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Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. This missingness hinders reproducibility, reduces statistical power, and makes it difficult to compare across samples or experiments. Although many methods exist for imputing missing values in proteomics data, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error, such as the mean-squared error between the imputed and the held-out observed values. Here we evaluate the performance of a set of commonly used imputation methods using three practical, “downstream-centric” criteria, which measure the ability of imputation methods to reconstruct differentially expressed peptides, identify new quantitative peptides, and improve peptide lower limit of quantification. Our evaluation spans several experiment types and acquisition strategies, including datadependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides, but that it can identify new quantitative peptides and improve peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that exisiting imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

Advances in library-based methods for peptide detection from data-independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high-quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico, but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate-controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide-window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

A fundamental challenge for any mass spectrometry-based proteomics experiment is the identification of the peptide that generated each acquired tandem mass spectrum. Although approaches that leverage known peptide sequence databases are widely used and effective for well-characterized model organisms, such methods cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to the acquired tandem mass spectra without prior information—de novopeptide sequencing—is valuable for gaining biological insights for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address thisde novosequencing problem, it remains an outstanding challenge, in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo’s superior performance improves the analysis of immunopeptideomics and metaproteomics experiments and allows us to delve deeper into the dark proteome.

A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular postprocessing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semisupervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semisupervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo.

The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.

A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular post-processing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semi-supervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semi-supervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub athttps://github.com/bittremieux/ANN-SoLo.

Data independent acquisition (DIA) mass spectrometry methods provide systematic and comprehensive quantification of the proteome; yet, relatively few open-source tools are available to analyze DIA proteomics experiments. Fewer still are tools that can leverage gas phase fractionated (GPF) chromatogram libraries to enhance the detection and quantification of peptides in these experiments. Here, we present nf-encyclopedia, an open-source NextFlow pipeline that connects three open-source tools—MSConvert, EncyclopeDIA, and MSstats—to analyze DIA proteomics experiments with or without chromatogram libraries. We demonstrate that nf-encyclopedia is reproducible both when run on a cloud platform or a local workstation and provides robust peptide and protein quantification. Additionally, we found that MSstats enhances protein-level quantitative performance over EncyclopeDIA alone. Finally, we benchmarked the ability nf-encyclopedia to scale to large experiments in the cloud by leveraging the parallelization of compute resources. The nf-encyclopedia pipeline is available under a permissive Apache 2.0 license—run it on your desktop, cluster, or in the cloud: https://github.com/TalusBio/nf-encyclopedia.

The volume of proteomics and mass spectrometry data available in public repositories continues to grow at a rapid pace as more researchers embrace open science practices. Open access to the data behind scientific discoveries has become critical to validate published findings and develop new computational tools. Here, we present ppx, a Python package that provides easy, programmatic access to the data stored in ProteomeXchange repositories, such as PRIDE and MassIVE. The ppx package can either be used as a command line tool or a Python package to retrieve the files and metadata associated with a project when provided its identifier. To demonstrate how ppx enhances reproducible research, we used ppx within a Snakemake workflow to reanalyze a published dataset with the open modification search tool ANN-SoLo and compared our reanalysis to the original results. We show that ppx readily integrates into workflows and our reanalysis produced results consistent with the original analysis. We envision that ppx will be a valuable tool for creating reproducible analyses, providing tool developers easy access to data for development, testing, and benchmarking, and enabling the use of mass spectrometry data in data-intensive analyses. The ppx package is freely available and open source under the MIT license at: https://github.com/wfondrie/ppx

Colwellia psychrerythraea is a marine psychrophilic bacterium known for its remarkable ability to maintain activity during long-term exposure to extreme subzero temperatures and correspondingly high salinities in sea ice. These microorganisms must have adaptations to both high salinity and low temperature to survive, be metabolically active, or grow in the ice. Here, we report on an experimental design that allowed us to monitor culturability, cell abundance, activity and proteomic signatures of C. psychrerythraea strain 34H (Cp34H) in subzero brines and supercooled sea water through long-term incubations under eight conditions with varying subzero temperatures, salinities and nutrient additions. Shotgun proteomics found novel metabolic strategies used to maintain culturability in response to each independent experimental variable, particularly in pathways regulating carbon, nitrogen and fatty acid metabolism. Statistical analysis of abundances of proteins uniquely identified in isolated conditions provide metabolism-specific protein biosignatures indicative of growth or survival in either increased salinity, decreased temperature, or nutrient limitation. Additionally, to aid in the search for extant life on other icy worlds, analysis of detected short peptides in −10°C incubations after 4 months identified over 500 potential biosignatures that could indicate the presence of terrestrial-like cold-active or halophilic metabolisms on other icy worlds.

Tandem mass spectrometry is the only high-throughput method for analyzing the protein content of complex biological samples and is thus the primary technology driving the growth of the field of proteomics. A key outstanding challenge in this field involves identifying the sequence of amino acids—the peptide—responsible for generating each observed spectrum, without making use of prior knowledge in the form of a peptide sequence database. Although various machine learning methods have been developed to address this de novo sequencing problem, challenges that arise when modeling tandem mass spectra have led to complex models that combine multiple neural networks and post-processing steps. We propose a simple yet powerful method for de novo peptide sequencing, Casanovo, that uses a transformer framework to map directly from a sequence of observed peaks (a mass spectrum) to a sequence of amino acids (a peptide). Our experiments show that Casanovo achieves state-of-the-art performance on a benchmark dataset using a standard cross-species evaluation framework which involves testing with spectra with never-before-seen peptide labels. Casanovo not only achieves superior performance but does so at a fraction of the model complexity and inference time required by other methods.

Advances in library-based methods for peptide detection from data independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.

Objectives Standard implementations of amyloid typing by liquid chromatography–tandem mass spectrometry use capabilities unavailable to most clinical laboratories. To improve accessibility of this testing, we explored easier approaches to tissue sampling and data processing. Methods We validated a typing method using manual sampling in place of laser microdissection, pairing the technique with a semiquantitative measure of sampling adequacy. In addition, we created an open-source data processing workflow (Crux Pipeline) for clinical users. Results Cases of amyloidosis spanning the major types were distinguishable with 100% specificity using measurements of individual amyloidogenic proteins or in combination with the ratio of λ and κ constant regions. Crux Pipeline allowed for rapid, batched data processing, integrating the steps of peptide identification, statistical confidence estimation, and label-free protein quantification. Conclusions Accurate mass spectrometry–based amyloid typing is possible without laser microdissection. To facilitate entry into solid tissue proteomics, newcomers can leverage manual sampling approaches in combination with Crux Pipeline and related tools.

Proteomics studies rely on the accurate assignment of peptides to the acquired tandem mass spectra-a task where machine learning algorithms have proven invaluable. We describe mokapot, which provides a flexible semisupervised learning algorithm that allows for highly customized analyses. We demonstrate some of the unique features of mokapot by improving the detection of RNA-cross-linked peptides from an analysis of RNA-binding proteins and increasing the consistency of peptide detection in a single-cell proteomics study.

Proteomics studies rely on the accurate assignment of peptides to the acquired tandem mass spectra—a task where machine learning algorithms have proven invaluable. We describe mokapot, which provides a flexible semi-supervised learning algorithm that allows for highly customized analyses. We demonstrate some of the unique features of mokapot by improving the detection of RNA-cross-linked peptides from an analysis of RNA-binding proteins and increasing the consistency of peptide detection in a single-cell proteomics study.

Machine learning methods have proven invaluable for increasing the sensitivity of peptide detection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, use semi-supervised algorithms to learn models directly from the datasets that they analyze. Although these methods are effective for many proteomics experiments, we suspected that they may be suboptimal for experiments of smaller scale. In this work, we found that the power and consistency of Percolator results was reduced as the size of the experiment was decreased. As an alternative, we propose a different operating mode for Percolator: learn a model with Percolator from a large dataset and use the learned model to evaluate the small-scale experiment. We call this a “static modeling” approach, in contrast to Percolator’s usual “dynamic model” that is trained anew for each dataset. We applied this static modeling approach to two settings: small, gel-based experiments and single-cell proteomics. In both cases, static models increased the yield of detected peptides and eliminated the model-induced variability of the standard dynamic approach. These results suggest that static models are a powerful tool for bringing the full benefits of Percolator and other semi-supervised algorithms to small-scale experiments., Graphical Abstract

Objective— Mutations affecting contractile-related proteins in the ECM (extracellular matrix), microfibrils, or vascular smooth muscle cells can predispose the aorta to aneurysms. We reported previously that the LRP1 (low-density lipoprotein receptor–related protein 1) maintains vessel wall integrity, and smLRP1 −/− mice exhibited aortic dilatation. The current study focused on defining the mechanisms by which LRP1 regulates vessel wall function and integrity. Approach and Results— Isometric contraction assays demonstrated that vasoreactivity of LRP1-deficient aortic rings was significantly attenuated when stimulated with vasoconstrictors, including phenylephrine, thromboxane receptor agonist U-46619, increased potassium, and L-type Ca 2+ channel ligand FPL-64176. Quantitative proteomics revealed proteins involved in actin polymerization and contraction were significantly downregulated in aortas of smLRP1 −/− mice. However, studies with calyculin A indicated that although aortic muscle from smLRP1 −/− mice can contract in response to calyculin A, a role for LRP1 in regulating the contractile machinery is not revealed. Furthermore, intracellular calcium imaging experiments identified defects in calcium release in response to a RyR (ryanodine receptor) agonist in smLRP1 −/− aortic rings and cultured vascular smooth muscle cells. Conclusions— These results identify a critical role for LRP1 in modulating vascular smooth muscle cell contraction by regulating calcium signaling events that potentially protect against aneurysm development.

Aortic aneurysms represent a significant clinical problem as they largely go undetected until a rupture occurs. Currently, an understanding of mechanisms leading to aneurysm formation is limited. Numerous studies clearly indicate that vascular smooth muscle cells play a major role in the development and response of the vasculature to hemodynamic changes and defects in these responses can lead to aneurysm formation. The LDL receptor-related protein 1 (LRP1) is major smooth muscle cell receptor that has the capacity to mediate the endocytosis of numerous ligands and to initiate and regulate signaling pathways. Genetic evidence in humans and mouse models reveal a critical role for LRP1 in maintaining the integrity of the vasculature. Understanding the mechanisms by which this is accomplished represents an important area of research, and likely involves LRP1’s ability to regulate levels of proteases known to degrade the extracellular matrix as well as its ability to modulate signaling events.

We report on the characterization of three different Surface Acoustic Wave Nebulization (SAWN) chip designs for use in mass spectrometric (MS) analysis of the bacterial glycolipid known as lipid A. We used three different statistical methods to objectively calculate MS noise level and, subsequently, signal-to-noise ratio for the purpose of choosing the optimum SAWN chip between three different designs. The best performing standing wave SAWN chip enabled MS detection of 125 fmol of the commercial standard monophosphoryl lipid A. All three chips allowed detection of lipid A extracted from 9 × 10 4 CFU of Francisella novicida. Finally, we show that SAWN-MS could be used to distinguish between different Gram-negative bacterial species based on their lipid A MS profiles, which has implications in the field of bacterial phenotyping.

Exosomes are 30–100 nm sized membrane vesicles released by cells into the extracellular space that mediate intercellular communication via transfer of proteins and other biological molecules. To better understand the role of these microvesicles in lung carcinogenesis, we employed a Triple SILAC quantitative proteomic strategy to examine the differential protein abundance between exosomes derived from an immortalized normal bronchial epithelial cell line and two non-small cell lung cancer (NSCLC) cell lines harboring distinct activating mutations in the cell signaling molecules: Kirsten rat sarcoma viral oncogene homolog (KRAS) or epidermal growth factor receptor (EGFR). In total, we were able to quantify 721 exosomal proteins derived from the three cell lines. Proteins associated with signal transduction, including EGFR, GRB2 and SRC, were enriched in NSCLC exosomes, and could actively regulate cell proliferation in recipient cells. This study's investigation of the NSCLC exosomal proteome has identified enriched protein cargo that can contribute to lung cancer progression, which may have potential clinical implications in biomarker development for patients with NSCLC. Biological significance The high mortality associated with lung cancer is a result of late-stage diagnosis of the disease. Current screening techniques used for early detection of lung cancer lack the specificity for accurate diagnosis. Exosomes are nano-sized extracellular vesicles, and the increased abundance of select protein cargo in exosomes derived from cancer cells may be used for diagnostic purposes. In this paper, we applied quantitative proteomic analysis to elucidate abundance differences in exosomal protein cargo between two NSCLC cell lines with distinctive oncogene mutations and an immortalized normal bronchial epithelial cell line. This study revealed proteins associated with cell adhesion, the extracellular matrix, and a variety of signaling molecules were enriched in NSCLC exosomes. The present data reveals a protein profile associated with NSCLC exosomes that suggests a role these vesicles have in the progression of lung carcinogenesis, as well as identifies several promising candidates that could be utilized as a multi-marker protein panel in a diagnostic platform for NSCLC.

Infectious diseases have a substantial global health impact. Clinicians need rapid and accurate diagnoses of infections to direct patient treatment and improve antibiotic stewardship. Current technologies employed in routine diagnostics are based on bacterial culture followed by morphological trait differentiation and biochemical testing, which can be time-consuming and labor-intensive. With advances in mass spectrometry (MS) for clinical diagnostics, the U.S. Food and Drug Administration has approved two microbial identification platforms based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis of microbial proteins. We recently reported a novel and complementary approach by comparing MALDI-TOF mass spectra of microbial membrane lipid fingerprints to identify ESKAPE pathogens. However, this lipid-based approach used a sample preparation method that required more than a working day from sample collection to identification. Here, we report a new method that extracts lipids efficiently and rapidly from microbial membranes using an aqueous sodium acetate (SA) buffer that can be used to identify clinically relevant Gram-positive and -negative pathogens and fungal species in less than an hour. The SA method also has the ability to differentiate antibiotic-susceptible and antibiotic-resistant strains, directly identify microbes from biological specimens, and detect multiple pathogens in a mixed sample. These results should have positive implications for the manner in which bacteria and fungi are identified in general hospital settings and intensive care units.

With the increased prevalence of multidrug-resistant Gram-negative bacteria, the use of colistin and other last-line antimicrobials is being revisited clinically. As a result, there has been an emergence of colistin-resistant bacterial species, including Acinetobacter baumannii and Klebsiella pneumoniae. The rapid identification of such pathogens is vitally important for the effective treatment of patients. We previously demonstrated that mass spectrometry of bacterial glycolipids has the capacity to identify and detect colistin resistance in a variety of bacterial species. In this study, we present a machine learning paradigm that is capable of identifying A. baumannii, K. pneumoniae and their colistin-resistant forms using a manually curated dataset of lipid mass spectra from 48 additional Gram-positive and -negative organisms. We demonstrate that these classifiers detect A. baumannii and K. pneumoniae in isolate and polymicrobial specimens, establishing a framework to translate glycolipid mass spectra into pathogen identifications.

Objective- Mutations affecting contractile-related proteins in the ECM (extracellular matrix), microfibrils, or vascular smooth muscle cells can predispose the aorta to aneurysms. We reported previously that the LRP1 (low-density lipoprotein receptor-related protein 1) maintains vessel wall integrity, and smLRP1

Objective: Mutations affecting proteins in the extracellular matrix (ECM), microfibrils, or vascular smooth muscle cells (VSMCs) that impact contractility can predispose individuals to thoracic aortic aneurysms. We reported previously that the low-density lipoprotein receptor-related protein 1 (LRP1) maintains vessel wall integrity, and smooth muscle LRP1-deficient ( smLRP1 -/- ) mice exhibited aortic dilatation. The current study focused on the descending thoracic aorta (DTA) and examined the role of LRP1 in VSMC contractility and its potential effect on the vascular ECM. Approach and Results: LRP1-deficient VSMCs exhibited a synthetic phenotype characterized by higher proliferation rates and an increase in synthetic organelles, mitochondria, multivesicular bodies, and macropinocytotic vesicles. LRP1-deficient VSMCs also displayed changes in their microfilament and actin structure that result in an inadequate interaction with the ECM. Quantitative proteomics identified proteins involved in actin polymerization and contraction that were downregulated significantly in the DTA of smLRP1 -/- mice. Further analysis by qRT-PCR revealed attenuated mRNA levels for α-1D adrenergic receptor ( adra1d ) and calcium voltage-gated channel subunit α1 C ( cacna1c ) in smLRP1 -/- aortas. Isometric contraction assays confirmed aberrant contraction of smLRP1 -/- aortic rings when stimulated with vasoconstrictors. Furthermore, intracellular calcium imaging identified defects in response to a ryanodine receptor agonist in smLRP1 -/- aortic rings. Conclusions: These results suggest that LRP1 is required for maintaining the VSMC contractile phenotype and identifies a novel role for LRP1 in calcium homeostasis that potentially protects against aneurysm development.

Exosomes are microvesicles of endocytic origin constitutively released by multiple cell types into the extracellular environment. With evidence that exosomes can be detected in the blood of patients with various malignancies, the development of a platform that uses exosomes as a diagnostic tool has been proposed. However, it has been difficult to truly define the exosome proteome due to the challenge of discerning contaminant proteins that may be identified via mass spectrometry using various exosome enrichment strategies. To better define the exosome proteome in breast cancer, we incorporated a combination of Tandem-Mass-Tag (TMT) quantitative proteomics approach and Support Vector Machine (SVM) cluster analysis of three conditioned media derived fractions corresponding to a 10 000g cellular debris pellet, a 100 000g crude exosome pellet, and an Optiprep enriched exosome pellet. The quantitative analysis identified 2 179 proteins in all three fractions, with known exosomal cargo proteins displaying at least a 2-fold enrichment in the exosome fraction based on the TMT protein ratios. Employing SVM cluster analysis allowed for the classification 251 proteins as “true” exosomal cargo proteins. This study provides a robust and vigorous framework for the future development of using exosomes as a potential multiprotein marker phenotyping tool that could be useful in breast cancer diagnosis and monitoring disease progression.

Rapid diagnostics that enable identification of infectious agents improve patient outcomes, antimicrobial stewardship, and length of hospital stay. Current methods for pathogen detection in the clinical laboratory include biological culture, nucleic acid amplification, ribosomal protein characterization, and genome sequencing. Pathogen identification from single colonies by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analysis of high abundance proteins is gaining popularity in clinical laboratories. Here, we present a novel and complementary approach that utilizes essential microbial glycolipids as chemical fingerprints for identification of individual bacterial species. Gram-positive and negative bacterial glycolipids were extracted using a single optimized protocol. Extracts of the clinically significant ESKAPE pathogens: Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp. were analyzed by MALDI-TOF-MS in negative ion mode to obtain glycolipid mass spectra. A library of glycolipid mass spectra from 50 microbial entries was developed that allowed bacterial speciation of the ESKAPE pathogens, as well as identification of pathogens directly from blood bottles without culture on solid medium and determination of antimicrobial peptide resistance. These results demonstrate that bacterial glycolipid mass spectra represent chemical barcodes that identify pathogens, potentially providing a useful alternative to existing diagnostics.

Dietary supplements have exhibited myriads of positive health effects on human health conditions and with the advent of new technological advances, including in the fields of proteomics, genomics, and metabolomics, biological and pharmacological activities of dietary supplements are being evaluated for their ameliorative effects in human ailments. Recent interests in understanding and discovering the molecular targets of phytochemical-gene-protein-metabolite dynamics resulted in discovery of a few protein signature candidates that could potentially be used to assess the effects of dietary supplements on human health. Persimmon (Diospyros kaki) is a folk medicine, commonly used as dietary supplement in China, Japan, and South Korea, owing to its different beneficial health effects including anti-diabetic implications. However, neither mechanism of action nor molecular biomarkers have been discovered that could either validate or be used to evaluate effects of persimmon on human health. In present study, Mass Spectrometry (MS)-based proteomic studies were accomplished to discover proteomic molecular signatures that could be used to understand therapeutic potentials of persimmon leaf extract (PLE) in diabetes amelioration. Saliva, serum, and urine samples were analyzed and we propose that salivary proteins can be used for evaluating treatment effectiveness and in improving patient compliance. The present discovery proteomics study demonstrates that salivary proteomic profile changes were found as a result of PLE treatment in prediabetic subjects that could specifically be used as potential protein signature candidates.

Solid-phase peptide synthesis has been applied to the preparation of phosphonate-derivatized oligoproline assemblies containing two different Ru(II) polypyridyl chromophores coupled via "click" chemistry. In water or methanol the assembly adopts the polyproline II (PPII) helical structure, which brings the chromophores into close contact. Excitation of the assembly on ZrO2 at the outer Ru(II) in 0.1 M HClO4 at 25 °C is followed by rapid, efficient intra-assembly energy transfer to the inner Ru(II) (k(EnT) = 3.0 × 10(7) s(-1), implying 96% relative efficiency). The comparable energy transfer rate constants in solution and on nanocrystalline ZrO2 suggest that the PPII structure is retained when bound to ZrO2. On nanocrystalline films of TiO2, excitation at the inner Ru(II) is followed by rapid, efficient injection into TiO2. Excitation of the outer Ru(II) is followed by rapid intra-assembly energy transfer and then by electron injection. The oligoproline/click chemistry approach holds great promise for the preparation of interfacial assemblies for energy conversion based on a family of assemblies having controlled compositions and distances between key functional groups.