A data-driven approach to studying enzymes and nanoparticles

Bringing together materials and life sciences by understanding the dynamic nature of catalyst configuration at the atomic level

Our motivation

Chemical conversions enabled by catalysis impact many areas including energy conversion, agriculture, and environmental protection. Developing a fundamental understanding of catalytic functionality leads to new improved catalysts.

Catalytic activity in living systems is enabled by enzymes. These proteins are also often used as drug targets due to their unique roles in regulating specific
chemical processes in the cell. Developing a fundamental understanding of enzyme structure leads to new improved drugs.

This project is supported by the National Science Foundation’s Harnessing the Data Revolution (HDR) Big Idea activity.

Latest News

Science & Engineering Challenges

What are some of the compelling science or engineering challenges the ALSDC Group faces? • Catalytic functionality is often enabled by structural dynamics which has been largely ignored up to now. • Our aim is to understand the dynamic nature of catalystconfiguration/conformation at the atomic level. • This brings together materials and life sciences. •Continue reading “Science & Engineering Challenges”

Accelerating Progress through Data-Driven Discoveries

Experimental advances New detectors now allow large quantities of high time resolution data revealing structural dynamics New structural biology techniques Large supercomputers Data science advances New high dimensional dynamic models now available New deep learning based image denoising techniques Move Beyond Current State of the Art By developing methods to provide a highly quantitative data-drivenContinue reading “Accelerating Progress through Data-Driven Discoveries”

Overcoming Obstacles in Data Science

Experimental measurements are very noisy Data have very high granularity (image sequences) Extracting interpretable high-level dynamic information How do we propose to overcome these obstacles? Denoising techniques Dimensionality reduction via clustering Dynamic modeling: learned Markov model + spectral analysis to quantify temporal stability and identify mixing rates

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