Fischer Samantha N, Claussen Erin R, Kourtis Savvas, Sdelci Sara, Orchard Sandra, Hermjakob Henning, Kustatscher Georg, Drew Kevin
Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA.
Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.
Mol Syst Biol. 2025 May 27. doi: 10.1038/s44320-025-00121-5.
Macromolecular protein complexes carry out most cellular functions. Unfortunately, we lack the subunit composition for many human protein complexes. To address this gap we integrated >25,000 mass spectrometry experiments using a machine learning approach to identify >15,000 human protein complexes. We show our map of protein complexes is highly accurate and more comprehensive than previous maps, placing nearly 70% of human proteins into their physical contexts. We globally characterize our complexes using mass spectrometry based protein covariation data (ProteomeHD.2) and identify covarying complexes suggesting common functional associations. hu.MAP3.0 generates testable functional hypotheses for 472 uncharacterized proteins which we support using AlphaFold modeling. Additionally, we use AlphaFold modeling to identify 5871 mutually exclusive proteins in hu.MAP3.0 complexes suggesting complexes serve different functional roles depending on their subunit composition. We identify expression as the primary way cells and organisms relieve the conflict of mutually exclusive subunits. Finally, we import our complexes to EMBL-EBI's Complex Portal ( https://www.ebi.ac.uk/complexportal/home ) and provide complexes through our hu.MAP3.0 web interface ( https://humap3.proteincomplexes.org/ ). We expect our resource to be highly impactful to the broader research community.
大分子蛋白质复合物执行大多数细胞功能。不幸的是，我们缺乏许多人类蛋白质复合物的亚基组成信息。为了填补这一空白，我们使用机器学习方法整合了超过25000个质谱实验，以识别超过15000个人类蛋白质复合物。我们表明，我们的蛋白质复合物图谱高度准确，比以前的图谱更全面，将近70%的人类蛋白质置于其物理背景中。我们使用基于质谱的蛋白质共变数据（ProteomeHD.2）对我们的复合物进行全局表征，并识别出表明共同功能关联的共变复合物。hu.MAP3.0为472个未表征的蛋白质生成了可测试的功能假设，我们使用AlphaFold建模对其进行了支持。此外，我们使用AlphaFold建模在hu.MAP3.0复合物中识别出5871个相互排斥的蛋白质，这表明复合物根据其亚基组成发挥不同的功能作用。我们确定表达是细胞和生物体缓解相互排斥亚基冲突的主要方式。最后，我们将我们的复合物导入欧洲生物信息学研究所（EMBL-EBI）的复合物门户（https://www.ebi.ac.uk/complexportal/home），并通过我们的hu.MAP3.0网络界面（https://humap3.proteincomplexes.org/）提供复合物。我们预计我们的资源将对更广泛的研究群体产生重大影响。
