Crystallography and protein–protein interactions: biological interfaces and crystal contacts

2008 ◽  
Vol 36 (6) ◽  
pp. 1438-1441 ◽  
Author(s):  
Bostjan Kobe ◽  
Gregor Guncar ◽  
Rebecca Buchholz ◽  
Thomas Huber ◽  
Bohumil Maco ◽  
...  

Crystallography is commonly used for studying the structures of protein–protein complexes. However, a crystal structure does not define a unique protein–protein interface, and distinguishing a ‘biological interface’ from ‘crystal contacts’ is often not straightforward. A number of computational approaches exist for distinguishing them, but their error rate is high, emphasizing the need to obtain further data on the biological interface using complementary structural and functional approaches. In addition to reviewing the computational and experimental approaches for addressing this problem, we highlight two relevant examples. The first example from our laboratory involves the structure of acyl-CoA thioesterase 7, where each domain of this two-domain protein was crystallized separately, but both yielded a non-functional assembly. The structure of the full-length protein was uncovered using a combination of complementary approaches including chemical cross-linking, analytical ultracentrifugation and mutagenesis. The second example involves the platelet glycoprotein Ibα–thrombin complex. Two groups reported the crystal structures of this complex, but all the interacting interfaces differed between the two structures. Our computational analysis did not fully resolve the reasons for the discrepancies, but provided interesting insights into the system. This review highlights the need to complement crystallographic studies with complementary experimental and computational approaches.

2007 ◽  
Vol 05 (02b) ◽  
pp. 579-592 ◽  
Author(s):  
ALEXIS S. IVANOV ◽  
OKSANA V. GNEDENKO ◽  
ANDREY A. MOLNAR ◽  
YURY V. MEZENTSEV ◽  
ANDREY V. LISITSA ◽  
...  

Protein–protein and protein–ligand interactions play a central role in biochemical reactions, and understanding these processes is an important task in different fields of biomedical science and drug discovery. Proteins often work in complex assemblies of several macromolecules and small ligands. The structural and functional description of protein–protein interactions (PPI) is very important for basic-, as well as applied research. The interface areas of protein complexes have unique structure and properties, so PPI represent prospective targets for a new generation of drugs. One of the key targets of PPI inhibitors are oligomeric enzymes. This report shows interactive links between virtual and experimental approaches in a total pipeline "from gene to drug" and using Surface Plasmon Resonance technology for experimentally assessing PPI. Our research is conducted on two oligomeric enzymes — HIV-1 protease (HIVp) (homo-dimer) and bacterial L-asparaginase (homo-tetramer). Using methods of molecular modeling and computational alanine scanning we obtained structural and functional description of PPI in these two enzymes. We also presented a real example of application of integral approach in searching inhibitors of HIVp dimerization — from virtual database mining up to experimental testing of lead compounds.


2011 ◽  
Vol 33 (1) ◽  
pp. 8-11
Author(s):  
Hung Xuan Ta ◽  
Liisa Holm

A great number of cellular behaviours are mediated by proteins which always carry out their functions by interacting with each other. Unravelling protein–protein interactions (PPIs) is one of the central goals in proteomics, which will decipher the molecular mechanisms underlying the biological functions and thereby help to understand human diseases on a system-wide level. A number of experimental techniques, especially high-throughput approaches, have resulted in a large amount of PPI data that still suffer from incompleteness and contradiction. Moreover, these experimental techniques are expensive, time-consuming and labour-intensive. Computational methods have emerged as complementary tools to experimental approaches to discover PPIs. Promisingly, computational methods can guide, assess and validate experimental data and finally predict novel PPIs.


2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic

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.


2020 ◽  
Vol 27 (37) ◽  
pp. 6306-6355 ◽  
Author(s):  
Marian Vincenzi ◽  
Flavia Anna Mercurio ◽  
Marilisa Leone

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.


Author(s):  
Rohan Dandage ◽  
Caroline M Berger ◽  
Isabelle Gagnon-Arsenault ◽  
Kyung-Mee Moon ◽  
Richard Greg Stacey ◽  
...  

Abstract Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein-protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein-protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein-protein interactions in the hybrid, for instance in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteins.


2018 ◽  
Vol 18 (20) ◽  
pp. 1719-1736 ◽  
Author(s):  
Sharanya Sarkar ◽  
Khushboo Gulati ◽  
Manikyaprabhu Kairamkonda ◽  
Amit Mishra ◽  
Krishna Mohan Poluri

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.


2017 ◽  
Vol 114 (9) ◽  
pp. 2224-2229 ◽  
Author(s):  
Daniel A. Weisz ◽  
Haijun Liu ◽  
Hao Zhang ◽  
Sundarapandian Thangapandian ◽  
Emad Tajkhorshid ◽  
...  

Photosystem II (PSII), a large pigment protein complex, undergoes rapid turnover under natural conditions. During assembly of PSII, oxidative damage to vulnerable assembly intermediate complexes must be prevented. Psb28, the only cytoplasmic extrinsic protein in PSII, protects the RC47 assembly intermediate of PSII and assists its efficient conversion into functional PSII. Its role is particularly important under stress conditions when PSII damage occurs frequently. Psb28 is not found, however, in any PSII crystal structure, and its structural location has remained unknown. In this study, we used chemical cross-linking combined with mass spectrometry to capture the transient interaction of Psb28 with PSII. We detected three cross-links between Psb28 and the α- and β-subunits of cytochrome b559, an essential component of the PSII reaction-center complex. These distance restraints enable us to position Psb28 on the cytosolic surface of PSII directly above cytochrome b559, in close proximity to the QB site. Protein–protein docking results also support Psb28 binding in this region. Determination of the Psb28 binding site and other biochemical evidence allow us to propose a mechanism by which Psb28 exerts its protective effect on the RC47 intermediate. This study also shows that isotope-encoded cross-linking with the “mass tags” selection criteria allows confident identification of more cross-linked peptides in PSII than has been previously reported. This approach thus holds promise to identify other transient protein–protein interactions in membrane protein complexes.


2018 ◽  
Vol 46 (6) ◽  
pp. 1593-1603 ◽  
Author(s):  
Chenkang Zheng ◽  
Patricia C. Dos Santos

Iron–sulfur (Fe–S) clusters are ubiquitous cofactors present in all domains of life. The chemistries catalyzed by these inorganic cofactors are diverse and their associated enzymes are involved in many cellular processes. Despite the wide range of structures reported for Fe–S clusters inserted into proteins, the biological synthesis of all Fe–S clusters starts with the assembly of simple units of 2Fe–2S and 4Fe–4S clusters. Several systems have been associated with the formation of Fe–S clusters in bacteria with varying phylogenetic origins and number of biosynthetic and regulatory components. All systems, however, construct Fe–S clusters through a similar biosynthetic scheme involving three main steps: (1) sulfur activation by a cysteine desulfurase, (2) cluster assembly by a scaffold protein, and (3) guided delivery of Fe–S units to either final acceptors or biosynthetic enzymes involved in the formation of complex metalloclusters. Another unifying feature on the biological formation of Fe–S clusters in bacteria is that these systems are tightly regulated by a network of protein interactions. Thus, the formation of transient protein complexes among biosynthetic components allows for the direct transfer of reactive sulfur and Fe–S intermediates preventing oxygen damage and reactions with non-physiological targets. Recent studies revealed the importance of reciprocal signature sequence motifs that enable specific protein–protein interactions and consequently guide the transactions between physiological donors and acceptors. Such findings provide insights into strategies used by bacteria to regulate the flow of reactive intermediates and provide protein barcodes to uncover yet-unidentified cellular components involved in Fe–S metabolism.


eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
José Ignacio Garzón ◽  
Lei Deng ◽  
Diana Murray ◽  
Sagi Shapira ◽  
Donald Petrey ◽  
...  

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein’s function. We provide annotations for most human proteins, including many annotated as having unknown function.


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