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Nature Methods (2024 )Cite this article aapptec peptide synthesizer
By using a limited-proteolysis strategy that employs a large amount of trypsin to generate peptides directly from native proteins, we found that ligand-induced protein local stability shifts can be sensitively detected on a proteome-wide scale. This enabled us to develop the peptide-centric local stability assay, a modification-free approach that achieves unprecedented sensitivity in proteome-wide target identification and binding-region determination. We demonstrate the broad applications of the peptide-centric local stability assay by investigating interactions across various biological contexts.
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The raw mass spectrometry proteomics data, protein identification and quantification results have been deposited with the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034606. The source data associated with Supplementary Figs. 1–21 are available via Figshare at https://doi.org/10.6084/m9.figshare.26886625 (ref. 28). The source data include all primary datasets and referenced datasets. The Supplementary Tables referenced in the Supplementary Notes are published alongside this paper. A step-by-step PELSA protocol has been deposited via protocols.io with the link https://doi.org/10.17504/protocols.io.q26g717x1gwz/v2 (ref. 29). Protein structures are download from PDB (https://www.rcsb.org/) with accession codes: 1FAP (FKBP1A-mTOR), 1U72 (DHFR), 5EHR (PTPN11), 2D8Y (LIMA1), 2PSN (ENO1), 4DMB (HDDC2), AF-Q9H2U2-F1-mod (PPA2), 1WAO (PPP5C), 5LN3 (human 26S Proteasome), 1BOZ and 4EJ1 (DHFR-folate), 1P4R (ATIC), 1RBY (GART) and 6KQY (LARS1). The reviewed human fasta database was downloaded from UniProt in June 2018 (https://www.uniprot.org/uniprotkb). Source data are provided with this paper.
The code of PELSA data processing pipeline is available via GitHub at https://github.com/DICP-1809/PELSA-Decipher.
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We thank M. Savitski from the European Molecular Biology Laboratory for the fruitful discussion, for revising this manuscript and the point-by-point replies during the revision process. This work was supported, in part, by funds from the National Key Research and Development Program of China (grant no. 2021YFA1302601 to M.Y.), the National Natural Science Foundation of China (grant nos. 92153302, 22437007 to M.Y., 22137002 to K.W., 92253303 to C.L., 22204033 to Y.L.), Dalian Science and Technology Innovation Fund (grant no. 2023JJ11CG006 to M.Y.), the innovation program of science and research from the DICP, CAS (grant nos. DICP I202109, DICP I202139 to M.Y.), the project of National Multidisciplinary Innovation Team of Traditional Chinese Medicine from National Administration of Traditional Chinese Medicine (grant no. ZYYCXTD-202004 to C.L.), the Youth Innovation Promotion Association of CAS (grant no. 2022279 to S.C.).
Present address: Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
Present address: Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
State Key Laboratory of Medical Proteomics, CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R&A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian, China
Kejia Li, Keyun Wang, Yan Wang, Lianji Xue, Yuying Ye, Zheng Fang, Jiawen Lyu, Haiyang Zhu, Yanan Li, Ting Yu, Xiaolei Zhang, Chengfei Ruan, Jiahua Zhou, Qi Wang & Mingliang Ye
University of Chinese Academy of Sciences, Beijing, China
Kejia Li, Lianji Xue, Yuying Ye, Zheng Fang, Jiawen Lyu, Haiyang Zhu, Ting Yu, Chengfei Ruan, Jiahua Zhou, Qi Wang & Mingliang Ye
Drug Discovery and Design Center, the Center for Chemical Biology, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
Shijie Chen, Feng Yang, Siqi Guo & Cheng Luo
MOE Key Laboratory of Bio-Intelligent Manufacturing, School of Bioengineering, Dalian University of Technology, Dalian, China
School of Pharmacy, Department of Hepatobiliary Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
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M.Y., K.L. and K.W. conceived and designed the project. K.L. developed the method, carried out the experiments and analyzed the results under the supervision of M.Y. S.C., F.Y. and S.G. measured the binding affinities of purified HSP90AA1 to HSP90 inhibitors and validated the off-targets identified for HSP90 inhibitors and R2HG under the supervision of C.L. K.W. advised on the experimental design. Y.W. set up the MS analysis methods, discussed the experiment results and edited the manuscript. L.X. performed the dose-dependent rapamycin/geldanamycin/tanespimycin/ganetespib PELSA experiments and tested different enzyme quantities. Y.Y. performed the fumarate-PELSA, succinate-PELSA, folate-TPP and leucine-TPP experiments. Z.F. gave help with data processing. J.L. helped in the experiment investigating PTM’s readers. H.Z. helped in data processing in the Zn2+ experiment. Y.L. helped to correct the manuscript. T.Y. advised on investigating the leucine-binding proteins. J.Z. helped in calculating the Euclidean distances between peptides and ligands. X.Z., C.R. and Q.W. gave scientific advice. M.D. discussed the results and revised the manuscript. K.L. and M.Y. wrote the paper with input from other authors.
Correspondence to Cheng Luo or Mingliang Ye.
A patent application related to this work has been filed by Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.
Nature Methods thanks Marcus Bantscheff and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Allison Doerr, in collaboration with the Nature Methods team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Local stability profiles of FKBP family proteins under 2 µM rapamycin treatment. Only peptides from FKBP domains display altered abundance. b, Dose-response curves of FKBP1A peptides (left) and FKBP1A protein (right) measured by PELSA in HeLa cell lysates toward rapamycin. The protein-level dose-response curve is generated by fitting the averaged fold change values of all qualified peptides across each rapamycin concentration. c, Binding affinities between rapamycin and FKBP1A peptides measured by LiP-Quant. DRC refers to the Pearson correlation coefficient of the dose-response curve; LiP-Quant score indicates the likelihood of being a genuine drug-binding protein. Data are extracted from the literature9. d, Protein-level dose-response curves of FKBP family proteins measured by PELSA.
a, Overlap analysis of staurosporine targets identified by PELSA in HeLa and K562 cell lysates. Kinases are considered true positive targets, while the non-kinase targets are listed based on their identification in HeLa and K562 cell lysates. b, Comparing the number of staurosporine targets determined in PELSA and thermal shift-based studies in K562 cell lysates. The percentage of kinase targets among all the determined staurosporine targets (that is, true positive rate, TPR) is indicated for each study.
Supplementary Tables 1–8 related to the Supplementary Notes.
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Li, K., Chen, S., Wang, K. et al. A peptide-centric local stability assay enables proteome-scale identification of the protein targets and binding regions of diverse ligands. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02553-7
DOI: https://doi.org/10.1038/s41592-024-02553-7
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