Merizo: a rapid and accurate protein domain segmentation method using invariant point attention.
Merizo: a rapid and accurate protein domain segmentation method using invariant point attention.
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DOI：
10.1038/s41467-023-43934-4
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发表时间：
2023-12-19
影响因子：
16.6
通讯作者：
Jones, David T.
中科院分区：
文献类型：
--
作者：
Lau, Andy M.;Kandathil, Shaun M.;Jones, David T.
Alphafold蛋白质结构数据库，其中包含超过2亿蛋白的预测，对其在丰富结构生物学研究及其他方面的潜力充满热情。当前，由于迫切需要允许其内容的有效遍历，发现和文档的工具，因此无法访问数据库。识别数据库中的领域区域是一项非平凡的努力，这样做将有助于我们对蛋白质结构和功能的理解，同时促进药物发现和比较基因组学。在这里，我们描述了一种称为Merizo的域分割的深度学习方法，该方法学会以自下而上的方式将残基聚集到域中。 Merizo在CATH域进行了训练，并通过自distillation对Alphafold2模型进行了微调，从而可以将其应用于实验和AlphaFold2模型。作为概念证明，我们将Merizo应用于人类蛋白质组，识别可与Cath代表域相匹配的40,818个推定域。
蛋白质包含称为域的模块化结构和功能单元。在这里，作者开发了Merizo，这是一种适用于实验结构以及Alphafold2产生的域分割的深度学习方法。
The AlphaFold Protein Structure Database, containing predictions for over 200 million proteins, has been met with enthusiasm over its potential in enriching structural biological research and beyond. Currently, access to the database is precluded by an urgent need for tools that allow the efficient traversal, discovery, and documentation of its contents. Identifying domain regions in the database is a non-trivial endeavour and doing so will aid our understanding of protein structure and function, while facilitating drug discovery and comparative genomics. Here, we describe a deep learning method for domain segmentation called Merizo, which learns to cluster residues into domains in a bottom-up manner. Merizo is trained on CATH domains and fine-tuned on AlphaFold2 models via self-distillation, enabling it to be applied to both experimental and AlphaFold2 models. As proof of concept, we apply Merizo to the human proteome, identifying 40,818 putative domains that can be matched to CATH representative domains. Proteins contain modular structural and functional units called domains. Here, the authors have developed Merizo, a deep learning method for domain segmentation applicable to experimental structures as well as those generated by AlphaFold2.
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影响因子：
14.9
作者：
Sillitoe I;Bordin N;Dawson N;Waman VP;Ashford P;Scholes HM;Pang CSM;Woodridge L;Rauer C;Sen N;Abbasian M;Le Cornu S;Lam SD;Berka K;Varekova IH;Svobodova R;Lees J;Orengo CA
通讯作者：
Orengo CA
影响因子：
14.9
作者：
Mistry J;Chuguransky S;Williams L;Qureshi M;Salazar GA;Sonnhammer ELL;Tosatto SCE;Paladin L;Raj S;Richardson LJ;Finn RD;Bateman A
通讯作者：
Bateman A
DOI：
10.1073/pnas.2214069120
发表时间：
2023-03-21
影响因子：
11.1
作者：
Schaeffer, R. Dustin;Zhang, Jing;Kinch, Lisa N.;Pei, Jimin;Cong, Qian;V. Grishin, Nick
通讯作者：
V. Grishin, Nick
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
5.8
作者：
Li, WZ;Jaroszewski, L;Godzik, A
通讯作者：
Godzik, A