Systematic identification of structure-specific protein-protein interactions.

The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis-mass spectrometry (LiP-MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP-MS detects well-characterized PPIs, including antibody-target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson's disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states.

ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

Link prediction in protein-protein interaction network: A similarity multiplied similarity algorithm with paths of length three.

Protein-protein interactions (PPIs) are crucial for various biological processes, and predicting PPIs is a major challenge. To solve this issue, the most common method is link prediction. Currently, the link prediction methods based on network Paths of Length Three (L3) have been proven to be highly effective. In this paper, we propose a novel link prediction algorithm, named SMS, which is based on L3 and protein similarities. We first design a mixed similarity that combines the topological structure and attribute features of nodes. Then, we compute the predicted value by summing the product of all similarities along the L3. Furthermore, we propose the Max Similarity Multiplied Similarity (maxSMS) algorithm from the perspective of maximum impact. Our computational prediction results show that on six datasets, including S. cerevisiae, H. sapiens, and others, the maxSMS algorithm improves the precision of the top 500, area under the precision-recall curve, and normalized discounted cumulative gain by an average of 26.99%, 53.67%, and 6.7%, respectively, compared to other optimal methods.

MR2CPPIS: Accurate prediction of protein-protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism.

Proteins play a vital role in various biological processes and achieve their functions through protein-protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://github.com/YyinGong/MR2CPPIS.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

A Semiquantitative Protein-Fragment Complementation Assay to Study Protein-Protein Interactions of the Polymerase Complex in Cellula.

Protein-fragment complementation assays (PCAs) are powerful tools to investigate protein-protein interactions in a cellular context. These are especially useful to study unstable proteins and weak interactions that may not resist protein isolation or purification. The PCA based on the reconstitution of the Gaussia princeps luciferase (split-luc) is a sensitive approach allowing the mapping of protein-protein interactions and the semiquantitative measurement of binding affinity. Here, we describe the split-luc protocol we used to map the viral interactome of measles virus polymerase complex.

Applying Reverse Genetics to Study Measles Virus Interactions with the Host.

The study of virus-host interactions is essential to achieve a comprehensive understanding of the viral replication process. The commonly used methods are yeast two-hybrid approach and transient expression of a single tagged viral protein in host cells followed by affinity purification of interacting cellular proteins and mass spectrometry analysis (AP-MS). However, by these approaches, virus-host protein-protein interactions are detected in the absence of a real infection, not always correctly compartmentalized, and for the yeast two-hybrid approach performed in a heterologous system. Thus, some of the detected protein-protein interactions may be artificial. Here we describe a new strategy based on recombinant viruses expressing tagged viral proteins to capture both direct and indirect protein partners during the infection (AP-MS in viral context). This way, virus-host protein-protein interacting co-complexes can be purified directly from infected cells for further characterization.

Protein features fusion using attributed network embedding for predicting protein-protein interaction.

BACKGROUND: Protein-protein interactions (PPIs) hold significant importance in biology, with precise PPI prediction as a pivotal factor in comprehending cellular processes and facilitating drug design. However, experimental determination of PPIs is laborious, time-consuming, and often constrained by technical limitations. METHODS: We introduce a new node representation method based on initial information fusion, called FFANE, which amalgamates PPI networks and protein sequence data to enhance the precision of PPIs' prediction. A Gaussian kernel similarity matrix is initially established by leveraging protein structural resemblances. Concurrently, protein sequence similarities are gauged using the Levenshtein distance, enabling the capture of diverse protein attributes. Subsequently, to construct an initial information matrix, these two feature matrices are merged by employing weighted fusion to achieve an organic amalgamation of structural and sequence details. To gain a more profound understanding of the amalgamated features, a Stacked Autoencoder (SAE) is employed for encoding learning, thereby yielding more representative feature representations. Ultimately, classification models are trained to predict PPIs by using the well-learned fusion feature. RESULTS: When employing 5-fold cross-validation experiments on SVM, our proposed method achieved average accuracies of 94.28%, 97.69%, and 84.05% in terms of Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori datasets, respectively. CONCLUSION: Experimental findings across various authentic datasets validate the efficacy and superiority of this fusion feature representation approach, underscoring its potential value in bioinformatics.

Hypothesis: evidence that the PRS gene products of Saccharomyces cerevisiae support both PRPP synthesis and maintenance of cell wall integrity.

The gene products of PRS1-PRS5 in Saccharomyces cerevisiae are responsible for the production of PRPP (5-phospho-D-ribosyl-α-1-pyrophosphate). However, it has been demonstrated that they are also involved in the cell wall integrity (CWI) signalling pathway as shown by protein-protein interactions (PPIs) with, for example Slt2, the MAP kinase of the CWI pathway. The following databases: SGD, BioGRID and Hit Predict, which collate PPIs from various research papers, have been scrutinized for evidence of PPIs between Prs1-Prs5 and components of the CWI pathway. The level of certainty in PPIs was verified by interaction scores available in the Hit Predict database revealing that well-documented interactions correspond with higher interaction scores and can be graded as high confidence interactions based on a score > 0.28, an annotation score ≥ 0.5 and a method-based high confidence score level of ≥ 0.485. Each of the Prs1-Prs5 polypeptides shows some degree of interaction with the CWI pathway. However, Prs5 has a vital role in the expression of FKS2 and Rlm1, previously only documented by reporter assay studies. This report emphasizes the importance of investigating interactions using more than one approach since every method has its limitations and the use of different methods, as described herein, provides complementary experimental and statistical data, thereby corroborating PPIs. Since the experimental data described so far are consistent with a link between PRPP synthetase and the CWI pathway, our aim was to demonstrate that these data are also supported by high-throughput bioinformatic analyses promoting our hypothesis that two of the five PRS-encoding genes contain information required for the maintenance of CWI by combining data from our targeted approach with relevant, unbiased data from high-throughput analyses.

Proximity Ligation Assay to Detect the Proximity Between Host Proteins and Viral Proteins of HIV-1.

The study of host-pathogen interaction often requires interrogating the protein-protein interactions and examining post-translational modifications of the proteins. Traditional protein detection strategies are limited in their sensitivity, specificity, and multiplexing capabilities. The Proximity Ligation Assay (PLA), a versatile and powerful molecular technique, can overcome these limitations. PLA blends the specificity of antibodies, two antibodies detecting two different epitopes on the same or two different proteins, with the amplification efficiency of a polymerase to allow highly specific and sensitive detection of low-abundant proteins, protein-protein interactions, or protein modifications. In this protocol, we describe the application of PLA to detect the proximity between HIV-1 Tat with one of its cellular partners, p65, in an infected host cell. The protocol could be applied to any other context with slight modifications. Of note, PLA can only confirm the physical proximity between two epitopes or proteins; however, the proximity need not necessarily allude to the functional interaction between the two proteins.

ReLo is a simple and rapid colocalization assay to identify and characterize direct protein-protein interactions.

The characterization of protein-protein interactions (PPIs) is fundamental to the understanding of biochemical processes. Many methods have been established to identify and study direct PPIs; however, screening and investigating PPIs involving large or poorly soluble proteins remains challenging. Here, we introduce ReLo, a simple, rapid, and versatile cell culture-based method for detecting and investigating interactions in a cellular context. Our experiments demonstrate that ReLo specifically detects direct binary PPIs. Furthermore, we show that ReLo bridging experiments can also be used to determine the binding topology of subunits within multiprotein complexes. In addition, ReLo facilitates the identification of protein domains that mediate complex formation, allows screening for interfering point mutations, and it is sensitive to drugs that mediate or disrupt an interaction. In summary, ReLo is a simple and rapid alternative for the study of PPIs, especially when studying structurally complex proteins or when established methods fail.

Protein-Protein Interactions Visualized by Bimolecular Fluorescence Complementation in Arabidopsis thaliana Protoplasts from Leaf.

Bimolecular fluorescence complementation (BiFC) is a powerful tool for studying protein-protein interactions in living cells. By fusing interacting proteins to fluorescent protein fragments, BiFC allows visualization of spatial localization patterns of protein complexes. This method has been adapted to a variety of expression systems in different organisms and is widely used to study protein interactions in plant cells. The Agrobacterium-mediated transient expression protocol for BiFC assays in Nicotiana benthamiana (N. benthamiana) leaf cells is widely used, but in this chapter, a method for BiFC assay using Arabidopsis thaliana protoplasts is presented.

Analysis of Protein Interactions in Patient-Derived Xenografts Using Immunoprecipitation.

Proteins are large, complex molecules that regulate multiple functions within the cell. The protein rarely functions as a single molecule, but rather interacts with one or more other proteins forming a dynamic network. Protein-protein interactions are critical for regulating the cell's response toward various stimuli from outside and inside the cell. Identification of protein-protein interactions enhanced our understanding of various biological processes in the living cell. Immunoprecipitation (IP) has been one of the standard and most commonly used biochemical methods to identify and confirm protein-protein interactions. IP uses a target protein-specific antibody conjugated with protein A/G affinity beads to identify molecules interacting with the target protein. Here, we describe the principle, procedure and challenges of the IP assay.

Denaturing mass photometry for rapid optimization of chemical protein-protein cross-linking reactions.

Chemical cross-linking reactions (XL) are an important strategy for studying protein-protein interactions (PPIs), including low abundant sub-complexes, in structural biology. However, choosing XL reagents and conditions is laborious and mostly limited to analysis of protein assemblies that can be resolved using SDS-PAGE. To overcome these limitations, we develop here a denaturing mass photometry (dMP) method for fast, reliable and user-friendly optimization and monitoring of chemical XL reactions. The dMP is a robust 2-step protocol that ensures 95% of irreversible denaturation within only 5 min. We show that dMP provides accurate mass identification across a broad mass range (30 kDa-5 MDa) along with direct label-free relative quantification of all coexisting XL species (sub-complexes and aggregates). We compare dMP with SDS-PAGE and observe that, unlike the benchmark, dMP is time-efficient (3 min/triplicate), requires significantly less material (20-100×) and affords single molecule sensitivity. To illustrate its utility for routine structural biology applications, we show that dMP affords screening of 20 XL conditions in 1 h, accurately identifying and quantifying all coexisting species. Taken together, we anticipate that dMP will have an impact on ability to structurally characterize more PPIs and macromolecular assemblies, expected final complexes but also sub-complexes that form en route.

Multi-level bioinformatics resources support drug target discovery of protein-protein interactions.

Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.

Cold Spot SCANNER: Colab Notebook for predicting cold spots in protein-protein interfaces.

BACKGROUND: Protein-protein interactions (PPIs) are conveyed through binding interfaces or surface patches on proteins that become buried upon binding. Structural and biophysical analysis of many protein-protein interfaces revealed certain unique features of these surfaces that determine the energetics of interactions and play a critical role in protein evolution. One of the significant aspects of binding interfaces is the presence of binding hot spots, where mutations are highly deleterious for binding. Conversely, binding cold spots are positions occupied by suboptimal amino acids and several mutations in such positions could lead to affinity enhancement. While there are many software programs for identification of hot spot positions, there is currently a lack of software for cold spot detection. RESULTS: In this paper, we present Cold Spot SCANNER, a Colab Notebook, which scans a PPI binding interface and identifies cold spots resulting from cavities, unfavorable charge-charge, and unfavorable charge-hydrophobic interactions. The software offers a Py3DMOL-based interface that allows users to visualize cold spots in the context of the protein structure and generates a zip file containing the results for easy download. CONCLUSIONS: Cold spot identification is of great importance to protein engineering studies and provides a useful insight into protein evolution. Cold Spot SCANNER is open to all users without login requirements and can be accessible at: https://colab. RESEARCH: google.com/github/sagagugit/Cold-Spot-Scanner/blob/main/Cold_Spot_Scanner.ipynb .

A Module for Analyzing Interactomes via APEX-MS Integrated into PatternLab for Proteomics.

Proximity labeling techniques, such as APEX-MS, provide valuable insights into proximal interactome mapping; however, the verification of biotinylated peptides is not straightforward. With this as motivation, we present a new module integrated into PatternLab for proteomics to enable APEX-MS data interpretation by targeting diagnostic fragment ions associated with APEX modifications. We reanalyzed a previously published APEX-MS data set and report a significant number of biotinylated peptides and, consequently, a confident set of proximal proteins. As the module is part of the widely adopted PatternLab for proteomics software suite, it offers users a comprehensive, easy, and integrated solution for data analysis. Given the broad utility of the APEX-MS technique in various biological contexts, we anticipate that our module will be a valuable asset to researchers, facilitating and enhancing interactome studies. PatternLab's APEX, including a usage protocol, is available at http://patternlabforproteomics.org/apex.

A protein network refinement method based on module discovery and biological information.

BACKGROUND: The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs. METHODS: Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules. RESULTS: To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

MEG-PPIS: a fast protein-protein interaction site prediction method based on multi-scale graph information and equivariant graph neural network.

MOTIVATION: Protein-protein interaction sites (PPIS) are crucial for deciphering protein action mechanisms and related medical research, which is the key issue in protein action research. Recent studies have shown that graph neural networks have achieved outstanding performance in predicting PPIS. However, these studies often neglect the modeling of information at different scales in the graph and the symmetry of protein molecules within three-dimensional space. RESULTS: In response to this gap, this article proposes the MEG-PPIS approach, a PPIS prediction method based on multi-scale graph information and E(n) equivariant graph neural network (EGNN). There are two channels in MEG-PPIS: the original graph and the subgraph obtained by graph pooling. The model can iteratively update the features of the original graph and subgraph through the weight-sharing EGNN. Subsequently, the max-pooling operation aggregates the updated features of the original graph and subgraph. Ultimately, the model feeds node features into the prediction layer to obtain prediction results. Comparative assessments against other methods on benchmark datasets reveal that MEG-PPIS achieves optimal performance across all evaluation metrics and gets the fastest runtime. Furthermore, specific case studies demonstrate that our method can predict more true positive and true negative sites than the current best method, proving that our model achieves better performance in the PPIS prediction task. AVAILABILITY AND IMPLEMENTATION: The data and code are available at https://github.com/dhz234/MEG-PPIS.git.

Mapping the global interactome of the ARF family reveals spatial organization in cellular signaling pathways.

The ADP-ribosylation factors (ARFs) and ARF-like (ARL) GTPases serve as essential molecular switches governing a wide array of cellular processes. In this study, we used proximity-dependent biotin identification (BioID) to comprehensively map the interactome of 28 out of 29 ARF and ARL proteins in two cellular models. Through this approach, we identified ∼3000 high-confidence proximal interactors, enabling us to assign subcellular localizations to the family members. Notably, we uncovered previously undefined localizations for ARL4D and ARL10. Clustering analyses further exposed the distinctiveness of the interactors identified with these two GTPases. We also reveal that the expression of the understudied member ARL14 is confined to the stomach and intestines. We identified phospholipase D1 (PLD1) and the ESCPE-1 complex, more precisely, SNX1, as proximity interactors. Functional assays demonstrated that ARL14 can activate PLD1 in cellulo and is involved in cargo trafficking via the ESCPE-1 complex. Overall, the BioID data generated in this study provide a valuable resource for dissecting the complexities of ARF and ARL spatial organization and signaling.