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Chao Hou

@chaohou.bsky.social
30 followers 56 following 21 posts

Protein dynamic, Multi conformation, Language model, Computational biology | Postdoc @Columbia | PhD 2023 & Bachelor 2020 @PKU1898 chaohou.netlify.app

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Reposted by Chao Hou
Nature Computational Science @natcomputsci.nature.com · 13/07/2026
📢Out now! @chaohou.bsky.social, @yshen.bsky.social and colleagues show that PLMs predict fitness best when outputs align with evolutionary patterns in homologs, with the peak performance occurring at moderate predicted sequence likelihoods. www.nature.com/articles/s43... 🔓 rdcu.be/ftBgt
nature.com
Understanding language model scaling for protein fitness prediction - Nature Computational Science
Larger language models do not always perform better at fitness prediction. Hou et al. found that performance depends on whether model outputs match evolutionary patterns in homologs, which is best ach...
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Reposted by Chao Hou
Nature Computational Science @natcomputsci.nature.com · 13/07/2026
An accompanying News & Views by Tong Wang is also available for this paper! www.nature.com/articles/s43... 🔓 rdcu.be/ftBie
nature.com
Protein fitness prediction with language models - Nature Computational Science
Protein language models are powerful computational tools for protein fitness prediction. The recent investigation into the relationship between model outputs and prediction accuracy reveals the scalin...
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Chao Hou @chaohou.bsky.social · 13/07/2026
🚨 Our paper of explaining why larger pLMs do NOT always perform better on fitness/variant-effect prediction was published. See our previous thread for the key insights, and check out the paper for the full story.
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Chao Hou @chaohou.bsky.social · 24/01/2026
Our work of protein language models trained on biophysical dynamics was just published in @pnas.org. URL: doi.org/10.1073/pnas...
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Chao Hou @chaohou.bsky.social · 25/08/2025
We just updated our manuscript "Understanding Language Model Scaling on Protein Fitness Prediction". Where we explained why larger pLMs don’t always perform better on mutation effect prediction. We extended beyond ESM2 to models like ESMC, ESM3, SaProt, and ESM-IF1. #ProteinLM
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Chao Hou @chaohou.bsky.social · 11/06/2025
⚠️ Caution when using ProteinGYM binary classification: some DMS_binarization_cutoff values appear inverted, some are totally unreasonable given the DMS_score shows a clear bimodal distribution.
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Reposted by Chao Hou
Aziz Zafar @aziz-zafar.bsky.social · 13/05/2025
How can we better understand pathogenic variants in intrinsically disordered regions (IDRs)? How do models such as AlphaMissense and ESM1b predict pathogenicity, when these regions typically exhibit lower genomic conservation than ordered regions? Read more: doi.org/10.1101/2025...
doi.org
Molecular dynamics simulations of intrinsically disordered protein regions enable biophysical interpretation of variant effect predictors
Predictive models for missense variant pathogenicity offer little functional interpretation for intrinsically disordered regions, since they rely on conservation and coevolution across homologous sequ...
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Reposted by Chao Hou
Kevin K. Yang 楊凱筌 @kevinkaichuang.bsky.social · 30/04/2025
Protein language model likelihood are better zero shot mutation effect predictions when they have perplexity 3-6 on the wildtype sequence. www.biorxiv.org/content/10.1...
Relationship between perplexity and zero shot performance.
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Chao Hou @chaohou.bsky.social · 29/04/2025
Why do large protein language models like ESM2-15B underperform compared to medium-sized ones like ESM2-650M in predicting mutation effects? 🤔 We dive into this issue in our new preprint—bringing insights into model scaling on mutation effect prediction. 🧬📉
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Chao Hou @chaohou.bsky.social · 17/04/2025
We have updated our protein lanuage model trained on structure dynamics. Our new models show significant better zero-shot performance on mutation effects of designed and viral proteins compared to ESM2. check the new preprint here: www.biorxiv.org/content/10.1...
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Reposted by Chao Hou
bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · 15/10/2024
SeqDance: A Protein Language Model for Representing Protein Dynamic Properties www.biorxiv.org/content/10.1101/202…
biorxiv.org
SeqDance: A Protein Language Model for Representing Protein Dynamic Properties https://www.biorxiv.org/content/10.1101/2024.10.11.617911v1
Proteins perform their functions by folding amino acid sequences into dynamic structural ensembles.
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