Linking structural features of protein complexes and biological function

Absolute protein quantification based on calibrated particle counting using electrospray-differential mobility analysis

The traceability of in vitro diagnostics or drug products is based on the accurate quantification of proteins. In this study, we developed an absolute quantification approach for proteins. This method is based on calibrated particle counting using electrospray-differential mobility analysis (ES-DMA) coupled with a condensation particle counter (CPC). The absolute concentration of proteins was quantified with the observed protein particle number measured with ES-DMA-CPC, and the detection efficiency was determined by calibrators. The measurement performance and quantitative level were verified using two certificated reference materials, BSA and NIMCmAb. The linear regression fit for the detection efficiency values of three reference materials and one highly purified protein (myoglobin, BSA, NIMCmAb and fibrinogen) indicated that the detection efficiency and the particle size distribution of these proteins exhibited a linear relationship. Moreover, to explore the suitability of the detection efficiency-particle size curve for protein quantification, the concentrations of three typical proteinaceous particles, including two high molecular weight proteins (NIST reference material 8671 and D-dimer) and one protein complex (glutathione S-transferase dimer), were determined. This work suggests that this calibrated particle counting method is an efficient approach for nondestructive, rapid and accurate quantification of proteins, especially for measuring proteinaceous particles with tremendous size and without reference standards.

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html.

GDockScore: a graph-based protein-protein docking scoring function

Summary

Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions.

Availability and implementation

The model implementation is available at https://gitlab.com/mcfeemat/gdockscore.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

The Knowns and Unknowns in Protein-Metabolite Interactions

Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.

Label-Free Single-Molecule Pulldown for the Detection of Released Cellular Protein Complexes

Precise and sensitive detection of intracellular proteins and complexes is key to the understanding of signaling pathways and cell functions. Here, we present a label-free single-molecule pulldown (LFSMP) technique for the imaging of released cellular protein and protein complexes with single-molecule sensitivity and low sample consumption down to a few cells per mm². LFSMP is based on plasmonic scattering imaging and thus can directly image the surface-captured molecules without labels and quantify the binding kinetics. In this paper, we demonstrate the detection principle for LFSMP, study the phosphorylation of protein complexes involved in a signaling pathway, and investigate how kinetic analysis can be used to improve the pulldown specificity. We wish our technique can contribute to uncovering the molecular mechanisms in cells with single-molecule resolution.

Frequent Assembly of Chimeric Complexes in the Protein Interaction Network of an Interspecies Yeast Hybrid

Hybrids between species often show extreme phenotypes, including some that take place at the molecular level. In this study, we investigated the phenotypes of an interspecies diploid hybrid in terms of protein–protein interactions inferred from protein correlation profiling. We used two yeast species, Saccharomyces cerevisiae and Saccharomyces uvarum, which are interfertile, but yet have proteins diverged enough to be differentiated using mass spectrometry. Most of the protein–protein interactions are similar between hybrid and parents, and are consistent with the assembly of chimeric complexes, which we validated using an orthogonal approach for the prefoldin complex. We also identified instances of altered protein–protein interactions in the hybrid, for instance, in complexes related to proteostasis and in mitochondrial protein complexes. Overall, this study uncovers the likely frequent occurrence of chimeric protein complexes with few exceptions, which may result from incompatibilities or imbalances between the parental proteomes.

Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes

Proteins involved in interactions throughout the course of evolution tend to co-evolve and compensatory changes may occur in interacting proteins to maintain or refine such interactions. However, certain residue pair alterations may prove to be detrimental for functional interactions. Hence, determining co-evolutionary pairings that could be structurally or functionally relevant for maintaining the conservation of an inter-protein interaction is important. Inter-protein co-evolution analysis in several complexes utilizing multiple existing methodologies suggested that co-evolutionary pairings can occur in spatially proximal and distant regions in inter-protein interactions. Subsequently, the Co-Var (Correlated Variation) method based on mutual information and Bhattacharyya coefficient was developed, validated, and found to perform relatively better than CAPS and EV-complex. Interestingly, while applying the Co-Var measure and EV-complex program on a set of protein-protein interaction complexes, co-evolutionary pairings were obtained in interface and non-interface regions in protein complexes. The Co-Var approach involves determining high degree co-evolutionary pairings that include multiple co-evolutionary connections between particular co-evolved residue positions in one protein with multiple residue positions in the binding partner. Detailed analyses of high degree co-evolutionary pairings in protein-protein complexes involved in cancer metastasis suggested that most of the residue positions forming such co-evolutionary connections mainly occurred within functional domains of constituent proteins and substitution mutations were also common among these positions. The physiological relevance of these predictions suggested that Co-Var can predict residues that could be crucial for preserving functional protein-protein interactions. Finally, Co-Var web server (http://www.hpppi.iicb.res.in/ishi/covar/index.html) that implements this methodology identifies co-evolutionary pairings in intra and inter-protein interactions.

PCprophet: a framework for protein complex prediction and differential analysis using proteomic data

Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein-protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography-sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein-protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography-MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.

ProAffiMuSeq: sequence-based method to predict the binding free energy change of protein-protein complexes upon mutation using functional classification

MOTIVATION

Protein-protein interactions are essential for the cell and mediate various functions. However, mutations can disrupt these interactions and may cause diseases. Currently available computational methods require a complex structure as input for predicting the change in binding affinity. Further, they have not included the functional class information for the protein-protein complex. To address this, we have developed a method, ProAffiMuSeq, which predicts the change in binding free energy using sequence-based features and functional class.

RESULTS

Our method shows an average correlation between predicted and experimentally determined ΔΔG of 0.73 and mean absolute error (MAE) of 0.86 kcal/mol in 10-fold cross-validation and correlation of 0.75 with MAE of 0.94 kcal/mol in the test dataset. ProAffiMuSeq was also tested on an external validation set and showed results comparable to structure-based methods. Our method can be used for large-scale analysis of disease-causing mutations in protein-protein complexes without structural information.

AVAILABILITY AND IMPLEMENTATION

Users can access the method at https://web.iitm.ac.in/bioinfo2/proaffimuseq/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Biological Assembly of Modular Protein Building Blocks as Sensing, Delivery, and Therapeutic Agents

Nature has evolved a wide range of strategies to create self-assembled protein nanostructures with structurally defined architectures that serve a myriad of highly specialized biological functions. With the advent of biological tools for site-specific protein modifications and de novo protein design, a wide range of customized protein nanocarriers have been created using both natural and synthetic biological building blocks to mimic these native designs for targeted biomedical applications. In this review, different design frameworks and synthetic decoration strategies for achieving these functional protein nanostructures are summarized. Key attributes of these designer protein nanostructures, their unique functions, and their impact on biosensing and therapeutic applications are discussed.

Recognition of PDL1/L2 by different induced-fit mechanisms of PD1: a comparative study of molecular dynamics simulations

The different binding mechanism for PD1/PDL1 and PD1/PDL2 complexes.

The R882H substitution in the human de novo DNA methyltransferase DNMT3A disrupts allosteric regulation by the tumor supressor p53

A myriad of protein partners modulate the activity of the human DNA methyltransferase 3A (DNMT3A), whose interactions with these other proteins are frequently altered during oncogenesis. We show here that the tumor suppressor p53 decreases DNMT3A activity by forming a heterotetramer complex with DNMT3A. Mutational and modeling experiments suggested that p53 interacts with the same region in DNMT3A as does the structurally characterized DNMT3L. We observed that the p53-mediated repression of DNMT3A activity is blocked by amino acid substitutions within this interface, but surprisingly, also by a distal DNMT3A residue, R882H. DNMT3A R882H occurs frequently in various cancers, including acute myeloid leukemia, and our results suggest that the effects of R882H and other DNMT3A mutations may go beyond changes in DNMT3A methylation activity. To further understand the dynamics of how protein-protein interactions modulate DNMT3A activity, we determined that p53 has a greater affinity for DNMT3A than for DNMT3L and that p53 readily displaces DNMT3L from the DNMT3A:DNMT3L heterotetramer. Interestingly, this occurred even when the preformed DNMT3A:DNMT3L complex was actively methylating DNA. The frequently identified p53 substitutions (R248W and R273H), whereas able to regulate DNMT3A function when forming the DNMT3A:p53 heterotetramer, no longer displaced DNMT3L from the DNMT3A:DNMT3L heterotetramer. The results of our work highlight the complex interplay between DNMT3A, p53, and DNMT3L and how these interactions are further modulated by clinically derived mutations in each of the interacting partners.

Small protein-protein interfaces rich in electrostatic are often linked to regulatory function

Protein-protein interaction (PPI) is critical for several biological functions in living cells through the formation of an interface. Therefore, it is of interest to characterize protein-protein interfaces using an updated non-redundant structural dataset of 2557 homo (identical subunits) and 393 hetero (different subunits) dimer protein complexes determined by X-ray crystallography. We analyzed the interfaces using van der Waals (vdW), hydrogen bonding and electrostatic energies. Results show that on average homo and hetero interfaces are similar. Hence, we further grouped the 2950 interfaces based on percentage vdW to total energies into dominant (≥60%) and sub-dominant (<60%) vdW interfaces. Majority (92%) of interfaces have dominant vdW energy with large interface size (146 ± 87 (homo) and 137 ± 76 (hetero) residues) and interface area (1622 ± 1135 Ų (homo) and 1579 ± 1060 Ų (hetero)). However, a proportion (8%) of interfaces have sub-dominant vdW energy with small interface size (85 ± 46 (homo) and 88 ± 36 (hetero) residues) and interface area (823 ± 538 Ų (homo) and 881 ± 377 Ų (hetero)). It is found that large interfaces have two-fold more interface area and interface size than small interfaces with increasing hydrogen bonding energy to interface size. However, small interfaces have three-fold more electrostatics energy than large interfaces with increasing electrostatics to interface size. Thus, 8% of complexes having small interfaces with limited interface area and sub-dominant vdW energy are rich in electrostatics. It is interesting to observe that complexes having small interfaces are often associated with regulatory function. Hence, the observed structural features with known molecular function provide insights for the better understanding of PPI.Communicated by Ramaswamy H. Sarma.

Computational analysis of hot spots and binding mechanism in the PD-1/PD-L1 interaction

Programmed cell death protein-1 (PD-1) is an important immunological checkpoint and plays a vital role in maintaining the peripheral tolerance of the human body by interacting with its ligand PD-L1. The overexpression of PD-L1 in tumor cells induces local immune suppression and helps the tumor cells to evade the endogenous anti-tumor immunity. Developing monoclonal antibodies against the PD-1/PD-L1 protein–protein interaction to block the PD-1/PD-L1 signaling pathway has demonstrated superior anti-tumor efficacy in a variety of solid tumors and has made a profound impact on the field of cancer immunotherapy in recent years. Although the X-ray crystal structure of the PD-1/PD-L1 complex has been solved, the detailed binding mechanism of the PD-1/PD-L1 interaction is not fully understood from a theoretical point of view. In this study, we performed computational alanine scanning on the PD-1/PD-L1 complex to quantitatively identify the hot spots in the PD-1/PD-L1 interaction and characterize its binding mechanisms at the atomic level. To the best of our knowledge, this is the first time that theoretical calculations have been used to systematically and quantitatively predict the hot spots in the PD-1/PD-L1 interaction. We hope that the predicted hot spots and the energy profile of the PD-1/PD-L1 interaction presented in this work can provide guidance for the design of peptide and small molecule drugs targeting PD-1 or PD-L1.

The hot spots quantitatively predicted by the recently developed MM/GBSA/IE method reveal a hydrophobic core in the PD-1/PD-L1 interaction.

Structure of the essential peptidoglycan amidotransferase MurT/GatD complex from Streptococcus pneumoniae

The universality of peptidoglycan in bacteria underlies the broad spectrum of many successful antibiotics. However, in our times of widespread resistance, the diversity of peptidoglycan modifications offers a variety of new antibacterials targets. In some Gram-positive species such as Streptococcus pneumoniae, Staphylococcus aureus, or Mycobacterium tuberculosis, the second residue of the peptidoglycan precursor, D-glutamate, is amidated into iso-D-glutamine by the essential amidotransferase MurT/GatD complex. Here, we present the structure of this complex at 3.0 Å resolution. MurT has central and C-terminal domains similar to Mur ligases with a cysteine-rich insertion, which probably binds zinc, contributing to the interface with GatD. The mechanism of amidation by MurT is likely similar to the condensation catalyzed by Mur ligases. GatD is a glutaminase providing ammonia that is likely channeled to the MurT active site through a cavity network. The structure and assay presented here constitute a knowledge base for future drug development studies.

The influence and manipulation of acid/base properties in drug discovery

There is a growing awareness of the importance of acid/base properties in medicinal chemistry research. In many drug classes, ionisable groups are present that make critical interactions with the receptor and are essential for potency. Yet the presence of these groups may cause problems with oral bioavailability, pharmacokinetics, or toxicity. Manipulating pK_a values during drug development or applying pro-drug techniques are strategies that can overcome potential deficits in a variety of these areas. Knowledge of drug ionisation states coupled with a consideration of pH-specific cellular environments can be used advantageously to target chemoresistance. As modern drug research ventures into drug candidates that exceed the rule of 5, such exploration requires an understanding of drug acid/base properties and how these factors affect ADMET characteristics.

Trichomonas vaginalis metalloproteinase TvMP50 is a monomeric Aminopeptidase P-like enzyme

Previously, metalloproteinase was isolated and identified from Trichomonas vaginalis, belonging to the aminopeptidase P-like metalloproteinase subfamily A/B, family M24 of clan MG, named TvMP50. The native and recombinant TvMP50 showed proteolytic activity, determined by gelatin zymogram, and a 50 kDa band, suggesting that TvMP50 is a monomeric active enzyme. This was an unexpected finding since other Xaa-Pro aminopeptidases/prolidases are active as a biological unit formed by dimers/tetramers. In this study, the evolutionary history of TvMP50 and the preliminary crystal structure of the recombinant enzyme determined at 3.4 Å resolution is reported. TvMP50 was shown to be a type of putative, eukaryotic, monomeric aminopeptidase P, and the crystallographic coordinates showed a monomer on a "pseudo-homodimer" array on the asymmetric unit that resembles the quaternary structure of the M24B dimeric family and suggests a homodimeric aminopeptidase P-like enzyme as a likely ancestor. Interestingly, TvMP50 had a modified N-terminal region compared with other Xaa-Pro aminopeptidases/prolidases with three-dimensional structures; however, the formation of the standard dimer is structurally unstable in aqueous solution, and a comparably reduced number of hydrogen bridges and lack of saline bridges were found between subunits A/B, which could explain why TvMP50 portrays monomeric functionality. Additionally, we found that the Parabasalia group contains two protein lineages with a "pita bread" fold; the ancestral monomeric group 1 was probably derived from an ancestral dimeric aminopeptidase P-type enzyme, and group 2 has a probable dimeric kind of ancestral eukaryotic prolidase lineage. The implications of such hypotheses are also presented.

Homo- and Heterodimerization of Proteins in Cell Signaling: Inhibition and Drug Design

Protein dimerization controls many physiological processes in the body. Proteins form homo-, hetero-, or oligomerization in the cellular environment to regulate the cellular processes. Any deregulation of these processes may result in a disease state. Protein-protein interactions (PPIs) can be inhibited by antibodies, small molecules, or peptides, and inhibition of PPI has therapeutic value. PPI drug discovery research has steadily increased in the last decade, and a few PPI inhibitors have already reached the pharmaceutical market. Several PPI inhibitors are in clinical trials. With advancements in structural and molecular biology methods, several methods are now available to study protein homo- and heterodimerization and their inhibition by drug-like molecules. Recently developed methods to study PPI such as proximity ligation assay and enzyme-fragment complementation assay that detect the PPI in the cellular environment are described with examples. At present, the methods used to design PPI inhibitors can be classified into three major groups: (1) structure-based drug design, (2) high-throughput screening, and (3) fragment-based drug design. In this chapter, we have described some of the experimental methods to study PPIs and their inhibition. Examples of homo- and heterodimers of proteins, their structural and functional aspects, and some of the inhibitors that have clinical importance are discussed. The design of PPI inhibitors of epidermal growth factor receptor heterodimers and CD2-CD58 is discussed in detail.

Prediction of interface residue based on the features of residue interaction network

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model.

Protein-protein interfaces are vdW dominant with selective H-bonds and (or) electrostatics towards broad functional specificity

Several catalysis, cellular regulation, immune function, cell wall assembly, transport, signaling and inhibition occur through Protein- Protein Interactions (PPI). This is possible with the formation of specific yet stable protein-protein interfaces. Therefore, it is of interest to understand its molecular principles using structural data in relation to known function. Several interface features have been documented using known X-ray structures of protein complexes since 1975. This has improved our understanding of the interface using structural features such as interface area, binding energy, hydrophobicity, relative hydrophobicity, salt bridges and hydrogen bonds. The strength of binding between two proteins is dependent on interface size (number of residues at the interface) and thus its corresponding interface area. It is known that large interfaces have high binding energy (sum of (van der Waals) vdW, H-bonds, electrostatics). However, the selective role played by each of these energy components and more especially that of vdW is not explicitly known. Therefore, it is important to document their individual role in known protein-protein structural complexes. It is of interest to relate interface size with vdW, H-bonds and electrostatic interactions at the interfaces of protein structural complexes with known function using statistical and multiple linear regression analysis methods to identify the prominent force. We used the manually curated non-redundant dataset of 278 hetero-dimeric protein structural complexes grouped using known functions by Sowmya et al. (2015) to gain additional insight to this phenomenon using a robust inter-atomic non-covalent interaction analyzing tool PPCheck (Anshul and Sowdhamini, 2015). This dataset consists of obligatory (enzymes, regulator, biological assembly), immune and nonobligatory (enzyme and regulator inhibitors) complexes. Results show that the total binding energy is more for large interfaces. However, this is not true for its individual energy factors. Analysis shows that vdW energies contribute to about 75% ± 11% on average among all complexes and it also increases with interface size (r2 ranging from 0.67 to 0.89 with p<0.01) at 95% confidence limit irrespective of molecular function. Thus, vdW is both dominant and proportional at the interface independent of molecular function. Nevertheless, H bond energy contributes to 15% ± 6.5% on average in these complexes. It also moderately increases with interface size (r2 ranging from 0.43 to 0.61 with p<0.01) only among obligatory and immune complexes. Moreover, there is about 11.3% ± 8.7% contribution by electrostatic energy. It increases with interface size specifically among non-obligatory regulator-inhibitors (r2 = 0.44). It is implied that both H-bonds and electrostatics are neither dominant nor proportional at the interface. Nonetheless, their presence cannot be ignored in binding. Therefore, H-bonds and (or) electrostatic energy having specific role for improved stability in complexes is implied. Thus, vdW is common at the interface stabilized further with selective H-bonds and (or) electrostatic interactions at an atomic level in almost all complexes. Comparison of this observation with residue level analysis of the interface is compelling. The role by H-bonds (14.83% ± 6.5% and r2 = 0.61 with p<0.01) among obligatory and electrostatic energy (8.8% ± 4.77% and r2 = 0.63 with p <0.01) among non-obligatory complexes within interfaces (class A) having more non-polar residues than surface is influencing our inference. However, interfaces (class B) having less non-polar residues than surface show 1.5 fold more electrostatic energy on average. The interpretation of the interface using inter-atomic (vdW, H-bonds, electrostatic) interactions combined with inter-residue predominance (class A and class B) in relation to known function is the key to reveal its molecular principles with new challenges.

Techniques for the Analysis of Protein-Protein Interactions in Vivo

Identifying key players and their interactions is fundamental for understanding biochemical mechanisms at the molecular level. The ever-increasing number of alternative ways to detect protein-protein interactions (PPIs) speaks volumes about the creativity of scientists in hunting for the optimal technique. PPIs derived from single experiments or high-throughput screens enable the decoding of binary interactions, the building of large-scale interaction maps of single organisms, and the establishment of cross-species networks. This review provides a historical view of the development of PPI technology over the past three decades, particularly focusing on in vivo PPI techniques that are inexpensive to perform and/or easy to implement in a state-of-the-art molecular biology laboratory. Special emphasis is given to their feasibility and application for plant biology as well as recent improvements or additions to these established techniques. The biology behind each method and its advantages and disadvantages are discussed in detail, as are the design, execution, and evaluation of PPI analysis. We also aim to raise awareness about the technological considerations and the inherent flaws of these methods, which may have an impact on the biological interpretation of PPIs. Ultimately, we hope this review serves as a useful reference when choosing the most suitable PPI technique.