Peptide Pairs Research Articles

Macrochirality of Self-Assembled and Co-assembled Supramolecular Structures of a Pair of Enantiomeric Peptides.

Although macrochirality of peptides’ supramolecular structures has been found to play important roles in biological activities, how macrochirality is determined by the molecular chirality of the constituted amino acids is still unclear. Here, two chiral peptides, Ac-LKLHLHLQLKLLLVLFLFLALK-NH2 (KK-11) and Ac-DKDHDHDQDKDL DVDFDFDADK-NH2 (KKd-11), which were composed entirely of either L- or D-amino acids, were designed for studying the chiral characteristics of the supramolecular microstructures. It was found that monocomponent KK-11 or KKd-11 self-assembled into right- or left-handed helical nanofibrils, respectively. However, when they co-assembled with concentration ratios varied from 1:9 to 9:1, achiral nanowire-like structures were formed. Both circular dichroism and Fourier transform infrared spectra indicated that the secondary structures changed when the peptides co-assembled. MD simulations indicated that KK-11 or KKd-11 exhibited a strong propensity to self-assemble into right-handed or left-handed nanofibrils, respectively. However, when KK-11 and KKd-11 were both presented in a solution, they had a higher probability to co-assemble instead of self-sort. MD simulations indicated that, in their mixtures, they formed nanowires without handedness feature, a good agreement with experimental observation. Our results shed light on the molecular mechanisms of the macrochirality of peptide supramolecular microstructures.

DeepNetBim: deep learning model for predicting HLA-epitope interactions based on network analysis by harnessing binding and immunogenicity information

BackgroundEpitope prediction is a useful approach in cancer immunology and immunotherapy. Many computational methods, including machine learning and network analysis, have been developed quickly for such purposes. However, regarding clinical applications, the existing tools are insufficient because few of the predicted binding molecules are immunogenic. Hence, to develop more potent and effective vaccines, it is important to understand binding and immunogenic potential. Here, we observed that the interactive association constituted by human leukocyte antigen (HLA)-peptide pairs can be regarded as a network in which each HLA and peptide is taken as a node. We speculated whether this network could detect the essential interactive propensities embedded in HLA-peptide pairs. Thus, we developed a network-based deep learning method called DeepNetBim by harnessing binding and immunogenic information to predict HLA-peptide interactions.ResultsQuantitative class I HLA-peptide binding data and qualitative immunogenic data (including data generated from T cell activation assays, major histocompatibility complex (MHC) binding assays and MHC ligand elution assays) were retrieved from the Immune Epitope Database database. The weighted HLA-peptide binding network and immunogenic network were integrated into a network-based deep learning algorithm constituted by a convolutional neural network and an attention mechanism. The results showed that the integration of network centrality metrics increased the power of both binding and immunogenicity predictions, while the new model significantly outperformed those that did not include network features and those with shuffled networks. Applied on benchmark and independent datasets, DeepNetBim achieved an AUC score of 93.74% in HLA-peptide binding prediction, outperforming 11 state-of-the-art relevant models. Furthermore, the performance enhancement of the combined model, which filtered out negative immunogenic predictions, was confirmed on neoantigen identification by an increase in both positive predictive value (PPV) and the proportion of neoantigen recognition.ConclusionsWe developed a network-based deep learning method called DeepNetBim as a pan-specific epitope prediction tool. It extracted the attributes of the network as new features from HLA-peptide binding and immunogenic models. We observed that not only did DeepNetBim binding model outperform other updated methods but the combination of our two models showed better performance. This indicates further applications in clinical practice.

Defining the Landscape of the Pauling-Corey Rippled Sheet: An Orphaned Motif Finding New Homes.

ConspectusWhen peptides are mixed with their mirror images in an equimolar ratio, two-dimensional periodic structural folds can form, in which extended peptide strands are arrayed with alternating chirality. The resultant topography class, termed the rippled β-sheet, was introduced as a theoretical concept by Pauling and Corey in 1953. Unlike other fundamental protein structural motifs identified around that time, including the α-helix and the pleated β-sheet, it took several decades before conclusive experimental data supporting the proposed rippled β-sheet motif were gained. Much of the key experimental evidence was provided over the course of the past decade through the concurrent efforts of our three laboratories. Studies that focused on developing new self-assembling hydrogel materials have shown that certain amphiphilic peptides form fibrils and hydrogel networks that are more rigid and have a higher thermodynamic stability when made from racemic peptide mixtures as opposed to pure enantiomers. Related interrogation of assemblies composed of mixtures of l- and d-amphiphilic peptides confirmed that the resulting fibrils were composed of alternating l/d peptides consistent with rippled β-sheets. It was also demonstrated that mirror-image amyloid beta (Aβ) could act as a molecular chaperone to promote oligomer-to-fibril conversion of the natural Aβ enantiomer, which was found to reduce Aβ neurotoxicity against different neuronal cell models. With a cross-disciplinary approach that combines experiment and theory, our three laboratories have demonstrated the unique biophysical, biochemical, and biological properties that arise upon mixing of peptide enantiomers, in consequence of rippled β-sheet formation. In this Account, we give an overview of the early history of the rippled β-sheet and provide a detailed structural description/definition of this motif relative to the pleated β-sheet. We then summarize the key findings, obtained on three unique sets of aggregating mirror-image peptide pairs through independent efforts of our three laboratories, and use these results to delineate the landscape of the rippled β-sheet structural motif to inspire future studies. Peptide sequence parameters that favor rippled β-sheet assembly are described, along with the accompanying kinetic and thermodynamic properties, as well as the resulting emergent physical properties of the assemblies. The Account then concludes with a brief overview of some key unresolved challenges in this nascent field. There is much potential for future applications of this unique supramolecular motif in the realm of materials design and biomedical research. We hope this Account will stimulate much-needed discussion of this fascinating structural class to eventually produce a fully quantitative, rational framework for the molecular engineering of rippled β-sheets in the future.

CATCH Peptides Coassemble into Structurally Heterogeneous β-Sheet Nanofibers with Little Preference to β-Strand Alignment.

Coassembling peptides offer an additional degree of freedom in the design of nanostructured biomaterials when compared to analogous self-assembling peptides. Yet, our understanding of how amino acid sequences encodes coassembled nanofiber structure is limited. Prior work on a charge-complementary pair, CATCH+ and CATCH- peptides, detected like-peptide nearest neighbors (CATCH+:CATCH+ and CATCH-:CATCH-) within coassembled β-sheet nanofibers; these self-associated peptide pairs marked a departure from an "ideal" coassembled structure. In this work, we employ solid-state NMR, isotope-edited FTIR, and coarse-grained molecular dynamics simulations to evaluate the alignment of β-strands within CATCH peptide nanofibers. Both experimental and computational results suggest that CATCH molecules coassemble into structurally heterogeneous nanofibers, which is consistent with our observations in another coassembling system, the King-Webb peptides. Within β-sheet nanofibers, β-strands were found to have nearest neighbors aligned in-register parallel, in-register antiparallel, and out-of-register. In comparison to the King-Webb peptides, CATCH nanofibers exhibit a greater degree of structural heterogeneity. By comparing the amino acid sequences of CATCH and King-Webb peptides, we can begin to unravel sequence-to-structure relationships, which may encode more precise coassembled β-sheet nanostructures.

PDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning.

In cross-linking mass spectrometry, the identification of cross-linked peptide pairs heavily relies on the ability of a database search engine to measure the similarities between experimental and theoretical MS/MS spectra. However, the lack of accurate ion intensities in theoretical spectra impairs the performance of search engines, in particular, on proteome scales. Here we introduce pDeepXL, a deep neural network to predict MS/MS spectra of cross-linked peptide pairs. To train pDeepXL, we used the transfer-learning technique because it facilitated the training with limited benchmark data of cross-linked peptide pairs. Test results on more than ten data sets showed that pDeepXL accurately predicted the spectra of both noncleavable DSS/BS3/Leiker cross-linked peptide pairs (>80% of predicted spectra have Pearson's r values higher than 0.9) and cleavable DSSO/DSBU cross-linked peptide pairs (>75% of predicted spectra have Pearson's r values higher than 0.9). pDeepXL also achieved the accurate prediction on unseen data sets using an online fine-tuning technique. Lastly, integrating pDeepXL into a database search engine increased the number of identified cross-link spectra by 18% on average.

Novel Cyclic Peptides from Lethal Amanita Mushrooms through a Genome-Guided Approach.

Most species in the genus Amanita are ectomycorrhizal fungi comprising both edible and poisonous mushrooms. Some species produce potent cyclic peptide toxins, such as α-amanitin, which places them among the deadliest organisms known to mankind. These toxins and related cyclic peptides are encoded by genes of the “MSDIN” family (named after the first five amino acid residues of the precursor peptides), and it is largely unknown to what extent these genes are expressed in the basidiocarps. In the present study, Amanita rimosa and Amanita exitialis were sequenced through the PacBio and Illumina techniques. Together with our two previously sequenced genomes, Amanita subjunquillea and Amanita pallidorosea, in total, 46 previously unknown MSDIN genes were discovered. The expression of over 80% of the MSDIN genes was demonstrated in A. subjunquillea. Through a combination of genomics and mass spectrometry, 12 MSDIN genes were shown to produce novel cyclic peptides. To further confirm the results, three of the cyclic peptides were chemically synthesized. The tandem mass spectrometry (MS/MS) spectra of the natural and the synthetic peptides shared a majority of the fragment ions, demonstrating an identical structure between each peptide pair. Collectively, the results suggested that the genome-guided approach is reliable for identifying novel cyclic peptides in Amanita species and that there is a large peptide reservoir in these mushrooms.

A top scoring pairs classifier for recent HIV infections.

Accurate incidence estimation of HIV infection from cross-sectional biomarker data is crucial for monitoring the epidemic and determining the impact of HIV prevention interventions. A key feature of cross-sectional incidence testing methods is the mean window period, defined as the average duration that infected individuals are classified as recently infected. Two assays available for cross-sectional incidence estimation, the BED capture immunoassay, and the Limiting Antigen (LAg) Avidity assay, measure a general characteristic of antibody response; performance of these assays can be affected and biased by factors such as viral suppression, resulting in sample misclassification and overestimation of HIV incidence. As availability and use of antiretroviral treatment increase worldwide, algorithms that do not include HIV viral load and are not impacted by viral suppression are needed for cross-sectional HIV incidence estimation. Using a phage display system to quantify antibody binding to over 3300 HIV peptides, we present a classifier based on top scoring peptide pairs that identifies recent infections using HIV antibody responses alone. Based on plasma samples from individuals with known dates of seroconversion, we estimated the mean window period for our classifier to be 217 days (95% confidence interval 183 to 257 days), compared to the estimated mean window period for the LAg-Avidity protocol of 106 days (76 to 146 days). Moreover, each of the four peptide pairs correctly classified more of the recent samples than the LAg-Avidity assay alone at the same classification accuracy for non-recent samples.

BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples.

The data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (DIA-MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide MS1 isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labeled peptide pairs through peptide fragment ions. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1 and MS2 levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax significantly improved the quantitative accuracy in SILAC and pSILAC samples by mitigating the ratio suppression of isotope-peptide pairs. We further applied BoxCarmax to measure protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.

Confident Identification of Citrullination and Carbamylation Assisted by Peptide Retention Time Prediction.

The chromatographic behavior of peptides carrying citrulline and homocitrulline residues in proteomic two-dimensional (2D) liquid chromatography-mass spectrometry (LC-MS) experiments has been investigated. The primary goal of this study was to determine the chromatographic conditions that allow differentiating between arginine citrullination and deamidation of asparagine based on retention data, improving the confidence of MS-based identifications. Carbamylation was used as a reference point due to a high degree of similarity between modification products and anticipated changes in chromatographic behavior. We applied 2D LC-MS/MS (a high-pH-low-pH reversed phase (RP), hydrophilic interaction liquid chromatography (HILIC)-low-pH RP, and strong cation exchange (SCX)-low-pH RP) to acquire retention data for modified-nonmodified peptide pairs in the four separation modes. Modifications of a standard protein mixture were induced enzymatically (PAD-2) or chemically (urea) for citrullination and carbamylation, respectively. Deamidation occurs spontaneously. Similar retention shifts were observed for all three modifications in a high-pH RP (decrease) and a low-pH RP (increase), thus limiting the applicability of this 2D LC combination. HILIC on bare silica and strong cation exchange separations have been probed to amplify the effect of charge loss upon citrullination, with SCX demonstrating the most differentiating power: the elimination of basic residues upon citrullination/carbamylation results in an ∼58 mM KCl retention decrease, while retention of deamidated products decreases slightly.

Complementary leucine zippering system for effective intracellular delivery of proteins by cell-penetrating peptides

A heterodimeric leucine zipper composed of a pair of leucine zipper peptides containing acidic or basic amino acid residues at appropriate positions in each peptide was used as a molecular glue to connect protein cargos to a cell-penetrating peptide (CPP) carrier. To investigate the hybridization properties by fluorescence experiments, we prepared an enhanced green fluorescent protein (EGFP) fused with an acidic leucine zipper (LzK), EGFP-LzK, and a basic leucine zipper (LzE) modified with a CPP, LzE-CPP. The LzK and LzE formed a 1:1 hybrid when EGFP-LzK and LzE-CPP were mixed in phosphate buffer saline, thereby conjugating the EGFP with the CPP. The formation of the 1:1 hybrid was confirmed by fluorescence spectra and fluorescence titration curves. Results from fluorescence microscopy experiments showed that EGFP was successfully delivered into cells by conjugating with the CPP via formation of the LzK/LzE hybrid. We also fused the apoptotic protein p53 with LzK (p53-LzK) and investigated the inhibition of cell proliferation of various cell lines by incubation with the p53-LzK/LzE-CPP hybrid. This hybrid was found to localize in nuclei and successfully inhibited cell-specific proliferation. The LzE/LzK zipper system inhibited cell proliferation more efficiently than the directly fused conjugate, p53-CPP. Our method will be a useful drug delivery system for delivering bioactive proteins to treat various diseases.

Liposome fusion with orthogonal coiled coil peptides as fusogens: the efficacy of roleplaying peptides.

Biological membrane fusion is a highly specific and coordinated process as a multitude of vesicular fusion events proceed simultaneously in a complex environment with minimal off-target delivery. In this study, we develop a liposomal fusion model system with specific recognition using lipidated derivatives of a set of four de novo designed heterodimeric coiled coil (CC) peptide pairs. Content mixing was only obtained between liposomes functionalized with complementary peptides, demonstrating both fusogenic activity of CC peptides and the specificity of this model system. The diverse peptide fusogens revealed important relationships between the fusogenic efficacy and the peptide characteristics. The fusion efficiency increased from 20% to 70% as affinity between complementary peptides decreased, (from KF ≈ 108 to 104 M−1), and fusion efficiency also increased due to more pronounced asymmetric role-playing of membrane interacting ‘K’ peptides and homodimer-forming ‘E’ peptides. Furthermore, a new and highly fusogenic CC pair (E3/P1K) was discovered, providing an orthogonal peptide triad with the fusogenic CC pairs P2E/P2K and P3E/P3K. This E3/P1k pair was revealed, via molecular dynamics simulations, to have a shifted heptad repeat that can accommodate mismatched asparagine residues. These results will have broad implications not only for the fundamental understanding of CC design and how asparagine residues can be accommodated within the hydrophobic core, but also for drug delivery systems by revealing the necessary interplay of efficient peptide fusogens and enabling the targeted delivery of different carrier vesicles at various peptide-functionalized locations.

Rapid construction of fluorescence quenching-based immunosensor Q-bodies using α-helical coiled-coil peptides.

Here, we report a rapid and efficient method to fabricate Quenchbodies (Q-bodies) that can detect targets with antigen-dependent fluorescence augmentation using a stable coiled-coil peptide pair, E4 and K4 (coiled Q-body, CQ-body). The CQ-body allowed antigen detection not only in buffer but also in 50% plasma. Furthermore, we describe FRET-type CQ-bodies using a dual-coloured K4 peptide, which allowed a more precise antigen quantification. Lastly, successful fabrication of nanobody-based CQ-body shows its applicability to a range of antibody fragments.

Mass-Based Protein Phylogenetic Approach to Identify Epistasis.

A mass-based protein phylogeny method, known as phylonumerics, is described to build phylogenetic-like trees using a purpose-built MassTree algorithm. These trees are constructed from sets of numerical mass map data for each protein without the need for gene or protein sequences. Such trees have been shown to be highly congruent with conventional sequence-based trees and provide a reliable means to study the evolutionary history of organisms. Mutations determined from the differences in the mass of peptide pairs across different mass sets are computed by the algorithm and displayed at branch nodes across the tree. By definition, since the trees display a phylogeny representing expressed proteins, all mutations are non-synonymous. The frequency of these mutations and a mutation score based on a sum of these frequencies weighted based upon their position to the root of the tree are output. The algorithm also outputs lists of pairs of mutations separated along interconnected branches of the tree. Those which co-occur or which occur consecutively, or near consecutively, and that are separated by a distance less than the average distance for all mutation pairs, are putatively assigned to be epistatic pairs. These pairs are examined further with a focus on non-conservative substitutions given their importance in driving structural and functional change and protein and organismal evolution. The application of the method is demonstrated for the H3 hemagglutinin protein of type A human H3N2 strains of the influenza virus. The most frequent ancestral mutations within epistatic pairs occur within antigenic site domains while the descendant mutations occur either at other antigenic sites or elsewhere in the protein. Both predominate at reported glycosylation sites. The results for this protein further support a "small steps" evolutionary model for the influenza virus where non-conservative mutations that involve the least structural change are favored over those involving substantive change, which may risk the virus's own extinction.

Open Search Strategy for Inferring the Masses of Cross-Link Adducts on Proteins.

Development of new reagents for protein cross-linking is constantly ongoing. The chemical formulas for the linker adducts formed by these reagents are usually deduced from expert knowledge and then validated by mass spectrometry. Clearly, it would be more rigorous to infer the chemical compositions of the adducts directly from the data without any prior assumptions on their chemistries. Unfortunately, the analysis tools that are currently available to detect chemical modifications on linear peptides are not applicable to the case of two cross-linked peptides. Here, we show that an adaptation of the open search strategy that works on linear peptides can be used to characterize cross-link modifications in pairs of peptides. We benchmark our approach by correctly inferring the linker masses of two well-known reagents, DSS and formaldehyde, to accuracies of a few parts per million. We then investigate the cross-linking chemistries of two poorly characterized reagents: EMCS and glutaraldehyde. In the case of EMCS, we find that the expected cross-linking chemistry is accompanied by a competing chemistry that targets other amino acid types. In the case of glutaraldehyde, we find that the chemical formula of the dominant linker is C5H4, which indicates a ringed aromatic structure. These results demonstrate how, with very little effort, our approach can yield nontrivial insights to better characterize new cross-linkers.

Spatial Multiplexing of Fluorescent Reporters for Imaging Signaling Network Dynamics

In order to analyze how a signal transduction network converts cellular inputs into cellular outputs, ideally one would measure the dynamics of many signals within the network simultaneously. We found that, by fusing a fluorescent reporter to a pair of self-assembling peptides, it could be stably clustered within cells at random points, distant enough to be resolved by a microscope but close enough to spatially sample the relevant biology. Because such clusters, which we call signaling reporter islands (SiRIs), can be modularly designed, they permit a set of fluorescent reporters to be efficiently adapted for simultaneous measurement of multiple nodes of a signal transduction network within single cells. We created SiRIs for indicators of second messengers and kinases and used them, in hippocampal neurons in culture and intact brain slices, to discover relationships between the speed of calcium signaling, and the amplitude of PKA signaling, upon receiving a cAMP-driving stimulus.

High-affinity antigen association to cationic liposomes via coiled coil-forming peptides induces a strong antigen-specific CD4+ T-cell response

Liposomes are widely investigated as vaccine delivery systems, but antigen loading efficiency can be low. Moreover, adsorbed antigen may rapidly desorb under physiological conditions. Encapsulation of antigens overcomes the latter problem but results in significant antigen loss during preparation and purification of the liposomes. Here, we propose an alternative attachment method, based on a complementary heterodimeric coiled coil peptide pair pepK and pepE.PepK was conjugated to cholesterol (yielding CPK) and pepE was covalently linked to model antigen OVA323 (yielding pepE-OVA323). CPK was incorporated in the lipid bilayer of cationic liposomes (180 nm in size). Antigen was associated more efficiently to functionalized liposomes (Kd 166 nM) than to cationic liposomes (Kd not detectable). In vivo co-localization of antigen and liposomes was strongly increased upon CPK-functionalization (35% -> 80%). CPK-functionalized liposomes induced 5-fold stronger CD4+ T-cell proliferation than non-functionalized liposomes in vitro. Both formulations were able to induce strong CD4+ T-cell expansion in mice, but more IFN-y and IL-10 production was observed after immunization with functionalized liposomes. In conclusion, antigen association via coiled coil peptide pair increased co-localization of antigen and liposomes, increased CD4+ T-cell proliferation in vitro and induced a stronger CD4+ T-cell response in vivo.

Shape-Shifting Peptide Nanomaterials: Surface Asymmetry Enables pH-Dependent Formation and Interconversion of Collagen Tubes and Sheets.

The fabrication of dynamic, transformable biomaterials that respond to environmental cues represents a significant step forward in the development of synthetic materials that rival their highly functional, natural counterparts. Here, we describe the design and synthesis of crystalline supramolecular architectures from charge-complementary heteromeric pairs of collagen-mimetic peptides (CMPs). Under appropriate conditions, CMP pairs spontaneously assemble into either 1D ultraporous (pore diameter >100 nm) tubes or 2D bilayer nanosheets due to the structural asymmetry that arises from heteromeric self-association. Crystalline collagen tubes represent a heretofore unobserved morphology of this common biomaterial. In-depth structural characterization from a suite of biophysical methods, including TEM, AFM, high-resolution cryo-EM, and SAXS/WAXS measurements, reveals that the sheet and tube assemblies possess a similar underlying lattice structure. The experimental evidence suggests that the tubular structures are a consequence of the self-scrolling of incipient 2D layers of collagen triple helices and that the scrolling direction determines the formation of two distinct structural isoforms. Furthermore, we show that nanosheets and tubes can spontaneously interconvert through manipulation of the assembly pH and systematic adjustment of the CMP sequence. Altogether, we establish initial guidelines for the construction of dynamically responsive 1D and 2D assemblies that undergo a structurally programmed morphological transition.

Directed co-evolution of interacting protein–peptide pairs by compartmentalized two-hybrid replication (C2HR)

Directed evolution methodologies benefit from read-outs quantitatively linking genotype to phenotype. We therefore devised a method that couples protein–peptide interactions to the dynamic read-out provided by an engineered DNA polymerase. Fusion of a processivity clamp protein to a thermostable nucleic acid polymerase enables polymerase activity and DNA amplification in otherwise prohibitive high-salt buffers. Here, we recapitulate this phenotype by indirectly coupling the Sso7d processivity clamp to Taq DNA polymerase via respective fusion to a high affinity and thermostable interacting protein–peptide pair. Escherichia coli cells co-expressing protein–peptide pairs can directly be used in polymerase chain reactions to determine relative interaction strengths by the measurement of amplicon yields. Conditional polymerase activity is further used to link genotype to phenotype of interacting protein–peptide pairs co-expressed in E. coli using the compartmentalized self-replication directed evolution platform. We validate this approach, termed compartmentalized two-hybrid replication, by selecting for high-affinity peptides that bind two model protein partners: SpyCatcher and the large fragment of NanoLuc luciferase. We further demonstrate directed co-evolution by randomizing both protein and peptide components of the SpyCatcher–SpyTag pair and co-selecting for functionally interacting variants.

Benefits of Collisional Cross Section Assisted Precursor Selection (caps-PASEF) for Cross-linking Mass Spectrometry

Ion mobility separates molecules in the gas-phase based on their physico-chemical properties, providing information about their size as collisional cross-sections. The timsTOF Pro combines trapped ion mobility with a quadrupole, collision cell and a TOF mass analyzer, to probe ions at high speeds with on-the-fly fragmentation. Here, we show that on this platform ion mobility is beneficial for cross-linking MS (XL-MS). Cross-linking reagents covalently link amino acids in proximity, resulting in peptide pairs after proteolytic digestion. These cross-linked peptides are typically present at low abundance in the background of normal peptides, which can partially be resolved by using enrichable cross-linking reagents. Even with a very efficient enrichable cross-linking reagent, like PhoX, the analysis of cross-linked peptides is still hampered by the co-enrichment of peptides connected to a partially hydrolyzed reagent - termed mono-linked peptides. For experiments aiming to uncover protein-protein interactions these are unwanted byproducts. Here, we demonstrate that gas-phase separation by ion mobility enables the separation of mono-linked peptides from cross-linked peptide pairs. A clear partition between these two classes is observed at a CCS of 500 Å2 and a monoisotopic mass of 2 kDa, which can be used for targeted precursor selection. A total of 50-70% of the mono-linked peptides are prevented from sequencing, allowing the analysis to focus on sequencing the relevant cross-linked peptide pairs. In applications to both simple proteins and protein mixtures and a complete highly complex lysate this approach provides a substantial increase in detected cross-linked peptides.

PAINT using proteins: A new brush for super-resolution artists.

PAINT (points accumulation for imaging in nanoscale topography) refers to methods that achieve the sparse temporal labeling required for super‐resolution imaging by using transient interactions between a biomolecule of interest and a fluorophore. There have been a variety of different implementations of this method since it was first described in 2006. Recent papers illustrate how transient peptide–protein interactions, rather than small molecule binding or DNA oligonucleotide duplex formation, can be employed to perform PAINT‐based single molecule localization microscopy (SMLM). We discuss the different approaches to PAINT using peptide and protein interactions, and their applications in vitro and in vivo. We highlight the important parameters to consider when selecting suitable peptide–protein interaction pairs for such studies. We also note the opportunities for protein scientists to apply their expertise in guiding the choice of peptide and protein pairs that are used. Finally, we discuss the potential for expanding super‐resolution imaging methods based on transient peptide–protein interactions, including the development of simultaneous multicolor imaging of multiple proteins and the study of very high and very low abundance proteins in live cells.