Abstract
An important goal of biological research is to explain and hopefully predict cell behavior from the molecular properties of cellular components. Accordingly, much work was done to build extensive “omic” datasets and develop theoretical methods, including computer simulation and network analysis to process as quantitatively as possible the parameters contained in these resources. Furthermore, substantial effort was made to standardize data presentation and make experimental results accessible to data scientists. However, the power and complexity of current experimental and theoretical tools make it more and more difficult to assess the capacity of gathered parameters to support optimal progress in our understanding of cell function. The purpose of this review is to focus on biomolecule interactions, the interactome, as a specific and important example, and examine the limitations of the explanatory and predictive power of parameters that are considered as suitable descriptors of molecular interactions. Recent experimental studies on important cell functions, such as adhesion and processing of environmental cues for decision-making, support the suggestion that it should be rewarding to complement standard binding properties such as affinity and kinetic constants, or even force dependence, with less frequently used parameters such as conformational flexibility or size of binding molecules.
Highlights
Why Is There a Need for Elaborate Parameters to Describe the Properties of Cellular Components?A long-term goal of cell biologists consists of explaining cell function with basic laws of physics and chemistry [1,2]
The purpose of this review is to focus on biomolecule interactions, the interactome, as a specific and important example, and examine the limitations of the explanatory and predictive power of parameters that are considered as suitable descriptors of molecular interactions
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 [129]. (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
Summary
Why Is There a Need for Elaborate Parameters to Describe the Properties of Cellular Components?. The simulation consists of starting from a reasonable conformation, usually on the basis of a structure obtained by an experimental method such as X-ray crystallography, and calculating the forces experienced by all atoms with empirical force fields in order to determine the displacement and velocity changes of individual atoms during a time step on the order of 1 fs (10−15 s) This procedure is repeated for at least several millions of steps, with periodic recording of the parameters, yielding “trajectory files” that can be processed to determine requested pieces of information. This strategy was, able to bring new insight into protein structure and function without any a priori model of investigated phenomena Applying this powerful approach to whole cells is clearly out of reach: the size of a typical cell is on the order of 10 μm, and the number of atoms is, 1515-fold higher than accessible with current MD simulations. While a more detailed discussion of MD would not fit into the scope of this review, this example clearly shows that any attempt at a quantitative understanding of cell function should require for many years a huge simplification and a choice of a restricted set of parameters that may strongly influence the outcome of modeling attempts
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