# Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures

*PRI Rank #6 · Topics and Trends in Most Cited Microbial Metabolic Engineering and Bioproduction Papers, Class of 2026*

*Canonical URL: https://pri.pepkio.com/top-papers/microbial-metabolic-engineering-and-bioproduction/2026/rank-6*

| Field | Value |
| --- | --- |
| Rank | #6 |
| 18m citations | 63 |
| Journal | Nature Communications |
| Year | 2024 |
| DOI | 10.1038/s41467-024-52533-w |
| Corresponding authors | Yuedong Yang |
| Institution | Sun Yat-sen University, China |

**Ranking page:** [Topics and Trends in Most Cited Microbial Metabolic Engineering and Bioproduction Papers, Class of 2026](https://pri.pepkio.com/top-papers/microbial-metabolic-engineering-and-bioproduction/2026)

**Paper link:** [10.1038/s41467-024-52533-w](https://doi.org/10.1038/s41467-024-52533-w)

## Topics

Enzyme Commission (EC) number · GraphEC · geometric graph learning · ESMFold-predicted structures · protein language model · Enzyme function prediction · label diffusion algorithm · homology information · Enzyme function prediction · Biocatalysis · Enzyme function prediction · protein structure-function relationship · Synthetic biology · Enzyme function prediction

## Cite this ranking

```
Pepkio Research Index (PRI). Topics and Trends in Most Cited Microbial Metabolic Engineering and Bioproduction Papers, Class of 2026. https://pri.pepkio.com/top-papers/microbial-metabolic-engineering-and-bioproduction/2026. Accessed 2026-07-29.

Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459
```