Noncovalently Associated Peptides Observed during Liquid Chromatography-Mass Spectrometry and Their Effect on Cross-Link Analyses

Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning

While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or for which few homologous sequences are known. Here we introduce AlphaLink, a modified version of the AlphaFold2 algorithm that incorporates experimental distance restraint information into its network architecture. By employing sparse experimental contacts as anchor points, AlphaLink improves on the performance of AlphaFold2 in predicting challenging targets. We confirm this experimentally by using the noncanonical amino acid photo-leucine to obtain information on residue-residue contacts inside cells by crosslinking mass spectrometry. The program can predict distinct conformations of proteins on the basis of the distance restraints provided, demonstrating the value of experimental data in driving protein structure prediction. The noise-tolerant framework for integrating data in protein structure prediction presented here opens a path to accurate characterization of protein structures from in-cell data.

Mass spectrometry uncovers intermediates and off-pathway complexes for SNARE complex assembly

The SNARE complex assembles from vesicular Synaptobrevin-2 as well as Syntaxin-1 and SNAP25 both anchored to the presynaptic membrane. It mediates fusion of synaptic vesicles with the presynaptic plasma membrane resulting in exocytosis of neurotransmitters. While the general sequence of SNARE complex formation is well-established, our knowledge on possible intermediates and stable off-pathway complexes is incomplete. We, therefore, follow the stepwise assembly of the SNARE complex and target individual SNAREs, binary sub-complexes, the ternary SNARE complex as well as interactions with Complexin-1. Using native mass spectrometry, we identify the stoichiometry of sub-complexes and monitor oligomerisation of various assemblies. Importantly, we find that interactions with Complexin-1 reduce multimerisation of the ternary SNARE complex. Chemical cross-linking provides detailed insights into these interactions suggesting a role for membrane fusion. In summary, we unravel the stoichiometry of intermediates and off-pathway complexes and compile a road map of SNARE complex assembly including regulation by Complexin-1.

Improved Peptide Backbone Fragmentation Is the Primary Advantage of MS-Cleavable Crosslinkers

Proteome-wide crosslinking mass spectrometry studies have coincided with the advent of mass spectrometry (MS)-cleavable crosslinkers that can reveal the individual masses of the two crosslinked peptides. However, recently, such studies have also been published with noncleavable crosslinkers, suggesting that MS-cleavability is not essential. We therefore examined in detail the advantages and disadvantages of using the commonly used MS-cleavable crosslinker, disuccinimidyl sulfoxide (DSSO). Indeed, DSSO gave rise to signature peptide fragments with a distinct mass difference (doublet) for nearly all identified crosslinked peptides. Surprisingly, we could show that it was not these peptide masses that proved the main advantage of MS cleavability of the crosslinker, but improved peptide backbone fragmentation which reduces the ambiguity of peptide identifications. This also holds true for another commonly used MS-cleavable crosslinker, DSBU. We show furthermore that the more intricate MS3-based data acquisition approaches lack sensitivity and specificity, causing them to be outperformed by the simpler and faster stepped higher-energy collisional dissociation (HCD) method. This understanding will guide future developments and applications of proteome-wide crosslinking mass spectrometry.

Leveraging Parameter Dependencies in High-Field Asymmetric Waveform Ion-Mobility Spectrometry and Size Exclusion Chromatography for Proteome-wide Cross-Linking Mass Spectrometry

Ion-mobility spectrometry shows great promise to tackle analytically challenging research questions by adding another separation dimension to liquid chromatography-mass spectrometry. The understanding of how analyte properties influence ion mobility has increased through recent studies, but no clear rationale for the design of customized experimental settings has emerged. Here, we leverage machine learning to deepen our understanding of field asymmetric waveform ion-mobility spectrometry for the analysis of cross-linked peptides. Knowing that predominantly m/z and then the size and charge state of an analyte influence the separation, we found ideal compensation voltages correlating with the size exclusion chromatography fraction number. The effect of this relationship on the analytical depth can be substantial as exploiting it allowed us to almost double unique residue pair detections in a proteome-wide cross-linking experiment. Other applications involving liquid- and gas-phase separation may also benefit from considering such parameter dependencies.

Leveraging crosslinking mass spectrometry in structural and cell biology

Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.

Structural basis for the Pr-Pfr long-range signaling mechanism of a full-length bacterial phytochrome at the atomic level

Phytochromes constitute a widespread photoreceptor family that typically interconverts between two photostates called Pr (red light–absorbing) and Pfr (far-red light–absorbing). The lack of full-length structures solved at the (near-)atomic level in both pure Pr and Pfr states leaves gaps in the structural mechanisms involved in the signal transmission pathways during the photoconversion. Here, we present the crystallographic structures of three versions from the plant pathogen Xanthomonas campestris virulence regulator XccBphP bacteriophytochrome, including two full-length proteins, in the Pr and Pfr states. The structures show a reorganization of the interaction networks within and around the chromophore-binding pocket, an α-helix/β-sheet tongue transition, and specific domain reorientations, along with interchanging kinks and breaks at the helical spine as a result of the photoswitching, which subsequently affect the quaternary assembly. These structural findings, combined with multidisciplinary studies, allow us to describe the signaling mechanism of a full-length bacterial phytochrome at the atomic level.

A Diazirine-Modified Membrane Lipid to Study Peptide/Lipid Interactions - Chances and Challenges

Although incorporation of photo‐activatable lipids into membranes potentially opens up novel avenues for investigating interactions with proteins, the question of whether diazirine‐modified lipids are suitable for such studies, remains under debate. Focusing on the potential for studying lipid/peptide interactions by cross‐linking mass spectrometry (XL‐MS), we developed a diazirine‐modified lipid (DiazPC), and examined its behaviour in membranes incorporating the model α‐helical peptide LAVA20. We observed an unexpected backfolding of the diazirine‐containing stearoyl chain of the lipid. This surprising behaviour challenges the potential application of DiazPC for future XL‐MS studies of peptide and protein/lipid interactions. The observations made for DiazPC most likely represent a general phenomenon for any type of membrane lipids with a polar moiety incorporated into the alkyl chain. Our finding is therefore of importance for future protein/lipid interaction studies relying on modified lipid probes.

Reliable identification of protein-protein interactions by crosslinking mass spectrometry

Protein-protein interactions govern most cellular pathways and processes, and multiple technologies have emerged to systematically map them. Assessing the error of interaction networks has been a challenge. Crosslinking mass spectrometry is currently widening its scope from structural analyses of purified multi-protein complexes towards systems-wide analyses of protein-protein interactions (PPIs). Using a carefully controlled large-scale analysis of Escherichia coli cell lysate, we demonstrate that false-discovery rates (FDR) for PPIs identified by crosslinking mass spectrometry can be reliably estimated. We present an interaction network comprising 590 PPIs at 1% decoy-based PPI-FDR. The structural information included in this network localises the binding site of the hitherto uncharacterised protein YacL to near the DNA exit tunnel on the RNA polymerase.

Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry

Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits the numbers of protein-protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein-protein interactions without affecting confidence. This approach is not limited to cell lysates and multi-dimensional separation but also improves considerably the analysis of crosslinked multiprotein complexes with a single chromatographic dimension. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of crosslinking mass spectrometry analyses.

Liposomes as Carriers of Membrane-Associated Proteins and Peptides for Mass Spectrometric Analysis

Membrane proteins are key players of the cell. Their structure and the interactions they form with their lipid environment are required to understand their function. Here we explore liposomes as membrane mimetics for mass spectrometric analysis of peripheral membrane proteins and peptides. Liposomes are advantageous over other membrane mimetics in that they are easy to prepare, can be varied in size and composition, and are suitable for functional assays. We demonstrate that they dissociate into lipid clusters in the gas phase of a mass spectrometer while intact protein and protein–lipid complexes are retained. We exemplify this approach by employing different liposomes including proteoliposomes of two model peptides/proteins differing in size. Our results pave the way for the general application of liposomes for mass spectrometric analysis of membrane‐associated proteins.

Cross-linking mass spectrometry uncovers protein interactions and functional assemblies in synaptic vesicle membranes

Synaptic vesicles are storage organelles for neurotransmitters. They pass through a trafficking cycle and fuse with the pre-synaptic membrane when an action potential arrives at the nerve terminal. While molecular components and biophysical parameters of synaptic vesicles have been determined, our knowledge on the protein interactions in their membranes is limited. Here, we apply cross-linking mass spectrometry to study interactions of synaptic vesicle proteins in an unbiased approach without the need for specific antibodies or detergent-solubilisation. Our large-scale analysis delivers a protein network of vesicle sub-populations and functional assemblies including an active and an inactive conformation of the vesicular ATPase complex as well as non-conventional arrangements of the luminal loops of SV2A, Synaptophysin and structurally related proteins. Based on this network, we specifically target Synaptobrevin-2, which connects with many proteins, in different approaches. Our results allow distinction of interactions caused by 'crowding' in the vesicle membrane from stable interaction modules.

RawVegetable - A data assessment tool for proteomics and cross-linking mass spectrometry experiments

We present RawVegetable, a software for mass spectrometry data assessment and quality control tailored toward shotgun proteomics and cross-linking experiments. RawVegetable provides four main modules with distinct features: (A) The charge state chromatogram that independently displays the ion current for each charge state; useful for optimizing the chromatography for highly charged ions and with lower XIC values such as those typically found in cross-linking experiments. (B) The XL-Artefact determination, which flags possible noncovalently associated peptides. (C) The TopN density estimation, for detecting retention time intervals of under or over-sampling, and (D) The chromatography reproducibility module, which provides pairwise comparisons between multiple experiments. RawVegetable, a tutorial, and the example data are freely available for academic use at: http://patternlabforproteomics.org/rawvegetable. SIGNIFICANCE: Chromatography optimization is a critical step for any shotgun proteomic or cross-linking mass spectrometry experiment. Here, we present a nifty solution with several key features, such as displaying individual charge state chromatograms, highlighting chromatographic regions of under- or over-sampling and checking for reproducibility.

Investigation of noncovalent interactions between peptides with potential intrinsic sequence patterns by mass spectrometry

RATIONALE

The conformation of a protein largely depends on the interactions between peptides. Specific and intrinsic sequence peptide patterns, such as DNA double helix backbones, may be present in proteins. A computational statistical deep learning method has supported this assumption, but it has not been experimentally proven. Mass spectrometry, as a fast and accurate experimental method, could be used to evaluate the interaction of biomolecules. The results would be of great value for further study of the mechanism of protein folding.

METHODS

Several potential intrinsic peptides were chosen by the deep learning method, including seven groups of pentapeptides and five groups of nonapeptides. The noncovalent interactions between mixed polypeptides were investigated by electrospray ionization mass spectrometry (ESI-MS) in full-scan and collision-induced dissociation (CID) modes. Molecular dynamics and molecular mechanics Poisson-Boltzmann surface area (MD-MM/PBSA) analyses were also performed to support the results.

RESULTS

The ESI-MS spectra showed that 11 of the 12 groups of mixed polypeptides formed binary and ternary complexes with relatively high stability. The binding between nonapeptide groups was stronger than that between pentapeptide groups according to the relative intensity. The binding energies calculated by the MM/PBSA binding energy tool also provided strong evidence for the combination of the complexes. Electrostatic interactions, hydrophobic interactions, and van der Waals forces were thought to stabilize the complexes according to the binding models.

CONCLUSIONS

The results implied the formation of stable complexes between polypeptides and identified their noncovalent interactions, proving that specific sequences and combinations with relatively strong binding ability exist in potential intrinsic sequences of peptides in protein structures.

In Situ Structural Restraints from Cross-Linking Mass Spectrometry in Human Mitochondria

The field of structural biology is increasingly focusing on studying proteins in situ, i.e., in their greater biological context. Cross-linking mass spectrometry (CLMS) is contributing to this effort, typically through the use of mass spectrometry (MS)-cleavable cross-linkers. Here, we apply the popular noncleavable cross-linker disuccinimidyl suberate (DSS) to human mitochondria and identify 5518 distance restraints between protein residues. Each distance restraint on proteins or their interactions provides structural information within mitochondria. Comparing these restraints to protein data bank (PDB)-deposited structures and comparative models reveals novel protein conformations. Our data suggest, among others, substrates and protein flexibility of mitochondrial heat shock proteins. Through this study, we bring forward two central points for the progression of CLMS towards large-scale in situ structural biology: First, clustered conflicts of cross-link data reveal in situ protein conformation states in contrast to error-rich individual conflicts. Second, noncleavable cross-linkers are compatible with proteome-wide studies.