Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to a perturbation, protein structure may be altered by various

molecular events, such as post-translational modifications, protein–protein interactions, aggregation, allostery or binding to other molecules. The ability to probe these structural changes

in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of biological processes and pathways. Here, we present an updated

protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide

spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. We introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the

identification of structurally altered peptides and differentially abundant proteins. The experimental procedures take 3 d, mass spectrometric measurement time and data processing depend on

sample number and statistical analysis typically requires ~1 d. These improvements expand the adaptability of LiP-MS and enable wide use in functional proteomics and translational

applications.

The mass spectrometry proteomics data presented in Fig. 8 and Extended Data Fig. 7 and 9 have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository27 with the

dataset identifier PXD031616 and PXD031627 (http://www.proteomexchange.org/). The mass spectrometry proteomics data presented in Fig. 6, Fig. 7 and Extended Data Fig. 8 were generated as

part of ref. 4. The mass spectrometry data presented in Fig. 9 were generated as part of ref. 5. The corresponding spectral libraries, Spectronaut Reports and statistical source data

presented in Figs. 6–9 and Extended Data Figs. 7 and 9 are available at https://doi.org/10.5281/zenodo.5749994.

The R Markdown notebooks for data analysis are available at https://doi.org/10.5281/zenodo.5749994. The MSstatsLiP R-package can be installed from Bioconductor

(https://www.bioconductor.org/packages/release/bioc/html/MSstats.html).

A Correction to this paper has been published: https://doi.org/10.1038/s41596-023-00808-9

Feng, Y. et al. Global analysis of protein structural changes in complex proteomes. Nat. Biotechnol. 32, 1036–1044 (2014).

We thank the Picotti laboratory for continuous input on the protocol optimization and the R package. I.P. and L.M. were supported by long-term EMBO postdoctoral fellowships (ALTF 846-2014

and ALTF 538-2016). This project received funding from the European Research Council (grant agreement no. 866004) and through the EPIC-XS Consortium (grant agreement no. 823839) both under

the European Union’s Horizon 2020 research and innovation program. It was supported by a Personalized Health and Related Technologies (PHRT) grant (PHRT-506), a Sinergia grant from the Swiss

National Science Foundation (SNSF grant CRSII5_177195) and the National Center of Competence in Research AntiResist funded by the SNSF (grant number 51NF40_180541).

These authors contributed equally: Liliana Malinovska, Valentina Cappelletti, Devon Kohler.

Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland

Liliana Malinovska, Valentina Cappelletti, Monika Pepelnjak, Patrick Stalder, Christian Dörig, Fabian Sesterhenn, Franziska Elsässer, Lucie Kralickova, Natalie de Souza & Paola Picotti

Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA

Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC Berlin), Berlin, Germany

Department of Mathematical Sciences, Kent State University, Kent, OH, USA

L.M., V.C. and N.d.S. wrote the paper. L.M., V.C. and I.P. optimized the original version of the protocol. M.P., P.S., C.D., F.E., L.K., N.B. and L.R. contributed to protocol optimization.

L.M., V.C., D.K., T.-H.T. and F.S. performed bioinformatic analysis of the data. D.K. and T.-H.T. optimized algorithms and tools for the R package MSstatsLiP. O.V. supervised the development

of the R package. P.P. supervised the optimization of the protocol.

N.B. and L.R. are employees of Biognosys AG (Zurich, Switzerland). P.P. is a scientific advisor for the company Biognosys AG (Zurich, Switzerland) and an inventor of a patent licensed by

Biognosys AG that covers the LiP-MS method used in this protocol. The remaining authors declare no competing interests.

Nature Protocols thanks the anonymous reviewers for their contribution to the peer review of this work.

Piazza, I. et al. Nat. Commun. 11, 4200 (2020): https://doi.org/10.1038/s41467-020-18071-x

Cappelletti, V. et al. Cell 184, 545–559.e22 (2021): https://doi.org/10.1016/j.cell.2020.12.021

This protocol is an update to Nat. Protoc. 12, 2391–2410 (2017): https://doi.org/10.1038/nprot.2017.100.

a and b, WebLogo diagram of the cleavage site of PK and the neighboring five amino acids in a mammalian cell lysate (a) and yeast cell lysate (b). Amino acids are plotted according to the

frequency of occurrence at the cleavage side and colored on the basis of their physicochemical properties. c, WebLogo diagram of the cleavage site of a specific protease (trypsin) in a

mammalian cell extract. d, Boxplots showing the distribution of secondary structure elements in half-tryptic peptides in different organisms. Box shows the quartiles of the dataset, median

values are represented by a vertical line in the center of the box, bars extend to the rest of the distribution and dots represent outliers. AA, amino acid.

a, Coefficient of variation of a LiP experiment performed in a thermocycler as described in this protocol (new) and performed in a boiling pot as described in Schopper et al.6 (old). The

median value is displayed below the plot. b, Coefficient of variation of a LiP experiment with DOC removal through centrifugation in individual reaction tubes as described in Schopper et

al.6 (old) and with DOC removal through filtration (new). The median value is displayed below the plot.

Principal component (PC) analysis of MS1 features. The colors indicate the different samples amounts in 50 μl.

a, Summary characterization of peptides in LiP-MS experiments performed as described in Fig. 2a. Left pie chart: Number of peptides identified across the different incubation times. Right

pie chart: Number of peptides identified across the different E:S ratios. b and c, Pairwise comparisons of peptide identifications and intensity changes for each incubation time (1–60 min)

versus the shortest incubation time (30 s). b, Fraction of peptides identified only in the higher digestion condition (red), only in the lower digestion condition (blue) or shared between

both conditions (white). c, Fraction of peptides with significantly changed intensities (|log2(fold change)| > 2, q-value 0.25). Proper mixing can counteract this effect. b, High CV (median

CV > 0.25) of peptide intensity for biological replicates in three samples. Evaporation can occur during PK inactivation as a result of opening lids. This leads to a global increase in

variability in all samples of an experiment. c, Distribution of HT peptides (gray) and fully tryptic peptides (black) in two samples. Insufficient inactivation of PK can lead to variations

in the proportions of HT peptides as reflected by the inconsistent trypticity content among replicates in sample 2. In this case, replicate 1 of sample 2 cannot be used for the statistical

analysis. d, PCA of fragment ion intensities in four samples. Spike-in of single proteins at high concentrations into a complex background can display poor separation of experimental groups

when using log2-transformed data (left panel). Using non-transformed data emphasizes the separation effect of the spike-in protein (right panel). e, Differential analysis of structurally

altered peptides, visualized in a volcano plot. Each point represents a peptide. Peptides passing the significance cutoff (|log2(FC)| > 1, q-value 1, q-value 2, q-value < 0.05); light green,

non-significant peptides. d, Structural barcodes visualizing fully tryptic peptides mapped along the sequence of aSyn in the M1 versus M2 comparison described in c and corrected for protein

abundance as indicated. Significant peptides are colored in dark green, and non-significant peptides are colored in light green. Gray regions indicate no identified fully tryptic peptide

matching this region. The position of the NAC region is indicated as a line below the barcode.

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