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

2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
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.

scholarly journals Applications of In-Cell NMR in Structural Biology and Drug Discovery

2019 ◽  
Vol 20 (1) ◽  
pp. 139 ◽  
Author(s):  
CongBao Kang
Keyword(s):  
Drug Discovery ◽  
Conformational Changes ◽  
Protein Interactions ◽  
Mammalian Cells ◽  
Structural Information ◽  
Living Cells ◽  
Bacterial Cells ◽  
Protein Protein Interactions ◽  
Nmr Studies ◽  
Post Translational Modifications

In-cell nuclear magnetic resonance (NMR) is a method to provide the structural information of a target at an atomic level under physiological conditions and a full view of the conformational changes of a protein caused by ligand binding, post-translational modifications or protein–protein interactions in living cells. Previous in-cell NMR studies have focused on proteins that were overexpressed in bacterial cells and isotopically labeled proteins injected into oocytes of Xenopus laevis or delivered into human cells. Applications of in-cell NMR in probing protein modifications, conformational changes and ligand bindings have been carried out in mammalian cells by monitoring isotopically labeled proteins overexpressed in living cells. The available protocols and successful examples encourage wide applications of this technique in different fields such as drug discovery. Despite the challenges in this method, progress has been made in recent years. In this review, applications of in-cell NMR are summarized. The successful applications of this method in mammalian and bacterial cells make it feasible to play important roles in drug discovery, especially in the step of target engagement.

Protein Interaction Domains: structural features and drug discovery applications (part 2)

2020 ◽  
Vol 27 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone
Keyword(s):  
Protein Interaction ◽  
Viral Infections ◽  
Structural Information ◽  
Data Bank ◽  
Structural Features ◽  
Modular Organization ◽  
Modular Architecture ◽  
Consensus Sequences ◽  
Interaction Domains ◽  
Catalytic Functions

Background: Proteins present a modular organization made up of several domains. Apart from domains playing catalytic functions, many others are crucial to recruit interactors. The latter domains can be defined "PIDs" (Protein Interaction Domains) and are responsible for pivotal outcomes in signal transduction and a certain array of normal physiological and disease-related pathways. Targeting such PIDs with small molecules and peptides able to modulate their interaction networks, may represent a valuable route to discover novel therapeutics. Objective: This work represents a continuation of a very recent review describing PIDs able to recognize post-translationally modified peptide segments. On the contrary, this second part concerns with PIDs that interact with simple peptide sequences provided with standard amino acids. Method: Crucial structural information on different domain subfamilies and their interactomes was gained by a wide search in different online available databases (including the PDB (Protein Data Bank), the Pfam (Protein family), and the SMART (Simple Modular Architecture Research Tool)). Pubmed was searched as well to explore the most recent literature related to the topic. Results and Conclusion: PIDs are multifaceted: they have all diverse structural features and can recognize several consensus sequences. PIDs can be linked to different diseases onset and progression, like cancer or viral infections and find applications in the personalized medicine field. Many efforts have been centered on peptide/peptidomimetic inhibitors of PIDs mediated interactions but much more work needs to be conducted to improve drug-likeness and interaction affinities of identified compounds.

scholarly journals The Role of Low Complexity Regions in Protein Interaction Modes: An Illustration in Huntingtin

2021 ◽  
Vol 22 (4) ◽  
pp. 1727
Author(s):  
Kristina Kastano ◽  
Pablo Mier ◽  
Miguel A. Andrade-Navarro
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Information ◽  
Low Complexity ◽  
Protein Interaction Data ◽  
Protein Protein Interactions ◽  
Post Translational Modifications ◽  
Evolutionarily Conserved ◽  
And Function

Low complexity regions (LCRs) are very frequent in protein sequences, generally having a lower propensity to form structured domains and tending to be much less evolutionarily conserved than globular domains. Their higher abundance in eukaryotes and in species with more cellular types agrees with a growing number of reports on their function in protein interactions regulated by post-translational modifications. LCRs facilitate the increase of regulatory and network complexity required with the emergence of organisms with more complex tissue distribution and development. Although the low conservation and structural flexibility of LCRs complicate their study, evolutionary studies of proteins across species have been used to evaluate their significance and function. To investigate how to apply this evolutionary approach to the study of LCR function in protein–protein interactions, we performed a detailed analysis for Huntingtin (HTT), a large protein that is a hub for interaction with hundreds of proteins, has a variety of LCRs, and for which partial structural information (in complex with HAP40) is available. We hypothesize that proteins RASA1, SYN2, and KAT2B may compete with HAP40 for their attachment to the core of HTT using similar LCRs. Our results illustrate how evolution might favor the interplay of LCRs with domains, and the possibility of detecting multiple modes of LCR-mediated protein–protein interactions with a large hub such as HTT when enough protein interaction data is available.

RING fingers and B-boxes: zinc-binding protein-protein interaction domains

1998 ◽  
Vol 76 (2-3) ◽  
pp. 351-358 ◽  
Author(s):  
Katherine LB Borden
Keyword(s):  
Conformational Changes ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Structural Features ◽  
Zinc Binding ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Biological Studies ◽  
Interaction Domains ◽  
Nmr Methods

The cysteine-rich zinc-binding motifs known as the RING and B-box are found in several unrelated proteins. Structural, biochemical, and biological studies of these motifs reveal that they mediate protein-protein interactions. Several RING-containing proteins are oncoproteins and recent data indicate that proapoptotic activities can be mediated through the RING. 1H NMR methods were used to determine the structures of RINGs and a B-box domain and to monitor the conformational changes these motifs undergo upon zinc ligation. This review discusses in detail the structural features of the RING and B-box domains. Further, possible structure function relationships for these motifs particularly in their role as protein interaction domains are discussed.Key words: RING, B-box, PML, NMR.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

scholarly journals Conserved Central Intraviral Protein Interactome of the Herpesviridae Family

mSystems ◽  
2019 ◽  
Vol 4 (5) ◽  
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.

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

2017 ◽  
Vol 114 (40) ◽  
pp. E8333-E8342 ◽  
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.

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

eLife ◽  
2015 ◽  
Vol 4 ◽  
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.

scholarly journals Modeling of Protein–Protein Interactions in Cytokinin Signal Transduction

2019 ◽  
Vol 20 (9) ◽  
pp. 2096 ◽  
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.

scholarly journals InterPep2: global peptide–protein docking using interaction surface templates

2020 ◽  
Vol 36 (8) ◽  
pp. 2458-2465 ◽  
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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