Structural Models Of Protein Complexes Research Articles

Structural proteomics of a bacterial mega membrane protein complex: FtsH-HflK-HflC

Recent advances in mass spectrometry (MS) yielding sensitive and accurate measurements along with developments in software tools have enabled the characterization of complex systems routinely. Thus, structural proteomics and cross-linking mass spectrometry (XL-MS) have become a useful method for structural modeling of protein complexes. Here, we utilized commonly used XL-MS software tools to elucidate the protein interactions within a membrane protein complex containing FtsH, HflK, and HflC, over-expressed in E. coli. The MS data were processed using MaxLynx, MeroX, MS Annika, xiSEARCH, and XlinkX software tools. The number of identified inter- and intra-protein cross-links varied among software. Each interaction was manually checked using the raw MS and MS/MS data and distance restraints to verify inter- and intra-protein cross-links. A total of 37 inter-protein and 148 intra-protein cross-links were determined in the FtsH-HflK-HflC complex. The 59 of them were new interactions on the lacking region of recently published structures. These newly identified interactions, when combined with molecular docking and structural modeling, present opportunities for further investigation. The results provide valuable information regarding the complex structure and function to decipher the intricate molecular mechanisms underlying the FtsH-HflK-HflC complex.

Higher-Order Structural Organization of the Mitochondrial Proteome Charted by In Situ Cross-Linking Mass Spectrometry

Mitochondria are densely packed with proteins, of which most are involved physically or more transiently in protein-protein interactions (PPIs). Mitochondria host among others all enzymes of the Krebs cycle and the oxidative phosphorylation pathway and are foremost associated with cellular bioenergetics. However, mitochondria are also important contributors to apoptotic cell death and contain their own genome indicating that they play additionally an eminent role in processes beyond bioenergetics. Despite intense efforts in identifying and characterizing mitochondrial protein complexes by structural biology and proteomics techniques, many PPIs have remained elusive. Several of these (membrane embedded) PPIs are less stable invitro hampering their characterization by most contemporary methods in structural biology. Particularly in these cases, cross-linking mass spectrometry (XL-MS) has proven valuable for the in-depth characterization of mitochondrial protein complexes in situ. Here, we highlight experimental strategies for the analysis of proteome-wide PPIs in mitochondria using XL-MS. We showcase the ability of in situ XL-MS as a tool to map suborganelle interactions and topologies and aid in refining structural models of protein complexes. We describe some of the most recent technological advances in XL-MS that may benefit the in situ characterization of PPIs even further, especially when combined with electron microscopy and structural modeling.

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.

Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and performed very well in estimating the global structure accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analyzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA.

CM2D3: Furnishing the Human Interactome with Structural Models of Protein Complexes Derived by Comparative Modeling and Docking

The human interactome is composed of around half a million interactions according to recent estimations and it is only for a small fraction of those that three-dimensional structural information is available. Indeed, the structural coverage of the human interactome is very low and given the complexity and time-consuming requirements of solving protein structures this problem will remain for the foreseeable future. Structural models, or predictions, of protein complexes can provide valuable information when the experimentally determined 3D structures are not available. Here we present CM2D3, a relational database containing structural models of the whole human interactome derived both from comparative modeling and data-driven docking. Starting from a consensus interactome derived from integrating several interactomics databases, a strategy was devised to derive structural models by computational means. Currently, CM2D3 includes 33338 structural models of which 5121 derived from comparative modeling and the remaining from docking. Of the latter, the structures of 14554 complexes were derived from monomers modeled by M4T while the rest were modeled with structures as predicted by AlphaFold2. Lastly, CM2D3 complements existing resources by focusing on models derived from both free-docking, as opposed to template-based docking, and hence expanding the available structural information on protein complexes to the scientific community.Database URL:http://www.bioinsilico.org/CM2D3

Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes.

Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics-inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end-to-end learning from molecular structures containing tens of thousands of atoms: a point-based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.

Driving integrative structural modeling with serial capture affinity purification

Streamlined characterization of protein complexes remains a challenge for the study of protein interaction networks. Here we describe serial capture affinity purification (SCAP), in which two separate proteins are tagged with either the HaloTag or the SNAP-tag, permitting a multistep affinity enrichment of specific protein complexes. The multifunctional capabilities of this protein-tagging system also permit in vivo validation of interactions using acceptor photobleaching Förster resonance energy transfer and fluorescence cross-correlation spectroscopy quantitative imaging. By coupling SCAP to cross-linking mass spectrometry, an integrative structural model of the complex of interest can be generated. We demonstrate this approach using the Spindlin1 and SPINDOC protein complex, culminating in a structural model with two SPINDOC molecules docked on one SPIN1 molecule. In this model, SPINDOC interacts with the SPIN1 interface previously shown to bind a lysine and arginine methylated sequence of histone H3. Our approach combines serial affinity purification, live cell imaging, and cross-linking mass spectrometry to build integrative structural models of protein complexes.

Improving Identification of In-organello Protein-Protein Interactions Using an Affinity-enrichable, Isotopically Coded, and Mass Spectrometry-cleavable Chemical Crosslinker

An experimental and computational approach for identification of protein-protein interactions by ex vivo chemical crosslinking and mass spectrometry (CLMS) has been developed that takes advantage of the specific characteristics of cyanurbiotindipropionylsuccinimide (CBDPS), an affinity-tagged isotopically coded mass spectrometry (MS)-cleavable crosslinking reagent. Utilizing this reagent in combination with a crosslinker-specific data-dependent acquisition strategy based on MS2 scans, and a software pipeline designed for integrating crosslinker-specific mass spectral information led to demonstrated improvements in the application of the CLMS technique, in terms of the detection, acquisition, and identification of crosslinker-modified peptides. This approach was evaluated on intact yeast mitochondria, and the results showed that hundreds of unique protein-protein interactions could be identified on an organelle proteome-wide scale. Both known and previously unknown protein-protein interactions were identified. These interactions were assessed based on their known sub-compartmental localizations. Additionally, the identified crosslinking distance constraints are in good agreement with existing structural models of protein complexes involved in the mitochondrial electron transport chain.

Structural modeling of protein complexes: Current capabilities and challenges

Proteins frequently interact with each other, and the knowledge of structures of the corresponding protein complexes is necessary to understand how they function. Computational methods are increasingly used to provide structural models of protein complexes. Not surprisingly, community-wide Critical Assessment of protein Structure Prediction (CASP) experiments have recently started monitoring the progress in this research area. We participated in CASP13 with the aim to evaluate our current capabilities in modeling of protein complexes and to gain a better understanding of factors that exert the largest impact on these capabilities. To model protein complexes in CASP13, we applied template-based modeling, free docking and hybrid techniques that enabled us to generate models of the topmost quality for 27 of 42 multimers. If templates for protein complexes could be identified, we modeled the structures with reasonable accuracy by straightforward homology modeling. If only partial templates were available, it was nevertheless possible to predict the interaction interfaces correctly or to generate acceptable models for protein complexes by combining template-based modeling with docking. If no templates were available, we used rigid-body docking with limited success. However, in some free docking models, despite the incorrect subunit orientation and missed interface contacts, the approximate location of protein binding sites was identified correctly. Apparently, our overall performance in docking was limited by the quality of monomer models and by the imperfection of scoring methods. The impact of human intervention on our results in modeling of protein complexes was significant indicating the need for improvements of automatic methods.

Natural language processing in text mining for structural modeling of protein complexes

BackgroundStructural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking. Our text-mining (TM) tool, which extracts binding site residues from the PubMed abstracts, was successfully applied to protein docking (Badal et al., PLoS Comput Biol, 2015; 11: e1004630). Still, many extracted residues were not relevant to the docking.ResultsWe present an extension of the TM tool, which utilizes natural language processing (NLP) for analyzing the context of the residue occurrence. The procedure was tested using generic and specialized dictionaries. The results showed that the keyword dictionaries designed for identification of protein interactions are not adequate for the TM prediction of the binding mode. However, our dictionary designed to distinguish keywords relevant to the protein binding sites led to considerable improvement in the TM performance. We investigated the utility of several methods of context analysis, based on dissection of the sentence parse trees. The machine learning-based NLP filtered the pool of the mined residues significantly more efficiently than the rule-based NLP. Constraints generated by NLP were tested in docking of unbound proteins from the DOCKGROUND X-ray benchmark set 4. The output of the global low-resolution docking scan was post-processed, separately, by constraints from the basic TM, constraints re-ranked by NLP, and the reference constraints. The quality of a match was assessed by the interface root-mean-square deviation. The results showed significant improvement of the docking output when using the constraints generated by the advanced TM with NLP.ConclusionsThe basic TM procedure for extracting protein-protein binding site residues from the PubMed abstracts was significantly advanced by the deep parsing (NLP techniques for contextual analysis) in purging of the initial pool of the extracted residues. Benchmarking showed a substantial increase of the docking success rate based on the constraints generated by the advanced TM with NLP.

Protein-Protein Docking Using Evolutionary Information.

The structural modeling of protein complexes by docking simulations has been attracting increasing interest with the rise of proteomics and of the number of experimentally identified binary interactions. Structures of unbound partners, either modeled or experimentally determined, can be used as input to sample as extensively as possible all putative binding modes and single out the most plausible ones. At the scoring step, evolutionary information contained in the joint multiple sequence alignments of both partners can provide key insights to recognize correct interfaces. Here, we describe a computational protocol based on the InterEvDock web server to exploit coevolution constraints in protein-protein docking methods. We provide methodology guidelines to prepare the input protein structures and generate improved alignments. We also explain how to extract and use the information returned by the server through the analysis of two representative examples.

Modeling of protein complexes in CAPRI Round 37 using template-based approach combined with model selection.

We participated in Round 37 of the Critical Assessment of PRediction of Interactions (CAPRI), held jointly with the 12th edition of the Critical Assessment of protein Structure Prediction (CASP12), having two major objectives. First, we intended to test the utility of our PPI3D web server in finding and selecting templates for comparative modeling of structures of protein complexes. Our second aim was to evaluate the ability of our model accuracy estimation method VoroMQA to score and rank structural models for protein-protein interactions. Using sequence search in PPI3D and HHpred servers we identified multimeric templates for 7 of 11 CAPRI targets, and models of at least acceptable quality were constructed for 6 of them. The clustering and visual analysis features implemented in the PPI3D software were instrumental in detecting alternative protein-protein interaction interfaces among the identified templates. When a single binding mode was observed for homologous proteins, the structural modeling of the protein complex was fairly straightforward, whereas choosing the correct interaction template from several alternatives turned out to be a difficult task requiring manual intervention. The combination of full structure and interaction interface VoroMQA scores effectively ranked structural models of protein complexes and selected models of better quality from the CAPRI Scoring sets. The overall results show possible uses of PPI3D and VoroMQA in structural modeling of protein-protein interactions and suggest ways for further improvements of both methods.

An Overview of Protein-Protein Interaction

Protein-Protein-Interactions (PPIs) are involved in almost all the cellular processes and understanding the structural basis of PPIs remains an important endeavor. The identification of the interface residues may shed light in many important aspects like drug development, elucidation of molecular pathways, generation of protein mimetics and understanding of disease mechanisms as well as development of docking methodologies to build structural models of protein complexes. Over the past few years, advances in high-throughput PPI identification techniques, such as yeast two-hybrid analysis and affinity purification coupled with mass spectrometry, have enabled the researchers to identify sets of interacting proteins in yeast, Drosophila and other organisms. Unfortunately, these experimental methods do not provide residue level insight into the structure of the interactions between the proteins. The uses of X-Ray crystallography and nuclear magnetic resonance (NMR) spectroscopy to determine the structural basis of an interaction are time consuming and overall expensive. In response to these difficulties, a number of different bioinformatics algorithms with varying degrees of accuracies have been developed that use a wide variety of data sources to predict PPIs and modes of binding between proteins. Machine learning techniques such as Support Vector Machines (SVMs) and Random Forests (RFs) have been used recently to solve problems such as prediction of catalytic residues and prediction and analysis of structure-based PPI interfaces. Previous machine learning approaches to the PPI interface prediction problems used features pertaining to evolutionary amino acid sequence conservation, phylogeny, and GO (Gene Ontology) protein annotation and, in most of the cases, protein structures. Till date there are very few computational methods available that are based solely on protein sequences. Keywords: Docking, NMR, Protein-protein interactions, RF, SVM, X-ray crystallography.

Simulated unbound structures for benchmarking of protein docking in the Dockground resource

BackgroundProteins play an important role in biological processes in living organisms. Many protein functions are based on interaction with other proteins. The structural information is important for adequate description of these interactions. Sets of protein structures determined in both bound and unbound states are essential for benchmarking of the docking procedures. However, the number of such proteins in PDB is relatively small. A radical expansion of such sets is possible if the unbound structures are computationally simulated.ResultsThe Dockground public resource provides data to improve our understanding of protein–protein interactions and to assist in the development of better tools for structural modeling of protein complexes, such as docking algorithms and scoring functions. A large set of simulated unbound protein structures was generated from the bound structures. The modeling protocol was based on 1 ns Langevin dynamics simulation. The simulated structures were validated on the ensemble of experimentally determined unbound and bound structures. The set is intended for large scale benchmarking of docking algorithms and scoring functions.ConclusionsA radical expansion of the unbound protein docking benchmark set was achieved by simulating the unbound structures. The simulated unbound structures were selected according to criteria from systematic comparison of experimentally determined bound and unbound structures. The set is publicly available at http://dockground.compbio.ku.edu.

A mass spectrometry–based hybrid method for structural modeling of protein complexes

We describe a method that integrates data derived from different mass spectrometric (MS) techniques with a modelling strategy for structural characterization of protein assemblies. We encoded structural data derived from native MS, bottom-up proteomics, ion mobility-MS and chemical cross-linking MS into modelling restraints to compute the most likely structure of a protein assembly. We used the method to generate near-native models for three known structures and characterized an assembly intermediate of the proteasomal base.

Intermolecular Contact Potentials for Protein-Protein Interactions Extracted from Binding Free Energy Changes upon Mutation.

Understanding and predicting the energetics of protein-protein interactions is fundamental to the structural modeling of protein complexes. Binding free energy can be approximated as a sum of pairwise atomic or residue contact energies, which are commonly inferred from contact frequencies observed in experimental protein structures. However, such statistically inferred potentials require certain assumptions and approximation. Here, we explore the possibility of deriving atomic and residue contact potentials directly from experimental binding free energy changes following mutation and present a number of such potentials. The first set of potentials is obtained by unweighted least-squares fitting and bootsrap aggregating. The second set is calculated using a weighting scheme optimized against absolute binding affinity data, so as to account for the over-representation of certain complexes, residues, and families of interactions. The congruence of the potentials with known physical chemistry is investigated. The potentials are further validated by ranking and clustering protein-protein docking poses.

GWIDD: a comprehensive resource for genome-wide structural modeling of protein-protein interactions

Protein-protein interactions are a key component of life processes. The knowledge of the three-dimensional structure of these interactions is important for understanding protein function. Genome-Wide Docking Database (http://gwidd.bioinformatics.ku.edu) offers an extensive source of data for structural studies of protein-protein complexes on genome scale. The current release of the database combines the available experimental data on the structure and characteristics of protein interactions with structural modeling of protein complexes for 771 organisms spanned over the entire universe of life from viruses to humans. The interactions are stored in a relational database with user-friendly interface that includes various search options. The search results can be interactively previewed; the structures, downloaded, along with the interaction characteristics.