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Keyword: ribosomal proteins ×Clear all

3 claims from 1 paper

1. Biochemistry, Genetics and Molecular Biology › Protein Structure and Dynamics

   Enhancing protein structure prediction accuracy by prioritizing important residues using protein language models

   Liu, Kang, Li and Cui · Communications Biology · 2026

   The authors built i-Fold, which adds language-model-derived residue importance scores to AlphaFold2 training, and report better structure predictions on two test sets without substantially higher computational cost.

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   1. UncheckedOn a 3,559-structure benchmark, the i-Fold model lowered prediction error and raised its success rate from 55.1% to 62.7% compared with AlphaFold2.“Evaluation on a benchmark test set of 3,559 protein structures reveals that i -Fold significantly improves accuracy (reduction in r.m.s.d., p = 0) and achieves a higher prediction success rate (7.6% improvement: 55.1% → 62.7%).”
   2. UncheckedOn 167 recently released protein structures, i-Fold's prediction success rate was 49.7% against 43.7% for AlphaFold2, a 6.0-point gain.“Consistent results were obtained on a completely independent test set of 167 recently released protein structures, where i -Fold again exhibited a higher prediction success rate (6.0% improvement: 43.7% → 49.7%) compared to AlphaFold2.”
   3. UncheckedThe i-Fold model shows particular gains over AlphaFold2 on ribosomal, membrane and orphan proteins, which AlphaFold2 typically finds hard to predict.“Notably, i -Fold demonstrates particular improvements for targets that are typically challenging for AlphaFold2, including ribosomal proteins, membrane proteins, and orphan proteins.”