Researchers develop model to predict insight about multivalent proteins

Researchers associated with the Center for Science & Engineering of Living Systems (CSELS) at the McKelvey School of Engineering have developed an open source computational model that allows scientists to generate predictive insights connecting molecular architectures to phase diagrams for multivalent proteins.

LAttice Simulation engine for Sticker and Spacer Interactions (LASSI) was designed in the lab of Rohit Pappu, CSELS director and Edwin H. Murty Professor of Engineering, by Furqan Dar, a graduate student in physics in Arts & Sciences, and former postdoctoral researcher Jeong-Mo Choi.

Details of the algorithm underlying LASSI were published recently in PLoS Computational Biology. Find the source code for LASSI at GitHub.

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