacting residues, respectively, are highly related to diseases. Our hDiSNet can provide detailed atomic interactions of human disease and their associated proteins with mutations. Our results show that the disease-related mutations are often located at the contacting residues forming the hydrogen bonds or conserved in the PPI family. In addition, hDiSNet provides the insights of the FGFR (EGFR)-MAPK pathway for interpreting the mechanisms of breast cancer and ErbB signaling pathway in brain cancer.

Conclusions

Our results demonstrate that hDiSNet can explore structural-based interactions insights for understanding the mechanisms of disease-associated proteins and their mutations. We believe that our method is useful to reconstruct structurally resolved PPI networks for interpreting structural genomics and disease associations.

     

Presence or Absence of Ras Dimerization Shows Distinct Kinetic Signature in Ras-Raf Interaction

Initiations of cell signaling pathways often occur through the formation of multiprotein complexes that form through protein-protein interactions. Therefore, detecting their presence is central to understanding the function of a cell signaling pathway, aberration of which often leads to fatal diseases, including cancers. However, the multiprotein complexes are often difficult to detect using microscopes due to their small sizes. Therefore, currently, their presence can be only detected through indirect means. In this article, we propose to investigate the presence or absence of protein complexes through some easily measurable kinetic parameters, such as activation rates. As a proof of concept, we investigate the Ras-Raf system, a well-characterized cell signaling system. It has been hypothesized that Ras dimerization is necessary to create activated Raf dimers. Although there are circumstantial evidences supporting the Ras dimerization hypothesis, direct proof of Ras dimerization is still inconclusive. In the absence of conclusive direct experimental proof, this hypothesis can only be examined through indirect evidences of Ras dimerization. In this article, using a multiscale simulation technique, we provide multiple criteria that distinguishes an activation mechanism involving Ras dimerization from another mechanism that does not involve Ras dimerization. The provided criteria will be useful in the investigation of not only Ras-Raf interaction but also other two-protein interactions.     

Computational redesign of the SHV-1 beta-lactamase/beta-lactamase inhibitor protein interface

β-lactamases are enzymes that catalyze the hydrolysis of β-lactam antibiotics. β-lactamase/β-lactamase inhibitor protein (BLIP) complexes are emerging as a well characterized experimental model system for studying protein-protein interactions. BLIP is a 165 amino acid protein that inhibits several class A β-lactamases with a wide range of affinities: picomolar affinity for K1; nanomolar affinity for TEM-1, SME-1, and BlaI; but only micromolar affinity for SHV-1 β-lactamase. The large differences in affinity coupled with the availability of extensive mutagenesis data and high-resolution crystal structures for the TEM-1/BLIP and SHV-1/BLIP complexes make them attractive systems for the further development of computational design methodology. We used EGAD, a physics-based computational design program, to redesign BLIP in an attempt to increase affinity for SHV-1. Characterization of several of designs and point mutants revealed that in all cases, the mutations stabilize the interface by 10- to 1000-fold relative to wild type BLIP. The calculated changes in binding affinity for the mutants were within a mean absolute error of 0.87 kcal/mol from the experimental values, and comparison of the calculated and experimental values for a set of 30 SHV-1/BLIP complexes yielded a correlation coefficient of 0.77. Structures of the two complexes with the highest affinity, SHV-1/BLIP (E73M) and SHV-1/BLIP (E73M, S130K, S146M), are presented at 1.7 Å resolution. While the predicted structures have much in common with the experimentally determined structures, they do not coincide perfectly; in particular a salt bridge between SHV-1 D104 and BLIP K74 is observed in the experimental structures, but not in the predicted design conformations. This discrepancy highlights the difficulty of modeling salt bridge interactions with a protein design algorithm that approximates side chains as discrete rotamers. Nevertheless, while local structural features of the interface were sometimes miscalculated, EGAD is globally successful in designing complexes with increased affinity.     

Structural interpretation of protein-protein interaction network

Background

Currently a huge amount of protein-protein interaction data is available from high throughput experimental methods. In a large network of protein-protein interactions, groups of proteins can be identified as functional clusters having related functions where a single protein can occur in multiple clusters. However experimental methods are error-prone and thus the interactions in a functional cluster may include false positives or there may be unreported interactions. Therefore correctly identifying a functional cluster of proteins requires the knowledge of whether any two proteins in a cluster interact, whether an interaction can exclude other interactions, or how strong the affinity between two interacting proteins is.

Methods

In the present work the yeast protein-protein interaction network is clustered using a spectral clustering method proposed by us in 2006 and the individual clusters are investigated for functional relationships among the member proteins. 3D structural models of the proteins in one cluster have been built – the protein structures are retrieved from the Protein Data Bank or predicted using a comparative modeling approach. A rigid body protein docking method (Cluspro) is used to predict the protein-protein interaction complexes. Binding sites of the docked complexes are characterized by their buried surface areas in the docked complexes, as a measure of the strength of an interaction.

Results

The clustering method yields functionally coherent clusters. Some of the interactions in a cluster exclude other interactions because of shared binding sites. New interactions among the interacting proteins are uncovered, and thus higher order protein complexes in the cluster are proposed. Also the relative stability of each of the protein complexes in the cluster is reported.

Conclusions

Although the methods used are computationally expensive and require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. These theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes.