Benchmarking protein language models for protein crystallization
Abstract The problem of protein structure determination is usually solved by X-ray crystallography. Several in silico deep learning methods have been developed to overcome the high attrition rate, cost of experiments and extensive trial-and-error settings, for predicting the crystallization propensi...
Saved in:
Main Authors: | Raghvendra Mall, Rahul Kaushik, Zachary A. Martinez, Matt W. Thomson, Filippo Castiglione |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-025-86519-5 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Non-Redundant Benchmark for Symmetric Protein Docking
by: Yumeng Yan, et al.
Published: (2019-06-01) -
S‐PLM: Structure‐Aware Protein Language Model via Contrastive Learning Between Sequence and Structure
by: Duolin Wang, et al.
Published: (2025-02-01) -
sORFdb – a database for sORFs, small proteins, and small protein families in bacteria
by: Julian M. Hahnfeld, et al.
Published: (2025-02-01) -
Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review
by: Jia-Ying Chen, et al.
Published: (2025-01-01) -
Innovation systems for emerging food technologies: evidence from the development of cultured proteins in Thailand
by: Waverly Eichhorst, et al.
Published: (2025-02-01)