# Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models

arXiv: https://arxiv.org/abs/2312.04019 · HTML: https://mlsanity.com/paper/2312.04019/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 72 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits](https://mlsanity.com/paper/2006.15426/) (2020)
- [PiFold: Toward effective and efficient protein inverse folding](https://mlsanity.com/paper/2209.12643/) (2022)
- [Learning Graph Models for Retrosynthesis Prediction](https://mlsanity.com/paper/2006.07038/) (2020)
- [AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB](https://mlsanity.com/paper/2202.01079/) (2022)
- [E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking](https://mlsanity.com/paper/2210.06069/) (2022)
- [Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design](https://mlsanity.com/paper/2106.13058/) (2021)
- [Target-aware Molecular Graph Generation](https://mlsanity.com/paper/2202.04829/) (2022)
- [Generative De Novo Protein Design with Global Context](https://mlsanity.com/paper/2204.10673/) (2022)
- [SemiRetro: Semi-template framework boosts deep retrosynthesis prediction](https://mlsanity.com/paper/2202.08205/) (2022)
- [Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement](https://mlsanity.com/paper/2305.15151/) (2023)
- [Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer](https://mlsanity.com/paper/2305.09480/) (2023)
- [VQPL: Vector Quantized Protein Language](https://mlsanity.com/paper/2310.04985/) (2023)
- [MotifRetro: Exploring the Combinability-Consistency Trade-offs in retrosynthesis via Dynamic Motif Editing](https://mlsanity.com/paper/2305.15153/) (2023)

## Built on by (most cited first, 1 of 1)

- [FoldToken: Learning Protein Language via Vector Quantization and Beyond](https://mlsanity.com/paper/2403.09673/) (2024)

## Abstract (arXiv)

Predicting protein stability changes induced by single-point mutations has been a persistent challenge over the years, attracting immense interest from numerous researchers. The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry, including drug development, protein evolution analysis, and enzyme synthesis. Despite the proposition of multiple methodologies aimed at addressing this issue, few approaches have successfully achieved optimal performance coupled with high computational efficiency. Two principal hurdles contribute to the existing challenges in this domain. The first is the complexity of extracting and aggregating sufficiently representative features from proteins. The second refers to the limited availability of experimental data for protein mutation analysis, further complicating the comprehensive evaluation of model performance on unseen data samples. With the advent of Large Language Models(LLM), such as the ESM models in protein research, profound interpretation of protein features is now accessibly aided by enormous training data. Therefore, LLMs are indeed to facilitate a wide range of protein research. In our study, we introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations. Furthermore, we have curated a dataset meticulously designed to preclude data leakage, corresponding to two extensively employed test datasets, to facilitate a more equitable model comparison.