Recent advances in predicting and modeling protein-protein interactions

Construction of a proximity labeling vector to identify protein-protein interactions in human stem cells

Identification of protein-protein interactions is essential for understanding protein functions in biological processes. While immunoprecipitation has traditionally been used to isolate proteins and their partners, it faces limitations in capturing transient interactions. Proximity labeling, particularly with the biotin ligase TurboID, addresses this challenge by enabling rapid and efficient identification of interacting proteins in vivo. Human induced pluripotent stem cells are valuable models for studying human development, however certain biological processes, such as differentiation, can be difficult to analyze because conventional transfection methods are challenging. Therefore, an alternative strategy for detection of interacting proteins is necessary. Here, we developed a novel system employing TurboID-fusion proteins within an integrative and inducible expression vector to investigate the interactome during stem cell differentiation. We validated our system by using U2AF2 and GFP as bait proteins, generated two distinct cell lines, and determining the minimum induction time required for optimal protein expression. Our results confirmed that the system did not alter the expected localization of U2AF2. Applying our system, we identified significant differences in the interactome of U2AF2 between the pluripotent and mesodermal differentiation stages, demonstrating that U2AF2 interacts with distinct protein sets following cell fate commitment. Our study successfully unveils a new tool for studying protein-protein interaction in human stem cells.

Computational methods for modeling protein-protein interactions in the AI era: Current status and future directions

The modeling of protein-protein interactions (PPIs) has been revolutionized by artificial intelligence, with deep learning and end-to-end frameworks such as AlphaFold and its derivatives now dominating the field. This review surveys the current computational landscape for predicting protein complex structures, outlining the role of traditional docking approaches as well as focusing on recent advances in AI-driven methods. We discuss key challenges, including protein flexibility, reliance on co-evolutionary signals, modeling of large assemblies, and interactions involving intrinsically disordered regions (IDRs). Recent innovations aimed at improving sampling diversity, integrating experimental data, and enhancing robustness are also highlighted. Although classical methods remain relevant in specific contexts, the continued evolution of AI-based tools offers transformative potential for structural biology. These advances are poised to deepen our understanding of biomolecular interactions and accelerate the design of therapeutic interventions.

Recent progress and future challenges in structure-based protein-protein interaction prediction

Protein-protein interactions (PPIs) play a fundamental role in cellular processes, and understanding these interactions is crucial for advances in both basic biological science and biomedical applications. This review presents an overview of recent progress in computational methods for modeling protein complexes and predicting PPIs based on 3D structures, focusing on the transformative role of artificial intelligence-based approaches. We further discuss the expanding biomedical applications of PPI research, including the elucidation of disease mechanisms, drug discovery, and therapeutic design. Despite these advances, significant challenges remain in predicting host-pathogen interactions, interactions between intrinsically disordered regions, and interactions related to immune responses. These challenges are worthwhile for future explorations and represent the frontier of research in this field.

Recent Chemical Biology Insights Towards Reversible Stapled Peptides

Peptides are increasingly recognized for their advantages over small molecules in the modulation of protein-protein interactions (PPIs), particularly in terms of potency and selectivity. "Staples" can be coupled to the amino acid residues of linear peptides to limit their conformation, improving the stability, membrane permeability, and resistance to proteolysis of peptides. However, the addition of staples can sometimes lead to the complete inactivation of the original peptide or result in extensive interactions that complicate biophysical analysis. Reversible stapled peptides provide an excellent solution to these issues. Besides, probes based on reversible stapled peptides are also indispensable tools for thoroughly investigating PPIs. Consequently, the development of diverse reversible stapling techniques for stapled peptides is crucial for broadening the applications of peptide molecules in drug discovery, drug delivery, and as tools in chemical biology research. This review aims to summarize representative chemical design strategies for reversible stapled peptides, focusing on reversible chemical stapling methods involving sulfhydryl, amino, and methylthio groups, as well as reversible modulation of the conformational states of stapled peptides. Additionally, we demonstrate some intriguing biological applications of stapled peptides and, finally, suggest future research directions in the field that will serve as references for related researchers.

The role of alpha-synuclein in synucleinopathy: Impact on lipid regulation at mitochondria-ER membranes

The protein alpha-synuclein (αSyn) plays a pivotal role in the pathogenesis of synucleinopathies, including Parkinson's disease and multiple system atrophy, with growing evidence indicating that lipid dyshomeostasis is a key phenotype in these neurodegenerative disorders. Previously, we identified that αSyn localizes, at least in part, to mitochondria-associated endoplasmic reticulum membranes (MAMs), which are transient functional domains containing proteins that regulate lipid metabolism, including the de novo synthesis of phosphatidylserine. In the present study, we analyzed the lipid composition of postmortem human samples, focusing on the substantia nigra pars compacta of Parkinson's disease and controls, as well as three less affected brain regions of Parkinson's donors. To further assess synucleinopathy-related lipidome alterations, similar analyses were performed on the striatum of multiple system atrophy cases. Our data reveal region- and disease-specific changes in the levels of lipid species. Specifically, our data revealed alterations in the levels of specific phosphatidylserine species in brain areas most affected in Parkinson's disease. Some of these alterations, albeit to a lesser degree, are also observed in multiple system atrophy. Using induced pluripotent stem cell-derived neurons, we show that αSyn regulates phosphatidylserine metabolism at MAM domains, and that αSyn dosage parallels the perturbation in phosphatidylserine levels. These findings support the notion that αSyn pathophysiology is linked to the dysregulation of lipid homeostasis, which may contribute to the vulnerability of specific brain regions in synucleinopathy. These findings have significant therapeutic implications.

Kinase-substrate prediction using an autoregressive model

Kinase-specific phosphorylation plays a critical role in cellular signaling and various diseases. However, even in model organisms, the substrates of most kinases remain unidentified. Currently, there is no reliable method to predict kinase-substrate relationships. In this study, we introduce an innovative approach leveraging an autoregressive model to predict kinase-substrate pairs. Unlike traditional methods focused on predicting site-specific phosphorylation, our approach addresses kinase-specific protein substrate prediction at the protein level. We redefine this problem as a special type of protein-protein interaction prediction task. Our model integrates protein large language model ESM-2 as the encoder and employs an autoregressive decoder to classify protein-kinase interactions in a binary fashion. We adopted a hard negative strategy, based on kinase embedding distances generated from ESM-2, to compel the model to effectively distinguish positive from negative data. We conducted a top‑k analysis to assess how well our model can prioritize the most likely kinase candidates. Our method is also capable of zero-shot prediction, meaning it can predict substrates for a kinase in case of no known substrates, which cannot be achieved by site-specific prediction methods. Our model's robust generalization to novel kinase and underrepresented groups showcases its versatility and broad utility. Code and data are available at https://github.com/farz1995/substrate_kinase_prediction.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Multi-tissue characterization of the constitutive heterochromatin proteome in Drosophila identifies a link between satellite DNA organization and transposon repression

Noncoding satellite DNA repeats are abundant at the pericentromeric heterochromatin of eukaryotic chromosomes. During interphase, sequence-specific DNA-binding proteins cluster these repeats from multiple chromosomes into nuclear foci known as chromocenters. Despite the pivotal role of chromocenters in cellular processes like genome encapsulation and gene repression, the associated proteins remain incompletely characterized. Here, we use 2 satellite DNA-binding proteins, D1 and Prod, as baits to characterize the chromocenter-associated proteome in Drosophila embryos, ovaries, and testes through quantitative mass spectrometry. We identify D1- and Prod-associated proteins, including known heterochromatin proteins as well as proteins previously unlinked to satellite DNA or chromocenters, thereby laying the foundation for a comprehensive understanding of cellular functions enabled by satellite DNA repeats and their associated proteins. Interestingly, we find that multiple components of the transposon-silencing piRNA pathway are associated with D1 and Prod in embryos. Using genetics, transcriptomics, and small RNA profiling, we show that flies lacking D1 during embryogenesis exhibit transposon expression and gonadal atrophy as adults. We further demonstrate that this gonadal atrophy can be rescued by mutating the checkpoint kinase, Chk2, which mediates germ cell arrest in response to transposon mobilization. Thus, we reveal that a satellite DNA-binding protein functions during embryogenesis to silence transposons, in a manner that is heritable across later stages of development.

Decoding Plant-Pathogen Interactions: A Comprehensive Exploration of Effector-Plant Transcription Factor Dynamics

In the coevolutionary process between plant pathogens and hosts, pathogen effectors, primarily proteinaceous, engage in interactions with host proteins, such as plant transcription factors (TFs), during the infection process. This review delves into the intricate interplay between TFs and effectors, a key aspect in the prolonged and complex battle between plants and pathogens. Effectors strategically manipulate TFs using diverse tactics. These include modulating activity of TFs, influencing their incorporation into multimeric complexes, directly changing TF expression levels, promoting their degradation via the ubiquitin-proteasome system, and inducing their subcellular relocalization. The review systematically presents documented interactions, elucidating key mechanisms and their profound impact on host-pathogen dynamics. It emphasises the central role of TFs in plant defence and investigates the convergent evolution of effectors targeting TFs. By providing this overview, we offer valuable insights into this dynamic interaction landscape and suggest potential directions for future research.

Martínez-Castilla L, García-Sandoval R, Ballester P, Ferrándiz C, Sanchez MDLP, García-Ponce B, Garay-Arroyo A, R. Álvarez-Buylla E. HDACs MADS-domain protein interaction: a case study of HDA15 and XAL1 in Arabidopsis thaliana

Cellular behavior, cell differentiation and ontogenetic development in eukaryotes result from complex interactions between epigenetic and classic molecular genetic mechanisms, with many of these interactions still to be elucidated. Histone deacetylase enzymes (HDACs) promote the interaction of histones with DNA by compacting the nucleosome, thus causing transcriptional repression. MADS-domain transcription factors are highly conserved in eukaryotes and participate in controlling diverse developmental processes in animals and plants, as well as regulating stress responses in plants. In this work, we focused on finding out putative interactions of Arabidopsis thaliana HDACs and MADS-domain proteins using an evolutionary perspective combined with bioinformatics analyses and testing the more promising predicted interactions through classic molecular biology tools. Through bioinformatic analyses, we found similarities between HDACs proteins from different organisms, which allowed us to predict a putative protein-protein interaction between the Arabidopsis thaliana deacetylase HDA15 and the MADS-domain protein XAANTAL1 (XAL1). The results of two-hybrid and Bimolecular Fluorescence Complementation analysis demonstrated in vitro and in vivo HDA15-XAL1 interaction in the nucleus. Likely, this interaction might regulate developmental processes in plants as is the case for this type of interaction in animals.

DPAM-AI: a domain parser for AlphaFold models powered by artificial intelligence

MOTIVATION

Due to the breakthrough in protein structure prediction by AlphaFold, the scientific community has access to 200 million predicted protein structures with near-atomic accuracy from the AlphaFold protein structure DataBase (AFDB), covering nearly the entire protein universe. Segmenting these models into domains and classifying them into an evolutionary hierarchy hold tremendous potential for unraveling essential insights into protein function.

RESULTS

We introduce DPAM-AI, a Domain Parser for AlphaFold Models based on Artificial Intelligence. DPAM-AI utilizes a convolutional neural network trained with previously classified domains in the Evolutionary Classification Of protein Domains (ECOD) database. DPAM-AI integrates inter-residue distances, predicted aligned errors, and sequence and structural alignments to previously classified domains detected via sequence (HHsuite) and structural (Dali) similarity searches. DPAM-AI has demonstrated its power through rigorous tests, excelling in several benchmark sets compared to its predecessor, DPAM, and other recently published domain parsers, Merizo and Chainsaw. We applied DPAM-AI to representative AFDB models for proteins classified in Pfam. We obtained representative 3D structures for 18 487 (89%) of the 20 795 Pfam families. The remaining families either (i) belong to viral proteins that were excluded from AFDB or (ii) do not adopt globular 3D structures. Our structure-aware domain delineation uncovered a considerable fraction (15%) of Pfam domains containing multiple structural and evolutionary units and refined the boundaries for over half.

AVAILABILITY AND IMPLEMENTATION

Pfam and corresponding DPAM-AI domains are at http://prodata.swmed.edu/DPAM-pfam/. Our code is deposited at https://github.com/Jsauce5p/DPAM/tree/dpam_ai, and updates will be released through https://github.com/CongLabCode/DPAM.

New insights into protein-protein interaction modulators in drug discovery and therapeutic advance

Protein-protein interactions (PPIs) are fundamental to cellular signaling and transduction which marks them as attractive therapeutic drug development targets. What were once considered to be undruggable targets have become increasingly feasible due to the progress that has been made over the last two decades and the rapid technological advances. This work explores the influence of technological innovations on PPI research and development. Additionally, the diverse strategies for discovering, modulating, and characterizing PPIs and their corresponding modulators are examined with the aim of presenting a streamlined pipeline for advancing PPI-targeted therapeutics. By showcasing carefully selected case studies in PPI modulator discovery and development, we aim to illustrate the efficacy of various strategies for identifying, optimizing, and overcoming challenges associated with PPI modulator design. The valuable lessons and insights gained from the identification, optimization, and approval of PPI modulators are discussed with the aim of demonstrating that PPI modulators have transitioned beyond early-stage drug discovery and now represent a prime opportunity with significant potential. The selected examples of PPI modulators encompass those developed for cancer, inflammation and immunomodulation, as well as antiviral applications. This perspective aims to establish a foundation for the effective targeting and modulation of PPIs using PPI modulators and pave the way for future drug development.

Structural comparison of homologous protein-RNA interfaces reveals widespread overall conservation contrasted with versatility in polar contacts

Protein-RNA interactions play a critical role in many cellular processes and pathologies. However, experimental determination of protein-RNA structures is still challenging, therefore computational tools are needed for the prediction of protein-RNA interfaces. Although evolutionary pressures can be exploited for structural prediction of protein-protein interfaces, and recent deep learning methods using protein multiple sequence alignments have radically improved the performance of protein-protein interface structural prediction, protein-RNA structural prediction is lagging behind, due to the scarcity of structural data and the flexibility involved in these complexes. To study the evolution of protein-RNA interface structures, we first identified a large and diverse dataset of 2,022 pairs of structurally homologous interfaces (termed structural interologs). We leveraged this unique dataset to analyze the conservation of interface contacts among structural interologs based on the properties of involved amino acids and nucleotides. We uncovered that 73% of distance-based contacts and 68% of apolar contacts are conserved on average, and the strong conservation of these contacts occurs even in distant homologs with sequence identity below 20%. Distance-based contacts are also much more conserved compared to what we had found in a previous study of homologous protein-protein interfaces. In contrast, hydrogen bonds, salt bridges, and π-stacking interactions are very versatile in pairs of protein-RNA interologs, even for close homologs with high interface sequence identity. We found that almost half of the non-conserved distance-based contacts are linked to a small proportion of interface residues that no longer make interface contacts in the interolog, a phenomenon we term "interface switching out". We also examined possible recovery mechanisms for non-conserved hydrogen bonds and salt bridges, uncovering diverse scenarios of switching out, change in amino acid chemical nature, intermolecular and intramolecular compensations. Our findings provide insights for integrating evolutionary signals into predictive protein-RNA structural modeling methods.

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.

Adaptive protein coevolution preserves telomere integrity

Many essential conserved functions depend, paradoxically, on proteins that evolve rapidly under positive selection. How such adaptively evolving proteins promote biological innovation while preserving conserved, essential functions remains unclear. Here, we experimentally test the hypothesis that adaptive protein-protein coevolution within an essential multi-protein complex mitigates the deleterious incidental byproducts of innovation under pressure from selfish genetic elements. We swapped a single, adaptively evolving subunit of a telomere protection complex from Drosophila yakuba into its close relative, D. melanogaster. The heterologous subunit uncovered a catastrophic interspecies incompatibility that caused lethal telomere fusions. Restoring six adaptively evolving sites on the protein-protein interaction surface, or introducing the D. yakuba interaction partner, rescued telomere integrity and viability. Our in vivo, evolution-guided manipulations illuminate how adaptive protein-protein coevolution preserves essential functions threatened by an evolutionary pressure to innovate.

Identification of Laccase Family of Auricularia auricula-judae and Structural Prediction Using Alphafold

Laccase is an enzyme that plays an important role in fungi, including lignin degradation, stress defense, and formation of fruiting bodies. Auricularia auricula-judae is a white-rot fungus in the Basidiomycota phylum, capable of delignifying wood. In this study, seven genes belonging to the laccase family were identified through de novo sequencing, containing Cu-Oxidase, Cu-Oxidase_2, and Cu-Oxidase_3 domains. Subsequently, the physical characteristics, phylogenetic relationships, protein secondary structure, and tertiary structure of the laccase family (AaLac1-AaLac7) were analyzed. Prediction of N-glycosylation sites identified 2 to 10 sites in the laccase family, with AaLac7 having the highest number of sites at 10. Sequence alignment and analysis of the laccase family showed high consistency in signature sequences. Phylogenetic analysis confirmed the relationship among laccases within the family, with AaLac3-AaLac4 and AaLac5-AaLac6 being closely positioned on the tree, exhibiting high similarity in tertiary structure predictions. This study identified and analyzed laccase family genes in Auricularia auricula-judae using de novo sequencing, offering a simple method for identifying and analyzing the laccase family in organisms with unknown genetic information.

In Silico Modeling of Myelin Oligodendrocyte Glycoprotein Disulfide Bond Reduction by Phosphine-Borane Complexes

BACKGROUND

Neurodegenerative diseases can cause vision loss by damaging retinal ganglion cells in the optic nerve. Novel phosphine-borane compounds (PBs) can protect these cells from oxidative stress via the reduction of disulfide bonds. However, the specific targets of these compounds are unknown. Proteomic evidence suggests that myelin oligodendrocyte glycoprotein (MOG) is a potential target. MOG is of significant interest due to its role in anti-MOG optic neuritis syndrome.

METHODS

We used in silico modeling to explore the structural consequences of cleaving the extracellular domain MOG disulfide bond, both in isolation and in complex with anti-MOG antibodies. The potential binding of PBs to this bond was examined using molecular docking.

RESULTS

Cleaving the disulfide bond of MOG altered the structure of MOG dimers and reduced their energetic favorability by 46.13 kcal/mol. The energy profiles of anti-MOG antibody complexes were less favorable when the disulfide bond of MOG was reduced in the monomeric state by 55.21 kcal/mol, but the reverse was true in the dimeric state. PBs exhibited reducing capabilities with the MOG extracellular disulfide bond, with this best-scoring compound binding with an energy of -28.54 kcal/mol to the MOG monomer and -24.97 kcal/mol to the MOG dimer.

CONCLUSIONS

These findings suggest that PBs can affect the structure of MOG dimers and the formation of antibody complexes by reducing the MOG disulfide bond. Structural changes in MOG could have implications for neurodegenerative diseases and anti-MOG syndrome.

Computational analysis of propeptide-containing proteins and prediction of their post-cleavage conformation changes

A propeptide is removed from a precursor protein to generate its active or mature form. Propeptides play essential roles in protein folding, transportation, and activation and are present in about 2.3% of reviewed proteins in the UniProt database. They are often found in secreted or membrane-bound proteins including proteolytic enzymes, hormones, and toxins. We identified a variety of globular and nonglobular Pfam domains in protein sequences designated as propeptides, some of which form intramolecular interactions with other domains in the mature proteins. Propeptide-containing enzymes mostly function as proteases, as they are depleted in other enzyme classes such as hydrolases acting on DNA and RNA, isomerases, and lyases. We applied AlphaFold to generate structural models for over 7000 proteins with propeptides having no less than 20 residues. Analysis of residue contacts in these models revealed conformational changes for over 300 proteins before and after the cleavage of the propeptide. Examples of conformation change occur in several classes of proteolytic enzymes in the families of subtilisins, trypsins, aspartyl proteases, and thermolysin-like metalloproteases. In most of the observed cases, cleavage of the propeptide releases the constraints imposed by the covalent bond between the propeptide and the mature protein, and cleavage enables stronger interactions between the propeptide and the mature protein. These findings suggest that post-cleavage propeptides could play critical roles in regulating the activity of mature proteins.

Unlocking protein-protein interactions in plants: a comprehensive review of established and emerging techniques

Protein-protein interactions orchestrate plant development and serve as crucial elements for cellular and environmental communication. Understanding these interactions offers a gateway to unravel complex protein networks that will allow a better understanding of nature. Methods for the characterization of protein-protein interactions have been around over 30 years, yet the complexity of some of these interactions has fueled the development of new techniques that provide a better understanding of the underlying dynamics. In many cases, the application of these techniques is limited by the nature of the available sample. While some methods require an in vivo set-up, others solely depend on protein sequences to study protein-protein interactions via an in silico set-up. The vast number of techniques available to date calls for a way to select the appropriate tools for the study of specific interactions. Here, we classify widely spread tools and new emerging techniques for the characterization of protein-protein interactions based on sample requirements while providing insights into the information that they can potentially deliver. We provide a comprehensive overview of commonly used techniques and elaborate on the most recent developments, showcasing their implementation in plant research.

African Swine Fever Virus Protein-Protein Interaction Prediction

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein-protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein-protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein-protein interactions, further validating their relevance. Two protein-protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

High throughput methods to study protein-protein interactions during host-pathogen interactions

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

Deus Ex Machina? The Rise of Artificial Intelligence in Toxicology

Artificial intelligence (AI) is rising rapidly, driven by big data, complex algorithms, and computing resources. Current research presented at the American Chemical Society Fall 2023 Meeting demonstrates AI to be a valuable predictive and supporting tool across all facets of toxicology.

Contribution of Artificial Intelligence to the Identification of Protein-Protein Interactions: A Case Study on PAR-3 and Its Partner Adapter Molecule Crk

Protein-protein interactions (PPIs) are known to be involved in most cellular functions, and a detailed knowledge of such interactions is essential for studying their role in normal and pathological conditions. Significant progress is being made in the identification of PPIs through advances in computational methods. In particular, the AlphaFold2 machine learning-based model has been shown to accelerate drug discovery process by predicting the 3D structure of protein complexes. In this chapter, a straightforward protocol for predicting interprotein interactions between PAR-3 and its protein partner adapter molecule crk is provided. Such artificial intelligence-based and publicly available approaches can provide a resource for further investigation of therapeutic drug targets.