The power of computational proteomics platforms to decipher protein-protein interactions.

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.

State of the art in protein-protein interactions within the fungi kingdom.

Proteins rarely exert their function by themselves. Protein-protein interactions (PPIs) regulate virtually every biological process that takes place in a cell. Such interactions are targets for new therapeutic agents against all sorts of diseases, through the screening and design of a variety of inhibitors. Here we discuss several aspects of PPIs that contribute to prediction of protein function and drug discovery. As the high-throughput techniques continue to release biological data, targets for fungal therapeutics that rely on PPIs are being proposed worldwide. Computational approaches have reduced the time taken to develop new therapeutic approaches. The near future brings the possibility of developing new PPI and interaction network inhibitors and a revolution in the way we treat fungal diseases.

Protein-Protein Interactions Efficiently Modeled by Residue Cluster Classes.

Predicting protein-protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to help improve these predictions, including choosing the proper descriptors of proteins to represent these interactions, among others. In the current work, we provide a representative protein structure that is amenable to PPI classification using machine learning approaches, referred to as residue cluster classes. Through sampling and optimization, we identified the best algorithm-parameter pair to classify PPI from more than 360 different training sets. We tested these classifiers against PPI datasets that were not included in the training set but shared sequence similarity with proteins in the training set to reproduce the situation of most proteins sharing sequence similarity with others. We identified a model with almost no PPI error (96-99% of correctly classified instances) and showed that residue cluster classes of protein pairs displayed a distinct pattern between positive and negative protein interactions. Our results indicated that residue cluster classes are structural features relevant to model PPI and provide a novel tool to mathematically model the protein structure/function relationship.

ppiGReMLIN: a graph mining based detection of conserved structural arrangements in protein-protein interfaces.

BACKGROUND: Protein-protein interactions (PPIs) are fundamental in many biological processes and understanding these interactions is key for a myriad of applications including drug development, peptide design and identification of drug targets. The biological data deluge demands efficient and scalable methods to characterize and understand protein-protein interfaces. In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a set of protein-protein complexes. Our method combines an unsupervised learning strategy with frequent subgraph mining in order to detect conserved structural arrangements (patterns) based on the physicochemical properties of atoms on protein interfaces. To assess the ability of ppiGReMLIN to point out relevant conserved substructures on protein-protein interfaces, we compared our results to experimentally determined patterns that are key for protein-protein interactions in 2 datasets of complexes, Serine-protease and BCL-2. RESULTS: ppiGReMLIN was able to detect, in an automatic fashion, conserved structural arrangements that represent highly conserved interactions at the specificity binding pocket of trypsin and trypsin-like proteins from Serine-protease dataset. Also, for the BCL-2 dataset, our method pointed out conserved arrangements that include critical residue interactions within the conserved motif LXXXXD, pivotal to the binding specificity of BH3 domains of pro-apoptotic BCL-2 proteins towards apoptotic suppressors. Quantitatively, ppiGReMLIN was able to find all of the most relevant residues described in literature for our datasets, showing precision of at least 69% up to 100% and recall of 100%. CONCLUSIONS: ppiGReMLIN was able to find highly conserved structures on the interfaces of protein-protein complexes, with minimum support value of 60%, in datasets of similar proteins. We showed that the patterns automatically detected on protein interfaces by our method are in agreement with interaction patterns described in the literature.

Selection of Antibody Fragments for CAR-T Cell Therapy from Phage Display Libraries.

CAR-T cell therapy emerged in the last years as a great promise to cancer treatment. Nowadays, there is a run to improve the breadth of its use, and thus, new chimeric antigen receptors (CAR) are being proposed. The antigen-binding counterpart of CAR is an antibody fragment, scFv (single chain variable fragment), that recognizes a membrane protein associated to a cancer cell. In this chapter, the use of human scFv phage display libraries as a source of new mAbs against surface antigen is discussed. Protocols focusing in the use of extracellular domains of surface protein in biotinylated format are proposed as selection antigen. Elution with unlabeled peptide and selection in solution is described. The analysis of enriched scFvs throughout the selection using NGS is also outlined. Taken together these protocols allow for the isolation of new scFvs able to be useful in the construction of new chimeric antigen receptors for application in cancer therapy.

Cell cycle and protein complex dynamics in discovering signaling pathways.

Signaling pathways are responsible for the regulation of cell processes, such as monitoring the external environment, transmitting information across membranes, and making cell fate decisions. Given the increasing amount of biological data available and the recent discoveries showing that many diseases are related to the disruption of cellular signal transduction cascades, in silico discovery of signaling pathways in cell biology has become an active research topic in past years. However, reconstruction of signaling pathways remains a challenge mainly because of the need for systematic approaches for predicting causal relationships, like edge direction and activation/inhibition among interacting proteins in the signal flow. We propose an approach for predicting signaling pathways that integrates protein interactions, gene expression, phenotypes, and protein complex information. Our method first finds candidate pathways using a directed-edge-based algorithm and then defines a graph model to include causal activation relationships among proteins, in candidate pathways using cell cycle gene expression and phenotypes to infer consistent pathways in yeast. Then, we incorporate protein complex coverage information for deciding on the final predicted signaling pathways. We show that our approach improves the predictive results of the state of the art using different ranking metrics.

Protein-protein interactions of the nicotinamide/nicotinate mononucleotide adenylyltransferase of Leishmania braziliensis.

BACKGROUND: Nicotinamide adenine dinucleotide (NAD) plays a central role in energy metabolism and integrates cellular metabolism with signalling and gene expression. NAD biosynthesis depends on the enzyme nicotinamide/nicotinate mononucleotide adenylyltransferase (NMNAT; EC: 2.7.7.1/18), in which converge the de novo and salvage pathways. OBJECTIVE: The purpose of this study was to analyse the protein-protein interactions (PPI) of NMNAT of Leishmania braziliensis (LbNMNAT) in promastigotes. METHODS: Transgenic lines of L. braziliensis promastigotes were established by transfection with the pSP72αneoαLbNMNAT-GFP vector. Soluble protein extracts were prepared, co-immunoprecipitation assays were performed, and the co-immunoprecipitates were analysed by mass spectrometry. Furthermore, bioinformatics tools such as network analysis were applied to generate a PPI network. FINDINGS: Proteins involved in protein folding, redox homeostasis, and translation were found to interact with the LbNMNAT protein. The PPI network indicated enzymes of the nicotinate and nicotinamide metabolic routes, as well as RNA-binding proteins, the latter being the point of convergence between our experimental and computational results. MAIN CONCLUSION: We constructed a model of PPI of LbNMNAT and showed its association with proteins involved in various functions such as protein folding, redox homeostasis, translation, and NAD synthesis.

Self-assembling functional programmable protein array for studying protein-protein interactions in malaria parasites.

BACKGROUND: Plasmodium vivax is the most widespread malarial species, causing significant morbidity worldwide. Knowledge is limited regarding the molecular mechanism of invasion due to the lack of a continuous in vitro culture system for these species. Since protein-protein and host-cell interactions play an essential role in the microorganism's invasion and replication, elucidating protein function during invasion is critical when developing more effective control methods. Nucleic acid programmable protein array (NAPPA) has thus become a suitable technology for studying protein-protein and host-protein interactions since producing proteins through the in vitro transcription/translation (IVTT) method overcomes most of the drawbacks encountered to date, such as heterologous protein production, stability and purification. RESULTS: Twenty P. vivax proteins on merozoite surface or in secretory organelles were selected and successfully cloned using gateway technology. Most constructs were displayed in the array expressed in situ, using the IVTT method. The Pv12 protein was used as bait for evaluating array functionality and co-expressed with P. vivax cDNA display in the array. It was found that Pv12 interacted with Pv41 (as previously described), as well as PvMSP142kDa, PvRBP1a, PvMSP8 and PvRAP1. CONCLUSIONS: NAPPA is a high-performance technique enabling co-expression of bait and query in situ, thereby enabling interactions to be analysed rapidly and reproducibly. It offers a fresh alternative for studying protein-protein and ligand-receptor interactions regarding a parasite which is difficult to cultivate (i.e. P. vivax).

The sequences of MinE responsible for its subcellular localization analyzed by competitive binding method in Escherichia coli.

The subcellular localization of a protein is important for its proper function. Escherichia coli MinE is a small protein with clear subcellular localization, which provides a good model to study protein localization mechanism. In the present study, a series of recombinant minEs truncated in one end or in the middle regions, fused with egfp, was constructed, and these recombinant proteins could compete to function with the chromosomal MinE. Our results showed that the sequences related to the subcellular localization of MinE span several functional domains, demonstrating that MinE positioning in cells depends on multiple factors. The eGFP fusions with some truncated MinE from N-terminal resulted in different cell phenotypes and localization features, implying that these fusions can interfere chromosomal MinE's function, similar to MinE36-88 phenotype in the previous report. The amino acid in the region (32-48) is sensitive to change MinE conformation and influence its dimerization. Some truncated protein structure could be unstable. Thus, the MinE localization is prerequisite for its proper anti-MinCD function and some new features of MinE were demonstrated. This approach can be extended for subcellular localization research for other essential proteins.

Protein complex prediction via dense subgraphs and false positive analysis.

Many proteins work together with others in groups called complexes in order to achieve a specific function. Discovering protein complexes is important for understanding biological processes and predict protein functions in living organisms. Large-scale and throughput techniques have made possible to compile protein-protein interaction networks (PPI networks), which have been used in several computational approaches for detecting protein complexes. Those predictions might guide future biologic experimental research. Some approaches are topology-based, where highly connected proteins are predicted to be complexes; some propose different clustering algorithms using partitioning, overlaps among clusters for networks modeled with unweighted or weighted graphs; and others use density of clusters and information based on protein functionality. However, some schemes still require much processing time or the quality of their results can be improved. Furthermore, most of the results obtained with computational tools are not accompanied by an analysis of false positives. We propose an effective and efficient mining algorithm for discovering highly connected subgraphs, which is our base for defining protein complexes. Our representation is based on transforming the PPI network into a directed acyclic graph that reduces the number of represented edges and the search space for discovering subgraphs. Our approach considers weighted and unweighted PPI networks. We compare our best alternative using PPI networks from Saccharomyces cerevisiae (yeast) and Homo sapiens (human) with state-of-the-art approaches in terms of clustering, biological metrics and execution times, as well as three gold standards for yeast and two for human. Furthermore, we analyze false positive predicted complexes searching the PDBe (Protein Data Bank in Europe) database in order to identify matching protein complexes that have been purified and structurally characterized. Our analysis shows that more than 50 yeast protein complexes and more than 300 human protein complexes found to be false positives according to our prediction method, i.e., not described in the gold standard complex databases, in fact contain protein complexes that have been characterized structurally and documented in PDBe. We also found that some of these protein complexes have recently been classified as part of a Periodic Table of Protein Complexes. The latest version of our software is publicly available at http://doi.org/10.6084/m9.figshare.5297314.v1.

Co-immunoprecipitation of Plant Receptor Kinases.

In order to comprehend the function of a particular protein, identification of the interacting protein partners is a useful approach. Co-immunoprecipitation (Co-IP) is employed to test physical interactions between proteins. Specific antibodies or antibodies against tagged versions can be used to immunoprecipitate the proteins. In this chapter, we describe a method to carry out Co-IP using recombinant membrane proteins expressed in yeast microsomal fractions.

Co-immunoprecipitation for Deciphering Protein Interactomes.

A single protein is often capable of binding with many partners, enabling potential effects on either protein, such as modifying its expression or activity. However, due to the complex nature of in vivo systems, it is often difficult to perform nontargeted assays with a protein of interest. Methods in discovery proteomics must be used to find potential interactors to pave the way for additional, more focused studies. This protocol describes the biological steps needed to create an interactome focused on a single protein target through co-immunoprecipitation.

The role of pi-interactions and hydrogen bonds in fully protective synthetic malaria vaccine development.

Analysis of our Plasmodium falciparum malaria parasite peptides' 1H-NMR database in the search for H-bonds and π-interactions led us to correlate their presence or absence with a peptide's particular immunological behavior. It was concluded that a 26.5 ± 1.5 Å between positions 1 to 9 of the HLA-DRß1* interacting region was necessary for proper docking of 20mer-long peptides and these MHC Class II molecules for full-protective immunity. Presence of intramolecular H-bonds or π-interactions leading to righ-handed α-helix or ß-turn conformation in this peptide's region induces different immune responses or none. PPIIL conformation and the absence of any intramolecular interaction thus became the first feature characterising our immune protection-inducing structures as malaria vaccine candidates.

Using the Gene Ontology tool to produce de novo protein-protein interaction networks with IS_A relationship.

Since the first assembled genomes, gene sequences alone have not been sufficient to understand complex metabolic processes involving several genes, each playing distinct roles. To identify their roles, a network of interactions, wherein each gene is a node, should be created. Edges connecting nodes are evidence of interaction, for instance, of gene products coexisting in the same cellular component. Such interaction networks are called protein-protein interactions (PPIs). After genome assembling, PPI mapping is used to predict the possibility of proteins interacting with other proteins based on literature evidence and several databases, thus enriching genome annotations. Identifying PPIs involves analyzing each possible protein pair for a set of features, for instance, participation in the same biological process and having the same function and status in a cellular component. Here, we investigated using the three categories of the Gene Ontology (GO) database for efficient PPI prediction, because it provides data about the three features exemplified here. For a broader conclusion, we investigated the genomes of ten different human pathogens, looking for commonality regarding the GO hierarchical relationship-denominated IS_A. The plasmids were examined separately from their main genomes. Protein pairs sharing at least one IS_A value were considered as interacting proteins. STRING results certified the probed interactions as sensitivity (score >0.75) and specificity (score <0.25) analysis. The average areas under the receiver operating characteristic curve for all organisms were 0.66 and 0.53 for their genomes and plasmids, respectively. Thus, GO categories alone could not potentially provide reliable PPI prediction. However, using additional features can improve predictions.

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.

BACKGROUND: Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. RESULTS: Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. CONCLUSIONS: The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

Vias de transdução de sinal e polimorfismo de Toll-like Receptors na carcinogenese por HPV / Toll-like Receptors signaling pathway and polymorphism on the HPV carcinogenesis

Seres humanos dependem incessantemente de um sistema de reconhecimento efetivo contra infecções para sobreviver. Dentre as diversas proteínas que compõem a resposta imune inata estão os receptores do tipo Toll (TLR Toll-like Receptors), que possuem a função de reconhecer padrões moleculares associados a patógenos e dar início a uma resposta imune adequada. O carcinoma do colo uterino é uma das principais causas de morte de mulheres por câncer mundialmente, sendo o terceiro tipo de câncer mais comum entre mulheres. Este tipo de neoplasia é vinculada etiologicamente à infecção pelo Papilomavírus humano (HPV). Dentre as principais proteínas virais, E6 e E7 são responsáveis pela manipulação dos processos celulares para promover ciclo viral, sendo essenciais no processo de transformação celular. Nesse contexto, o objetivo deste trabalho foi investigar a importância da via de sinalização de TLRs sobre a infecção por HPV. O polimorfismo rs5743836, na região promotora de TLR9, capaz de alterar a expressão deste receptor, foi estudado quanto à influência sobre a história natural da infecção por HPV em uma coorte de mulheres brasileiras; nenhuma associação relevante foi encontrada, indicando que este polimorfismo não interfere significativamente na resposta à infecção e risco de desenvolvimento de lesões no colo do útero causadas por HPV. Proteínas componentes da via de TLRs demonstraram serem alvos de interação com E6 de HPV16; dentre elas, o notável adaptador MyD88 e IKKε, enzima ativadora de importantes transfatores do sistema imune. Estas interações foram aqui estudadas. A interação de E6 com MyD88 resultou em estabilização da proteína viral, o que parece não depender do sítio LxxLL presente em MyD88, como ocorre com outros parceiros moleculares de E6. O sítio de interação de E6 com IKKε coincide com a região onde se localiza o sítio catalítico desta enzima, sugerindo a ação de E6 na ativação de proteínas alvo de IKKε. Esta interação foi observada em queratinócitos, células alvo das infecções por HPV. A produção de citocinas foi afetada por E6 de HPV16, resultando num aumento da quantidade de IL-8 e IL-6; a indução desta citocina poderia ser explicada pela ativação de IKKε. Estes resultados apontam para a capacidade do HPV16 de interferir com o sistema imune, contribuindo para o processo de carcinogênese

Humans constantly rely on an effective recognition system against infections in order to survive. Among various proteins that compose the innate immune response, Toll-like Receptors (TLRs) have the role to recognize pathogen associated molecular patterns and initiate a proper immune response. The cervical cancer is one of the main causes of women death worldwide, being the third most common cancer type among women. This type of neoplasia is etiologically associated with the Human papillomavirus (HPV) infection. E6 and E7, two main viral proteins, are responsible for manipulating the cellular processes to promote the virus' life-cycle, being essential to the cellular transformation process. In the context, the objective of this work was to investigate the relevance of the TLR signaling pathway on the HPV infection. The rs5743836 polymorphism, in the TLR9 promoter region, capable of altering this receptor's expression, was studied regarding its influence on the natural history of HPV infection in a Brazilian women cohort; no relevant association was found, indicating that this polymorphism does not interfere significantly in the infection response and risk of developing cervix lesions caused by HPV. Component proteins of TLR pathway were shown to be interaction targets of HPV16 E6; among them, the notable adaptor MyD88 and IKKε, enzyme that activates important immune system transfactors. These interactions were studied in this work. The interaction of E6 with MyD88 resulted in the stabilization of the viral protein, which seems independent of the LxxLL site present on MyD88, as in other E6 molecular partners. The interaction site on IKK with E6 matches with the region containing the enzyme's catalytic site, suggesting an influence of E6 in the activation of IKKε target proteins. This interaction was observed in keratinocytes, natural targets of HPV infections. The cytokines production was altered by HPV16 E6, resulting in an increase of IL-8 and IL-6 concentration; the induction of the latter could be explained by the activation of IKKε. These results point to the ability of HPV16 of interfering with the immune system, contributing to the carcinogenesis process

Proteomic Study to Survey the CIGB-552 Antitumor Effect.

CIGB-552 is a cell-penetrating peptide that exerts in vitro and in vivo antitumor effect on cancer cells. In the present work, the mechanism involved in such anticancer activity was studied using chemical proteomics and expression-based proteomics in culture cancer cell lines. CIGB-552 interacts with at least 55 proteins, as determined by chemical proteomics. A temporal differential proteomics based on iTRAQ quantification method was performed to identify CIGB-552 modulated proteins. The proteomic profile includes 72 differentially expressed proteins in response to CIGB-552 treatment. Proteins related to cell proliferation and apoptosis were identified by both approaches. In line with previous findings, proteomic data revealed that CIGB-552 triggers the inhibition of NF-κB signaling pathway. Furthermore, proteins related to cell invasion were differentially modulated by CIGB-552 treatment suggesting new potentialities of CIGB-552 as anticancer agent. Overall, the current study contributes to a better understanding of the antitumor action mechanism of CIGB-552.

Extracting physical chemistry from mechanics: a new approach to investigate DNA interactions with drugs and proteins in single molecule experiments.

In this review we focus on the idea of establishing connections between the mechanical properties of DNA-ligand complexes and the physical chemistry of DNA-ligand interactions. This type of connection is interesting because it opens the possibility of performing a robust characterization of such interactions by using only one experimental technique: single molecule stretching. Furthermore, it also opens new possibilities in comparing results obtained by very different approaches, in particular when comparing single molecule techniques to ensemble-averaging techniques. We start the manuscript reviewing important concepts of DNA mechanics, from the basic mechanical properties to the Worm-Like Chain model. Next we review the basic concepts of the physical chemistry of DNA-ligand interactions, revisiting the most important models used to analyze the binding data and discussing their binding isotherms. Then, we discuss the basic features of the single molecule techniques most used to stretch DNA-ligand complexes and to obtain "force × extension" data, from which the mechanical properties of the complexes can be determined. We also discuss the characteristics of the main types of interactions that can occur between DNA and ligands, from covalent binding to simple electrostatic driven interactions. Finally, we present a historical survey of the attempts to connect mechanics to physical chemistry for DNA-ligand systems, emphasizing a recently developed fitting approach useful to connect the persistence length of DNA-ligand complexes to the physicochemical properties of the interaction. Such an approach in principle can be used for any type of ligand, from drugs to proteins, even if multiple binding modes are present.

Interaction Fingerprints and Their Applications to Identify Hot Spots.

Binding recognition is in the core of how nature controls processes in living cells, how enzyme-substrate binding leads to catalysis and how drugs modulate enzymes and receptors to convey a desirable physiological response. Thus, understanding binding recognition in a systematic manner is paramount, not only to understand biological processes but also to be able to design and discover new bioactive compounds. One such way to analyze binding interactions is through the development of binding interaction fingerprints. Here, we present the methodology to develop interaction fingerprints with three different software platforms along with two representative examples.

I-COMS: Interprotein-COrrelated Mutations Server.

Interprotein contact prediction using multiple sequence alignments (MSAs) is a useful approach to help detect protein-protein interfaces. Different computational methods have been developed in recent years as an approximation to solve this problem. However, as there are discrepancies in the results provided by them, there is still no consensus on which is the best performing methodology. To address this problem, I-COMS (interprotein COrrelated Mutations Server) is presented. I-COMS allows to estimate covariation between residues of different proteins by four different covariation methods. It provides a graphical and interactive output that helps compare results obtained using different methods. I-COMS automatically builds the required MSA for the calculation and produces a rich visualization of either intraprotein and/or interprotein covariating positions in a circos representation. Furthermore, comparison between any two methods is available as well as the overlap between any or all four methodologies. In addition, as a complementary source of information, a matrix visualization of the corresponding scores is made available and the density plot distribution of the inter, intra and inter+intra scores are calculated. Finally, all the results can be downloaded (including MSAs, scores and graphics) for comparison and visualization and/or for further analysis.