# Interplay of inherent promiscuity and specificity in protein biochemical function with applications to drug discovery and exome analysis

> **NIH NIH R35** · GEORGIA INSTITUTE OF TECHNOLOGY · 2021 · $490,958

## Abstract

PROJECT SUMMARY
Although the past two decades witnessed the large-scale analyses of cellular components, e.g. exomes, their
impact on drug discovery and precision medicine has been modest. For example, 6/7 drug candidates failed
safety and 3/4 failed efficacy in recent FDA clinical trials. These unsolved, but related issues, safety and efficacy,
reflect significant gaps in understanding of the triangular interrelationship between diseases, molecular function,
and drug treatments. A key conceptual limitation of contemporary drug discovery is the often implicitly assumed
single drug for a single protein target disease model. In reality, most diseases are caused by multiple
malfunctioning molecules. Whether it be disease treatment or precision medicine diagnostics, there is often an
inability to identify disease-associated mode of action (MOA) proteins. To begin to address these issues, in the
current MIRA proposal, we developed a promising protein structure and network-based Artificial Intelligence (AI)
approach, MEDICASCY, to predict disease-associated MOA proteins, drug indications, side effects and efficacy;
however, much more needs to be done. Here, we propose to build on our successes and develop an integrated
AI-based approach, MEDICASCY-X, that addresses the following: The first step in determining a drug’s MOA
and off-target interactions is to identity its protein targets. This requires the structures of all human proteins and
their complexes. While we predicted suitable models for at least one domain in 97% of human proteins, using
deep learning, we will predict the structures of the missing domains, domain-domain orientations and protein-
protein complexes. We will extend small molecule virtual ligand screening (VLS) to predict binding affinities
based on the insight that interacting ring-protein subpocket geometries and chemistry are conserved across
protein families, are often privileged chemical structures and are likely low free energy complexes. Cryptic protein
pockets, recently recognized as important drug targets, will be predicted and included in our VLS approach.
Antibody-based immunotherapies are powerful but have similar safety and efficacy issues as small-molecules;
thus, their safety and efficacy will be predicted by MEDICASCY-X. While MEDICASCY works on an “averaged
human”, MEDICASCY-X will consider individual genetic and epigenetic profiles to make it a true precision
medicine tool. We will predict which MOA proteins should be targeted and if a protein’s MOA is due to a loss or
gain of function. The same framework will predict synergistic drug-drug interactions. Another way to prioritize
MOA proteins is by disease comorbidity: proteins occurring in multiple diseases are likely important. If disease
comorbidity can be predicted, we will construct the “Phylogenetic” Tree(s) of Diseases that would facilitate a
deeper understanding of disease interrelationships. As proof of principle of the effectiveness of the algorithms
being...

## Key facts

- **NIH application ID:** 10149528
- **Project number:** 2R35GM118039-06
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** JEFFREY SKOLNICK
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $490,958
- **Award type:** 2
- **Project period:** 2016-05-06 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10149528

## Citation

> US National Institutes of Health, RePORTER application 10149528, Interplay of inherent promiscuity and specificity in protein biochemical function with applications to drug discovery and exome analysis (2R35GM118039-06). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10149528. Licensed CC0.

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