# Modeling Project

> **NIH NIH P01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2020 · $541,205

## Abstract

PROJECT SUMMARY: MODELING PROJECT
Our overall aim is to develop, apply, and distribute computational tools that predict the activities for individual
enzymes and for multiple enzymes that function together in a biochemical pathway. We integrate concepts
and methods derived from comparative protein structure modeling, structure-based ligand docking, integrative
structural biology, chemoinformatics, and systems biology. Specific Aim 1. A major focus has been the
development and application of structure-based approaches to predict substrates for enzymes of unknown
function; when experimental structures are unavailable, we have demonstrated that comparative protein
models can be productively used. While we have had substantial success, key challenges remain to enable
routine, successful application, including: sampling both covalent and non-covalent intermediates, expanding
from simple substrate prediction to predicting activities across a metabolic pathway, and an elementary lack of
the right metabolites in our docking libraries. 1a. We will explore a new covalent-docking method to allow
sampling both non-covalent intermediates as may be suitable for many enzyme families. 1b. We will broaden
our efforts from docking potential substrates against a single target, to doing so against candidate members of
an entire pathway; this will be key information to integrative modeling of metabolic pathways. 1c. To address
the problem of missing metabolites, we will use chemoinformatics to infer metabolites from the docking of
synthetic libraries, which span a much larger chemical space, and have fewer gaps, than the extant metabolite
libraries. Specific Aim 2. We aim to develop, apply, and distribute a method for mapping metabolic pathways
that will identify the enzymes and ligands in a pathway as well as their order. The goal will be achieved by
integrating structural and systems information, such as data from virtual screening, cheminformatics, genomic
context, ligand binding experiments, and metabolomics. Specific Aim 3. The Metabolism Project (supported by
the Protein Core) will test predictions of metabolite docking on individual enzymes, and predictions of
integrative mapping for metabolic pathways, providing crucial feedback to evaluate and improve the methods.
The Ligand Discovery Project will provide data for integrative mapping, and structure determination efforts will
facilitate comparative protein modeling and, in select cases, test predictions of protein-ligand complex
structures generated by metabolite docking. Specific Aim 4. We will make our tools widely used, including,
when possible, by non-expert scientists. Thus, we will automate and distribute our open source packages, web
servers, databases, benchmarks, and sample applications. These will include optimized libraries of metabolites
in dockable forms and an optimized tool for docking these metabolites, a tool for covalent docking, tools within
the ZINC family of programs and databases to seek...

## Key facts

- **NIH application ID:** 9918945
- **Project number:** 5P01GM118303-05
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** MATTHEW P JACOBSON
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $541,205
- **Award type:** 5
- **Project period:** — → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9918945

## Citation

> US National Institutes of Health, RePORTER application 9918945, Modeling Project (5P01GM118303-05). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/9918945. Licensed CC0.

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