Protein-Protein Interface Identification by Site-Specific Photo-Cross-linking/Cleavage in Mammalian Cells.

Identification of protein-protein interfaces is necessary for understanding and regulating biological events. Genetic code expansion enables site-specific photo-cross-linking by introducing photo-reactive non-canonical amino acids into proteins at defined positions during translation. This technology is widely used for analyzing protein-protein interactions and is applicable in mammalian cells. However, the identification of the cross-linked region still remains challenging. Our new protocol enables its identification by pre-installing a site-specific cleavage site, an α-hydroxy acid (Nε-allyloxycarbonyl-α-hydroxyl-L-lysine acid, AllocLys-OH), into the target protein. Alkaline treatment cleaves the crosslinked complex at the position of the α-hydroxy acid residue and thus helps to identify which side of the cleavage site, either closer to the N-terminus or C-terminus, the crosslinked site is located on within the target protein. A series of AllocLys-OH introductions narrows down the crosslinked region. This combination of site-specific crosslinking and cleavage promises to be useful for revealing binding interfaces and protein complex geometries. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Search for crosslinkable sites Basic Protocol 2: Site-specific photo-cross-linking/cleavage.

Repurposing Proximity-Dependent Protein Labeling (BioID2) for Protein Interaction Mapping in E. coli.

Methods that identify protein-protein interactions are essential for understanding molecular mechanisms controlling biological systems. Proximity-dependent labeling has proven to be a valuable method for revealing protein-protein interaction networks in living cells. A mutant form of the biotin protein ligase enzyme from Aquifex aeolicus (BioID2) underpins this methodology by producing biotin that is attached to proteins that enter proximity to it. This labels proteins for capture, extraction, and identification. In this chapter, we present a toolkit for BioID2 specifically adapted for use in E. coli, exemplified by the chemotaxis protein CheA. We have created plasmids containing BioID2 as expression cassettes for proteins (e.g., CheA) fused to BioID2 at either the N or C terminus, optimized with an 8 × GGS linker. We provide a methodology for expression and verification of CheA-BioID2 fusion proteins in E. coli cells, the in vivo biotinylation of interactors by protein-BioID2 fusions, and extraction and analysis of interacting proteins that have been biotinylated.

Approaches to Backbone Flexibility in Protein-Protein Docking.

Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.

Sampling and Scoring in Protein-Protein Docking.

Protein-protein docking is considered one of the most important techniques supporting experimental proteomics. Recent developments in the field of computer science helped to improve this computational technique so that it better handles the complexity of protein nature. Sampling algorithms are responsible for the generation of numerous protein-protein ensembles. Unfortunately, a primary docking output comprises a set of both near-native poses and decoys. Application of the efficient scoring function helps to differentiate poses with the most favorable properties from those that are very unlikely to represent a natural state of the complex. This chapter explains the importance of sampling and scoring in the process of protein-protein docking. Moreover, it summarizes advances in the field.

From Multiple Protein Docking to Protein-Protein Docking at Interactome Level.

Molecular docking is used to anticipate the optimal orientation of a particular molecule to a target to form a stable complex. It makes predictions about the 3D structure of any complex based on the binding characteristics of the ligand and the target receptor usually a protein. It is an exceptionally useful tool, which is used as a model to study how ligands attach to proteins. Docking can also be used for studying the interaction of ligands and proteins to analyze inhibitory efficacy. The ligand may also be a protein, making it possible to study interactions between two different proteins using the numerous docking tools available for basic research on protein interactions. The protein-protein docking is a crucial approach to understanding the protein interactions and predicting the structure of protein complexes that have not yet been experimentally determined. Moreover, the protein-protein interactions can predict the function of target proteins and the drug-like properties of molecules. Therefore, protein docking assists in uncovering insights into protein interactions and also aids in a better understanding of molecular pathways/mechanisms. This chapter comprehends the various tools for protein-protein docking (pairwise and multiple), including their methodologies and analysis of output as results.

Docking Foundations: From Rigid to Flexible Docking.

Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods. The latter derive from techniques originally published and applied almost half a century ago and are based on interdisciplinary knowledge from the nexus of the fields of biology, chemistry, and physics about protein sequences, structures, and their folding. Protein-protein docking, the main protagonist of this chapter, is routinely treated as an integral part of protein research. Herein, we describe the basic foundations of the whole process in general terms, but step by step from protein representations through docking methods and evaluation of complexes to their final validation.

Software and Databases for Protein-Protein Docking.

Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking.

Machine Learning Methods in Protein-Protein Docking.

An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field. However, despite the rapid development of technology and the progress in developing new approaches, many aspects remain challenging to solve, such as predicting conformational changes in proteins, or more "trivial" issues as high-quality data in huge quantities.Therefore, this chapter focuses on a short introduction to various AI approaches to study protein-protein interactions, followed by a description of the most up-to-date algorithms and programs used for this purpose. Yet, given the considerable pace of development in this hot area of computational science, at the time you read this chapter, the development of the algorithms described, or the emergence of new (and better) ones should come as no surprise.

Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants.

Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.

Molecular Dynamics Simulations in Protein-Protein Docking.

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

Computational Methods to Target Protein-Protein Interactions.

The chapter emphasizes the importance of understanding protein-protein interactions in cellular mechanisms and highlights the role of computational modeling in predicting these interactions. It discusses sequence-based approaches such as evolutionary trace (ET), correlated mutation analysis (CMA), and subtractive correlated mutation (SCM) for identifying crucial amino acid residues, considering interface conservation or evolutionary changes. The chapter also explores methods like differential ET, hidden-site class model, and spatial cluster detection (SCD) for interface specificity and spatial clustering. Furthermore, it examines approaches combining structural and sequential methodologies and evaluates modeled predictions through initiatives like critical assessment of prediction of interactions (CAPRI). Additionally, the chapter provides an overview of various software programs used for molecular docking, detailing their search, sampling, refinement and scoring stages, along with innovative techniques and tools like normal mode analysis (NMA) and adaptive Poisson-Boltzmann solver (APBS) for electrostatic calculations. These computational and experimental approaches are crucial for unraveling protein-protein interactions and aid in developing potential therapeutics for various diseases.

Assessment of Protein-Protein Docking Models Using Deep Learning.

Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes have been determined by biophysical experimental methods, such as X-ray crystallography and cryogenic electron microscopy. However, as experimental methods are costly in resources, many computational methods have been developed that model protein complex structures. One of the difficulties in computational protein complex modeling (protein docking) is to select the most accurate models among many models that are usually generated by a docking method. This article reviews advances in protein docking model assessment methods, focusing on recent developments that apply deep learning to several network architectures.

Detection and Analysis of Short Linear Motif-Based Protein-Protein Interactions with SLiMAn2 Web Server.

Interactomics is bringing a deluge of data regarding protein-protein interactions (PPIs) which are involved in various molecular processes in all types of cells. However, this information does not easily translate into direct and precise molecular interfaces. This limits our understanding of each interaction network and prevents their efficient modulation. A lot of the detected interactions involve recognition of short linear motifs (SLiMs) by a folded domain while others rely on domain-domain interactions. Functional SLiMs hide among a lot of spurious ones, making deeper analysis of interactomes tedious. Hence, actual contacts and direct interactions are difficult to identify.Consequently, there is a need for user-friendly bioinformatic tools, enabling rapid molecular and structural analysis of SLiM-based PPIs in a protein network. In this chapter, we describe the use of the new webserver SLiMAn to help digging into SLiM-based PPIs in an interactive fashion.

Proximity Ligation Assay for the Analysis of Iron-Mediated Protein-Protein Interactions in the Nucleus.

Iron forms essential cofactors used by many nuclear enzymes involved in genome maintenance. However, unchaperoned nuclear iron may represent a threat to the surrounding genetic material as it promotes redox toxicity that may affect DNA integrity. Safely handling intracellular iron implies metal transfer and cofactor assembly processes based on protein-protein interactions. Identifying those interactions commonly occurs via high-throughput approaches using affinity purification or proximity labeling coupled with mass spectrometry analysis. However, these methods do not identify the subcellular location of the interactions. The one-on-one confirmation of proposed nuclear interactions is also challenging. Many approaches used to look at protein interactions are not tailored for looking at the nucleus because the methods used to solubilize nuclear content are harsh enough to disrupt those transient interactions. Here, we describe step-by-step the use of Proximity Ligation Assay (PLA) to analyze iron-mediated protein-protein interactions in the nucleus of cultured human cells. PLA allows the subcellular visualization of the interactions via the in situ detection of the two interacting proteins using fluorescence confocal microscopy. Briefly, cells are fixed, blocked, permeabilized, and incubated with primary antibodies directed to target proteins. Primary antibodies are recognized using PLA probes consisting of one PLUS and one MINUS oligonucleotide-labeled secondary antibody. If the two proteins are close enough (<40 nm), the PLA probes are ligated and used as the template for rolling circle amplification (RCA) with fluorescently labeled oligonucleotides that yield a signal detectable using fluorescence confocal microscopy. A fluorescently labeled membrane-specific stain (WGA) and the DNA-specific probe DAPI are used to identify cellular and nuclear boundaries, respectively. Confocal images are then analyzed using the CellProfiler software to confirm the abundance and localization of the studied protein-protein interactions.

Leveraging a self-cleaving peptide for tailored control in proximity labeling proteomics.

Protein-protein interactions play an important biological role in every aspect of cellular homeostasis and functioning. Proximity labeling mass spectrometry-based proteomics overcomes challenges typically associated with other methods and has quickly become the current state of the art in the field. Nevertheless, tight control of proximity-labeling enzymatic activity and expression levels is crucial to accurately identify protein interactors. Here, we leverage a T2A self-cleaving peptide and a non-cleaving mutant to accommodate the protein of interest in the experimental and control TurboID setup. To allow easy and streamlined plasmid assembly, we built a Golden Gate modular cloning system to generate plasmids for transient expression and stable integration. To highlight our T2A Split/link design, we applied it to identify protein interactions of the glucocorticoid receptor and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid and non-structural protein 7 (NSP7) proteins by TurboID proximity labeling. Our results demonstrate that our T2A split/link provides an opportune control that builds upon previously established control requirements in the field.

Multiplexed mapping of the interactome of GPCRs with receptor activity-modifying proteins.

Receptor activity-modifying proteins (RAMPs) form complexes with G protein-coupled receptors (GPCRs) and may regulate their cellular trafficking and pharmacology. RAMP interactions have been identified for about 50 GPCRs, but only a few GPCR-RAMP complexes have been studied in detail. To elucidate a comprehensive GPCR-RAMP interactome, we created a library of 215 dual epitope-tagged (DuET) GPCRs representing all GPCR subfamilies and coexpressed each GPCR with each of the three RAMPs. Screening the GPCR-RAMP pairs with customized multiplexed suspension bead array (SBA) immunoassays, we identified 122 GPCRs that showed strong evidence for interaction with at least one RAMP. We screened for interactions in three cell lines and found 23 endogenously expressed GPCRs that formed complexes with RAMPs. Mapping the GPCR-RAMP interactome expands the current system-wide functional characterization of RAMP-interacting GPCRs to inform the design of selective therapeutics targeting GPCR-RAMP complexes.

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.

Systematic Identification of Kinase-Substrate Relationship by Integrated Phosphoproteome and Interactome Analysis.

The sensitivity of phosphorylation site identification by mass spectrometry (MS)-based phosphoproteomics has improved significantly. However, the lack of kinase-substrate relationship (KSR) data has hindered improvement of the range and accuracy of kinase activity prediction using phosphoproteome data. We herein describe the application of a systematic identification of KSR by integrated phosphoproteome and interactome analysis using doxycycline (Dox)-induced target kinase-overexpressing HEK-293 cells.

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications.

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.