Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?
1. Introduction: Why Is There a Need for Elaborate Parameters to Describe the Properties of Cellular Components?
- Define and identify informative cell properties (i.e., features or attributes). The choice of considering a particular property is somewhat arbitrary and depends on the function that is being explored. As an example, the set of gene transcription rates may be thought to account for a cell differentiation state . More transient properties are cell polarization, i.e., asymmetrical organization, or motility state: whether a cell is immobile on a surface or migrating throughout a living organism. The activity of a particular signaling pathway is another example. A cell state may be defined as the time-dependent set of values of a suitable group of properties.
- Define and identify the pieces of information, or signals, provided to cells by the extracellular medium. A signal may consist of the application of a force on a region of the cell membrane or binding of a ligand to a cell surface receptor. As will be discussed below, multiple binding interactions are continually formed and broken, and a choice may be necessary to define significant signals, which depends on the function that is being explored. Additionally, the effect of a signal may be strongly dependent on its localization and temporal evolution. These features must, therefore, be included in the parameters used to describe signals.
- Discover rules allowing to predict the temporal evolution of cell states as a function of received signals. It must be emphasized that the way rules are expressed may involve somewhat hidden assumptions. Thus, it may be implicitly assumed that the description of a cell state does not need to include its history, i.e., that cell evolution may be described as a Markov process, an assumption that is not always warranted [4,5].
2. The Initial Choice of Parameters Used to Describe Cell Properties Strongly Influences the Performance of Quantitative Methods Currently Used to Study Cell Function
2.1. An Exhaustive Ab Initio Description of Cell Function Seems out of Reach in the near Future, and It Is not as “Parameter-Independent” as Might Be Thought: Lessons from Molecular Dynamics
2.2. The “Omic” Approach: Representative Examples of Strategies Used to Analyze Huge Datasets
2.2.1. Graph Theory, Networks, and Logic-Based Models
2.2.2. Viewing Cells as Mobile Points Moving on a Multidimensional Landscape
2.3. Data Processing with Multivariate Statistics and Machine Learning
3. Is There a Need for a More Precise Description of Biomolecule Interactions?
3.1. Current State of Interactome Databases
- The term “interaction” may refer to different phenomena. Physical binding and unbinding of a molecular pair in solution can be quantified with high accuracy with standard techniques, as will be detailed below. High throughput maps of binary interactions involving about 17,500 human proteins (about 90% of the protein-coding genome) were built with the standard yeast two-hybrid method, yielding about 53,000 interactions . The validity of results was fed into public databases such as IntAct (https://www.ebi.ac.uk/intact/ accessed on 7 December 2021) after careful validation . However, as was well acknowledged by the authors, the occurrence of physiologically relevant molecular interactions requires that molecular partners might encounter each other within cells. Additionally, molecular interactions may display significant differences in vitro and within cells . Affinity purification-mass spectrometry (AP-MS) is currently used to obviate this difficulty [65,66]. This relies on the use of cells expressing tagged “baits” that may be purified after cell lysis before identification of binding partners with quantitative mass spectrometry. As a recent example, a network of 118,162 interactions among 14,586 proteins was obtained after affinity-purification of 10,128 proteins expressed by human epithelial kidney cells, yielding the Bioplex 3.0 network . Interestingly, comparison with data obtained on another cell line (from human colorectal carcinoma) revealed, as expected, significant differences between interatomic networks found with two different cell types. Additionally, when AP-MS was studied to monitor, for a period of time of 600 s, primary T lymphocytes subjected to antigen-receptor mediated stimulation, the expected evolution of signalosome interactions was clearly evidenced . In conclusion, while a reliable and nearly exhaustive network of the physical interaction of human proteins under standard conditions may be available, an exhaustive description of interactions involving relevant epigenetic states of the proteome and cellular environments is currently out of reach [21,60], despite the impressive amount of information gathered by combining data mining and experiments, as exemplified by the STRING database . As clearly stated : “It remains infeasible to assemble a reference interactome map by systematically identifying endogenous protein–protein interactions (PPIs) in thousands of physiological and pathological cellular contexts”.
- As already mentioned in the aforementioned examples, the quantitative properties of biomolecule interactions may display huge variations. As an example, the dissociation constant of biomolecular bonds may vary between picomolar values (as exemplified by hormone-receptor interaction) and millimolar values , which may be considered to represent ultra-weak interactions, of which the biological importance is, however, recognized . The lifetime of a ligand-receptor bond may vary between less than a second (as was sometimes reported on cadherins  or antigen-antibody pairs ) and hours.
3.2. Parameters Currently Available to Describe Biomolecule Interactions
3.2.1. Interaction between Soluble Molecules and Surface-Bound Receptors
3.2.2. Interaction between Surface-Bound Ligands and Receptors
3.2.3. Additional Parameters May Be Obtained with Computer Simulation
3.3. Physical and Biological Significance of Parameters Allowing to Account for Biomolecule Interactions
3.3.1. The Equilibrium Constant
- It is difficult to use Equation (1) to assess the biological significance of a reported interaction. Indeed, it would reasonable to conclude that an interaction AB is significant if either Ka[A] or Ka[B] is not too close to zero. But this would require to know the value of [A] close to a molecule [B] or [B] close to [A], which is not always the case. As a striking example, it was recently shown that spatio-temporal cAMP signaling is under precise control of nanoscale domains . Additionally, local molecular crowding may alter effective concentrations . More generally, the in vivo affinity of a reaction may differ significantly from the affinity measured in a standard buffer. Thus, the qualitative demonstration of a physical interaction between two molecules within a cell as evidenced with AP-MS  may be more informative than quantitative in vitro affinity measurement.
- Many biomolecule interactions involve surface-bound molecules. Indeed, as was recently emphasized , nearly 30% of human genes encode membrane proteins. Unfortunately, as already explained , the affinity between surface-bound molecules is difficult to derive from 3D affinity, and even to define: the outcome of an encounter between two bound molecules is dependent on a number of parameters independent of binding sites such as molecular length and flexibility , lateral mobility, and distance between surfaces .
3.3.2. Kinetic Rates Provide an Informative Means of Accounting for Biomolecule Interactions
- First, biomolecular attachments often appear as multistep reactions, the description of which may require numerous parameters to account for several energy barriers in reaction paths. The number of required parameters was somewhat reduced when it was found that the kinetics of bond formation between different antigen and antibody might be modeled as a progression along a rugged energy landscape, accounted for by a single effective diffusion constant, and matched the intuitively appealing interpretation that bond formation required a minimal contact time that was estimated as a few milliseconds [94,95,96]. Thus, in contrast with the predictions of Equation (2), a fairly long contact may not always be replaced with many transient ones to allow bond formation.
- Second, the on-rate is difficult to define under 2D conditions, since it depends on many properties independent of the molecular binding interface [75,92]. Additionally, the bond lifetime is certainly as dependent on the disruptive forces exerted by surfaces, as it depends on the intrinsic bond stability .
3.3.3. Accounting for the Effect of Forces on Bonds
- Integrin mediated cell adhesion. Integrins are important mediators of cell adhesion. A well-known peptide ligand of integrins (the RGD tripeptide) was bound to surfaces with DNA constructs of varying strengths. Cell adhesion required that the linkers be able to resist a force of at least 40 pN for 2 s .
- Signaling. Similarly, the use of calibrated DNA tethers showed that notch signaling required that the strength of the ligand attachment to a surface be higher than 12 pN . When T lymphocytes were made to contact ligands deposited on a probe connected to a BFP allowing real-time determination of interaction force, the triggering of a calcium rise indicative of cell activation was correlated to the application of a force of about 10 pN on the TCR . Additionally, a force of 10 pN applied on TCRs with an optical trap was reported to strongly increase signaling . Furthermore, some reports supported the hypothesis that TCR signaling required that the TCR-ligand bond behave as a catch bond [119,120]. More work is, however, needed to assess the generality of this requirement . More recently, it was suggested that talin, a cytoskeleton associated molecules that acted as a force sensor, could filter mechanical noise as a consequence of specific mechanical properties .
- Ligand discrimination. The capacity of membrane receptors to discriminate between a high number of potential ligands is an essential requirement for cell function. It is, therefore, important to emphasize that forces may play an important role in ligand discrimination. The capacity of B lymphocytes to select and extract antigens bound to surfaces was shown to rely on forces [121,122]. Forces were also reported to modulate the preference of αvβ3 integrin for fibronectin or vitronectin .
3.3.4. Receptor Length and Conformational Dynamics
- In addition to the molecular properties of binding sites, the efficiency of the bond formation between surface-attached molecules is strongly dependent on the length and flexibility of binding molecules so that binding sites might contact each other with a suitable orientation . As a well-known example, the remarkable capacity of P-selectin molecules to tether rapidly flowing leukocytes in blood vessels is partly due to the unusually high length of the ligand-receptor couple (nearly 0.1 µm). Length and flexibility also play an important role in modulating multivalent attachments that are key to the avidity of interactions. Avidity might be loosely defined as some kind of “functional affinity” .
- Receptor-mediated signaling is also highly dependent on the capacity of binding molecules to form multivalent attachments, since clustering of membrane molecules is often a key event of signal generation . Indeed, an early step of the triggering of a signaling cascade is often the phosphorylation of a dedicated site on a molecule by a kinase brought close to this site. The molecular reach of molecules such as kinases or phosphatases is obviously dependent on the size and conformational flexibility of involved molecules . Another important role of receptor length in the signaling process is the reorganization of intercellular contact zones as a consequence of the exclusion of bulky molecules from tight contact zones generated by short ligand-receptor couples. The importance of this mechanism was well demonstrated since the exclusion of the bulky CD45 phosphatase was found sufficient to trigger a signaling cascade in T lymphocytes [125,126,127].
- The formation of multimolecular assemblies plays an important role in signal generation (as related to signalosome formation) as well as cell structural organization. Conformation flexibility may play a key role in this process  if bond formation results in the transient appearance of docking sites that may recruit additional molecules. Two points may be mentioned in this respect. (i) For the sake of simplicity, model systems used to study molecular associations most often rely on binary interactions with the underlying assumption that they are additive, which is not always warranted . (ii) Recent progress in molecular dynamics may strongly improve our understanding of reaction paths and transient molecular states, which may increase our interest in ternary and multimolecular interactions.
4. Discussion and Conclusions: What Is the Take-Home Message?
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Bongrand, P. Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function? Curr. Issues Mol. Biol. 2022, 44, 505-525. https://doi.org/10.3390/cimb44020035
Bongrand P. Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function? Current Issues in Molecular Biology. 2022; 44(2):505-525. https://doi.org/10.3390/cimb44020035Chicago/Turabian Style
Bongrand, Pierre. 2022. "Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?" Current Issues in Molecular Biology 44, no. 2: 505-525. https://doi.org/10.3390/cimb44020035