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# Molecules interact. But how strong and how much?

Kathleen M Weimer, Boglarka Zambo, Gergő Gógl

► To cite this version:

Kathleen M Weimer, Boglarka Zambo, Gergő Gógl. Molecules interact. But how strong and how much?. BioEssays, 2023, 45 (6), pp.e2300007. 10.1002/bies.202300007. hal-04818810

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# Molecules interact. But how strong and how much?

Kathleen Weimer

Boglarka Zambo

Gergo Gogl

<figure><img src="image_3.png" /></figure>

Institut de Génétique et de Biologie
Moléculaire et Cellulaire (IGBMC), INSERM
U1258/CNRS UMR 7104/Université de
Strasbourg, Illkirch, France

**Correspondence**

Gergo Gogl, Institut de Génétique et de
Biologie Moléculaire et Cellulaire (IGBMC),
INSERM U1258/CNRS UMR 7104/Université
de Strasbourg, 1 rue Laurent Fries, BP 10142,
F-67404 Illkirch, France.

Email: goglg@igbmc.fr

**Abstract**

Interactomics aims to characterize all interactions formed between molecules that comprise our body. Although it emerged from quantitative biophysics, it has devolved into a predominantly qualitative field of science over the past decades. Due to technical limitations at its onset, almost all tools in interactomics are qualitative, which persists in defining the discipline. Here, we argue that interactomics needs to return to a quantitative direction because the technical achievements of the last decade have overcome the original limitations that forced its current path. In contrast to qualitative interactomics which is constrained to charting lists of observed interactions, quantitative interactomics can also uncover answers to key questions such as the strength of interactions or how many of certain complexes can form in cells, thus providing researchers with more immediate proxies for understanding and predicting biological processes.

**KEYWORDS**

interactomics, protein-protein interactions, quantitative biology

## INTRODUCTION

Biological processes are governed by noncovalent complexes at all levels of cellular life. Thanks to high throughput interaction mapping, we can now view the human protein-protein interactome with remarkable breadth and depth; yet, our view remains incomplete because current practices fail to incorporate the quantitative properties of these interactions. Due to recent advances, we can now also begin to integrate a quantitative perspective and understand the interactome on a much more nuanced and complete level than ever before.

Fundamental principles underpinning the formation of complexes have been formulated in the past centuries by pioneers in physical-chemistry and biophysics.[1] Modern biochemists carry on their legacy by developing refined theoretical and experimental approaches to investigate the thermodynamic, kinetic, or equilibrium binding properties of noncovalent complexes.[2-5] Although these methods are both precise and accurate, they require specialized expertise. Con-

sequently, only a handful of interactions could be studied using bio-physical approaches. This changed rapidly when biology entered the genomic era, allowing scientists to develop alternative, more accessible approaches for investigating noncovalent interactions in order to map entire interactomes proteome-wide.[6,7]

The rift that has emerged between this new field of interactomics and conventional biophysics is profound (Figure 1). Biophysics seeks to characterize complexes through their binding properties, whereas in contrast interactomics lists all interactions that form detectably between molecules of our cells. As a result, interactomics became a qualitative field of science where molecules either “bind” or “do not bind” and interactomes are routinely displayed, analyzed, and even predicted as simple “social networks of biomolecules”.[8-10] In this work we present key properties and evaluate limitations of interactomics from the perspective of biochemistry, thus demonstrating the advantages of quantitative interactomics over current qualitative practices and reasoning why the field needs to find a way back to its biophysical roots.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2023 The Authors. *BioEssays* published by Wiley Periodicals LLC.

<figure><img src="image_4.png" /></figure>
<figure><img src="image_5.png" /><figcaption>**FIGURE 1** Qualitative interactomic, biochemical, and quantitative interactomic description of molecular interactions. On the left panel, an imaginary interaction network of "molecule A" is shown indicating six molecules, of which molecules B-E detectably bind based on qualitative interactomic measurements. All of these interactions, even the ones that do not appear as a binder in the first panel, can be described with a set of intrinsic binding constants, as exemplified in the middle panel. Such intrinsic biophysical binding constants constitute the affinity interactome, as exemplified on the right panel for the same interaction network shown in the left panel. Quantitative affinity-interatomic profiles immediately enable the ranking of the observed interactions which is not possible with simple qualitative interactomic network graphs. In addition, the detection thresholds of quantitative assays are better defined than "binding thresholds" of qualitative interactomic assays, increasing the reproducibility and reliability of interactomes.</figcaption></figure>

## QUALITATIVE INTERACTOMICS AND REMNANTS OF QUANTITATIVE FEATURES

A key milestone in the transition of biophysics into interactomics was the first description of a two-hybrid experiment in 1989, which allowed the discovery of complexes at a previously unprecedented pace.[11,12] Two-hybrid measures the possibility of interactions between pairs of molecules expressed in engineered environments. The Human Reference Interactome project applied this method between cca. 17,500 protein molecules and measured 53,000 interactions formed between human proteins.[13] Although these interactions were detected by the two-hybrid assay, the reacting molecules may never actually be present in the same environment in concentrations sufficient to form complexes in large quantities. For this reason, many alternative approaches were invented to measure the in situ amount of complexes in cells, or entire complexomes.

In the OpenCell project, affinity purification coupled to mass spectrometry (AP-MS) was used to find interaction partners of proteins labeled at endogenous levels, identifying 30,000 potential complexes using cca. 1000 protein baits.[14] In contrast to complexes identified by two-hybrid, partners of these interactions are certainly present at an adequate concentration in normal cellular context and are therefore able to form detectable amounts of complexes. However, with these methods, interactions are subject to being overlooked due to the detection threshold of mass spectrometry. To circumvent this, the BioPlex project adopted a similar assay, but with overexpressed baits, managing to identify more than 120,000 complexes with approximately 10,000 protein baits.[15,16] More recently, proximity labeling-based approaches, such as APEX or BioID, have become attractive for interactomic studies because these method often succeed in identifying even those highly transient interactions that are typically not detected by standard AP-MS or 2-hybrid experiments.[17,18] In these assays, bait proteins are produced tagged with biotin ligase in situ

and the biotinylated proteins are captured using standard pulldown methods.[19] Finally, while two-hybrid, AP-MS and BioID are directed approaches (focused on a specific pair of molecules or a specific bait), co-fractionation based methods attempt to isolate and identify all complexes directly from cellular extracts that are stable enough to stay together during fractionation.[20,21]

All methods discussed above generate qualitative interactomic data, that is, lists of molecules that interact detectably. As in all experimental methods, interactomic approaches have a detection threshold. It is based on this limiting factor and the measured signal that we classify pairs of molecules as either "interacting" or "not interacting." However, even if we use the same word for the observed interactions of different assays, the measured signals can be of different origins. The underlying units of measurement of observed "interactions" are either equilibrium binding constants or concentrations of complexes. While the binding constants are intrinsic parameters of a system (i.e., they are constant between different states of the system), the concentration of reactants and ultimately the complex formation are extrinsic parameters (i.e., different in different states of the system).

Some of these methods study intrinsic binding preferences (e.g., 2-hybrid), while other approaches shed light on extrinsic features of cellular complexomes (e.g., AP-MS or co-fractionation). For methods that study extrinsic cellular complexomes or, in other words, relative amounts of complexes, the remnants of quantitative features are often still present within qualitative data. For example, the BioPlex project could show that the measured interaction networks change with cell type.[16] One may also measure the apparent molar ratio of bait and captured prey in AP-MS experiments in order to calculate an apparent "stoichiometry" for every observed interaction.[14,22] Such sophisticated AP-MS based studies can even reveal dynamic changes in the complexome.[23] Co-fractionation can also give an intricate insight into complexomic rearrangements of stable complexes under different proteomic conditions.[24,25] Finally, we recently showed that relative
enrichment values of a given prey bound to multiple baits, immobilized in equal amounts during AP-MS, correlate with their relative intrinsic affinity differences.[26,27] Taken together, this indicates that although it is highly convenient to use qualitative interactomic methods, we should not forget the biophysical basis of complex formation when it comes to interpreting our data.

At last, we need to discuss how these remaining quantitative features in qualitative complexomics can be used to study perturbations in cells or differences between cell types. Although we can certainly use these observations to pinpoint key hotspots of complexomic variations, one has to consider that it is currently impossible to measure cellular complexomes without perturbing them. For example, in any assay where complexes are captured on solid supports and separated from their matrix through washing steps, the binding equilibrium is disrupted, resulting in partial or complete dissociation of obtained complexes in accordance to the applied washing protocols and the kinetic constants of the interactions.[28] We will later discuss how novel methods in quantitative interactomics can help overcome this obstacle to give a more complete view of the complexome.

## THE ISSUE OF REPRODUCIBILITY IN QUALITATIVE INTERACTOMICS

From the beginning, qualitative interactomics faces a reproducibility crisis. This was initially realized when the first proteome-scale protein-protein interactome maps were charted in independent studies, revealing surprisingly large discrepancies between their results.[29] Since then, many interactomic studies have encountered the same reproducibility issue and attempt to find approaches to minimize this discrepancy, for example, by standardizing experimental procedures.[7] In part, the apparently poor reproducibility has a clear biological background.[30] For example, as mentioned above, 2-hybrid qualitatively studies intrinsic interactomes, but AP-MS qualitatively studies cellular complexomes. Consequently, 2-hybrid identifies pairs of molecules that could directly interact, while AP-MS identifies complexes that may contain indirectly bound components. Apart from these, much of the inconsistency between results comes from the thresholding of the measured signals.

As aforementioned, although rarely discussed otherwise in biological studies, all experimental methods have various kinds of detection thresholds and other bottlenecks. When these differ and, for example, one compares qualitative interactomes measured with low and high affinity detection thresholds, the overlap is expected to be smaller than when comparing results from two different methods with similar thresholds. Related to this issue is the concern of the negatome – pairs of molecules for which no interaction is observed. The interactions belonging to the negatome were found to have affinities or concentrations below the detection thresholds in experiments.[31,32] One should only concern analyzing the negatome when they can be paired with explicit detection thresholds, otherwise they cannot be relativized. Partners that appear to be “not interacting” could also appear as “interacting” in an assay with a lower detection threshold.[4] As

previously established, one way to circumvent such limitations is to use highly standardized methods with similar binding thresholds.[7] Another solution is to completely change experimental strategies, measuring affinity interactomes as opposed to charting qualitative interactomes.

At last, validation metrics of qualitative interactomes are done by qualitatively comparing the data with reference interactomes, such as the calculation of Jaccard indexes or precision/recall values. It was recently shown that the most commonly used reference interactome for human protein-protein interactions (PPIs), CORUM, mostly consists of the highest affinity interactions of our interactome and was simultaneously found to be markedly underrepresented in weaker interactions that are inevitably more abundant in nature.[14,33] If comprehensive affinity interactomic databases would exist, the results of any measurement could be compared with published affinity values by calculating explicit affinity differences directly resulting in standardized, accurate and reproducible ΔΔG values.

## ALTERNATIVES TO QUALITATIVE INTERACTOMICS

Many biochemists and biophysicists have already recognized the above limitations of interactomics. For example, the founders of AxCell Biosciences Corp. in the early 2000s were early to recognize the advantages of quantitative interactomics, and attempted to map affinities of every promiscuous domain-motif mediated interaction network by the mid-2000s.[34] Although their goals went unfinished, others proceeded with similar goals and measured (relative or absolute) affinities of large interaction networks of domain-motif interactions.[27,35-39] Scientists today have access to dozens of orthogonal experimental approaches that can quantify affinity interactomes at proteomic scales of either minimal binding fragments or even full-length proteins, but mainstream interactomics still remains mostly qualitative.

Measuring the estimated billions of interactions comprising the full human proteome and their variations can be a daunting task.[27] Taking into consideration that many PPIs are mediated through specific domain-motif interactions, efforts to measure the hundreds of millions of interactions constituting the full fragmental interactome appear to be much more feasible.[40] A common feature of these interactions is that they can be easily studied through their minimal binding fragments, such as using synthetic peptides and small globular domains. Among the numerous fragmentomic tools that researchers can use to study fragmental interactomes, phage display provides the highest throughput, as well as great sensitivity, yet it is only capable of identifying motif-mediated interactions and not quantifying their properties.[41]

Many low throughput techniques are available that can easily determine a handful of fragmentomic interactions with great precision and high accuracy. For example, surface plasmon resonance (SPR), biolayer interferometry (BLI), and isothermal calorimetry (ITC). However, these typically cannot be scaled up, preventing them from being suitable for large scale interactomic studies. Many optical assays can be employed at equally high precision and accuracy, as
well as at higher throughput, such as fluorescence polarization (FP), microscale thermophoresis (MST), or MRBLE-pep.[37,42,43] Although these methods are typically more easily scalable and can be combined with peptide or protein arrays, they are resource intensive and can often be used only with special constraints due to technical limitations. Our recently developed fragmentomic holdup assay does not require any special instrumentation and can be used for any type of interaction determination.[27] We applied this method to measure all possible equilibrium binding constants between 50% of all human PDZ domains and approximately 10% of all predicted human PDZ-binding motifs in a combinatorial way, characterizing more than 65,000 interactions and quantifying more than 18,000 dissociation constants.

Regardless of their obvious benefits, fragmentomic approaches only shed light on localized, site-specific properties of interactions. While a well-defined minimal interacting fragment pair may result in the measurement of a singular high-quality affinity constant, it does not consider the global impact of multiple binding sites (e.g., cooperativity or avidity) or explain the distinct binding properties of proteoforms (resulting in allostery, autoinhibition, etc.).[2,44] To this end, investigating the properties of full-length PPIs are equally necessary. Methods for studying full-length PPIs share many challenges with fragmentomic approaches, but one of the most important is reagent preparation. Virtually all low-, medium-, and even high-throughput methods can be used to study interactions of full-length proteins, but purifying even a handful of active full-length proteins can take years of extensive work. Therefore, although feasible for a few cases, the same methods are not practical for studying interactions of full-length proteins at the proteomic scale. As mentioned above, efforts have been undertaken to map the Human Reference Interactome via the 2-hybrid system.[13] Although this was done with a qualitative version of the assay, a tri-fluorescent 2-hybrid assay was also developed that can study interactions quantitatively.[45] Other fluorescence- or luminescence-based assays have also been adapted to quantitatively study PPIs, such as Forster Resonance Energy Transfer (FRET), AlphaScreen, Homogeneous Time Resolved Fluorescence (HTFR), and Bioluminescence Resonance Energy Transfer (BRET), or using supported membrane sheets.[46-50] Although these methods are capable of characterizing interactions of full-length molecules, they are limited to the study of the properties of a single pair of molecules at a time and, therefore, still difficult to scale to the proteomic level. We have recently developed an approach called native holdup (nHU), which is capable of estimating equilibrium binding constants between a single recombinant bait and all proteins that are detectable from cell extracts.[51] With only a single nHU experiment coupled to a multiplex mass spectrometry readout, we showed that it is possible to get estimations about several thousands of equilibrium binding constants, all measured with full-length endogenous proteins and even large multicomponent complexes, exceeding the scalability of most alternative approaches. Although these approaches allow the measurement of the biophysical properties of intact macromolecular interactions often in their semi-native environment, they always lead to a single apparent affinity constant. However, as we have introduced, when discussing fragmental interactomics, proteins exist in a plethora of states referred to as proteoforms.[52]

Regardless, these apparent affinities could readily provide important biological insight even if proteoforms are initially ignored and later could be refined, for example, with targeted fragmentomic characterizations.

In addition to the lack of standardized and scalable techniques that the community has agreed to widely adopt, one of the key barriers preventing the qualitative-to-quantitative transition of interactomics is the lack of universal quantitative affinity interactomic databases.[3] Compendiums of both low- and high-throughput interactomic studies provide extensive lists of interactions that allow scientists today to be able to find dozens to hundreds of putative interaction partners for any given protein of interest without doing any experiments. Public databases compiled of these results, such as IntAct, BioGrid, DIP, or STRING, are of unquestionable value.[53-56] Although some of these databases, like Intact, already record biophysical information when available, they were originally created to handle qualitative data and were only adopted later to capture quantitative information. Consequently, they do not capture all possible information for quantitative interactomics and they display interactomes in the form of qualitative interaction graphs, further solidifying the qualitative notion of the field. Previously developed databases fully dedicated for quantitative interactomics were highly specialized and could only store narrow types of data, usually measured with a particular technique.[27,57-61] Hopefully, as quantitative interactomic approaches become more widespread, new general biophysics-based databases will emerge, and current ones will adopt the quantitative view of the field.

## COMPUTATIONAL QUANTITATIVE INTERACTOMICS

The recent machine learning boom has impacted nearly all domains and the field of interactomics is no exception. For over 50 years, predictive modeling of affinities has been an area of research interest, as the determination of drug-target affinities is critical to drug development and computational approaches require less resource investment than experimental ones.[62] Binding affinity prediction most often relies on the analysis of experimental or computationally docked structures of complexes using various algorithms.[63,64] With the recent advent of machine learning-based protein structure prediction tools, for example, AlphaFold, this approach is no longer limited by the availability of an experimentally solved structure or by the accuracy of molecular docking, as affinity predictions can be performed using accurately predicted models generated by advanced machine learning methods.[65] However important it may be to keep in mind that these are simply predictions, they serve as a good basis to evaluate candidates for further validation through experimental interactomic studies. For example, a recent approach used AlphaFold prediction with competing interactions to predict their relative binding affinities, however, their evaluation did not consider that binding affinities are determined not only by bound states, but also their free and all intermediate states as well.[66]

Despite the many applications for machine learning in interactomics, the field is currently self-limited by the mentioned lack of
<figure><img src="image_6.png" /><figcaption>**FIGURE 2** From interactomes to complexomes through the proteome. Intrinsic affinity interactomes (far left) can be combined with absolute proteomes of any cell type or state (middle left) in order to calculate the cellular complexome (middle right). This complexome can be used to re-rank partners according to their estimated cellular abundances (far right). While the units of measurement of intrinsic affinity interactomes are some sort of physical quantities, like binding energies, the units of complexomes are concentrations. These can be also converted to a number of complexes per cell, as approximately $10^{-4}$ nM molar concentration corresponds to 1 molecule/complex per cell in eukaryotes.[71] Note that for an entire organism, the interactome remains constant, while the proteome and the complexome vary between cells. The intrinsic equilibrium binding affinity profile of the PDZ-binding motif of ATP2B4 was obtained from the ProfAff database and the absolute HEK293T proteome was obtained from the OpenCell database.[14,27] The simplest bimolecular binding model was used to estimate the amounts of complexes.</figcaption></figure>

available affinity data and repositories to store them. Machine learning is a data driven method and it is known that "a dumb algorithm with lots of data beats a clever one with modest amounts of it."[67] The accuracy of this method is highly dependent on the availability of a large, high-quality training data set, where data quality can impact the model's predictive performance and insufficient data can prevent generalizability to new data.[68] That said, quantitative interactomic data naturally lends itself to machine learning and it is expected that experimental and computational quantitative interactomics will grow hand in hand.

## ESTIMATING STEADY-STATE OF COMPLEXOMES AND THEIR DYNAMIC CHANGES

Interactions can be studied by their intrinsic (e.g., binding constants) or extrinsic (e.g., cellular concentrations) parameters. Thanks to modern proteomics, we have easy ways to estimate the total bulk concentrations of protein molecules present in cellular extracts.[69] One of the main benefits of quantitative interactomics is that it could be used to convert experimental proteome information into estimated complexomes using computational modeling.

Using first-principle modeling, we can approximate the amounts of formed complexes under binding equilibrium, by combining these absolute concentrations with intrinsic affinities for every molecule pair.[26,27,51,70] Simple estimations of steady states can be very coarse, as they only consider the presence of interacting molecules in isotropic cells at complete binding equilibrium (Figure 2). Still, even an estimated complexome could provide a useful picture of biological mechanisms.[71-74] While quantitative proteomics showed us a reference scale about the total amounts of protein molecules in cells, we still lack such a scale for molecular complexes. Certainly, all molecular complexes are somewhat less abundant as their ingredients, but by how much? Are they roughly equal since the complexome is dominated by extremely stable complexes? Or are only a small fraction of molecules

in complex at a time? These simplistic calculations are also sufficient to show how weaker affinity complexes can arise in larger quantities than stronger ones due to higher abundances of their reactants. Since these calculations are simple and fast, they can also be used to rapidly translate observed proteomic-perturbations into complexomic perturbations, revealing how the complexomic landscape is rearranged by various factors, such as by mutations or by different environmental signals.

A more complete complexomic calculation can be performed by extending the number of reactants in the calculations from binary complexes (pairs of molecules) to whole signaling pathways or even the entire complexome, taking into account series of competing-cooperating interactions. For example, when members of the same domain family compete to bind a group of proteins containing a family of cognate recognition motifs, the overall steady-state of the complexomic network is determined by the affinities of all possible interactions, as well as the total concentration of every participating molecule. Using total cellular concentrations and either directly measured kinetic rate constants or arbitrary kinetic constants calculated from equilibrium binding affinities, rule-based binding simulations can be performed to estimate the overall steady-state of the complete system.[75]

Complexomic modeling can also be used to study dynamic cellular processes and not only steady-state binding equilibriums. For example, cellular signal transduction is a dynamic process, where the cellular content, for example, phosphorylation states, continuously changes over time. Quantitative interactomics can also be extended to study the properties of modification-dependent interactions, as we have shown in the past on phosphorylation and acetylation-dependent interactions.[76,77] These, in combination with phosphoproteomic data, can help us predict dynamic changes in complexomes during complex signaling events.[78] However, for future large-scale dynamic modeling, we will also need other parameters in addition to equilibrium binding constants, such as reaction rate constants and other kinetic parameters, whose measurement will likely be the next big challenge of quantitative interactomics.
The spatial resolution of any complexomic modeling is bound to the spatial resolution of the proteomic measurement, but not to the intrinsic biophysical properties, which do not have spatial dimensions. Cellular compartmentalization, a property inherent to eukaryotic cells, certainly leads to differential complexes across the cell. Fortunately, there are alternative approaches to bulk proteomics to quantify amounts of molecules in space.[79,80] Spatial proteome-complexome relationships were already implied based on past qualitative data, and future quantitative progress in the field may reveal the spatial self-organization principles of highly interconnected molecular networks in cells.[14,81] Although we may still be far from reaching the coverage of quantitative affinity interactomes sufficient for this type of complex first-principle modeling, establishing the clear conceptual difference between interactomes and complexes is definitely the first step towards this goal.

## COMPARATIVE QUANTITATIVE INTERACTOMICS AND COMPLEXOMICS

In many instances, interactomics is used to measure similarities or differences in binding properties between molecules. For example, the qualitative interactomes of many near-complete protein families were studied in depth to connect the particular roles of family-members with certain biological processes.[18,82,83] One of the main advantages of quantitative biophysical measurements is that instead of making qualitative deductions based on a simple list of interaction partners, we can use standardized and accurate metrics of similarities, such as ΔΔG values. This is often achieved by studying the affinity interactomes of a series of proteins, or even entire protein families, against a concise set of known interaction partners, such as peptide motifs, using conventional biophysical methods.[37,84] When quantitative interactomic approaches are performed on the proteomic scale, instead of analyzing a few selected individual interaction partners, we can consider entire binding profiles and quantify interactomic similarities through multidimensional affinity spaces by calculating Euclidean distances (e.g., cumulative ΔΔG values). As a result, quantitative interactomics is capable of determining a comprehensible, unbiased and reproducible picture of interactomic similarity, a critical asset to understanding differences between close paralogous proteins or differences that occur in turn due to post-translational modifications, or genetic mutations.

Any change in affinity interactomes, for example, induced by genetic variations, is expected to lead to altered cellular physiology. Nevertheless, much is still not yet understood regarding the interactome-phenotype relationship, because many layers of biological networks, in increasing levels of complexity, span between intrinsic properties and macroscopic phenotypes. Qualitative studies have shown that mutations drastically altering the interactome are often associated with diseases and that different mutations in the same gene causing different interactomic profiles are associated with distinguishable phenotypes.[85] Since these comparative interactomes were studied in a qualitative manner, experimentally surveyed interactomic impacts were categorized as either quasi-WT resulting in no detectable

changes, edgetic resulting in partial loss or gain of interactions, or quasi-null resulting in total abolishment of interactions. One clear disadvantage of qualitative assays is that they only consider "interacting" and "not interacting" molecules; therefore, mutations causing relative perturbations in biophysical traits of interactions go undetected. Interactions unaffected by mutations according to qualitative interactomic assays may still display enhanced or weakened affinities, and these unaccounted for effects could also contribute to phenotypes through slightly altered complexes. The accumulation of small relative variations in affinities or concentrations of several interactions can contribute to the development of disease just as much as large perturbations.

As briefly discussed above, much attention has been paid in the past not only to study the interactomic differences of different proteins or their variants, but also to study differences in the interaction networks formed in different cellular contexts.[9] Since the affinity interactome is an intrinsic property of an organism, biophysical constants remain the same in every cell of our body, unless somatic mutations occur. However, each cell has a unique proteome, which can vary markedly between different tissues; their unique cellular complexes can be used to measure changes in amounts of formed complexes between different cellular types and states. Related to this problem, pathogenic hijacking is of particular importance. Many host-pathogen interactions occur through PPIs where newly introduced pathogenic components interact and perturb an already established host interaction network.[86,87] As a constant intrinsic physical property, affinities of the host network remain unchanged, albeit the affinity interactomes of certain host proteins are extended with new interactions formed with the hijacking proteins. However, depending on the type of the infected host cell, large variations are expected in hijacked complexes.[26] In addition, infection can often trigger extensive proteomic changes in the host cell.[27,88] Shifts in the abundance of available partners are reflected during complex formation, leading to different complexomic landscapes at various stages of infection, and studying these changes may not only give us insight into the pathophysiology of infectious disease, but can help identify key targets at critical time points during infection to block pathogenic reprogramming in cells.

## UNDERSTANDING SPECIFICITY AND PROMISCUITY IN THE LIGHT OF UNBIASED INTERACTOMES

Specific interactions can be described according to the law of mass action, while nonspecific binding refers to different kinds of interactions, such as those resulting from the Debye-Hückel theory.[1] For simplicity, if we assume specific binding between any pairs of protein molecules and if we only consider a single proteoform per protein, the entire unbiased human affinity PPI interactome consists of 300,000,000 "combinatorial" interactions. Albeit the numerical values of these affinities may slightly change with changing conditions in theory, in practice they can be considered constants because most conditions, such as temperature, pressure, or ionic strength, only vary
<figure><img src="image_7.png" /><figcaption>**FIGURE 3** Affinity weighting can reveal key determinants of optimal binding. For interactions mediated by short linear motifs, sequence LOGO representation is often used to reveal specificity determinants. We show three examples of different LOGO representations of PDZ interactions with or without affinity weighting using data taken from the ProfAff database.[27] Sequences of interaction partners were taken above a 4.0 pK<sub>d</sub> affinity threshold (red dashed line) and the number of partner sequences with affinities above the threshold used for calculations is indicated on the affinity profiles. In affinity-weighted LOGO calculations, the same set of interacting peptide sequences was used but their corresponding affinities were also considered as described in detail before.[27] In frequency LOGO calculations, the frequencies of each amino acid are counted at every motif position. In relative frequency LOGO calculations, the amino acid frequencies of interacting peptides are normalized to the amino acid frequencies of all assayed peptides (including the “non-binder” sequences). In entropy LOGO calculations, the frequencies of each amino acid are converted to Shannon entropies. Red arrows indicate some marked differences that only appear after affinity weighting; preference for Leu over Val at the C-terminal position of ARHGEF12 interacting peptides; preference for aromatic residues of TX1BP3 interacting peptides and the preference of an Arg residue for high-affinity MAGI1 interaction motifs.</figcaption></figure>

within a very narrow range under physiological conditions, with the
notable exception of concentrations of certain key components, such as
Mg²⁺ or H₃O⁺ ions. While most of these combinatorial interactions are
likely to display extremely weak affinities, they are unlikely to lead to
substantial complex formation under physiological cellular conditions.
However, even these interactions are likely to contribute to the forma-
tion of cellular complexomes and therefore should be considered when
discussing specificity or promiscuity.[89]

Based on more than 65,000 measured interactions including more
than 18,000 quantified steady-state fragmentomic affinities of PDZ
domains, we have found that the binding free energies follow an
exponential distribution that may relate to the Boltzmann distribu-
tion, similarly to the distribution of other energy-related properties of
molecules and interactions.[27] Although this observation was made
for highly similar domain-motif interactions, the affinities of more
diverse types of interactions show similar trends.[51] Consequently,
affinity interactomes are dominated by weak interactions, and only a
small fraction of all detectable partners mediate the strongest affini-
ties. Based on the overall shape of such affinity profile or, in other
words, based on the affinity distribution of the interactome, the
promiscuity of the selected molecule can be determined[77] (Figure 3).
A highly promiscuous molecule has many interaction partners with sim-
ilar affinities, none of which display outstanding affinity. In contrast,
a less promiscuous molecule will have at least one interaction part-
ner, which will display much greater affinity than the others. While it
is tempting to call such a molecule highly specific or to say that its

interactome is specific, it should be kept in mind that even the weaker
affinity interactions are specific by definition. Therefore, from a prag-
matic biochemical perspective, it is highly improbable to find molecules
with exclusively specific interactomic profiles, and it is plausible that
even the most specific molecules will display weak affinities against all
other molecules present in our body.

Since quantitative interactomics is most advanced for domain-motif
interactions, we could not avoid mentioning the specificity determi-
nants of such interactions in the light of affinity profiles. Convention-
ally, binding preferences of these interactions are often studied with
bioinformatic approaches, such as position-specific scoring matrices
(PSSM), and specificity determinants are often visualized in the form
of sequence LOGOs.[90-92] However, most of these approaches do not
discriminate between interaction partners that display strong or weak
affinities and only define “binders” and “nonbinders.” We previously
proposed that such calculations could be modified in a way to take
affinity information into account and this way, affinity-weighted PSSMs
or LOGO representation could be an important asset to visualize
determinants of high affinity interactions.[27]

CONCLUSION

Quantitative interactomics has already shown in many cases how
powerful can it be in answering key biological questions. In this
essay, instead of providing a comprehensive review about these major
achievements, we tried to focus on important limitations of current trends and provide an alternative perspective that could go beyond the boundaries of current interactomics. In the light of recent advances, many of the original technical limitations of interactomics, which can stand in the way of quantitative measurements, have almost been overcome. We believe that the time has come for interactomics to return to its quantitative roots. Qualitative interactomics has already given us a nearly comprehensive map of the molecular wiring of our cells.[93] Although there will be much to come until proteome-wide comprehensive affinity maps will be available, on a case-by-case basis, researchers can already consider alternative quantitative approaches over conventional ones. With the help of these, there are deeper questions that can be addressed, such as how strong are the intrinsic affinities of complexes of cellular molecules or how much can be found from them under given cellular conditions. Ultimately, turning the field into a more quantitative direction will help us to describe the cellular distribution of complexes and their alterations in disease.

## ACKNOWLEDGMENTS

We are grateful to all our colleagues and friends for their enlightening discussions and support. In particular, we would like to thank Gilles Trave for our stimulating discussions, which have inspired most of the ideas in this article.

## CONFLICT OF INTEREST STATEMENT

The authors have no conflict of interest to declare.

## DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

## ORCID

Gergo Gogl https://orcid.org/0000-0002-8597-3711

## REFERENCES

1. Wyman, J., & Gill, S. J. (1990). *Binding and linkage*. University Science Books, Mill Valley, CA.

2. Ivarsson, Y., & Jemth, P. (2019). Affinity and specificity of motif-based protein-protein interactions. *Current Opinion in Structural Biology*, **54**, 26-33. https://doi.org/10.1016/j.sbi.2018.09.009

3. Jarmoskaite, I., Alsadhan, I., Vaidyanathan, P. P., & Herschlag, D. (2020). How to measure and evaluate binding affinities. *ELife*, **9**, 1-34. https://doi.org/10.7554/ELIFE.57264

4. Pollard, T. D. (2010). A guide to simple and informative binding assays. *Molecular Biology of the Cell*, **21**(23), 4061-4067. https://doi.org/10.1091/mbc.E10-08-0683

5. Corzo, J., & Santamaria, M. (2006). Time, the forgotten dimension of ligand binding teaching. *Biochemistry and Molecular Biology Education*, **34**(6), 413-416. https://doi.org/10.1002/bmb.2006494034062678

6. Bludau, I., & Aebersold, R. (2020). Proteomic and interactomic insights into the molecular basis of cell functional diversity. *Nature Reviews Molecular Cell Biology*, **21**(6), 327-340. https://doi.org/10.1038/s41580-020-0231-2

7. Varjosalo, M., Sacco, R., Stukalov, A., Van Drogen, A., Planyavsky, M., Hauri, S., Aebersold, R., Bennett, K. L., Colinge, J., Gstaiger, M., & Superti-Furga, G. (2013). Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS. *Nature Methods*, **10**(4), 307-314. https://doi.org/10.1038/nmeth.2400

8. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: A software environment for integrated models. *Genome Research*, **13**(11), 2498-2504. https://doi.org/10.1101/gr.1239303

9. Vidal, M., Cusick, M. E., & Barabási, A. L. (2011). Interactome networks and human disease. *Cell*, **144**(6), 986-998. https://doi.org/10.1016/j.cell.2011.02.016

10. Kovács, I. A., Luck, K., Spirohn, K., Wang, Y., Pollis, C., Schlabach, S., Bian, W., Kim, D. K., Kishore, N., Hao, T., Calderwood, M. A., Vidal, M., & Barabási, A. L. (2019). Network-based prediction of protein interactions. *Nature Communications*, **10**(1), 1-8. https://doi.org/10.1038/s41467-019-09177-y

11. Fields, S., & Song, O. K. (1989). A novel genetic system to detect protein-protein interactions. *Nature*, **340**(6230), 245-246. https://doi.org/10.1038/340245a0

12. Young, K. H. (1998). Yeast two-hybrid: So many interactions, (in) so little time. *Biology of Reproduction*, **58**(2), 302-311. https://doi.org/10.1095/biolreprod58.2.302

13. Luck, K., Kim, D. K., Lambourne, L., Spirohn, K., Begg, B. E., Bian, W., Brignall, R., Cafarelli, T., Campos-Laborie, F. J., Charoteaux, B., Choi, D., Coté, A. G., Daley, M., Deimling, S., Desbuleux, A., Dricot, A., Gebbia, M., Hardy, M. F., Kishore, N., ... Calderwood, M. A. (2020). A reference map of the human binary protein interactome. *Nature*, **580**(7803), 402-408. https://doi.org/10.1038/s41586-020-2188-x

14. Cho, N. H., Cheveralls, K. C., Brunner, A.-D., Kim, K., Michaelis, A. C., Raghavan, P., Kobayashi, H., Savy, L., Li, J. Y., Canaj, H., Kim, J. Y. S., Stewart, E. M., Gnann, C., McCarthy, F., Cabrera, J. P., Brunetti, R. M., Chhun, B. B., Dingle, G., Hein, M. Y., ... Leonetti, M. D. (2022). OpenCell: Endogenous tagging for the cartography of human cellular organization. *Science*, **375**, eabi6983. https://doi.org/10.1126/science.abi6983

15. Huttlin, E. L., Bruckner, R. J., Paulo, J. A., Cannon, J. R., Ting, L., Baltier, K., Colby, G., Gebreab, F., Gygi, M. P., Parzen, H., Szpyt, J., Tam, S., Zarraga, G., Pontano-Vaites, L., Swarup, S., White, A. E., Schweppe, D. K., Rad, R., Erickson, B. K., ... Harper, J. W. (2017). Architecture of the human interactome defines protein communities and disease networks. *Nature*, **545**(7655), 505-509. https://doi.org/10.1038/nature22366

16. Huttlin, E. L., Bruckner, R. J., Navarrete-Perea, J., Cannon, J. R., Baltier, K., Gebreab, F., Gygi, M. P., Thornock, A., Zarraga, G., Tam, S., Szpyt, J., Gassaway, B. M., Panov, A., Parzen, H., Fu, S., Golbazi, A., Maenpaa, E., Stricker, K., Guha Thakurta, S., ... Gygi, S. P. (2021). Dual proteome-scale networks reveal cell-specific remodeling of the human interactome. *Cell*, **184**(11), 3022-3040.e28. https://doi.org/10.1016/j.cell.2021.04.011

17. Rhee, H., Zou, P., Udeshi, N. D., Martell, J. D., Mootha, V. K., Carr, S. A., & Ting, A. Y. (2013). Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging. *Science*, **339**(3), 1328.

18. Göös, H., Kinnunen, M., Salokas, K., Tan, Z., Liu, X., Yadav, L., Zhang, Q., Wei, G. H., & Varjosalo, M. (2022). Human transcription factor protein interaction networks. *Nature Communications*, **13**(1), 1-16. https://doi.org/10.1038/s41467-022-28341-5

19. Roux, K. J., Kim, D. I., Burke, B., & May, D. G. (2018). BioID: A screen for protein-protein interactions. *Current Protocols in Protein ScienceCurrent Protocols in Protein Science*, **91**(1), 19.23.1-19.23.15. https://doi.org/10.1002/cpps.51

20. Skinnider, M. A., & Foster, L. J. (2021). Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments. *Nature Methods*, **18**(7), 806-815. https://doi.org/10.1038/s41592-021-01194-4

21. Havugimana, P. C., Goel, R. K., Phanse, S., Youssef, A., Padhorny, D., Kotelnikov, S., Kozakov, D., & Emili, A. (2022). Scalable multiplex co-fractionation/mass spectrometry platform for accelerated protein interactome discovery. *Nature Communications*, **13**(1), 1-8. https://doi.org/10.1038/s41467-022-31809-z
22. Hein, M. Y., Hubner, N. C., Poser, I., Cox, J., Nagaraj, N., Toyoda, Y., Gak, I. A., Weisswange, I., Mansfeld, J., Buchholz, F., Hyman, A. A., & Mann, M. (2015). A Human Interactome in three quantitative dimensions organized by stoichiometries and abundances. *Cell*, **163**(3), 712–723. https://doi.org/10.1016/j.cell.2015.09.053

23. Ciuffa, R., Uliana, F., Mannion, J., Mehnert, M., Tenev, T., Marulli, C., Satanowski, A., Keller, L. M. L., Ramírez, P. N. R., Ori, A., Gstaiger, M., Meier, P., & Aebersold, R. (2022). Novel biochemical, structural, and systems insights into inflammatory signaling revealed by contextual interaction proteomics. *Proceedings of the National Academy of Sciences of the United States of America*, **119**(40), 1–12. https://doi.org/10.1073/pnas.2117175119

24. Salas, D., Stacey, R. G., Akinlaja, M., & Foster, L. J. (2020). Next-generation interactomics: Considerations for the use of co-elution to measure protein interaction networks. *Molecular and Cellular Proteomics*, **19**(1), 1–10. https://doi.org/10.1074/mcp.R119.001803

25. Bludau, I., Nicod, C., Martelli, C., Xue, P., Heusel, M., Fossati, A., Uliana, F., Frommelt, F., Aebersold, R., & Collins, B. C. (2022). Rapid profiling of protein complex re-organization in perturbed systems. *BioRxiv*, 1–33. https://www.biorxiv.org/content/10.1101/2021.12.17.473177v2

26. Gogl, G., Tugaeva, K., Eberling, P., Kostmann, C., Trave, G., & Sluchanko, N. N. (2021). Hierarchized phosphotarget binding by the seven human 14-3-3 isoforms. *Nature Communications*, **12**(1677), 1–12. https://doi.org/10.1038/s41467-021-21908-8

27. Gogl, G., Zambo, B., Kostmann, C., Cousido-Siah, A., Morlet, B., Durbesson, F., Negroni, L., Eberling, P., Jane, P., Nomine, Y., Zeke, A., Ostergaard, S., Monsellier, E., Vincentelli, R., & Trave, G. (2022). Quantitative fragmentomics allow affinity mapping of interactomes. *Nature Communications*, **13**(1), 1–18.

28. Luck, K., & Travé, G. (2011). Phage display can select over-hydrophobic sequences that may impair prediction of natural domain-peptide interactions. *Bioinformatics*, **27**(7), 899–902. https://doi.org/10.1093/bioinformatics/btr060

29. Goll, J., & Uetz, P. (2006). The elusive yeast interactome. *Genome Biology*, **7**(6), 223. https://doi.org/10.1186/gb-2006-7-6-223

30. Sharifi Tabar, M., Parsania, C., Chen, H., Su, X. D., Bailey, C. G., & Rasko, J. E. J. (2022). Illuminating the dark protein-protein interactome. *Cell Reports Methods*, **2**(8), 100275. https://doi.org/10.1016/j.crmeth.2022.100275

31. Mei, S., & Zhang, K. (2019). Neglog: Homology-based negative data sampling method for genome-scale reconstruction of human protein-protein interaction networks. *International Journal of Molecular Sciences*, **20**(20), 5075. https://doi.org/10.3390/ijms20205075

32. Blohm, P., Frishman, G., Smialowski, P., Goebels, F., Wachinger, B., Ruepp, A., & Frishman, D. (2014). Negatome 2.0: A database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis. *Nucleic Acids Research*, **42**(D1), D396–D400. https://doi.org/10.1093/nar/gkt1079

33. Giurgiu, M., Reinhard, J., Brauner, B., Dunger-Kaltenbach, I., Fobo, G., Frishman, G., Montrone, C., & Ruepp, A. (2019). CORUM: The comprehensive resource of mammalian protein complexes -2019. *Nucleic Acids Research*, **47**(D1), D559–D563. https://doi.org/10.1093/nar/gky973

34. Hu, H., Columbus, J., Zhang, Y., Wu, D., Lian, L., Yang, S., Goodwin, J., Luczak, C., Carter, M., Chen, L., James, M., Davis, R., Sudol, M., Rodwell, J., & Herrero, J. J. (2004). A map of WW domain family interactions. *Proteomics*, **4**(3), 643–655. https://doi.org/10.1002/pmic.200300632

35. Smith, M. J., Hardy, W. R., Murphy, J. M., Jones, N., & Pawson, T. (2006). Screening for PTB domain binding partners and ligand specificity using proteome-derived NPXY peptide arrays. *Molecular and Cellular Biology*, **26**(22), 8461–8474. https://doi.org/10.1128/mcb.01491-06

36. Stiffler, M. A., Chen, J. R., Grantcharova, V. P., Lei, Y., Fuchs, D., Allen, J. E., Zaslavskaia, L. A., & MacBeath, G. (2007). PDZ domain binding selectivity is optimized across the mouse proteome. *Science*, **317**(10), 364–370.

37. Simon, M., Gógl, G., Ecsédi, P., Póti Á., Kardos, J., & Nyitray, L. (2020). High throughput competitive fluorescence polarization assay reveals functional redundancy in the S100 protein family. *FEBS Journal*, **287**(13), 1–13. https://febs.onlinelibrary.wiley.com/doi/10.1111/febs.15175

38. Delalande, F., Gogl, G., Rohrbacher, A., Kostmann, C., Eberling, P., Carapito, C., Travé, G., & Monsellier, E. (2022). The Holdup Multiplex, an assay for high-throughput measurement of protein-ligand affinity constants using a mass-spectrometry readout. *BioRxiv*, 1–36.

39. Hause, R. J., Leung, K. K., Barkinge, J. L., Ciaccio, M. F., Chuu, C., & Jones, R. B. (2012). Comprehensive binary interaction mapping of SH2 domains via fluorescence polarization reveals novel functional diversification of ErbB receptors. *PLoS ONE*, **7**(9), e44471. https://doi.org/10.1371/journal.pone.0044471

40. Tompa, P., Davey, N. E., Gibson, T. J., & Babu, M. M. (2014). A million peptide motifs for the molecular biologist. *Molecular Cell*, **55**(2), 161–169. https://doi.org/10.1016/j.molcel.2014.05.032

41. Benz, C., Ali, M., Krystkowiak, I., Simonetti, L., Sayadi, A., Mihalic, F., Kliche, J., Andersson, E., Jemth, P., Davey, N. E., & Ivarsson, Y. (2022). Proteome-scale mapping of binding sites in the unstructured regions of the human proteome. *Molecular Systems Biology*, **18**(1), 1–27. https://doi.org/10.15252/msb.202110584

42. Jerabek-Willemsen, M., André, T., Wanner, R., Roth, H. M., Duhr, S., Baaske, P., & Breitsprecher, D. (2014). MicroScale Thermophoresis: Interaction analysis and beyond. *Journal of Molecular Structure*, **1077**, 101–113. https://doi.org/10.1016/j.molstruc.2014.03.009

43. Nguyen, H. Q., Roy, J., Harink, B., Damle, N. P., Latorraca, N. R., Baxter, B. C., Brower, K., Longwell, S. A., Kortemme, T., Thorn, K. S., Cyert, M. S., & Fordyce, P. M. (2019). Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads. *ELife*, 8(e40499), 1–28. https://doi.org/10.7554/eLife.40499

44. Stefan, M. I., & Le Novère, N. (2013). Cooperative binding. *PLoS Computational Biology*, **9**(6), 2–7. https://doi.org/10.1371/journal.pcbi.1003106

45. Cluet, D., Amri, I., Vergier, B., Léault, J., Audibert, A., Grosjean, C., Calabrési, D., & Spichty, M. (2020). A quantitative tri-fluorescent yeast two-hybrid system: From flow cytometry to in cellula affinities. *Molecular and Cellular Proteomics*, **19**(4), 701–715. https://doi.org/10.1074/mcp.TIR119.001692

46. Song, Y., Madahar, V., & Liao, J. (2011). Development of FRET assay into quantitative and high-throughput screening technology platforms for protein-protein interactions. *Annals of Biomedical Engineering*, **39**(4), 1224–1234. https://doi.org/10.1007/s10439-010-0225-x

47. Yasgar, A., Jadhav, A., Simeonov, A., & Coussens, N. P. (2016). AlphaScreen-based assays: Ultra-high-throughput screening for small-molecule inhibitors of challenging enzymes and protein-protein interactions. *High Throughput Screening*, 1, 77–98. https://doi.org/10.1007/978-1-4939-3673-1

48. Degorce, F., Card, A., Soh, S., Trinquet, E., Knapik, G. P., & Xie, B. (2009). HTRF: A technology tailored for drug discovery - A review of theoretical aspects and recent applications. *Current Chemical Genomics*, **3**(1), 915–933. https://doi.org/10.1007/s10236-017-1064-1

49. Trepte, P., Kruse, S., Kostova, S., Hoffmann, S., Buntru, A., Tempelmeier, A., Secker, C., Diez, L., Schulz, A., Klockmeier, K., Zenkner, M., Golusik, S., Rau, K., Schnoegl, S., Garner, C. C., & Wanker, E. E. (2018). LuTHy: A double-readout bioluminescence-based two-hybrid technology for quantitative mapping of protein-protein interactions in mammalian cells. *Molecular Systems Biology*, **14**(7), 1–20. https://doi.org/10.15252/msb.20178071

50. Erlendsson, S., Thorsen, T. S., Vauquelin, G., Ammendrup-Johnsen, I., Wirth, V., Martinez, K. L., Teilum, K., Gether, U., & Madsen, K. L. (2019). Mechanisms of pdz domain scaffold assembly illuminated by use of supported cell membrane sheets. *ELife*, 8, 1–27. https://doi.org/10.7554/eLife.39180
51. Zambo, B., Morlet, B., Negroni, L., Trave, G., & Gogl, G. (2022). Native holdup (nHU) to measure binding affinities from cell extracts. *Science Advances*, 8(51), eade3828.

52. Aebersold, R., Agar, J. N., Amster, I. J., Baker, M. S., Bertozzi, C. R., Boja, E. S., Costello, C. E., Cravatt, B. F., Fenselau, C., Garcia, B. A., Ge, Y., Gunawardena, J., Hendrickson, R. C., Hergenrother, P. J., Huber, C. G., Ivanov, A. R., Jensen, O. N., Jewett, M. C., Kelleher, N. L., ... Zhang, B. (2018). How many human proteoforms are there? *Nature Chemical Biology*, 14(3), 206–214. https://doi.org/10.1038/nchembio.2576

53. Orchard, S., Ammari, M., Aranda, B., Breuza, L., Briganti, L., Broackes-Carter, F., Campbell, N. H., Chavali, G., Chen, C., Del-Toro, N., Duesbury, M., Dumousseau, M., Galeota, E., Hinz, U., Iannuccelli, M., Jagannathan, S., Jimenez, R., Khadake, J., Lagreid, A., ... Hermjakob, H. (2014). The MIntAct project - IntAct as a common curation platform for 11 molecular interaction databases. *Nucleic Acids Research*, 42(D1), D358–D363. https://doi.org/10.1093/nar/gkt1115

54. Oughtred, R., Rust, J., Chang, C., Breitkreutz, B. J., Stark, C., Willems, A., Boucher, L., Leung, G., Kolas, N., Zhang, F., Dolma, S., Coulombe-Huntington, J., Chatr-aryamontri, A., Dolinski, K., & Tyers, M. (2021). The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. *Protein Science*, 30(1), 187–200. https://doi.org/10.1002/pro.3978

55. Salwinski, L., Miller, C. S., Smith, A. J., Pettit, F. K., Bowie, J. U., & Eisenberg, D. (2004). The database of interacting proteins: 2004 update. *Nucleic Acids Research*, 32(DATABASE ISS.), 449D–451. https://doi.org/10.1093/nar/gkh086

56. Szklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., Doncheva, N. T., Legeay, M., Fang, T., Bork, P., Jensen, L. J., & von Mering, C. (2021). The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. *Nucleic Acids Research*, 49 manuscript. https://doi.org/10.1093/nar/gkab835

57. Wassermann, A. M., & Bajorath, J. (2011). BindingDB and ChEMBL: Online compound databases for drug discovery. *Expert Opinion on Drug Discovery*, 6(7), 683–687. https://doi.org/10.1517/17460441.2011.579100

58. Wang, R., Fang, X., Lu, Y., Yang, C. Y., & Wang, S. (2005). The PDBbind database: Methodologies and updates. *Journal of Medicinal Chemistry*, 48(12), 4111–4119. https://doi.org/10.1021/jm048957q

59. Nikam, R., Kulandaisamy, A., Harini, K., Sharma, D., & Michael Gromiha, M. (2021). ProThermDB: Thermodynamic database for proteins and mutants revisited after 15 years. *Nucleic Acids Research*, 49(D1), D420–D424. https://doi.org/10.1093/nar/gkaa1035

60. Siva Shanmugam, N. R., Jino Blessy, J., Veluraja, K., & Michael Gromiha, M. (2020). ProCaff: Protein-carbohydrate complex binding affinity database. *Bioinformatics*, 36(11), 3615–3617. https://doi.org/10.1093/bioinformatics/btaa141

61. Harini, K., Srivastava, A., Kulandaisamy, A., & Gromiha, M. M. (2022). ProNAB: Database for binding affinities of protein-nucleic acid complexes and their mutants. *Nucleic Acids Research*, 50(D1), D1528–D1534. https://doi.org/10.1093/nar/gkab848

62. Kastritis, P. L., & Bonvin, A. M. J. J. (2013). On the binding affinity of macromolecular interactions: Daring to ask why proteins interact. *Journal of the Royal Society Interface*, 10(79), 20120835. https://doi.org/10.1098/rsif.2012.0835

63. Yan, Z., Guo, L., Hu, L., & Wang, J. (2013). Specificity and affinity quantification of protein-protein interactions. *Bioinformatics* (Oxford, England), 29(9), 1127–1133. https://doi.org/10.1093/bioinformatics/btt121

64. Wang, D. D., Chan, M. T., & Yan, H. (2021). Structure-based protein-ligand interaction fingerprints for binding affinity prediction. *Computational and Structural Biotechnology Journal*, 19, 6291–6300. https://doi.org/10.1016/j.csbj.2021.11.018

65. Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A.,

Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., ... Hassabis, D. (2021). Highly accurate protein structure prediction with AlphaFold. *Nature*, 596, 583–589. https://doi.org/10.1038/s41586-021-03819-2

66. Chang, L., & Perez, A. (2022). Ranking peptide binders by affinity with AlphaFold. *Angewandte Chemie International Edition*, 2022, e202213362. https://doi.org/10.1002/anie.202213362

67. Domingos, P. (2012). A few useful things to know about machine learning. *Communications of the ACM*, 55(10), 78–87. https://dl.acm.org/citation.cfm?id=2347755

68. Dias, R., & Kolaczkowski, B. (2017). Improving the accuracy of high-throughput protein-protein affinity prediction may require better training data. *BMC Bioinformatics*, 18(suppl 5), 7–18. https://doi.org/10.1186/s12859-017-1533-z

69. Wiśniewski, J. R., Hein, M. Y., Cox, J., & Mann, M. (2014). A “proteomic ruler” for protein copy number and concentration estimation without spike-in standards. *Molecular and Cellular Proteomics*, 13(12), 3497–3506. https://doi.org/10.1074/mcp.M113.037309

70. Catozzi, S., Halasz, M., & Kiel, C. (2021). Predicted 'wiring landscape' of Ras-effector interactions in 29 human tissues. *Npj Systems Biology and Applications*, 7(1), 1–15. https://doi.org/10.1038/s41540-021-00170-0

71. Milo, R., Jorgensen, P., Moran, U., Weber, G., & Springer, M. (2009). BioNumbers—The database of key numbers in molecular and cell biology. *Nucleic Acids Research*, 38(1), D750–D753. https://doi.org/10.1093/nar/gkp889

72. Flamholz, A., Phillips, R., & Milo, R. (2014). The quantified cell. *Molecular Biology of the Cell*, 25(22), 3497–3500. https://doi.org/10.1091/mbc.E14-09-1347

73. Phillips, R., & Milo, R. (2009). A feeling for the numbers in biology. *Proceedings of the National Academy of Sciences of the United States of America*, 106(51), 21465–21471. https://doi.org/10.1073/pnas.0907732106

74. Liberali, P., & Pelkmans, L. (2012). Towards quantitative cell biology. *Nature Cell Biology*, 14(12), 1233. https://doi.org/10.1038/ncb2648

75. Harris, L. A., Hogg, J. S., Tapia, J. J., Sekar, J. A. P., Gupta, S., Korsunsky, I., Arora, A., Barua, D., Sheehan, R. P., & Faeder, J. R. (2016). BioNetGen 2.2: Advances in rule-based modeling. *Bioinformatics*, 32(21), 3366–3368. https://doi.org/10.1093/bioinformatics/btw469

76. Gogl, G., Jane, P., Caillet-Saguy, C., Kostmann, C., Bich, G., Cousido-Siah, A., Nyitray, L., Vincentelli, R., Wolff, N., Nomine, Y., Sluchanko, N. N., & Trave, G. (2020). Dual specificity PDZ- and 14-3-3-binding motifs: A structural and interactomics study. *Structure*, 28(7), 747–759.e3. https://doi.org/10.1016/j.str.2020.03.010

77. Jané, P., Gógl, G., Kostmann, C., Bich, G., Girault, V., Caillet-Saguy, C., Eberling, P., Vincentelli, R., Wolff, N., Travé, G., & Nominé, Y. (2020). Interactomic affinity profiling by holdup assay: acetylation and distal residues impact the PDZome-binding specificity of PTEN phosphatase. *PloS One*, 15(12), 1–24. https://doi.org/10.1371/journal.pone.0244613

78. Gógl, G., Biri-Kovács, B., Durbesson, F., Jane, P., Nomine, Y., Kostmann, C., Bilics, V., Simon, M., Reményi, A., Vincentelli, R., Trave, G., & Nyitray, L. (2019). Rewiring of RSK-PDZ interactome by linear motif phosphorylation. *Journal of Molecular Biology*, 431(6), 1234–1249. https://doi.org/10.1016/j.jmb.2019.01.038

79. Politi, A. Z., Cai, Y., Walther, N., Hossain, M. J., Koch, B., Wachsmuth, M., & Ellenberg, J. (2018). Quantitative mapping of fluorescently tagged cellular proteins using FCS-calibrated four-dimensional imaging. *Nature Protocols*, 13(6), 1445–1464. https://doi.org/10.1038/nprot.2018.040

80. Lundberg, E., & Borner, G. H. H. (2019). Spatial proteomics: A powerful discovery tool for cell biology. *Nature Reviews Molecular Cell Biology*, 20(5), 285–302. https://doi.org/10.1038/s41580-018-0094-y

81. Qin, Y., Huttlin, E. L., Winsnes, C. F., Gosztyla, M. L., Wacheul, L., Kelly, M. R., Blue, S. M., Zheng, F., Chen, M., Schaffer, L. V., Licon, K.,
Bäckström, A., Vaites, L. P., Lee, J. J., Ouyang, W., Liu, S. N., Zhang, T., Silva, E., Park, J., ... Ideker, T. (2021). A multi-scale map of cell structure fusing protein images and interactions. *Nature*, **600**(7889), 536–542.
https://doi.org/10.1038/s41586-021-04115-9

82. Neveu, G., Cassonnet, P., Vidalain, P. O., Rolloy, C., Mendoza, J., Jones, L., Tangy, F., Muller, M., Demeret, C., Tafforeau, L., Lotteau, V., Rabourdin-Combe, C., Travé, G., Dricot, A., Hill, D. E., Vidal, M., Favre, M., & Jacob, Y. (2012). Comparative analysis of virus-host interactions with a mammalian high-throughput protein complementation assay based on Gaussia princeps luciferase. *Methods*, **58**(4), 349–359.
https://doi.org/10.1016/j.ymeth.2012.07.029

83. Buljan, M., Ciuffa, R., van Drogen, A., Vichalkovski, A., Mehnert, M., Rosenberger, G., Lee, S., Varjosalo, M., Pernas, L. E., Spegg, V., Snijder, B., Aebersold, R., & Gstaiger, M. (2020). Kinase interaction network expands functional and disease roles of human kinases. *Molecular Cell*, **79**(3), 504–520.e9.
https://doi.org/10.1016/j.molcel.2020.07.001

84. Garai, Á., Zeke, A., Gógl, G., Törő, I., Ferenc, F., Blankenburg, H., Bárkai, T., Varga, J., Alexa, A., Emig, D., Albrecht, M., & Reményi, A. (2012). Specificity of linear motifs that bind to a common mitogen-activated protein kinase docking groove. *Science Signaling*, **5**(245), ra74.
https://doi.org/10.1126/scisignal.2003004.Specificity

85. Sahni, N., Yi, S., Taipale, M., Fuxman Bass, J. I., Coulombe-Huntington, J., Yang, F., Peng, J., Weile, J., Karras, G. I., Wang, Y., Kovács, I. A., Kamburov, A., Krykbaeva, I., Lam, M. H., Tucker, G., Khurana, V., Sharma, A., Liu, Y. Y., Yachie, N., ... Vidal, M. (2015). Widespread macromolecular interaction perturbations in human genetic disorders. *Cell*, **161**(3), 647–660.
https://doi.org/10.1016/j.cell.2015.04.013

86. Davey, N. E., Travé, G., & Gibson, T. J. (2011). How viruses hijack cell regulation. *Trends in Biochemical Sciences*, **36**(3), 159–169.
https://doi.org/10.1016/j.tibs.2010.10.002

87. Sámano-Sánchez, H., & Gibson, T. J. (2020). Mimicry of short linear motifs by bacterial pathogens: A drugging opportunity. *Trends in Biochemical Sciences*, **45**(6), 526–544.
https://doi.org/10.1016/j.tibs.2020.03.003

88. Paget-Bailly, P., Meznad, K., Bruyère, D., Perrard, J., Herfs, M., Jung, A. C., Mougin, C., Prétet, J. L., & Baguet, A. (2019). Comparative RNA sequencing reveals that HPV16 E6 abrogates the effect of E6*I on ROS metabolism. *Scientific Reports*, **9**(1), 1–15.
https://doi.org/10.1038/s41598-019-42393-6

89. Hofmann, J. L., Maheshwari, A. J., Sunol, A. M., Endy, D., & Zia, R. N. (2022). Ultra-weak protein-protein interactions can modulate proteome-wide searching and binding. *BioRxiv*, 1-36.
https://www.biorxiv.org/content/10.1101/2022.09.30.510365v1

90. Krystkowiak, I., Manguy, J., & Davey, N. E. (2018). PSSMSearch: A server for modeling, visualization, proteome-wide discovery and annotation of protein motif specificity determinants. *Nucleic Acids Research*, **46**(W1), W235–W241.
https://doi.org/10.1093/nar/gky426

91. Schneider, T. D., & Stephens, R. M. (1990). Sequence logos: A new way to display consensus sequences. *Nucleic Acids Research*, **18**(20), 6097–6100.
https://doi.org/10.1093/nar/18.20.6097

92. Crooks, G. E., Hon, G., Chandonia, J. M., & Brenner, S. E. (2004). WebLogo: A sequence logo generator. *Genome Research*, **14**(6), 1188–1190.
https://doi.org/10.1101/gr.849004

93. Lambourne, L., Yadav, A., Wang, Y., Desbuleux, A., Kim, D.-K., Cafarelli, T., Pons, C., Kovács, I. A., Jailkhani, N., Schlabach, S., Ridder, D. D., Luck, K., Bian, W., Shen, Y., Yang, Z., Mee, M. W., Helmy, M., Jacob, Y., Lemmens, I., ... Vidal, M. (2022). Binary interactome models of inner-versus outer-complexome organisation. *BioRxiv*, 1-89.
http://biorxiv.org/content/early/2022/07/22/2021.03.16.435663 abstract

**How to cite this article:** Weimer, K., Zambo, B., & Gogl, G. (2023). Molecules interact. But how strong and how much? *BioEssays*, **45**, e2300007.
https://doi.org/10.1002/bies.202300007