Squidly harnesses enzyme functional hierarchy and contrastive learning to efficiently predict catalytic residues from sequence
· 2026-09-04 · 原文
Enzymes present a sustainable alternative to traditional chemical industries, drug synthesis, and bioremediation applications. Because catalytic residues are the key amino acids that drive enzyme function, their accurate prediction facilitates enzyme function prediction. Sequence similarity-based approaches such as BLAST are fast but require previously annotated homologues. Machine-learning (ML) approaches aim to overcome this limitation; however, current gold-standard ML-based methods require high-quality 3D structures limiting their application to large datasets. To address these challenges, we developed Squidly, a sequence-only tool that leverages contrastive representation learning with a biology-informed, rationally designed pairing scheme to distinguish catalytic from non-catalytic r
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1. 人话版
Enzymes present a sustainable alternative to traditional chemical industries, drug synthesis, and bioremediation applications.
Because catalytic residues are the key amino acids that drive enzyme function, their accurate prediction facilitates enzyme function prediction.
2. 领域脉络
来源板块：板块二 · 顶级期刊。
3. 机制拆解
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4. 证据与数字
Machine-learning (ML) approaches aim to overcome this limitation; however, current gold-standard ML-based methods require high-quality 3D structures limiting their application to large datasets.
5. 反例与边界
Sequence similarity-based approaches such as BLAST are fast but require previously annotated homologues.
To address these challenges, we developed Squidly, a sequence-only tool that leverages contrastive representation learning with a biology-informed, rationally designed pairing scheme to distinguish catalytic from non-catalytic r
6. 跨领域连接与意外收获
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7. 可复用方法
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8. 术语表
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