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However, this conceptual framework was limited to monospecific multivalent interactions between proteins of certain topologies and was also not practically implementable to quickly analyze and design a wide range of molecular systems 26, 27, 28, 29. This approach provided highly resolved mechanistic insights into the dynamical events that underlie simple multivalent interactions and indicated a means with which to extend existing experimental techniques-such as surface plasmon resonance (SPR)-to macromolecular systems previously beyond the scope of quantitative analysis due to their complexity and heterogeneity 24, 25. We previously developed a conceptualization of multivalency that described the noncanonical signatures of multivalent receptor–ligand interactions as the flux through an interconnected network of configurational microstates 23.
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Given that multivalency is a powerful design element of natural and synthetic systems that is easy to implement yet difficult to predict, there is a need for holistic, quantitative molecular frameworks that can integrate existing approaches in the literature, that are extensible across the multivalency design landscape, that render molecular inputs and kinetic outputs in experimentally testable formats, and that can be presented as simple interactive tools for researchers performing model-guided experimentation. These models range from fundamental treatments of linker-driven bivalent interactions 14, 16, to system-specific descriptions of complex ligand recognition 17, 18, 19, to coarse-grained approaches that model multisite engagement at surfaces 20, 21, 22. The expansive utility of multivalency has driven multiple computational approaches to describe aspects of multivalent interactions 14, 15, 16, 17, 18, 19, 20, 21, 22. Deriving from multiple binding elements within sets of interacting molecules, multivalency is used to regulate intracellular compartmentalization 1, 2, 3, 4, 5, high-avidity interactions 6, 7, 8, 9, ultrasensitivity 10, and dynamics and selectivity of molecular recognition 11, 12, 13. Multivalent interactions are fundamental building blocks of supramolecular systems.
![cytoscape tutorial cytoscape tutorial](https://image.slidesharecdn.com/2016cytoscape3-170419000626/95/2016-cytoscape-33-tutorial-17-638.jpg)
MVsim and instructional tutorials are freely available at.
![cytoscape tutorial cytoscape tutorial](https://image.slidesharecdn.com/2015cytoscape3-150519192514-lva1-app6892/95/2015-cytoscape-32-tutorial-9-638.jpg)
Further, to illustrate the conceptual insights into multivalent systems that MVsim can provide, we apply it to quantitatively predict the ultrasensitivity and performance of multivalent-encoded protein logic gates, evaluate the inherent programmability of multispecificity for selective receptor targeting, and extract rate constants of conformational switching for the SARS-CoV-2 spike protein and model its binding to ACE2 as well as multivalent inhibitors of this interaction.
![cytoscape tutorial cytoscape tutorial](https://image.slidesharecdn.com/2016cytoscape3-170419000626/95/2016-cytoscape-33-tutorial-20-638.jpg)
To demonstrate the utility and versatility of MVsim, we first show that both monospecific and multispecific multivalent ligand-receptor interactions, with their noncanonical binding kinetics, can be accurately simulated. Here we present MVsim, an application suite built around a configurational network model of multivalency to facilitate the quantification, design, and mechanistic evaluation of multivalent binding phenomena through a simple graphical user interface. Arising through multiple binding elements, multivalency can specify the avidity, duration, cooperativity, and selectivity of biomolecular interactions, but quantitative prediction and design of these properties has remained challenging.
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