MVsim is a toolset for quantifying and designing multivalent interactions

Bence Bruncsics, Wes Errington, Casim A. Sarkar

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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. 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. 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. 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. MVsim and instructional tutorials are freely available at

Original languageEnglish (US)
Article number5029
JournalNature communications
Issue number1
StatePublished - Dec 2022

Bibliographical note

Funding Information:
We thank Dr. Péter Antal for the critical review of the calculations of effective concentration in MVsim. This work was supported by funding from the National Laboratory of Artificial Intelligence through the National Research Development and Innovation Office under the auspices of the Ministry for Innovation and Technology and from the National Research, Development, and Innovation Fund of Hungary (TKP2021-EGA-02) (B.B.); from the National Institutes of Health (R35GM136309, R01GM113985, and R21EB022258 to C.A.S.); and from the Institute for Engineering in Medicine at the University of Minnesota (COVID-19 Rapid Response Grant to C.A.S.). The Biacore S200 instrument was made available through a shared instrument grant (S10OD021539) from the Office of Research Infrastructure Programs at the National Institutes of Health.

Publisher Copyright:
© 2022, The Author(s).


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