# Advanced approaches to protein structure prediction

> **NIH NIH R35** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $514,097

## Abstract

Abstract
The success of genome sequencing over the last four decades has resulted in a rapidly increasing gap between
the number of known protein sequences and the number of known protein structures and functions. Because
protein sequence on its own cannot tell us what each molecule does in cells, the large-scale absence of protein
structure and function information severely hinders the progress of contemporary biological and medical studies.
These gaps in understanding strongly call for efficient computational approaches for automated, yet highly
accurate protein structure prediction and function annotation. The PI’s lab has a successful track record in
developing and disseminating high-quality structural bioinformatics methods which have been widely used by
the global community. In this project, the lab seeks to develop new advanced methods for both tertiary and
quaternary protein structure prediction. Built on the tools and databases previously developed in the PI’s lab,
new deep neural-network based techniques will be extended to residue-level intra- and inter-chain contact- and
distance-map predictions. These predictions will then be used to constrain the conformational searching space
of threading-based fragment assembly simulations, with the aim to significantly improve the accuracy and
success rate of structure modeling of monomeric proteins and protein-protein interactions (PPIs), especially for
the difficult targets that lack homologous templates in the Protein Data Bank. Next, the structure and PPI network
information will be used to help elucidate multiple levels of biological and biomedical functions for protein
molecules, including mutation-induced changes in protein stability and human disease predictions. The long-
term goals of this project are to significantly improve the state of the art of protein structure prediction and to
narrow the gap between the abundance of protein sequence information and the dearth of protein structure and
function data, thus significantly enhancing the usefulness and impact of structural bioinformatics. Success in this
project will also help reveal the general principles governing the fundamental relations across sequence,
structure and function of protein molecules.

## Key facts

- **NIH application ID:** 10132358
- **Project number:** 5R35GM136422-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Yang Zhang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $514,097
- **Award type:** 5
- **Project period:** 2020-04-01 → 2021-12-24

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10132358

## Citation

> US National Institutes of Health, RePORTER application 10132358, Advanced approaches to protein structure prediction (5R35GM136422-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10132358. Licensed CC0.

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