Quick Links:

C3.ai DTI Webpage

Events

Information on Call for Proposals

Proposal Matchmaking (Archive from 2020 CFP)

C3.ai DTI Training Materials

C3 Administration (password protected)

Have Questions? Please contact one of us:

• Jay Roloff, jayr@illinois.edu (Executive Director, c3.ai.DTI)
• R. Srikant, rsrikant@illinois.edu (Co-Director, c3.ai.DTI)
• Tandy Warnow, warnow@illinois.edu (Co-chief Scientist, c3.ai.DTI)

Announcing our Third Call for Proposals:

AI to Transform Cybersecurity and Secure Critical Infrastructure

It is anticipated that up to USD $10 million in Research Awards will be awarded from this Call for Proposals. Proposals can request funding of USD $100,000 to $1,000,000 for an initial period of one (1) year.

C3DTI will also make available up to USD $2 million in Azure Cloud computing resources, supercomputing resources at UIUC’s NCSA and LBNL’s NERSC, and free, unlimited access to the C3 AI Suite hosted on the Microsoft Azure Cloud. 

**** We will be offering a virtual session with information on the CFP including an opportunity to ask questions early in January. Please keep an eye out on you email and this wiki page for more information. ****

Proposals are Due February 7, 2022

Awards will be made in March 2022 with start dates of around June 1, 2022

Announcements

Colloquium on Digital Transformation Science

April 15, 3 pm CT

AI-Enabled Deep Mutational Scanning of Interaction between SARS-CoV-2 Spike Protein S and Human ACE2 Receptor

Diwakar Shukla, Assistant Professor, Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign

REGISTER FOR ZOOM WEBINAR

The rapid and escalating spread of SARS-CoV-2 poses an immediate public health emergency. The viral spike protein S binds ACE2 on host cells to initiate molecular events that release the viral genome intracellularly. Soluble ACE2 inhibits entry of both SARS and SARS-2 coronaviruses by acting as a decoy for S binding sites, and is a candidate for therapeutic and prophylactic development. Deep mutational scans is one of the approaches that could provide such a detailed map of protein-protein interactions. However, this technique suffers from several issues such as experimental noise, expensive experimental protocol, and lack of techniques that could provide second or higher-order mutation effects. In this talk, we describe an approach that employs a recently developed platform, TLmutation, that could enable rapid investigation of sequence-structure-function relationship of proteins. In particular, we employ a transfer learning approach to generate high-fidelity scans from noisy experimental data and transfer the knowledge from single point mutation data to generate higher-order mutational scans from the single amino-acid substitution data. Using deep mutagenesis, variants of ACE2 will be identified with increased binding to the receptor binding domain of S at a cell surface. We plan to employ the information from the preliminary mutational landscape to generate the high order mutations in ACE2 that could enhance binding to S protein. We also aim to investigate this problem using distributed computing approaches to understand the underlying physics of the spike protein and ACE2 interaction.

Quick Links:

C3.ai DTI Webpage

Events

Information on Call for Proposals

Proposal Matchmaking

C3.ai DTI Training Materials Overview (password protected)

C3 Administration (password protected)

Have Questions? Please contact one of us:

• Jay Roloff, jayr@illinois.edu (Executive Director, c3.ai.DTI)
• R. Srikant, rsrikant@illinois.edu (Co-Director, c3.ai.DTI)
• Tandy Warnow, warnow@illinois.edu (Co-chief Scientist, c3.ai.DTI)