Modeling of Protein–Protein Interactions in Cytokinin Signal Transduction

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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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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Interfacial Peptides as Affinity Modulating Agents of Protein-Protein Interactions

Biomolecules ◽

10.3390/biom12010106 ◽

2022 ◽

Vol 12 (1) ◽

pp. 106

Author(s):

Pavel V. Ershov ◽

Yuri V. Mezentsev ◽

Alexis S. Ivanov

Keyword(s):

Protein Interactions ◽

Targeted Delivery ◽

Protein Complexes ◽

Protein Protein Interactions ◽

Good Efficiency ◽

Cellular Targets ◽

Proteolytic Stability ◽

Clinically Significant

The identification of disease-related protein-protein interactions (PPIs) creates objective conditions for their pharmacological modulation. The contact area (interfaces) of the vast majority of PPIs has some features, such as geometrical and biochemical complementarities, “hot spots”, as well as an extremely low mutation rate that give us key knowledge to influence these PPIs. Exogenous regulation of PPIs is aimed at both inhibiting the assembly and/or destabilization of protein complexes. Often, the design of such modulators is associated with some specific problems in targeted delivery, cell penetration and proteolytic stability, as well as selective binding to cellular targets. Recent progress in interfacial peptide design has been achieved in solving all these difficulties and has provided a good efficiency in preclinical models (in vitro and in vivo). The most promising peptide-containing therapeutic formulations are under investigation in clinical trials. In this review, we update the current state-of-the-art in the field of interfacial peptides as potent modulators of a number of disease-related PPIs. Over the past years, the scientific interest has been focused on following clinically significant heterodimeric PPIs MDM2/p53, PD-1/PD-L1, HIF/HIF, NRF2/KEAP1, RbAp48/MTA1, HSP90/CDC37, BIRC5/CRM1, BIRC5/XIAP, YAP/TAZ–TEAD, TWEAK/FN14, Bcl-2/Bax, YY1/AKT, CD40/CD40L and MINT2/APP.

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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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Evolutionary diversification of protein–protein interactions by interface add-ons

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1707335114 ◽

2017 ◽

Vol 114 (40) ◽

pp. E8333-E8342 ◽

Cited By ~ 13

Author(s):

Maximilian G. Plach ◽

Florian Semmelmann ◽

Florian Busch ◽

Markus Busch ◽

Leonhard Heizinger ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Complexes ◽

Evolutionary Strategy ◽

Structural Level ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Evolutionary Diversification

Cells contain a multitude of protein complexes whose subunits interact with high specificity. However, the number of different protein folds and interface geometries found in nature is limited. This raises the question of how protein–protein interaction specificity is achieved on the structural level and how the formation of nonphysiological complexes is avoided. Here, we describe structural elements called interface add-ons that fulfill this function and elucidate their role for the diversification of protein–protein interactions during evolution. We identified interface add-ons in 10% of a representative set of bacterial, heteromeric protein complexes. The importance of interface add-ons for protein–protein interaction specificity is demonstrated by an exemplary experimental characterization of over 30 cognate and hybrid glutamine amidotransferase complexes in combination with comprehensive genetic profiling and protein design. Moreover, growth experiments showed that the lack of interface add-ons can lead to physiologically harmful cross-talk between essential biosynthetic pathways. In sum, our complementary in silico, in vitro, and in vivo analysis argues that interface add-ons are a practical and widespread evolutionary strategy to prevent the formation of nonphysiological complexes by specializing protein–protein interactions.

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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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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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In silico analysis of protein-protein interfaces: Tools and approaches

International Journal of Research in Pharmaceutical Sciences ◽

10.26452/ijrps.v11i4.3960 ◽

2020 ◽

Vol 11 (4) ◽

pp. 7539-7548

Author(s):

Christina Nilofer ◽

Arumugam Mohanapriya

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

In Silico Analysis ◽

Physicochemical Characteristics ◽

Structural Features ◽

Interface Residue ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Molecular Functions

Two or more proteins interact in vivo to perform complex molecular functions including catalysis, regulation, assembly, immunity and inhibition through the formation of stable interfaces. This interaction is governed by several factors that are selective, sensitive and specific in nature. Several interface features has been documented since 1975. The study of these interface features of proteins and their dynamicity during interaction with different proteins help understanding the mechanisms underlying diverse molecular functions and its biological processes. Computational tools greatly assist in studying such interface features that determine the interaction between two or more proteins, and in this context, this review enumerates the different interface features reported thus far along with the tools that aid in deciphering protein features (physicochemical characteristics, binding site and interface residue prediction and hotspot residues) along with their approaches that are employed in the prediction these features. Also, the review discusses the advantages and limitations of experimental techniques and computational biological tools deployed for deciphering the protein-protein interactions. Altogether, the review will provide insights into the optimal tools and different strategies involved in protein interaction studies that would facilitate the researchers to understand the protein structural features and molecular principles of protein-protein interaction with known functions.

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Mapping, Structure and Modulation of PPI

Frontiers in Chemistry ◽

10.3389/fchem.2021.718405 ◽

2021 ◽

Vol 9 ◽

Author(s):

Elisa Martino ◽

Sara Chiarugi ◽

Francesco Margheriti ◽

Gianpiero Garau

Keyword(s):

Free Energy ◽

Protein Interactions ◽

Hot Spots ◽

Molecular Level ◽

Protein Complexes ◽

Binding Free Energy ◽

Detailed Knowledge ◽

Protein Protein Interactions ◽

Advanced Technologies ◽

Successful Design

Because of the key relevance of protein–protein interactions (PPI) in diseases, the modulation of protein-protein complexes is of relevant clinical significance. The successful design of binding compounds modulating PPI requires a detailed knowledge of the involved protein-protein system at molecular level, and investigation of the structural motifs that drive the association of the proteins at the recognition interface. These elements represent hot spots of the protein binding free energy, define the complex lifetime and possible modulation strategies. Here, we review the advanced technologies used to map the PPI involved in human diseases, to investigate the structure-function features of protein complexes, and to discover effective ligands that modulate the PPI for therapeutic intervention.

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The CcmC–CcmE interaction during cytochrome c maturation by System I is driven by protein–protein and not protein–heme contacts

Journal of Biological Chemistry ◽

10.1074/jbc.ra118.005024 ◽

2018 ◽

Vol 293 (43) ◽

pp. 16778-16790 ◽

Cited By ~ 5

Author(s):

Shevket H. Shevket ◽

Diego Gonzalez ◽

Jared L. Cartwright ◽

Colin Kleanthous ◽

Stuart J. Ferguson ◽

...

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Plant Mitochondria ◽

Site Directed Mutagenesis ◽

Covalent Attachment ◽

Protein Protein Interactions ◽

Post Translational Modification ◽

Cytochrome C Maturation ◽

Heme Cofactor

Cytochromes c are ubiquitous proteins, essential for life in most organisms. Their distinctive characteristic is the covalent attachment of heme to their polypeptide chain. This post-translational modification is performed by a dedicated protein system, which in many Gram-negative bacteria and plant mitochondria is a nine-protein apparatus (CcmA–I) called System I. Despite decades of study, mechanistic understanding of the protein–protein interactions in this highly complex maturation machinery is still lacking. Here, we focused on the interaction of CcmC, the protein that sources the heme cofactor, with CcmE, the pivotal component of System I responsible for the transfer of the heme to the apocytochrome. Using in silico analyses, we identified a putative interaction site between these two proteins (residues Asp47, Gln50, and Arg55 on CcmC; Arg73, Asp101, and Glu105 on CcmE), and we validated our findings by in vivo experiments in Escherichia coli. Moreover, employing NMR spectroscopy, we examined whether a heme-binding site on CcmE contributes to this interaction and found that CcmC and CcmE associate via protein–protein rather than protein–heme contacts. The combination of in vivo site-directed mutagenesis studies and high-resolution structural techniques enabled us to determine at the residue level the mechanism for the formation of one of the key protein complexes for cytochrome c maturation by System I.

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SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features

International Journal of Molecular Sciences ◽

10.3390/ijms21197281 ◽

2020 ◽

Vol 21 (19) ◽

pp. 7281

Author(s):

A. J. Preto ◽

Irina S. Moreira

Keyword(s):

Protein Interactions ◽

Hot Spots ◽

Scientific Community ◽

Protein Complexes ◽

Protein Sequences ◽

Prediction Methods ◽

Fasta File ◽

Protein Protein Interactions ◽

Extremely Randomized Trees ◽

Protein Hot Spots

Protein Hot-Spots (HS) are experimentally determined amino acids, key to small ligand binding and tend to be structural landmarks on protein–protein interactions. As such, they were extensively approached by structure-based Machine Learning (ML) prediction methods. However, the availability of a much larger array of protein sequences in comparison to determined tree-dimensional structures indicates that a sequence-based HS predictor has the potential to be more useful for the scientific community. Herein, we present SPOTONE, a new ML predictor able to accurately classify protein HS via sequence-only features. This algorithm shows accuracy, AUROC, precision, recall and F1-score of 0.82, 0.83, 0.91, 0.82 and 0.85, respectively, on an independent testing set. The algorithm is deployed within a free-to-use webserver, only requiring the user to submit a FASTA file with one or more protein sequences.

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