IDR-targeting compounds suppress HPV genome replication via disruption of phospho-BRD4 association with DNA damage response factors.

Compounds binding to the bromodomains of bromodomain and extra-terminal (BET) family proteins, particularly BRD4, are promising anticancer agents. Nevertheless, side effects and drug resistance pose significant obstacles in BET-based therapeutics development. Using high-throughput screening of a 200,000-compound library, we identified small molecules targeting a phosphorylated intrinsically disordered region (IDR) of BRD4 that inhibit phospho-BRD4 (pBRD4)-dependent human papillomavirus (HPV) genome replication in HPV-containing keratinocytes. Proteomic profiling identified two DNA damage response factors-53BP1 and BARD1-crucial for differentiation-associated HPV genome amplification. pBRD4-mediated recruitment of 53BP1 and BARD1 to the HPV origin of replication occurs in a spatiotemporal and BRD4 long (BRD4-L) and short (BRD4-S) isoform-specific manner. This recruitment is disrupted by phospho-IDR-targeting compounds with little perturbation of the global transcriptome and BRD4 chromatin landscape. The discovery of these protein-protein interaction inhibitors (PPIi) not only demonstrates the feasibility of developing PPIi against phospho-IDRs but also uncovers antiviral agents targeting an epigenetic regulator essential for virus-host interaction and cancer development.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions.

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Recent advances in predicting and modeling protein-protein interactions.

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

ATG7(2) Interacts With Metabolic Proteins and Regulates Central Energy Metabolism.

Macroautophagy/autophagy is an essential catabolic process that targets a wide variety of cellular components including proteins, organelles, and pathogens. ATG7, a protein involved in the autophagy process, plays a crucial role in maintaining cellular homeostasis and can contribute to the development of diseases such as cancer. ATG7 initiates autophagy by facilitating the lipidation of the ATG8 proteins in the growing autophagosome membrane. The noncanonical isoform ATG7(2) is unable to perform ATG8 lipidation; however, its cellular regulation and function are unknown. Here, we uncovered a distinct regulation and function of ATG7(2) in contrast with ATG7(1), the canonical isoform. First, affinity-purification mass spectrometry analysis revealed that ATG7(2) establishes direct protein-protein interactions (PPIs) with metabolic proteins, whereas ATG7(1) primarily interacts with autophagy machinery proteins. Furthermore, we identified that ATG7(2) mediates a decrease in metabolic activity, highlighting a novel splice-dependent function of this important autophagy protein. Then, we found a divergent expression pattern of ATG7(1) and ATG7(2) across human tissues. Conclusively, our work uncovers the divergent patterns of expression, protein interactions, and function of ATG7(2) in contrast to ATG7(1). These findings suggest a molecular switch between main catabolic processes through isoform-dependent expression of a key autophagy gene.

CDHu40: a novel marker gene set of neuroendocrine prostate cancer.

Prostate cancer (PCa) is the most prevalent cancer affecting American men. Castration-resistant prostate cancer (CRPC) can emerge during hormone therapy for PCa, manifesting with elevated serum prostate-specific antigen levels, continued disease progression, and/or metastasis to the new sites, resulting in a poor prognosis. A subset of CRPC patients shows a neuroendocrine (NE) phenotype, signifying reduced or no reliance on androgen receptor signaling and a particularly unfavorable prognosis. In this study, we incorporated computational approaches based on both gene expression profiles and protein-protein interaction networks. We identified 500 potential marker genes, which are significantly enriched in cell cycle and neuronal processes. The top 40 candidates, collectively named CDHu40, demonstrated superior performance in distinguishing NE PCa (NEPC) and non-NEPC samples based on gene expression profiles. CDHu40 outperformed most of the other published marker sets, excelling particularly at the prognostic level. Notably, some marker genes in CDHu40, absent in the other marker sets, have been reported to be associated with NEPC in the literature, such as DDC, FOLH1, BEX1, MAST1, and CACNA1A. Importantly, elevated CDHu40 scores derived from our predictive model showed a robust correlation with unfavorable survival outcomes in patients, indicating the potential of the CDHu40 score as a promising indicator for predicting the survival prognosis of those patients with the NE phenotype. Motif enrichment analysis on the top candidates suggests that REST and E2F6 may serve as key regulators in the NEPC progression.

A comprehensive review and comparison of existing computational methods for protein function prediction.

Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.

Design of rigid protein-protein interaction inhibitors enables targeting of undruggable Mcl-1.

The structure-based design of small-molecule inhibitors targeting protein-protein interactions (PPIs) remains a huge challenge as the drug must bind typically wide and shallow protein sites. A PPI target of high interest for hematological cancer therapy is myeloid cell leukemia 1 (Mcl-1), a prosurvival guardian protein from the Bcl-2 family. Despite being previously considered undruggable, seven small-molecule Mcl-1 inhibitors have recently entered clinical trials. Here, we report the crystal structure of the clinical-stage inhibitor AMG-176 bound to Mcl-1 and analyze its interaction along with clinical inhibitors AZD5991 and S64315. Our X-ray data reveal high plasticity of Mcl-1 and a remarkable ligand-induced pocket deepening. Nuclear Magnetic Resonance (NMR)-based free ligand conformer analysis demonstrates that such unprecedented induced fit is uniquely achieved by designing highly rigid inhibitors, preorganized in their bioactive conformation. By elucidating key chemistry design principles, this work provides a roadmap for targeting the largely untapped PPI class more successfully.

Structure function analysis of ADP-dependent cyanobacterial phosphofructokinase reveals new phylogenetic grouping in the PFK-A family.

Dependent on the light conditions, photosynthetic organisms switch between carbohydrate synthesis or breakdown, for which the reversibility of carbohydrate metabolism, including glycolysis, is essential. Kinetic regulation of phosphofructokinase (PFK), a key-control point in glycolysis, was studied in the cyanobacterium Synechocystis sp. PCC 6803. The two PFK iso-enzymes (PFK- A1, PFK-A2), were found to use ADP instead of ATP, and have similar kinetic characteristics, but differ in their allosteric regulation. PFK-A1 is inhibited by 3- phosphoglycerate, a product of the Calvin-Benson-Bassham cycle, while PFK-A2 is inhibited by ATP, which is provided by photosynthesis. This regulation enables cyanobacteria to switch PFK off in light, and on in darkness. ADP dependence has not been reported before for the PFK-A enzyme family, and was thought to be restricted to the PFK-B ribokinase superfamily. Phosphate donor specificity within the PFK-A family could be related to specific binding motifs in ATP-, ADP-, and PPi-dependent PFK-As. Phylogenetic analysis revealed a distribution of ADP-PFK-As in cyanobacteria and in a few alphaproteobacteria, which was confirmed in enzymatic studies.

Protein-protein interaction and site prediction using transfer learning.

The advanced language models have enabled us to recognize protein-protein interactions (PPIs) and interaction sites using protein sequences or structures. Here, we trained the MindSpore ProteinBERT (MP-BERT) model, a Bidirectional Encoder Representation from Transformers, using protein pairs as inputs, making it suitable for identifying PPIs and their respective interaction sites. The pretrained model (MP-BERT) was fine-tuned as MPB-PPI (MP-BERT on PPI) and demonstrated its superiority over the state-of-the-art models on diverse benchmark datasets for predicting PPIs. Moreover, the model's capability to recognize PPIs among various organisms was evaluated on multiple organisms. An amalgamated organism model was designed, exhibiting a high level of generalization across the majority of organisms and attaining an accuracy of 92.65%. The model was also customized to predict interaction site propensity by fine-tuning it with PPI site data as MPB-PPISP. Our method facilitates the prediction of both PPIs and their interaction sites, thereby illustrating the potency of transfer learning in dealing with the protein pair task.

HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction.

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein-Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data.

HN-PPISP: a hybrid network based on MLP-Mixer for protein-protein interaction site prediction.

MOTIVATION: Biological experimental approaches to protein-protein interaction (PPI) site prediction are critical for understanding the mechanisms of biochemical processes but are time-consuming and laborious. With the development of Deep Learning (DL) techniques, the most popular Convolutional Neural Networks (CNN)-based methods have been proposed to address these problems. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in protein sequences. Current methods cannot efficiently explore the nature of Position Specific Scoring Matrix (PSSM), secondary structure and raw protein sequences by processing them all together. For PPI site prediction, how to effectively model the PPI context with attention to prediction remains an open problem. In addition, the long-distance dependencies of PPI features are important, which is very challenging for many CNN-based methods because the innate ability of CNN is difficult to outperform auto-regressive models like Transformers. RESULTS: To effectively mine the properties of PPI features, a novel hybrid neural network named HN-PPISP is proposed, which integrates a Multi-layer Perceptron Mixer (MLP-Mixer) module for local feature extraction and a two-stage multi-branch module for global feature capture. The model merits Transformer, TextCNN and Bi-LSTM as a powerful alternative for PPI site prediction. On the one hand, this is the first application of an advanced Transformer (i.e. MLP-Mixer) with a hybrid network for sequence-based PPI prediction. On the other hand, unlike existing methods that treat global features altogether, the proposed two-stage multi-branch hybrid module firstly assigns different attention scores to the input features and then encodes the feature through different branch modules. In the first stage, different improved attention modules are hybridized to extract features from the raw protein sequences, secondary structure and PSSM, respectively. In the second stage, a multi-branch network is designed to aggregate information from both branches in parallel. The two branches encode the features and extract dependencies through several operations such as TextCNN, Bi-LSTM and different activation functions. Experimental results on real-world public datasets show that our model consistently achieves state-of-the-art performance over seven remarkable baselines. AVAILABILITY: The source code of HN-PPISP model is available at https://github.com/ylxu05/HN-PPISP.

Machine learning on protein-protein interaction prediction: models, challenges and trends.

Protein-protein interactions (PPIs) carry out the cellular processes of all living organisms. Experimental methods for PPI detection suffer from high cost and false-positive rate, hence efficient computational methods are highly desirable for facilitating PPI detection. In recent years, benefiting from the enormous amount of protein data produced by advanced high-throughput technologies, machine learning models have been well developed in the field of PPI prediction. In this paper, we present a comprehensive survey of the recently proposed machine learning-based prediction methods. The machine learning models applied in these methods and details of protein data representation are also outlined. To understand the potential improvements in PPI prediction, we discuss the trend in the development of machine learning-based methods. Finally, we highlight potential directions in PPI prediction, such as the use of computationally predicted protein structures to extend the data source for machine learning models. This review is supposed to serve as a companion for further improvements in this field.

Protein-protein interaction network-based integration of GWAS and functional data for blood pressure regulation analysis.

BACKGROUND: It is valuable to analyze the genome-wide association studies (GWAS) data for a complex disease phenotype in the context of the protein-protein interaction (PPI) network, as the related pathophysiology results from the function of interacting polyprotein pathways. The analysis may include the design and curation of a phenotype-specific GWAS meta-database incorporating genotypic and eQTL data linking to PPI and other biological datasets, and the development of systematic workflows for PPI network-based data integration toward protein and pathway prioritization. Here, we pursued this analysis for blood pressure (BP) regulation. METHODS: The relational scheme of the implemented in Microsoft SQL Server BP-GWAS meta-database enabled the combined storage of: GWAS data and attributes mined from GWAS Catalog and the literature, Ensembl-defined SNP-transcript associations, and GTEx eQTL data. The BP-protein interactome was reconstructed from the PICKLE PPI meta-database, extending the GWAS-deduced network with the shortest paths connecting all GWAS-proteins into one component. The shortest-path intermediates were considered as BP-related. For protein prioritization, we combined a new integrated GWAS-based scoring scheme with two network-based criteria: one considering the protein role in the reconstructed by shortest-path (RbSP) interactome and one novel promoting the common neighbors of GWAS-prioritized proteins. Prioritized proteins were ranked by the number of satisfied criteria. RESULTS: The meta-database includes 6687 variants linked with 1167 BP-associated protein-coding genes. The GWAS-deduced PPI network includes 1065 proteins, with 672 forming a connected component. The RbSP interactome contains 1443 additional, network-deduced proteins and indicated that essentially all BP-GWAS proteins are at most second neighbors. The prioritized BP-protein set was derived from the union of the most BP-significant by any of the GWAS-based or the network-based criteria. It included 335 proteins, with ~ 2/3 deduced from the BP PPI network extension and 126 prioritized by at least two criteria. ESR1 was the only protein satisfying all three criteria, followed in the top-10 by INSR, PTN11, CDK6, CSK, NOS3, SH2B3, ATP2B1, FES and FINC, satisfying two. Pathway analysis of the RbSP interactome revealed numerous bioprocesses, which are indeed functionally supported as BP-associated, extending our understanding about BP regulation. CONCLUSIONS: The implemented workflow could be used for other multifactorial diseases.

An amphiphilic dansyl based multianalyte sensor for the detection of Hg2+, PPi, and TNP: A three-in-one chemical sensor.

A fluorescent dansyl-based amphiphilic probe, 5-(dimethylamino)-N-hexadecylnaphthalene-1-sulfonamide (DLC), was synthesized and characterized to detect multiple analytes at different sensing environments. In acetonitrile, DLC detects nitro explosives such as trinitrophenol (TNP) and 2,4-dinitrophenol (2,4-DNP) by an emission "on-off" response method, and the detection limits (LOD) were estimated to be as low as 4.3 µM and 17.4 µM, respectively. Amphiphilic long chains of the probe were embedded into lipid bilayers to form nanoscale vesicles DLC.Ves. Nanovesicular probe DLC.Ves was found to be highly selective for Hg2+ among other metal ions and for pyrophosphate (PPi) ions among various anions. DLC.Ves could detect Hg2+ with a turn "on-off" emission and PPi with ratiometric change in emission at 525 nm. It is proposed that DLC.Ves could detect Hg2+ via the Hg2+-induced aggregation quenching mechanism and PPi through the Hydrogen bonding. The LODs are estimated as 6.41 µM and 70.9 µM for Hg2+ and PPi, respectively. 1H NMR, SEM, and fluorescence lifetime measurements confirmed the binding mechanism. Thus, it is believed that the simple fluorescent probe DLC could be a prominent sensor to detect multiple analytes depending on the sensing medium.

CREB-Regulated Transcriptional Coactivator 2 Proteome Landscape is Modulated by SREBF1.

cAMP response element-binding protein (CREB) regulated transcriptional coactivator 2 (CRTC2) is a critical transcription factor that maintains glucose homeostasis by activating CREB. Energy homeostasis is maintained through multiple pathways; therefore, CRTC2 may interact with other transcription factors, particularly under metabolic stress. CRTC2 liver-specific KO mice were created, and the global proteome, phosphoproteome, and acetylome from liver tissue under high-fat diet conditions were analyzed using liquid chromatography-tandem mass spectrometry and bioinformatics analysis. Differentially regulated proteins (DRPs) were enriched in metabolic pathways, which were subsequently corroborated through animal experiments. The consensus DRPs from these datasets were used as seed proteins to generate a protein-protein interaction network using STRING, and GeneMANIA identified fatty acid synthase as a mutually relevant protein. In an additional local-protein-protein interaction analysis of CRTC2 and fatty acid synthase with DRPs, sterol regulatory element binding transcription factor 1 (SREBF1) was the common mediator. CRTC2-CREB and SREBF1 are transcription factors, and DNA-binding motif analysis showed that multiple CRTC2-CREB-regulated genes possess SREBF1-binding motifs. This indicates the possible induction by the CRTC2-SREBF1 complex, which is validated through luciferase assay. Therefore, the CRTC2-SREBF1 complex potentially modulates the transcription of multiple proteins that fine-tune cellular metabolism under metabolic stress.

DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning.

BACKGROUND: Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein-protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep learning methods have progressively been implemented for the prediction of PPI sites within proteins, the task of enhancing their predictive performance remains an arduous challenge. RESULTS: In this paper, we propose a novel PPI site prediction model (DGCPPISP) based on a dynamic graph convolutional neural network and a two-stage transfer learning strategy. Initially, we implement the transfer learning from dual perspectives, namely feature input and model training that serve to supply efficacious prior knowledge for our model. Subsequently, we construct a network designed for the second stage of training, which is built on the foundation of dynamic graph convolution. CONCLUSIONS: To evaluate its effectiveness, the performance of the DGCPPISP model is scrutinized using two benchmark datasets. The ensuing results demonstrate that DGCPPISP outshines competing methods in terms of performance. Specifically, DGCPPISP surpasses the second-best method, EGRET, by margins of 5.9%, 10.1%, and 13.3% for F1-measure, AUPRC, and MCC metrics respectively on Dset_186_72_PDB164. Similarly, on Dset_331, it eclipses the performance of the runner-up method, HN-PPISP, by 14.5%, 19.8%, and 29.9% respectively.

Using protein language models for protein interaction hot spot prediction with limited data.

BACKGROUND: Protein language models, inspired by the success of large language models in deciphering human language, have emerged as powerful tools for unraveling the intricate code of life inscribed within protein sequences. They have gained significant attention for their promising applications across various areas, including the sequence-based prediction of secondary and tertiary protein structure, the discovery of new functional protein sequences/folds, and the assessment of mutational impact on protein fitness. However, their utility in learning to predict protein residue properties based on scant datasets, such as protein-protein interaction (PPI)-hotspots whose mutations significantly impair PPIs, remained unclear. Here, we explore the feasibility of using protein language-learned representations as features for machine learning to predict PPI-hotspots using a dataset containing 414 experimentally confirmed PPI-hotspots and 504 PPI-nonhot spots. RESULTS: Our findings showcase the capacity of unsupervised learning with protein language models in capturing critical functional attributes of protein residues derived from the evolutionary information encoded within amino acid sequences. We show that methods relying on protein language models can compete with methods employing sequence and structure-based features to predict PPI-hotspots from the free protein structure. We observed an optimal number of features for model precision, suggesting a balance between information and overfitting. CONCLUSIONS: This study underscores the potential of transformer-based protein language models to extract critical knowledge from sparse datasets, exemplified here by the challenging realm of predicting PPI-hotspots. These models offer a cost-effective and time-efficient alternative to traditional experimental methods for predicting certain residue properties. However, the challenge of explaining why specific features are important for determining certain residue properties remains.

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples.

Protein-protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks.

Cytoplasmic Shotgun Proteomic Points to Key Proteins and Pathways in Temozolomide-Resistant Glioblastoma Multiforme.

Temozolomide (TMZ) is the first line of chemotherapy to treat primary brain tumors of the type glioblastoma multiforme (GBM). TMZ resistance (TMZR) is one of the main barriers to successful treatment and is a principal factor in relapse, resulting in a poor median survival of 15 months. The present paper focuses on proteomic analyses of cytosolic fractions from TMZ-resistant (TMZR) LN-18 cells. The experimental workflow includes an easy, cost-effective, and reproducible method to isolate subcellular fraction of cytosolic (CYTO) proteins, mitochondria, and plasma membrane proteins for proteomic studies. For this study, enriched cytoplasmic fractions were analyzed in replicates by nanoflow liquid chromatography tandem high-resolution mass spectrometry (nLC-MS/MS), and proteins identified were quantified using a label-free approach (LFQ). Statistical analysis of control (CTRL) and temozolomide-resistant (TMZR) proteomes revealed proteins that appear to be differentially controlled in the cytoplasm. The functions of these proteins are discussed as well as their roles in other cancers and TMZ resistance in GBM. Key proteins are also described through biological processes related to gene ontology (GO), molecular functions, and cellular components. For protein-protein interactions (PPI), network and pathway involvement analyses have been performed, highlighting the roles of key proteins in the TMZ resistance phenotypes. This study provides a detailed insight into methods of subcellular fractionation for proteomic analysis of TMZ-resistant GBM cells and the potential to apply this approach to future large-scale studies. Several key proteins, protein-protein interactions (PPI), and pathways have been identified, underlying the TMZ resistance phenotype and highlighting the proteins' biological functions.

Studying protein-protein interactions: Latest and most popular approaches.

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.