Identification Of Interacting Proteins Research Articles

Split Proteins and Reassembly Modules for Biological Applications.

Split systems, modular entities enabling controlled biological processes, have become instrumental in biological research. This review highlights their utility across applications like gene regulation, protein interaction identification, and biosensor development. Covering significant progress over the last decade, it revisits traditional split proteins such as GFP, luciferase, and inteins, and explores advancements in technologies like Cas proteins and base editors. We also examine reassembly modules and their applications in diverse fields, from gene regulation to therapeutic innovation. This review offers a comprehensive perspective on the recent evolution of split systems in biological research.

The permissive binding theory of cancer.

The later stages of cancer, including the invasion and colonization of new tissues, are actively mysterious compared to earlier stages like primary tumor formation. While we lack many details about both, we do have an apparently successful explanatory framework for the earlier stages: one in which genetic mutations hold ultimate causal and explanatory power. By contrast, on both empirical and conceptual grounds, it is not currently clear that mutations alone can explain the later stages of cancer. Can a different type of molecular change do better? Here, I introduce the "permissive binding theory" of cancer, which proposes that novel protein binding interactions are the key causal and explanatory entity in invasion and metastasis. It posits that binding is more abundant at baseline than we observe because it is restricted in normal physiology; that any large perturbation to physiological state revives this baseline abundance, unleashing many new binding interactions; and that a subset of these cause the cellular functions at the heart of oncogenesis, especially invasion and metastasis. Significant physiological perturbations occur in cancer cells in very early stages, and generally become more extreme with progression, providing interactions that continually fuel invasion and metastasis. The theory is compatible with, but not limited to, causal roles for the diverse molecular changes observed in cancer (e.g. gene expression or epigenetic changes), as these generally act causally upstream of proteins, and so may exert their effects by changing the protein binding interactions that occur in the cell. This admits the possibility that molecular changes that appear quite different may actually converge in creating the same few protein complexes, simplifying our picture of invasion and metastasis. If correct, the theory offers a concrete therapeutic strategy: targeting the key novel complexes. The theory is straightforwardly testable by large-scale identification of protein interactions in different cancers.

Differential Proteome Profiling Analysis under Pesticide Stress by the Use of a Nano-UHPLC-MS/MS Untargeted Proteomic-Based Approach on a 3D-Developed Neurospheroid Model: Identification of Protein Interactions, Prognostic Biomarkers, and Potential Therapeutic Targets in Human IDH Mutant High-Grade Gliomas.

High-grade gliomas represent the most common group of infiltrative primary brain tumors in adults associated with high invasiveness, agressivity, and resistance to therapy, which highlights the need to develop potent drugs with novel mechanisms of action. The aim of this study is to reveal changes in proteome profiles under stressful conditions to identify prognostic biomarkers and altered apoptogenic pathways involved in the anticancer action of human isocitrate dehydrogenase (IDH) mutant high-grade gliomas. Our protocol consists first of a 3D in vitro developing neurospheroid model and then treatment by a pesticide mixture at relevant concentrations. Furthermore, we adopted an untargeted proteomic-based approach with high-resolution mass spectrometry for a comparative analysis of the differentially expressed proteins between treated and nontreated spheroids. Our analysis revealed that the majority of altered proteins were key members in glioma pathogenesis, implicated in the cellular metabolism, biological regulation, binding, and catalytic and structural activity and linked to many cascading regulatory pathways. Our finding revealed that grade-IV astrocytomas promote the downstream of the mitogen-activated-protein-kinases/extracellular-signal-regulated kinase (MAPK1/ERK2) pathway involving massive calcium influx. The gonadotrophin-releasing-hormone signaling enhances MAKP activity and may serve as a negative feedback compensating regulator. Thus, our study can pave the way for effective new therapeutic and diagnostic strategies to improve the overall survival.

Investigating Cryptosporidium spp. Using Genomic, Proteomic and Transcriptomic Techniques: Current Progress and Future Directions.

Cryptosporidiosis is a widespread disease caused by the parasitic protozoan Cryptosporidium spp., which infects various vertebrate species, including humans. Once unknown as a gastroenteritis-causing agent, Cryptosporidium spp. is now recognized as a pathogen causing life-threatening disease, especially in immunocompromised individuals such as AIDS patients. Advances in diagnostic methods and increased awareness have led to a significant shift in the perception of Cryptosporidium spp. as a pathogen. Currently, genomic and proteomic studies play a main role in understanding the molecular biology of this complex-life-cycle parasite. Genomics has enabled the identification of numerous genes involved in the parasite's development and interaction with hosts. Proteomics has allowed for the identification of protein interactions, their function, structure, and cellular activity. The combination of these two approaches has significantly contributed to the development of new diagnostic tools, vaccines, and drugs for cryptosporidiosis. This review presents an overview of the significant achievements in Cryptosporidium research by utilizing genomics, proteomics, and transcriptomics approaches.

Graph-BERT and language model-based framework for protein–protein interaction identification

Identification of protein–protein interactions (PPI) is among the critical problems in the domain of bioinformatics. Previous studies have utilized different AI-based models for PPI classification with advances in artificial intelligence (AI) techniques. The input to these models is the features extracted from different sources of protein information, mainly sequence-derived features. In this work, we present an AI-based PPI identification model utilizing a PPI network and protein sequences. The PPI network is represented as a graph where each node is a protein pair, and an edge is defined between two nodes if there exists a common protein between these nodes. Each node in a graph has a feature vector. In this work, we have used the language model to extract feature vectors directly from protein sequences. The feature vectors for protein in pairs are concatenated and used as a node feature vector of a PPI network graph. Finally, we have used the Graph-BERT model to encode the PPI network graph with sequence-based features and learn the hidden representation of the feature vector for each node. The next step involves feeding the learned representations of nodes to the fully connected layer, the output of which is fed into the softmax layer to classify the protein interactions. To assess the efficacy of the proposed PPI model, we have performed experiments on several PPI datasets. The experimental results demonstrate that the proposed approach surpasses the existing PPI works and designed baselines in classifying PPI.

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

Incorporation of gene ontology in identification of protein interactions from biomedical corpus: a multi-modal approach

Incorporation of gene ontology in identification of protein interactions from biomedical corpus: a multi-modal approach

Identification and Affinity Determination of Protein-Antibody and Protein-Aptamer Epitopes by Biosensor-Mass Spectrometry Combination.

Analytical methods for molecular characterization of diagnostic or therapeutic targets have recently gained high interest. This review summarizes the combination of mass spectrometry and surface plasmon resonance (SPR) biosensor analysis for identification and affinity determination of protein interactions with antibodies and DNA-aptamers. The binding constant (KD) of a protein–antibody complex is first determined by immobilizing an antibody or DNA-aptamer on an SPR chip. A proteolytic peptide mixture is then applied to the chip, and following removal of unbound material by washing, the epitope(s) peptide(s) are eluted and identified by MALDI-MS. The SPR-MS combination was applied to a wide range of affinity pairs. Distinct epitope peptides were identified for the cardiac biomarker myoglobin (MG) both from monoclonal and polyclonal antibodies, and binding constants determined for equine and human MG provided molecular assessment of cross immunoreactivities. Mass spectrometric epitope identifications were obtained for linear, as well as for assembled (“conformational”) antibody epitopes, e.g., for the polypeptide chemokine Interleukin-8. Immobilization using protein G substantially improved surface fixation and antibody stabilities for epitope identification and affinity determination. Moreover, epitopes were successfully determined for polyclonal antibodies from biological material, such as from patient antisera upon enzyme replacement therapy of lysosomal diseases. The SPR-MS combination was also successfully applied to identify linear and assembled epitopes for DNA–aptamer interaction complexes of the tumor diagnostic protein C-Met. In summary, the SPR-MS combination has been established as a powerful molecular tool for identification of protein interaction epitopes.

Computed structures of core eukaryotic protein complexes.

Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coevolution analysis and deep-learning–based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of yeast proteins, identify 1505 likely to interact, and build structure models for 106 previously unidentified assemblies and 806 that have not been structurally characterized. These complexes, which have as many as five subunits, play roles in almost all key processes in eukaryotic cells and provide broad insights into biological function.

Identification of protein interactions of grapevine fanleaf virus RNA-dependent RNA polymerase during infection of Nicotiana benthamiana by affinity purification and tandem mass spectrometry.

The RNA-dependent RNA polymerase (1EPol) is involved in replication of grapevine fanleaf virus (GFLV, Nepovirus, Secoviridae) and causes vein clearing symptoms in Nicotiana benthamiana. Information on protein 1EPol interaction with other viral and host proteins is scarce. To study protein 1EPol biology, three GFLV infectious clones, i.e. GHu (a symptomatic wild-type strain), GHu-1EK802G (an asymptomatic GHu mutant) and F13 (an asymptomatic wild-type strain), were engineered with protein 1EPol fused to a V5 epitope tag at the C-terminus. Following Agrobacterium tumefaciens -mediated delivery of GFLV clones in N. benthamiana and protein extraction at seven dpi, when optimal 1EPol:V5 accumulation was detected, two viral and six plant putative interaction partners of V5-tagged protein 1EPol were identified for the three GFLV clones by affinity purification and tandem mass spectrometry. This study provides insights into the protein interactome of 1EPol during GFLV systemic infection in N. benthamiana and lays the foundation for validation work.

Tissue- and isoform-specific protein complex analysis with natively processed bait proteins

Protein-protein interaction analysis is an important tool to elucidate the function of proteins and protein complexes as well as their dynamic behavior. To date, the analysis of tissue- or even cell- or compartment-specific protein interactions is still relying on the availability of specific antibodies suited for immunoprecipitation. Here, we aimed at establishing a method that allows identification of protein interactions and complexes from intact tissues independent of specific, high affinity antibodies used for protein pull-down and isolation. Tagged bait proteins were expressed in human HEK293T cells and residual interactors removed by SDS. The resulting tag-fusion protein was then used as bait to pull proteins from tissue samples. Tissue-specific interactions were reproducibly identified from porcine retina as well as from retinal pigment epithelium using the ciliary protein lebercilin as bait. Further, murine heart-specific interactors of two gene products of the 3′,5′-cyclic guanosine monophosphate (cGMP)-dependent protein kinase type 1 (cGK1) were investigated. Here, specific interactions were associated with the cGK1α and β gene products, that differ only in their unique amino-terminal region comprising about 100 aa. As such, the new protocol provides a fast and reliable method for tissue-specific protein complex analysis which is independent of the availability or suitability of antibodies for immunoprecipitation. SignificanceProtein-protein interaction in the functional relevant tissue is still difficult due to the dependence on specific antibodies or bait production in bacteria or insect cells. Here, the tagged protein of interest is produced in a human cell line and bound proteins are gently removed using SDS. Because applying the suitable SDS concentration is a critical step, different SDS solutions were tested to demonstrate their influence on interactions and the clean-up process. The established protocol enabled a tissue-specific analysis of the ciliary proteins lebercilin and TMEM107 using pig eyes. In addition, two gene products of the 3′,5′-cyclic guanosine monophosphate (cGMP)-dependent protein kinase type 1 showed distinct protein interactions in mouse heart tissue. With the easy, fast and cheap protocol presented here, deep insights in tissue-specific and functional relevant protein complex formation is possible.

Systematic evaluation of machine learning methods for identifying human–pathogen protein–protein interactions

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

Targeted Identification of Protein Interactions in Eukaryotic mRNA Translation.

To identify protein-protein interactions and phosphorylated amino acid sites in eukaryotic mRNA translation, replicate TAP-MudPIT and control experiments are performed targeting Saccharomyces cerevisiae genes previously implicated in eukaryotic mRNA translation by their genetic and/or functional roles in translation initiation, elongation, termination, or interactions with ribosomal complexes. Replicate tandem affinity purifications of each targeted yeast TAP-tagged mRNA translation protein coupled with multidimensional liquid chromatography and tandem mass spectrometry analysis are used to identify and quantify copurifying proteins. To improve sensitivity and minimize spurious, nonspecific interactions, a novel cross-validation approach is employed to identify the most statistically significant protein-protein interactions. Using experimental and computational strategies discussed herein, the previously described protein composition of the canonical eukaryotic mRNA translation initiation, elongation, and termination complexes is calculated. In addition, statistically significant unpublished protein interactions and phosphorylation sites for S. cerevisiae's mRNA translation proteins and complexes are identified.

Multimodal deep representation learning for protein interaction identification and protein family classification

BackgroundProtein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge.ResultsIn this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods.ConclusionTo the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.

Genetically Encoded Residue-Selective Photo-Crosslinker to Capture Protein-Protein Interactions in Living Cells

•Genetically encoded lysine-selective protein photo-crosslinker in living cells•Capturing protein interactions with lysine-selectivity in a temporal-resolved manner•Producing residue-predictable photo-crosslinking site and crosslinked peptides•Capture of elusive enzyme-substrate interaction and validation of lysine PTM site Protein-protein interactions play pivotal roles in virtually all cellular processes. The identification of transient and weak protein interactions in living cells still remains challenging with traditional strategies. Combining advantages of temporal control and the unique ability of residue selectivity, photo-crosslinkers offer are a powerful tool for identification of dynamic, weak, and transient protein interactions in living cells with specificity, accuracy, and reliability, thus, for better understanding of biological processes and pathophysiology. Investigating photo-crosslinking with residue selectivity may lead to the discovery of new photo-crosslinkers and help in expanding the repertoire of current protein crosslinking technology and interactive proteomics. Consequently, this approach will have an effect on a wide range of fields involving protein-protein and ligand-receptor interactions such as basic biological studies, interactive proteomics, protein structural studies, and chemical biology. The genetically encoded non-selective photo-crosslinkers suffer from unpredictable crosslinking sites and false positive interacting proteins, thus, the identification of protein interactions and mapping accurate interaction interfaces remain largely elusive in living systems. Here, we report genetically encoded, residue-selective protein photo-crosslinker enabling covalent crosslinking of the interacting proteins with proximal lysine, upon UV light activation in vitro and in living cells. Using this photo-crosslinker, we demonstrate a crosslinking strategy to capture protein-protein interactions with residue selectivity in a temporal-resolved manner. In addition, this crosslinking strategy produces the lysine-selective crosslinking site and predictable crosslinked peptide, which serve as the direct evidence for protein-protein interactions and facilitate mass spectrometry analysis. More importantly, this photo-crosslinker enables the capture of elusive enzyme-substrate interaction with directly interacting lysine and validation of acetylation site of the substrate. This strategy represents higher spatiotemporal resolution, accuracy, and reliability for investigating dynamic, transient, and weak protein-protein interactions in living systems. The genetically encoded non-selective photo-crosslinkers suffer from unpredictable crosslinking sites and false positive interacting proteins, thus, the identification of protein interactions and mapping accurate interaction interfaces remain largely elusive in living systems. Here, we report genetically encoded, residue-selective protein photo-crosslinker enabling covalent crosslinking of the interacting proteins with proximal lysine, upon UV light activation in vitro and in living cells. Using this photo-crosslinker, we demonstrate a crosslinking strategy to capture protein-protein interactions with residue selectivity in a temporal-resolved manner. In addition, this crosslinking strategy produces the lysine-selective crosslinking site and predictable crosslinked peptide, which serve as the direct evidence for protein-protein interactions and facilitate mass spectrometry analysis. More importantly, this photo-crosslinker enables the capture of elusive enzyme-substrate interaction with directly interacting lysine and validation of acetylation site of the substrate. This strategy represents higher spatiotemporal resolution, accuracy, and reliability for investigating dynamic, transient, and weak protein-protein interactions in living systems. Protein-protein interactions play crucial roles in regulation of many biological processes. However, the identification of dynamic, transient, and weak protein-protein interactions in living cells still remains highly challenging with traditional strategies.1Berggård T. Linse S. James P. Methods for the detection and analysis of protein-protein interactions.Proteomics. 2007; 7: 2833-2842Crossref PubMed Scopus (422) Google Scholar, 2Dunham W.H. Mullin M. Gingras A.C. Affinity-purification coupled to mass spectrometry: basic principles and strategies.Proteomics. 2012; 12: 1576-1590Crossref PubMed Scopus (208) Google Scholar In comparison with non-covalent binding approaches such as immunoprecipitation and affinity purification, crosslinking of protein complexes with covalent linkage irreversibly captures the interacting protein partners and tolerates stringent processing, conferring higher reliability, specificity, and accuracy.3Yu C. Huang L. Cross-linking mass spectrometry: an emerging technology for interactomics and structural biology.Anal. Chem. 2018; 90: 144-165Crossref PubMed Scopus (173) Google Scholar, 4Sinz A. Cross-linking/mass spectrometry for studying protein structures and protein-protein interactions: where are we now and where should we go from here?.Angew. Chem. Int. Ed. 2018; 57: 6390-6396Crossref PubMed Scopus (111) Google Scholar, 5Preston G.W. Wilson A.J. Photo-induced covalent cross-linking for the analysis of biomolecular interactions.Chem. Soc. Rev. 2013; 42: 3289-3301Crossref PubMed Scopus (135) Google Scholar, 6Pham N.D. Parker R.B. Kohler J.J. Photocrosslinking approaches to interactome mapping.Curr. Opin. Chem. Biol. 2013; 17: 90-101Crossref PubMed Scopus (96) Google Scholar The stable covalent linkage formation with proximal residues potentially enabling the capture and identification of dynamic, weak, and transient protein interactions. Several elegant methodologies have been developed via chemical crosslinkers.3Yu C. Huang L. Cross-linking mass spectrometry: an emerging technology for interactomics and structural biology.Anal. Chem. 2018; 90: 144-165Crossref PubMed Scopus (173) Google Scholar, 4Sinz A. Cross-linking/mass spectrometry for studying protein structures and protein-protein interactions: where are we now and where should we go from here?.Angew. Chem. Int. Ed. 2018; 57: 6390-6396Crossref PubMed Scopus (111) Google Scholar, 7Young M.M. Tang N. Hempel J.C. Oshiro C.M. Taylor E.W. Kuntz I.D. Gibson B.W. Dollinger G. High throughput protein fold identification by using experimental constraints derived from intramolecular cross-links and mass spectrometry.Proc. Natl. Acad. Sci. USA. 2000; 97: 5802-5806Crossref PubMed Scopus (392) Google Scholar, 8Herzog F. Kahraman A. Boehringer D. Mak R. Bracher A. Walzthoeni T. Leitner A. Beck M. Hartl F.U. Ban N. et al.Structural probing of a protein phosphatase 2A network by chemical cross-linking and mass spectrometry.Science. 2012; 337: 1348-1352Crossref PubMed Scopus (312) Google Scholar, 9Shi Y. Pellarin R. Fridy P.C. Fernandez-Martinez J. Thompson M.K. Li Y.Y. Wang Q.J. Sali A. Rout M.P. Chait B.T. A strategy for dissecting the architectures of native macromolecular assemblies.Nat. Methods. 2015; 12: 1135-1138Crossref PubMed Scopus (83) Google Scholar Chemical crosslinkers coupled with mass spectrometry (MS) analysis are versatile, but the reactive small molecules used are generally incompatible with living systems, which therefore have been mainly restricted to large protein complexes with stable interactions. The highly reactive small molecules simultaneously react with all targeted residues in pairs on protein surfaces with non-selectivity and generate excess number of covalent linkages, which will complicate MS analysis and artificially alter protein interactions and protein structure conformation.10Kalkhof S. Sinz A. Chances and pitfalls of chemical cross-linking with amine-reactive N-hydroxysuccinimide esters.Anal. Bioanal. Chem. 2008; 392: 305-312Crossref PubMed Scopus (197) Google Scholar, 11Mädler S. Bich C. Touboul D. Zenobi R. Chemical cross-linking with NHS esters: a systematic study on amino acid reactivities.J. Mass Spectrom. 2009; 44: 694-706Crossref PubMed Scopus (199) Google Scholar, 12Merkley E.D. Rysavy S. Kahraman A. Hafen R.P. Daggett V. Adkins J.N. Distance restraints from crosslinking mass spectrometry: mining a molecular dynamics simulation database to evaluate lysine-lysine distances.Protein Sci. 2014; 23: 747-759Crossref PubMed Scopus (175) Google Scholar, 13Chen Z.A. Jawhari A. Fischer L. Buchen C. Tahir S. Kamenski T. Rasmussen M. Lariviere L. Bukowski-Wills J.C. Nilges M. et al.Architecture of the RNA polymerase II-TFIIF complex revealed by cross-linking and mass spectrometry.EMBO J. 2010; 29: 717-726Crossref PubMed Scopus (324) Google Scholar Alternatively, photo-crosslinking of protein complexes in a covalent manner has become a highly valuable technique for mapping dynamic or weak protein-protein interactions.5Preston G.W. Wilson A.J. Photo-induced covalent cross-linking for the analysis of biomolecular interactions.Chem. Soc. Rev. 2013; 42: 3289-3301Crossref PubMed Scopus (135) Google Scholar, 6Pham N.D. Parker R.B. Kohler J.J. Photocrosslinking approaches to interactome mapping.Curr. Opin. Chem. Biol. 2013; 17: 90-101Crossref PubMed Scopus (96) Google Scholar, 14Nguyen T.A. Cigler M. Lang K. Expanding the genetic code to study protein-protein interactions.Angew. Chem. Int. Ed. 2018; 57: 14350-14361Crossref PubMed Scopus (60) Google Scholar, 15Coin I. Application of non-canonical crosslinking amino acids to study protein-protein interactions in live cells.Curr. Opin. Chem. Biol. 2018; 46: 156-163Crossref PubMed Scopus (35) Google Scholar, 16Yang Y. Song H. Chen P.R. Genetically encoded photocrosslinkers for identifying and mapping protein-protein interactions in living cells.IUBMB Life. 2016; 68: 879-886Crossref PubMed Scopus (34) Google Scholar Specifically, site-specific incorporation of photo-crosslinkers in a protein of interest has emerged as an attractive strategy for investigation of protein-protein interactions with temporal control in living cells, with an aim to map protein interaction interfaces, to capture and identify dynamic, weak, and transient interactions.17Chin J.W. Santoro S.W. Martin A.B. King D.S. Wang L. Schultz P.G. Addition of p-azido-L-phenylalanine to the genetic code of Escherichia coli.J. Am. Chem. Soc. 2002; 124: 9026-9027Crossref PubMed Scopus (552) Google Scholar, 18Chin J.W. Martin A.B. King D.S. Wang L. Schultz P.G. Addition of a photocrosslinking amino acid to the genetic code of Escherichia coli.Proc. Natl. Acad. Sci. USA. 2002; 99: 11020-11024Crossref PubMed Scopus (526) Google Scholar, 19Hino N. Okazaki Y. Kobayashi T. Hayashi A. Sakamoto K. Yokoyama S. Protein photo-cross-linking in mammalian cells by site-specific incorporation of a photoreactive amino acid.Nat. Methods. 2005; 2: 201-206Crossref PubMed Scopus (195) Google Scholar, 20Ai H.W. Shen W. Sagi A. Chen P.R. Schultz P.G. Probing protein-protein interactions with a genetically encoded photo-crosslinking amino acid.ChemBioChem. 2011; 12: 1854-1857Crossref PubMed Scopus (90) Google Scholar, 21Zhang M. Lin S. Song X. Liu J. Fu Y. Ge X. Fu X. Chang Z. Chen P.R. A genetically incorporated crosslinker reveals chaperone cooperation in acid resistance.Nat. Chem. Biol. 2011; 7: 671-677Crossref PubMed Scopus (189) Google Scholar, 22Chou C.J. Uprety R. Davis L. Chin J.W. Deiters A. Genetically encoding an aliphatic diazirine for protein photocrosslinking.Chem. Sci. 2011; 2: 480-483Crossref Scopus (73) Google Scholar, 23Hino N. Oyama M. Sato A. Mukai T. Iraha F. Hayashi A. Kozuka-Hata H. Yamamoto T. Yokoyama S. Sakamoto K. Genetic incorporation of a photo-crosslinkable amino acid reveals novel protein complexes with GRB2 in mammalian cells.J. Mol. Biol. 2011; 406: 343-353Crossref PubMed Scopus (43) Google Scholar, 24Coin I. Perrin M.H. Vale W.W. Wang L. Photo-cross-linkers incorporated into G-protein-coupled receptors in mammalian cells: a ligand comparison.Angew. Chem. Int. Ed. 2011; 50: 8077-8081Crossref PubMed Scopus (50) Google Scholar, 25Coin I. Katritch V. Sun T. Xiang Z. Siu F.Y. Beyermann M. Stevens R.C. Wang L. Genetically encoded chemical probes in cells reveal the binding path of urocortin-I to CRF class B GPCR.Cell. 2013; 155: 1258-1269Abstract Full Text Full Text PDF PubMed Scopus (132) Google Scholar, 26Yang Y. Song H. He D. Zhang S. Dai S. Lin S. Meng R. Wang C. Chen P.R. Genetically encoded protein photocrosslinker with a transferable mass spectrometry-identifiable label.Nat. Commun. 2016; 7: 12299Crossref PubMed Scopus (58) Google Scholar, 27He D. Xie X. Yang F. Zhang H. Su H. Ge Y. Song H. Chen P.R. Quantitative and comparative profiling of protease substrates through a genetically encoded multifunctional photocrosslinker.Angew. Chem. Int. Ed. 2017; 56: 14521-14525Crossref PubMed Scopus (35) Google Scholar This strategy holds enormous potential for application to myriad normal and pathological processes, especially, where receptors dynamically signal and traffic in response to ligand-induced activation, followed by light trigger to crosslink downstream protein interactions.19Hino N. Okazaki Y. Kobayashi T. Hayashi A. Sakamoto K. Yokoyama S. Protein photo-cross-linking in mammalian cells by site-specific incorporation of a photoreactive amino acid.Nat. Methods. 2005; 2: 201-206Crossref PubMed Scopus (195) Google Scholar, 25Coin I. Katritch V. Sun T. Xiang Z. Siu F.Y. Beyermann M. Stevens R.C. Wang L. Genetically encoded chemical probes in cells reveal the binding path of urocortin-I to CRF class B GPCR.Cell. 2013; 155: 1258-1269Abstract Full Text Full Text PDF PubMed Scopus (132) Google Scholar The incorporated photo-crosslinkers can generate highly reactive intermediates under ultraviolet (UV) irradiation (Figure 1A).14Nguyen T.A. Cigler M. Lang K. Expanding the genetic code to study protein-protein interactions.Angew. Chem. Int. Ed. 2018; 57: 14350-14361Crossref PubMed Scopus (60) Google Scholar These reactive species insert into proximal C–H and heteroatom–H bonds, enabling stable covalent linkages formation with interacting protein partners. Despite widespread success of these photo-crosslinkers in chemical biology and proteomics area, the non-selective crosslinking of any nearby residue of protein complex increases background crosslinking and produces unpredictable crosslinking sites, resulting in a challenge for subsequent tandem MS analysis,3Yu C. Huang L. Cross-linking mass spectrometry: an emerging technology for interactomics and structural biology.Anal. Chem. 2018; 90: 144-165Crossref PubMed Scopus (173) Google Scholar, 4Sinz A. Cross-linking/mass spectrometry for studying protein structures and protein-protein interactions: where are we now and where should we go from here?.Angew. Chem. Int. Ed. 2018; 57: 6390-6396Crossref PubMed Scopus (111) Google Scholar, 19Hino N. Okazaki Y. Kobayashi T. Hayashi A. Sakamoto K. Yokoyama S. Protein photo-cross-linking in mammalian cells by site-specific incorporation of a photoreactive amino acid.Nat. Methods. 2005; 2: 201-206Crossref PubMed Scopus (195) Google Scholar, 28Kleiner P. Heydenreuter W. Stahl M. Korotkov V.S. Sieber S.A. A whole proteome inventory of background photocrosslinker binding.Angew. Chem. Int. Ed. 2017; 56: 1396-1401Crossref PubMed Scopus (69) Google Scholar dramatically making it complicated to map accurate interaction interfaces and leading to false positive interacting partners.28Kleiner P. Heydenreuter W. Stahl M. Korotkov V.S. Sieber S.A. A whole proteome inventory of background photocrosslinker binding.Angew. Chem. Int. Ed. 2017; 56: 1396-1401Crossref PubMed Scopus (69) Google Scholar In addition, the reactive intermediates also can be readily quenched or convert to the less reactive species, resulting in short half-life (nanosecond to microsecond range),14Nguyen T.A. Cigler M. Lang K. Expanding the genetic code to study protein-protein interactions.Angew. Chem. Int. Ed. 2018; 57: 14350-14361Crossref PubMed Scopus (60) Google Scholar which will lead to lower crosslinking efficiency and increase the incidence of nonspecific crosslinking of interacting proteins. Clearly, residue-selective photo-crosslinkers are highly in demand, which could confer greater decoding precision of protein-protein interactions and facilitate structural studies of protein complexes.26Yang Y. Song H. He D. Zhang S. Dai S. Lin S. Meng R. Wang C. Chen P.R. Genetically encoded protein photocrosslinker with a transferable mass spectrometry-identifiable label.Nat. Commun. 2016; 7: 12299Crossref PubMed Scopus (58) Google Scholar, 29Yang B. Wu H. Schnier P.D. Liu Y. Liu J. Wang N. DeGrado W.F. Wang L. Proximity-enhanced SuFEx chemical cross-linker for specific and multitargeting cross-linking mass spectrometry.Proc. Natl. Acad. Sci. USA. 2018; 115: 11162-11167Crossref PubMed Scopus (42) Google Scholar Recently, tetrazole derived unnatural amino acid (Uaa) was genetically encoded as an excellent photo-crosslinker (Figure 1B) (mPyTK) to achieve site-selective protein photo-crosslinking in living cells.30Tian Y. Jacinto M.P. Zeng Y. Yu Z. Qu J. Liu W.R. Lin Q. Genetically encoded 2-aryl-5-carboxytetrazoles for site-selective protein photo-cross-linking.J. Am. Chem. Soc. 2017; 139: 6078-6081Crossref PubMed Scopus (47) Google Scholar, 31Tian Y.L. Lin Q. Genetic encoding of 2-aryl-5-carboxytetrazole-based protein photo-cross-linkers.Chem. Commun. (Camb.). 2018; 54: 4449-4452Crossref PubMed Google Scholar The tetrazoles are previously designed for photoclick reaction with alkenes,32Lim R.K. Lin Q. Photoinducible bioorthogonal chemistry: a spatiotemporally controllable tool to visualize and perturb proteins in live cells.Acc. Chem. Res. 2011; 44: 828-839Crossref PubMed Scopus (181) Google Scholar while the nitrile imine intermediates photogenerated from tetrazoles could react with several nucleophilic groups (e.g., thiol groups,33Feng W. Li L. Yang C. Welle A. Trapp O. Levkin P.A. UV-induced tetrazole-thiol reaction for polymer conjugation and surface functionalization.Angew. Chem. Int. Ed. 2015; 54: 8732-8735Crossref PubMed Scopus (51) Google Scholar, 34An P. Lewandowski T.M. Erbay T.G. Liu P. Lin Q. Sterically shielded, stabilized nitrile imine for rapid bioorthogonal protein labeling in live cells.J. Am. Chem. Soc. 2018; 140: 4860-4868Crossref PubMed Scopus (69) Google Scholar carboxylic acids,35Li Z. Qian L. Li L. Bernhammer J.C. Huynh H.V. Lee J.S. Yao S.Q. Tetrazole photoclick chemistry: reinvestigating its suitability as a bioorthogonal reaction and potential applications.Angew. Chem. Int. Ed. 2016; 55: 2002-2006Crossref PubMed Scopus (132) Google Scholar, 36Zhao S. Dai J. Hu M. Liu C. Meng R. Liu X. Wang C. Luo T. Photo-induced coupling reactions of tetrazoles with carboxylic acids in aqueous solution: application in protein labelling.Chem. Commun. (Camb.). 2016; 52: 4702-4705Crossref PubMed Google Scholar tryptophan,37Siti W. Khan A.K. de Hoog H.P. Liedberg B. Nallani M. Photo-induced conjugation of tetrazoles to modified and native proteins.Org. Biomol. Chem. 2015; 13: 3202-3206Crossref PubMed Google Scholar amine,35Li Z. Qian L. Li L. Bernhammer J.C. Huynh H.V. Lee J.S. Yao S.Q. Tetrazole photoclick chemistry: reinvestigating its suitability as a bioorthogonal reaction and potential applications.Angew. Chem. Int. Ed. 2016; 55: 2002-2006Crossref PubMed Scopus (132) Google Scholar and histidine38Zhang Y. Liu W. Zhao Z.K. Nucleophilic trapping nitrilimine generated by photolysis of diaryltetrazole in aqueous phase.Molecules. 2013; 19: 306-315Crossref PubMed Scopus (28) Google Scholar ) to give ligation products. Indeed, it is a creative strategy to develop Uaa from tetrazole to capture transient protein-protein interactions in living cells, however, photogenerated intermediate potentially reacts with above mentioned nucleophiles in addition to carboxylic acid residues, providing linkages with unpredictable selectivity in complex biological environments (Figure 1B).34An P. Lewandowski T.M. Erbay T.G. Liu P. Lin Q. Sterically shielded, stabilized nitrile imine for rapid bioorthogonal protein labeling in live cells.J. Am. Chem. Soc. 2018; 140: 4860-4868Crossref PubMed Scopus (69) Google Scholar, 35Li Z. Qian L. Li L. Bernhammer J.C. Huynh H.V. Lee J.S. Yao S.Q. Tetrazole photoclick chemistry: reinvestigating its suitability as a bioorthogonal reaction and potential applications.Angew. Chem. Int. Ed. 2016; 55: 2002-2006Crossref PubMed Scopus (132) Google Scholar Therefore, methods to access photo-crosslinking capable of temporal resolution39Yang B. Tang S. Ma C. Li S.T. Shao G.C. Dang B. DeGrado W.F. Dong M.Q. Wang P.G. Ding S. et al.Spontaneous and specific chemical cross-linking in live cells to capture and identify protein interactions.Nat. Commun. 2017; 8: 2240Crossref PubMed Scopus (55) Google Scholar, 40Xiang Z. Ren H. Hu Y.S. Coin I. Wei J. Cang H. Wang L. Adding an unnatural covalent bond to proteins through proximity-enhanced bioreactivity.Nat. Methods. 2013; 10: 885-888Crossref PubMed Scopus (100) Google Scholar, 41Chen X.H. Xiang Z. Hu Y.S. Lacey V.K. Cang H. Wang L. Genetically encoding an electrophilic amino acid for protein stapling and covalent binding to native receptors.ACS Chem. Biol. 2014; 9: 1956-1961Crossref PubMed Scopus (68) Google Scholar and residue selectivity for capturing and identifying transient and weak protein-protein interactions in living cells still remain highly challenging and desirable. Herein, we have developed a genetically encoded, lysine-selective protein photo-crosslinker, which is capable of covalently crosslinking interacting protein partners with proximal lysine upon UV light activation in vitro and in living cells (Figure 1C). Using this photo-crosslinker (o-nitrobenzyl alcohol derived lysine, o-NBAK), we demonstrated a crosslinking approach enabling capture of protein-protein interactions in a temporally controlled manner with residue selectivity, producing lysine-selective crosslinking sites, and predictable crosslinked peptides. In addition, this photo-crosslinker enables capture of enzyme-substrate interaction with directly interacting lysine and validation of acetylation site of the substrate. This method represents specificity, accuracy, and reliability for investigation of protein-protein interactions in vitro as well as in living E.coli cells. o-Nitrobenzyl alcohol (o-NBA) derivatives generate corresponding aryl-nitroso compounds via photoisomerization, upon UV light activation (Figure 2A).42Klán P. Šolomek T. Bochet C.G. Blanc A. Givens R. Rubina M. Popik V. Kostikov A. Wirz J. Photoremovable protecting groups in chemistry and biology: reaction mechanisms and efficacy.Chem. Rev. 2013; 113: 119-191Crossref PubMed Scopus (1138) Google Scholar, 43Zhao H. Sterner E.S. Coughlin E.B. Theato P. o-nitrobenzyl alcohol derivatives: opportunities in polymer and materials science.Macromolecules. 2012; 45: 1723-1736Crossref Scopus (426) Google Scholar These intermediates have been successfully employed for condensation with amines to form stable indazolones (Figure 2B).44Kurth M.J. Olmstead M.M. Haddadin M.J. Claimed 2,1-benzisoxazoles are indazalones.J. Org. Chem. 2005; 70: 1060-1062Crossref PubMed Scopus (38) Google Scholar Inspired by these examples as well as the mechanism of photochemical reaction of o-NBA derivatives,42Klán P. Šolomek T. Bochet C.G. Blanc A. Givens R. Rubina M. Popik V. Kostikov A. Wirz J. Photoremovable protecting groups in chemistry and biology: reaction mechanisms and efficacy.Chem. Rev. 2013; 113: 119-191Crossref PubMed Scopus (1138) Google Scholar, 43Zhao H. Sterner E.S. Coughlin E.B. Theato P. o-nitrobenzyl alcohol derivatives: opportunities in polymer and materials science.Macromolecules. 2012; 45: 1723-1736Crossref Scopus (426) Google Scholar we envisioned that the reactive intermediate photogenerated from o-NBAK would smoothly react with proximal lysine in buffer, providing stable indazolone product upon UV light activation (Figure 2C), thus, the genetically encoded o-NBAK could be applied as a novel lysine-selective photo-crosslinker for capturing and identifying protein-protein interactions in living cells (Figures 1C and 2C). To evaluate this hypothesis, we began with o-NBAK (Figure 1C; Supplemental Experimental Procedures) to investigate the potential cross-reactivity with a variety of biological functional groups under UV light activation in aqueous media (Figures S1–S4; Table S1). To our delight, we found that o-NBAK reacted rapidly with lysine after 10 min of UV light activation, providing stable indazolone as ligation product with high yield (>70%, detected by HPLC), within 1 h incubation at 37°C, in buffer conditions (Figures S1 and S2). In contrast, no detectable stable ligation products were found from the reactions of aryl-nitroso compound and other natural amino acids with different sidechains (Table S1; Figure S3).45Lo Conte M.L. Carroll K.S. Chemoselective ligation of sulfinic acids with aryl-nitroso compounds.Angew. Chem. Int. Ed. 2012; 51: 6502-6505Crossref PubMed Scopus (53) Google Scholar The photogenerated aryl-nitroso intermediate could be detected on UPLC-MS analysis process (Figure S2) and half-life is more than 30 min in buffer conditions, which is more stable than other photogenerated reactive species from Uaas.14Nguyen T.A. Cigler M. Lang K. Expanding the genetic code to study protein-protein interactions.Angew. Chem. Int. Ed. 2018; 57: 14350-14361Crossref PubMed Scopus (60) Google Scholar, 34An P. Lewandowski T.M. Erbay T.G. Liu P. Lin Q. Sterically shielded, stabilized nitrile imine for rapid bioorthogonal protein labeling in live cells.J. Am. Chem. Soc. 2018; 140: 4860-4868Crossref PubMed Scopus (69) Google Scholar These results indicated that o-NBAK efficiently and selectively reacted with lysine upon UV light activation, providing ligation product, which could serve as stable covalent linkage for lysine-selective photo-crosslinking. The genetic incorporation of Uaas with novel structures at specific sites of proteins offers a powerful tool to explore and manipulate protein structure and function both in vitro and in vivo.46Liu C.C. Schultz P.G. Adding new chemistries to the genetic code.Annu. Rev. Biochem. 2010; 79: 413-444Crossref PubMed Scopus (1278) Google Scholar Recently, pyrrolysyl-tRNA synthetase (PylRS) and its mutated variants have shown considerable versatility for incorporating a large number of lysine derivatives into proteins in living cells.47Wan W. Tharp J.M. Liu W.R. Pyrrolysyl-tRNA synthetase: an ordinary enzyme but an outstanding genetic code expansion tool.Biochim. Biophys. Acta. 2014; 1844: 1059-1070Crossref PubMed Scopus (255) Google Scholar In this context, we focused on tRNApylCUA/MmPylRS pair for genetic incorporation of o-NBAK as photo-crosslinker into proteins. To site-specifically incorporate the o-NBAK into proteins, the PylRS mutant should efficiently charge o-NBAK. Based on the crystal structure of Methanosarcina mazei (Mm) PylRS in complex with Pyl-AMP,48Kavran J.M. Gundllapalli S. O'Donoghue P. Englert M. Söll D. Steitz T.A. Structure of pyrrolysyl-tRNA synthetase, an archaeal enzyme for genetic code innovation.Proc. Natl. Acad. Sci. USA. 2007; 104: 11268-11273Crossref PubMed Scopus (156) Google Scholar docking models (Figure 3A) suggested that the active site of PylRS mutant (Y306A/Y384F) is capable of accommodating o-NBAK, implying that the orthogonal tRNApylCUA/MmPylRS (Y306A/Y384F) is able to recognize o-NBAK.49Yanagisawa T. Hino N. Iraha F. Mukai T. Sakamoto K. Yokoyama S. Wide-range protein photo-crosslinking achieved by a genetically encoded N(epsilon)-(benzyloxycarbonyl)lysine derivative with a diazirinyl moiety.Mol. Biosyst. 2012; 8: 1131-1135Crossref PubMed Scopus (40) Google Scholar Therefore, we examined the incorporation efficiency and specificity of o-NBAK into Schistosoma japonicum glutathione-S-transferase (GST) protein in E. coli. Guided by structural analysis of GST dimer (PDB ID: 1Y6E), we introduced a TAG codon at position 97 and a C-terminal His tag into GST sequence (GST-97TAG). A plasmid of GST mutant co-expressed with another plasmid of tRNApylCUA/MmPylRS (Y306A/Y384F) in E. coli. In absence of o-NBAK, no full-length GST protein was detected (Figure 3B), when 1 mM o-NBAK was added in growth media, full-length GST was obtained in good yield (ca. 4.0 mg L−1, Figure S14). The purified GST-97o-NBAK was analyzed by electrospray ionization time-of-flight MS (ESI-TOF MS) (Figure 3C). The masses indicate that only o-NBAK was incorporated in GST and no peaks corresponding to other amino acids were observed. Furthermore, GST-97o-NBAK protein was digested with trypsin and analyzed by tandem MS (Figure 3D), a series of y and b ions clearly indicate that o-NBAK was incorporated at position 97, specified by the TAG codon. These results demonstrate that mutated MmPylRS (Y306A/Y384F) can efficiently recognize o-NBAK (Figure S14) and only o-NBAK was incorporated at TAG-encoded position in GST, and that o-NBAK was stable during protein synthesis in E. coli and protein purification process. As proof of principle, we chose to crosslink GST homodimer as o-NBAK enabling photo-crosslinking with proximal lysine inter-molecularly (Figure 4A). Based on GST dimer crystal structure, A97 located at a distance of ∼15 Å from a lysine residue (Lys44) from the opposite monomer, which indicated that o-NBAK at position 97 would be able to generate covalent linkage with Lys44 between GST dimer.29Yang B. Wu H. Schnier P.D. Liu Y. Liu J. Wang N. DeGrado W.F. Wang L. Proximity-enhanced SuFEx chemical cross-linker for specific and multitargeting cross-linking mass spectrometry.Proc. Natl. Acad. Sci. USA. 2018; 115: 11162-11167Crossref PubMed Scopus (42) Google Scholar, 30Tian Y. Jacinto M.P. Zeng Y. Yu Z. Qu J. Liu W.R. Lin Q. Genetically encoded 2-aryl-5-carboxytetrazoles for site-selective protein photo-

Diversified Application of Barcoded PLATO (PLATO-BC) Platform for Identification of Protein Interactions

Proteins usually associate with other molecules physically to execute their functions. Identifying these interactions is important for the functional analysis of proteins. Previously, we reported the parallel analysis of translated ORFs (PLATO) to couple ribosome display of full-length ORFs with affinity enrichment of mRNA/protein/ribosome complexes for the “bait” molecules, followed by the deep sequencing analysis of mRNA. However, the sample processing, from extraction of precipitated mRNA to generation of DNA libraries, includes numerous steps, which is tedious and may cause the loss of materials. Barcoded PLATO (PLATO-BC), an improved platform was further developed to test its application for protein interaction discovery. In this report, we tested the antisera-antigen interaction using serum samples from patients with inclusion body myositis (IBM). Tripartite motif containing 21 (TRIM21) was identified as a potentially new IBM autoantigen. We also expanded the application of PLATO-BC to identify protein interactions for JQ1, single ubiquitin peptide, and NS5 protein of Zika virus. From PLATO-BC analyses, we identified new protein interactions for these “bait” molecules. We demonstrate that Ewing sarcoma breakpoint region 1 (EWSR1) binds to JQ1 and their interactions may interrupt the EWSR1 association with acetylated histone H4. RIO kinase 3 (RIOK3), a newly identified ubiquitin-binding protein, is preferentially associated with K63-ubiquitin chain. We also find that Zika NS5 protein interacts with two previously unreported host proteins, par-3 family cell polarity regulator (PARD3) and chromosome 19 open reading frame 53 (C19orf53), whose attenuated expression benefits the replication of Zika virus. These results further demonstrate that PLATO-BC is capable of identifying novel protein interactions for various types of “bait” molecules.

Towards improving proximity labeling by the biotin ligase BirA.

The discovery and validation of protein-protein interactions provides a knowledge base that is critical for defining protein networks and how they underpin the biology of the cell. Identification of protein interactions that are highly transient, or sensitive to biochemical disruption, can be very difficult. This challenge has been met by proximity labeling methods which generate reactive species that chemically modify neighboring proteins. The most widely used proximity labeling method is BioID, which features a mutant biotin ligase BirA(Arg118Gly), termed BirA*, fused to a protein of interest. Here, we explore how amino acid substitutions at Arg118 affect the biochemical properties of BirA. We found that relative to wild-type BirA, the Arg118Lys substitution both slightly reduced biotin affinity and increased the release of reactive biotinyl-5'-AMP. BioID using a BirA(Arg118Lys)-Lamin A fusion enabled identification of PCNA as a lamina-proximal protein in HEK293T cells, a finding that was validated by immunofluorescence microscopy. Our data expand on the concept that proximity labeling by BirA fused to proteins of interest can be modulated by amino acid substitutions that affect biotin affinity and the release of biotinyl-5'-AMP.

Natural language processing in text mining for structural modeling of protein complexes

BackgroundStructural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking. Our text-mining (TM) tool, which extracts binding site residues from the PubMed abstracts, was successfully applied to protein docking (Badal et al., PLoS Comput Biol, 2015; 11: e1004630). Still, many extracted residues were not relevant to the docking.ResultsWe present an extension of the TM tool, which utilizes natural language processing (NLP) for analyzing the context of the residue occurrence. The procedure was tested using generic and specialized dictionaries. The results showed that the keyword dictionaries designed for identification of protein interactions are not adequate for the TM prediction of the binding mode. However, our dictionary designed to distinguish keywords relevant to the protein binding sites led to considerable improvement in the TM performance. We investigated the utility of several methods of context analysis, based on dissection of the sentence parse trees. The machine learning-based NLP filtered the pool of the mined residues significantly more efficiently than the rule-based NLP. Constraints generated by NLP were tested in docking of unbound proteins from the DOCKGROUND X-ray benchmark set 4. The output of the global low-resolution docking scan was post-processed, separately, by constraints from the basic TM, constraints re-ranked by NLP, and the reference constraints. The quality of a match was assessed by the interface root-mean-square deviation. The results showed significant improvement of the docking output when using the constraints generated by the advanced TM with NLP.ConclusionsThe basic TM procedure for extracting protein-protein binding site residues from the PubMed abstracts was significantly advanced by the deep parsing (NLP techniques for contextual analysis) in purging of the initial pool of the extracted residues. Benchmarking showed a substantial increase of the docking success rate based on the constraints generated by the advanced TM with NLP.

The Protein Interactome of Streptococcus pneumoniae and Bacterial Meta-interactomes Improve Function Predictions.

The functions of roughly a third of all proteins in Streptococcus pneumoniae, a significant human-pathogenic bacterium, are unknown. Using a yeast two-hybrid approach, we have determined more than 2,000 novel protein interactions in this organism. We augmented this network with meta-interactome data that we defined as the pool of all interactions between evolutionarily conserved proteins in other bacteria. We found that such interactions significantly improved our ability to predict a protein's function, allowing us to provide functional predictions for 299 S.pneumoniae proteins with previously unknown functions. IMPORTANCE Identification of protein interactions in bacterial species can help define the individual roles that proteins play in cellular pathways and pathogenesis. Very few protein interactions have been identified for the important human pathogen S.pneumoniae. We used an experimental approach to identify over 2,000 new protein interactions for S.pneumoniae, the most extensive interactome data for this bacterium to date. To predict protein function, we used our interactome data augmented with interactions from other closely related bacteria. The combination of the experimental data and meta-interactome data significantly improved the prediction results, allowing us to assign possible functions to a large number of poorly characterized proteins.

Protease-Inhibitor Interaction Predictions: Lessons on the Complexity of Protein–Protein Interactions

Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data, including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features, thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of nonproteolytic and noninhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task and thereby highlight limitations of computational interaction prediction methods.