ChikvInt: a Chikungunya virus–host protein–protein interaction database

Chikungunya is a fast-mutating virus causing Chikungunya virus disease (ChikvD) with a significant load of disability-adjusted life years (DALY) around the world. The outbreak of this virus is significantly higher in the tropical countries. Several experiments have identified crucial viral–host protein–protein interactions (PPIs) between Chikungunya Virus (Chikv) and the human host. However, no standard database that catalogs this PPI information exists. Here we develop a Chikv-Human PPI database, ChikvInt, to facilitate understanding ChikvD disease pathogenesis and the progress of vaccine studies. ChikvInt consists of 109 interactions and is available at www.chikvint.com .     

AraPPISite: a database of fine-grained protein–protein interaction site annotations for Arabidopsis thaliana

Knowledge about protein interaction sites provides detailed information of protein–protein interactions (PPIs). To date, nearly 20,000 of PPIs from Arabidopsis thaliana have been identified. Nevertheless, the interaction site information has been largely missed by previously published PPI databases. Here, AraPPISite, a database that presents fine-grained interaction details for A. thaliana PPIs is established. First, the experimentally determined 3D structures of 27 A. thaliana PPIs are collected from the Protein Data Bank database and the predicted 3D structures of 3023 A. thaliana PPIs are modeled by using two well-established template-based docking methods. For each experimental/predicted complex structure, AraPPISite not only provides an interactive user interface for browsing interaction sites, but also lists detailed evolutionary and physicochemical properties of these sites. Second, AraPPISite assigns domain–domain interactions or domain–motif interactions to 4286 PPIs whose 3D structures cannot be modeled. In this case, users can easily query protein interaction regions at the sequence level. AraPPISite is a free and user-friendly database, which does not require user registration or any configuration on local machines. We anticipate AraPPISite can serve as a helpful database resource for the users with less experience in structural biology or protein bioinformatics to probe the details of PPIs, and thus accelerate the studies of plant genetics and functional genomics. AraPPISite is available at http://systbio.cau.edu.cn/arappisite/index.html.     

The Arabidopsis ESCRT protein�Cprotein interaction network

In yeast, endosomal sorting of monoubiquitylated transmembrane proteins is performed by a subset of the 19 "class E vacuolar protein sorting" proteins. The core machinery consists of 11 proteins that are organised in three complexes termed ESCRT I-III (endosomal sorting complex required for transport I-III) and is conserved in eukaryotic cells. While the pathway is well understood in yeast and animals, the plant ESCRT system is largely unexplored. At least one sequence homolog for each ESCRT component can be found in the Arabidopsis genome. Generally, sequence conservation between yeast/animals and the Arabidopsis proteins is low. To understand details about participating proteins and complex organization we have performed a systematic pairwise yeast two hybrid analysis of all Arabidopsis proteins showing homology to the ESCRT core machinery. Positive interactions were validated using bimolecular fluorescence complementation. In our experiments, most putative ESCRT components exhibited interactions with other ESCRT components that could be shown to occur on endosomes suggesting that despite their low homology to their yeast and animal counterparts they represent functional components of the plant ESCRT pathway.     

IRAA: A statistical tool for investigating a protein–protein interaction interface from multiple structures

Understanding protein–protein interactions (PPIs) is fundamental to infer how different molecular systems work. A major component to model molecular recognition is the buried surface area (BSA), that is, the area that becomes inaccessible to solvent upon complex formation. To date, many attempts tried to connect BSA to molecular recognition principles, and in particular, to the underlying binding affinity. However, the most popular approach to calculate BSA is to use a single (or in some cases few) bound structures, consequently neglecting a wealth of structural information of the interacting proteins derived from ensembles corresponding to their unbound and bound states. Moreover, the most popular method inherently assumes the component proteins to bind as rigid entities. To address the above shortcomings, we developed a Monte Carlo method-based Interface Residue Assessment Algorithm (IRAA), to calculate a combined distribution of BSA for a given complex. Further, we apply our algorithm to human ACE2 and SARS-CoV-2 Spike protein complex, a system of prime importance. Results show a much broader distribution of BSA compared to that obtained from only the bound structure or structures and extended residue members of the interface with implications to the underlying biomolecular recognition. We derive that specific interface residues of ACE2 and of S-protein are consistently highly flexible, whereas other residues systematically show minor conformational variations. In effect, IRAA facilitates the use of all available structural data for any biomolecular complex of interest, extracting quantitative parameters with statistical significance, thereby providing a deeper biophysical understanding of the molecular system under investigation.     

Role of MERIT40 in stabilization of BRCA1 complex: A protein–protein interaction study

MERIT40 is a novel associate of the BRCA1-complex, thus play an essential role in DNA damage repair mechanism. It is the least implicit protein and its structural and functional aspects of regulating the stability of BRCA1–MERIT40 complex remain equivocal. Analysis of protein–protein interactions between BRCA1 and its cellular binding partners like ABRAXAS, RAP80 and MERIT40 would help to understand the role of protein complex integrity in DNA repair mechanism. The recombinant proteins were purified and their structural aspects were elucidated by spectroscopic methods. Interaction analysis was carried out to determine binding partners of MERIT40. MERIT40 showed interaction with bridging molecule, called ABRAXAS, thus generate a scaffold among various members which further stabilizes the entire complex. It acts as an adapter molecule by interacting with BRCA1-BRCT in non-phosphorylation dependent manner. The feature enlighten on structural and interaction profile of BRCA1-complex member to elucidate their role in complex stability and DNA repair process.     

Phospho-tyrosine dependent protein–protein interaction network

Post-translational protein modifications, such as tyrosine phosphorylation, regulate protein–protein interactions (PPIs) critical for signal processing and cellular phenotypes. We extended an established yeast two-hybrid system employing human protein kinases for the analyses of phospho-tyrosine (pY)-dependent PPIs in a direct experimental, large-scale approach. We identified 292 mostly novel pY-dependent PPIs which showed high specificity with respect to kinases and interacting proteins and validated a large fraction in co-immunoprecipitation experiments from mammalian cells. About one-sixth of the interactions are mediated by known linear sequence binding motifs while the majority of pY-PPIs are mediated by other linear epitopes or governed by alternative recognition modes. Network analysis revealed that pY-mediated recognition events are tied to a highly connected protein module dedicated to signaling and cell growth pathways related to cancer. Using binding assays, protein complementation and phenotypic readouts to characterize the pY-dependent interactions of TSPAN2 (tetraspanin 2) and GRB2 or PIK3R3 (p55γ), we exemplarily provide evidence that the two pY-dependent PPIs dictate cellular cancer phenotypes.     

ModuleSearch: finding functional modules in a protein–protein interaction network

Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein–protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein–protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.     

Multi-agent-based bio-network for systems biology: protein–protein interaction network as an example

Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.     

α-casein micelles-membranes interaction: Flower-like lipid protein coaggregates formation

BackgroundEnvironmental conditions regulate the association/aggregation states of proteins and their action in cellular compartments. Analysing protein behaviour in presence of lipid membranes is fundamental for the comprehension of many functional and dysfunctional processes. Here, we present an experimental study on the interaction between model membranes and α-casein. α-casein is the major component of milk proteins and it is recognised to play a key role in performing biological functions. The conformational properties of this protein and its capability to form supramolecular structures, like micelles or irreversible aggregates, are key effectors in functional and pathological effects.MethodsBy means of quantitative fluorescence imaging and complementary spectroscopic methods, we were able to characterise α-casein association state and the course of events induced by pH changes, which regulate the interaction of this molecule with membranes.ResultsThe study of these complex dynamic events revealed that the initial conformation of the protein critically regulates the fate of α-casein, size and structure of the newly formed aggregates and their effect on membrane structures. Disassembly of micelles due to modification in electrostatic interactions results in increased membrane structure rigidity which accompanies the formation of protein lipid flower-like co-aggregates with protein molecules localised in the external part.General significanceThese results may contribute to the comprehension of how the initial state of a protein establishes the course of events that occur upon changes in the molecular environment. These events which may occur in cells may be essential to functional, pathological or therapeutical properties specifically associated to casein proteins.     

Diaretinopathy database –A Gene database for diabetic retinopathy

Diabetic retinopathy, is a microvascular complication of diabetes mellitus and is a major cause of adult blindness. Despite advances in diagnosis and treatment the pathogenesis of diabetic retinopathy is not well understood. Results from epidemiological studies of diabetic patients suggest that there are familial predispositions to diabetes and to diabetic retinopathy. Therefore the main purpose of this database is to help both scientists and doctors in studying the candidate genes responsible for causing diabetic retinopathy. For each candidate gene official symbol, chromosome map, number of exons, GT-AG introns, motif, polymorphic variation and 3D structure are given respectively. In addition to molecular class and function of these genes, this database also provides links to download the corresponding nucleotide and amino acid sequences in FASTA format which may be further used for computational approaches. Therefore this database will increase the understanding of the genetics underlying the development or progression of diabetic retinopathy and will have an impact on future diagnostic, prevention and intervention strategies.

Availability

The database is freely available at http: diaretinopathydatabase.com     

Structural basis of protein–protein interaction studied by NMR

This paper describes efforts of the structural genomics project in the nuclear magnetic resonance (NMR) laboratory at the University of Science and Technology of China. This structural genomics project is biological-functional driven. Targets are mainly selected from two systems: proteins related with regulation of gene expression in humans and other eukaryotes, and proteins existing in the cell junction in humans. The majority of proteins selected from these two systems are related with human health and diseases, and some are potential drug targets. Twenty-five protein structures from Homo sapiens and other eukaryotes have been determined during last 5 years in this laboratory. Nuclear magnetic resonance (NMR) spectroscopy is highly suited to investigate molecular interactions at a close physiological condition and is particularly suited for the study of low-affinity, transient complexes. It can provide information on protein surface interaction, their complex structure, and their dynamic properties during protein recognition. Several examples are given in this paper.     

Role for protein–protein interaction databases in human genetics

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.     

A computerized protein–protein interaction modeling study of ampicillin antibody specificity in relation to biosensor development

Nonspecific interactions between immobilized biomolecules and interfering proteins significantly impede biosensor development and commercialization. Advances in bioinformatics and computer technology have facilitated a greater understanding of biological interactions. We employed two different protein–protein docking programs to simulate the nonspecific interaction between ampicillin antibody and potential interfering proteins (human serum albumin and ovalbumin). To evaluate the contact and probability of association with the active site of the antibody, different amino acid chains from human serum albumin (HSA) and ovalbumin (OVA) were modeled in the simulation. In addition, a well-known specific immune complex, lysozyme and lysozyme antibody, was simulated for comparison. The results demonstrated that the cluster density of nonspecific interactions was smaller than the specific interaction between lysozyme and antibody, and that the dock scores were scattered. However, the active site of ampicillin antibody was prone to nonspecific protein interactions. The strength of interaction was different for specific binding and nonspecific binding. These results provide a platform for detecting the probability of nonspecific interactions and for improving methods of biosensor detection construction with reduced nonspecific adsorption.     

A new potential approach to block HIV-1 replication via protein–protein interaction and strand-transfer inhibition

Therapeutic treatment of AIDS is recently characterized by a crescent effort towards the identification of multiple ligands able to target different steps of HIV-1 life cycle. Taking into consideration our previously obtained SAR information and combining some important chemical structural features we report herein the synthesis of novel benzyl-indole derivatives as anti-HIV agents. Through this work we identified new dual target small molecules able to inhibit both IN-LEDGF/p75 interaction and the IN strand-transfer step considered as two crucial phases of viral life cycle.     

Thermodynamic database supports deciphering protein–nucleic acid interactions

The thermodynamics of protein–nucleic acid interactions (PNIs) is crucial for elucidating the mechanisms of molecular recognition and pathological consequences. The Protein–Nucleic Acid Thermodynamics Database (PNATDB) is a database containing experimentally determined thermodynamic parameters along with sequence, structural, and function data, which is available free online.     

How do oncoprotein mutations rewire protein–protein interaction networks?

The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein–protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein–protein interaction networks and drive the cancer phenotype.     

A. baumannii histone acetyl transferase Hpa2: optimization of homology modeling,analysis of protein–protein interaction and virtual screening

In the current scenario, widespread multidrug resistivity in ESKAPE pathogens demands identification of novel drug targets to keep their infections at bay. For this purpose, we have identified a novel target Hpa2 of A. baumannii, a member of GNAT superfamily of HATs. But due to sequence identity of equal or less than 35%, the correct sequence alignment and construction of 3D monomeric and dimeric models of Hpa2 having optimal structural parameters is a troublesome task. To circumvent these problems, we have designed an easy and optimized protocol for Hpa2 monomer modeling, and for generation of dimeric Hpa2 model using data-driven protein–protein docking experiment. Improvement in the structural features of generated model is an onerous process and generally achieved by paying time and computational cost. Herein, it is achieved by reconciliation of FoldX commands which takes less time in execution. Evaluations performed to validate structural parameters and stability of monomeric and dimeric Hpa2 attests to its quality. Analysis of interfacial residues, energy terms and RMSD values indicated a clear correlation between experimental and theoretical interface properties of the dimers, corroborating to the regime used for Hpa2 dimer generation. Structural information from the refined models was used for virtual screening of substrate-derived library and polyamines to achieve a new platform for developing A. baumannii inhibitory molecules. Molecules showing preferential binding at the dimer interface could be used as allosteric inhibitors. Binding of polyamines with model illustrated the same binding pattern as described experimentally in case of yeast Hpa2.