Confirmation of intersubunit connectivity and topology of designed protein complexes by native MS

Computational protein design provides the tools to expand the diversity of protein complexes beyond those found in nature. Understanding the rules that drive proteins to interact with each other enables the design of protein–protein interactions to generate specific protein assemblies. In this work, we designed protein–protein interfaces between dimers and trimers to generate dodecameric protein assemblies with dihedral point group symmetry. We subsequently analyzed the designed protein complexes by native MS. We show that the use of ion mobility MS in combination with surface-induced dissociation (SID) allows for the rapid determination of the stoichiometry and topology of designed complexes. The information collected along with the speed of data acquisition and processing make SID ion mobility MS well-suited to determine key structural features of designed protein complexes, thereby circumventing the requirement for more time- and sample-consuming structural biology approaches.

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Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

Current Medicinal Chemistry ◽

10.2174/0929867326666190620101637 ◽

2020 ◽

Vol 27 (37) ◽

pp. 6306-6355 ◽

Cited By ~ 2

Author(s):

Marian Vincenzi ◽

Flavia Anna Mercurio ◽

Marilisa Leone

Keyword(s):

Drug Discovery ◽

Protein Interaction ◽

Protein Interactions ◽

Structural Information ◽

Protein Complexes ◽

Structural Features ◽

Protein Protein Interactions ◽

Modular Architecture ◽

Post Translational Modifications ◽

Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

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Conserved Central Intraviral Protein Interactome of the Herpesviridae Family

mSystems ◽

10.1128/msystems.00295-19 ◽

2019 ◽

Vol 4 (5) ◽

Cited By ~ 1

Author(s):

Anna Hernández Durán ◽

Kay Grünewald ◽

Maya Topf

Keyword(s):

Protein Interactions ◽

Computational Models ◽

Functional Organization ◽

Driving Forces ◽

Protein Complexes ◽

Structural Features ◽

System Level ◽

Protein Protein Interactions ◽

Protein Interactome ◽

Essential Components

ABSTRACT Protein interactions are major driving forces behind the functional phenotypes of biological processes. As such, evolutionary footprints are reflected in system-level collections of protein-protein interactions (PPIs), i.e., protein interactomes. We conducted a comparative analysis of intraviral protein interactomes for representative species of each of the three subfamilies of herpesviruses (herpes simplex virus 1, human cytomegalovirus, and Epstein-Barr virus), which are highly prevalent etiologic agents of important human diseases. The intraviral interactomes were reconstructed by combining experimentally supported and computationally predicted protein-protein interactions. Using cross-species network comparison, we then identified family-wise conserved interactions and protein complexes, which we defined as a herpesviral “central” intraviral protein interactome. A large number of widely accepted conserved herpesviral protein complexes are present in this central intraviral interactome, encouragingly supporting the biological coherence of our results. Importantly, these protein complexes represent most, if not all, of the essential steps required during a productive life cycle. Hence the central intraviral protein interactome could plausibly represent a minimal infectious interactome of the herpesvirus family across a variety of hosts. Our data, which have been integrated into our herpesvirus interactomics database, HVint2.0, could assist in creating comprehensive system-level computational models of this viral lineage. IMPORTANCE Herpesviruses are an important socioeconomic burden for both humans and livestock. Throughout their long evolutionary history, individual herpesvirus species have developed remarkable host specificity, while collectively the Herpesviridae family has evolved to infect a large variety of eukaryotic hosts. The development of approaches to fight herpesvirus infections has been hampered by the complexity of herpesviruses’ genomes, proteomes, and structural features. The data and insights generated by our study add to the understanding of the functional organization of herpesvirus-encoded proteins, specifically of family-wise conserved features defining essential components required for a productive infectious cycle across different hosts, which can contribute toward the conceptualization of antiherpetic infection strategies with an effect on a broader range of target species. All of the generated data have been made freely available through our HVint2.0 database, a dedicated resource of curated herpesvirus interactomics purposely created to promote and assist future studies in the field.

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Complex+: Aided Decision-Making for the Study of Protein Complexes

10.1101/744656 ◽

2019 ◽

Author(s):

Mehrnoosh Oghbaie ◽

Petr Šulc ◽

David Fenyö ◽

Michael Pennock ◽

John LaCava

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Cell Biology ◽

Protein Complexes ◽

Conclusive Evidence ◽

Computational Techniques ◽

Protein Assemblies ◽

Data Types ◽

Research Strategies ◽

Protein Interactome

AbstractProteins are the chief effectors of cell biology and their functions are typically carried out in the context of multi-protein assemblies; large collections of such interacting protein assemblies are often referred to as interactomes. Knowing the constituents of protein complexes is therefore important for investigating their molecular biology. Many experimental methods are capable of producing data of use for detecting and inferring the existence of physiological protein complexes. Each method has associated pros and cons, affecting the potential quality and utility of the data. Numerous informatic resources exist for the curation, integration, retrieval, and processing of protein interactions data. While each resource may possess different merits, none are definitive and few are wieldy, potentially limiting their effective use by non-experts. In addition, contemporary analyses suggest that we may still be decades away from a comprehensive map of a human protein interactome. Taken together, we are currently unable to maximally impact and improve biomedicine from a protein interactome perspective – motivating the development of experimental and computational techniques that help investigators to address these limitations. Here, we present a resource intended to assist investigators in (i) navigating the cumulative knowledge concerning protein complexes and (ii) forming hypotheses concerning protein interactions that may yet lack conclusive evidence, thus (iii) directing future experiments to address knowledge gaps. To achieve this, we integrated multiple data-types/different properties of protein interactions from multiple sources and after applying various methods of regularization, compared the protein interaction networks computed to those available in the EMBL-EBI Complex Portal, a manually curated, gold-standard catalog of macromolecular complexes. As a result, our resource provides investigators with reliable curation of bona fide and candidate physical interactors of their protein or complex of interest, prompting due scrutiny and further validation when needed. We believe this information will empower a wider range of experimentalists to conduct focused protein interaction studies and to better select research strategies that explicitly target missing information.

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Contacts-based prediction of binding affinity in protein–protein complexes

eLife ◽

10.7554/elife.07454 ◽

2015 ◽

Vol 4 ◽

Cited By ~ 119

Author(s):

Anna Vangone ◽

Alexandre MJJ Bonvin

Keyword(s):

Binding Affinity ◽

Conformational Changes ◽

Protein Interactions ◽

Prediction Accuracy ◽

Protein Complexes ◽

Structural Features ◽

Specific Protein ◽

Strong Impact ◽

Protein Protein Interactions ◽

Almost All

Almost all critical functions in cells rely on specific protein–protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein–protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein–protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.

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Modeling of Protein–Protein Interactions in Cytokinin Signal Transduction

International Journal of Molecular Sciences ◽

10.3390/ijms20092096 ◽

2019 ◽

Vol 20 (9) ◽

pp. 2096 ◽

Cited By ~ 5

Author(s):

Dmitry V. Arkhipov ◽

Sergey N. Lomin ◽

Yulia A. Myakushina ◽

Ekaterina M. Savelieva ◽

Dmitry I. Osolodkin ◽

...

Keyword(s):

Protein Interactions ◽

Hot Spots ◽

Protein Complexes ◽

Structural Features ◽

Signaling Proteins ◽

In Planta ◽

Protein Protein Interactions ◽

Response Regulators ◽

Protein Surfaces

The signaling of cytokinins (CKs), classical plant hormones, is based on the interaction of proteins that constitute the multistep phosphorelay system (MSP): catalytic receptors—sensor histidine kinases (HKs), phosphotransmitters (HPts), and transcription factors—response regulators (RRs). Any CK receptor was shown to interact in vivo with any of the studied HPts and vice versa. In addition, both of these proteins tend to form a homodimer or a heterodimeric complex with protein-paralog. Our study was aimed at explaining by molecular modeling the observed features of in planta protein–protein interactions, accompanying CK signaling. For this purpose, models of CK-signaling proteins’ structure from Arabidopsis and potato were built. The modeled interaction interfaces were formed by rather conserved areas of protein surfaces, complementary in hydrophobicity and electrostatic potential. Hot spots amino acids, determining specificity and strength of the interaction, were identified. Virtual phosphorylation of conserved Asp or His residues affected this complementation, increasing (Asp-P in HK) or decreasing (His-P in HPt) the affinity of interacting proteins. The HK–HPt and HPt–HPt interfaces overlapped, sharing some of the hot spots. MSP proteins from Arabidopsis and potato exhibited similar properties. The structural features of the modeled protein complexes were consistent with the experimental data.

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InterPep2: global peptide–protein docking using interaction surface templates

Bioinformatics ◽

10.1093/bioinformatics/btaa005 ◽

2020 ◽

Vol 36 (8) ◽

pp. 2458-2465 ◽

Cited By ~ 2

Author(s):

Isak Johansson-Åkhe ◽

Claudio Mirabello ◽

Björn Wallner

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Structural Features ◽

Protein Docking ◽

Supplementary Information ◽

Peptide Ligand ◽

Protein Protein Interactions ◽

Intrinsically Disordered ◽

Intrinsically Disordered Regions ◽

Improved Performance

Abstract Motivation Interactions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming and difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability. Results InterPep2 is a freely available method for predicting the structure of peptide–protein interactions. Improved performance is obtained by using templates from both peptide–protein and regular protein–protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide–protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide–protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18). Availability and implementation The program is available from: http://wallnerlab.org/InterPep2. Supplementary information Supplementary data are available at Bioinformatics online.

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High-Field Asymmetric Waveform Ion Mobility Spectrometry and Native Mass Spectrometry: Analysis of Intact Protein Assemblies and Protein Complexes

Analytical Chemistry ◽

10.1021/acs.analchem.0c00649 ◽

2020 ◽

Vol 92 (10) ◽

pp. 6811-6816 ◽

Cited By ~ 4

Author(s):

Oliver J. Hale ◽

Eva Illes-Toth ◽

Todd H. Mize ◽

Helen J. Cooper

Keyword(s):

Mass Spectrometry ◽

Ion Mobility ◽

High Field ◽

Protein Complexes ◽

Native Mass Spectrometry ◽

Mass Spectrometry Analysis ◽

Intact Protein ◽

Asymmetric Waveform ◽

Protein Assemblies ◽

Spectrometry Analysis

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Spherical polar Fourier assembly of protein complexes with arbitrary point group symmetry

Journal of Applied Crystallography ◽

10.1107/s1600576715022931 ◽

2016 ◽

Vol 49 (1) ◽

pp. 158-167 ◽

Cited By ~ 23

Author(s):

David W. Ritchie ◽

Sergei Grudinin

Keyword(s):

Protein Complexes ◽

Complex Structure ◽

Point Group ◽

Arbitrary Point ◽

Group Symmetry ◽

Test Cases ◽

Point Group Symmetry ◽

Group Symmetries ◽

The Given ◽

Ab Inito

A novel fast Fourier transform-basedab initodocking algorithm calledSAMis presented, for building perfectly symmetrical models of protein complexes with arbitrary point group symmetry. The basic approach uses a novel and very fast one-dimensional symmetry-constrained spherical polar Fourier search to assemble cyclicCnsystems from a given protein monomer. Structures with higher-order (Dn,T,OandI) point group symmetries may be built using a subsequent symmetry-constrained Fourier domain search to assemble trimeric sub-units. The results reported here show that theSAMalgorithm can correctly assemble monomers of up to around 500 residues to produce a near-native complex structure with the given point group symmetry in 17 out of 18 test cases. TheSAMprogram may be downloaded for academic use at http://sam.loria.fr/.

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Classification and prediction of protein–protein interaction interface using machine learning algorithm

Scientific Reports ◽

10.1038/s41598-020-80900-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Subhrangshu Das ◽

Saikat Chakrabarti

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Learning Algorithm ◽

Protein Complexes ◽

Three Dimensional ◽

3D Models ◽

Structural Features ◽

Support Vector ◽

Protein Protein Interaction ◽

Significant Difference

AbstractStructural insight of the protein–protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein–protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein–protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein–protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/.

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