High throughput sequencing of in vitro selections of mRNA-displayed peptides: data analysis and applications.

In vitro selection using mRNA display is currently a widely used method to isolate functional peptides with desired properties. The analysis of high throughput sequencing (HTS) data from in vitro evolution experiments has proven to be a powerful technique but only recently has it been applied to mRNA display selections. In this Perspective, we introduce aspects of mRNA display and HTS that may be of interest to physical chemists. We highlight the potential of HTS to analyze in vitro selections of peptides and review recent advances in the application of HTS analysis to mRNA display experiments. We discuss some possible issues involved with HTS analysis and summarize some strategies to alleviate them. Finally, the potential for future impact of advancing HTS analysis on mRNA display experiments is discussed.

Predicting Sequence Features, Function, and Structure of Proteins Using MESSA.

MEta-Server for protein Sequence Analysis (MESSA) is a tool that facilitates widespread protein sequence analysis by gathering structural (local sequence properties and three-dimensional structure) and functional (annotations from SWISS-PROT, Gene Ontology terms, and enzyme classification) predictions for a query protein of interest. MESSA uses multiple well-established tools to offer consensus-based predictions on important aspects of protein sequence analysis. Being freely available for noncommercial users and with a user-friendly interface, MESSA serves as an umbrella platform that overcomes the absence of a comprehensive tool for predictive protein analysis. This article reveals how to access MESSA via the Web and shows how to input a protein sequence to analyze using the MESSA web server. It also includes a detailed explanation of the output from MESSA to aid in better interpretation of results. © 2019 by John Wiley & Sons, Inc.

Software-aided workflow for predicting protease-specific cleavage sites using physicochemical properties of the natural and unnatural amino acids in peptide-based drug discovery.

Peptide drugs have been used in the treatment of multiple pathologies. During peptide discovery, it is crucially important to be able to map the potential sites of cleavages of the proteases. This knowledge is used to later chemically modify the peptide drug to adapt it for the therapeutic use, making peptide stable against individual proteases or in complex medias. In some other cases it needed to make it specifically unstable for some proteases, as peptides could be used as a system to target delivery drugs on specific tissues or cells. The information about proteases, their sites of cleavages and substrates are widely spread across publications and collected in databases such as MEROPS. Therefore, it is possible to develop models to improve the understanding of the potential peptide drug proteolysis. We propose a new workflow to derive protease specificity rules and predict the potential scissile bonds in peptides for individual proteases. WebMetabase stores the information from experimental or external sources in a chemically aware database where each peptide and site of cleavage is represented as a sequence of structural blocks connected by amide bonds and characterized by its physicochemical properties described by Volsurf descriptors. Thus, this methodology could be applied in the case of non-standard amino acid. A frequency analysis can be performed in WebMetabase to discover the most frequent cleavage sites. These results were used to train several models using logistic regression, support vector machine and ensemble tree classifiers to map cleavage sites for several human proteases from four different families (serine, cysteine, aspartic and matrix metalloproteases). Finally, we compared the predictive performance of the developed models with other available public tools PROSPERous and SitePrediction.

Electronic transport in a graphene single layer: application in amino acid sensing.

We modeled a type of field-effect transistor device based on graphene for the recognition of amino acids with a potential application in the building of a protein sequencer. The theoretical model used was a combination of density functional theory (DFT) with the non-equilibrium Green's function (NEGF) in order to describe the coherent transport in molecular devices. First, we studied the physisorption of each amino acid on a graphene sheet and we reported the adsorption energy, the adsorption distances, the equilibrium configuration and the charge transfer of ten amino acids that can be considered as representative of all of the amino acids: histidine (His), alanine (Ala), aspartic acid (Asp), tyrosine (Tyr), arginine (Arg), glutamic acid (Glu), glycine (Gly), phenylalanine (Phe), proline (Pro) and lysine (Lys). As a result, significant differences were found in the density of states (DOS) after adsorption and there was a change in the semi-metallic character of the graphene due to the lysine and arginine interactions. Furthermore, we noticed changes in the electrical characteristics of the devices, as the amino acids adsorbed onto the surface of the graphene. The curves of current vs. bias voltage (I-Vb) display a distinct response for each amino acid, i.e. the I-Vb curves produce a characteristic footprint for each amino acid. We identified a possible rectification mechanism related to the voltage profile asymmetry, where the amino acids can control the transport characteristics in the device, i.e. Lys and Phe amino acids physisorbed on graphene act as a molecular diode, where electrons can easily flow in one direction and decrease in the other. This may be promising for the prospect of biosensors: graphene could be used as an amino acid detector.

TiO2 Nanowires from Wet-Corrosion Synthesis for Peptide Sequencing Using Laser Desorption/Ionization Time-of-Flight Mass Spectrometry.

In this work, TiO2 nanowires synthesized from a wet-corrosion process were presented for peptide sequencing by photocatalytic reaction with UV radiation. For the photocatalytic decomposition of peptides, the peptide sample was dropped on a target plate containing synthesized TiO2 nanowire zones and UV-irradiated. Subsequently, the target plate was analyzed by laser desorption/ionization time-of-flight (LDI-TOF) mass spectrometry using the synthesized TiO2 nanowires as a solid matrix. The feasibility of peptide sequencing based on the photocatalytic reaction with the synthesized TiO2 nanowires was demonstrated using six types of peptides GHP9 (G1-H-P-Q-G2-K1-K2-K3-K4, 1006.59 Da), BPA-1(K1-S1-L-E-N-S2-Y-G1-G2-G3-K2-K3-K4, 1394.74 Da), PreS1(F1-G-A-N1-S-N2-N3-P1-D1-W-D2-F2-N4-P2-N5, 1707.68 Da), HPQ peptide-1 (G-Y-H-P-Q-R-K, 884.45 Da), HPQ peptide-2 (K-R-H-P-Q-Y-G, 884.45 Da), and HPQ peptide-3 (R-Y-H-P-Q-G-K, 884.45 Da). The identification of three different peptides with the same molecular weight was also demonstrated by using the synthesized TiO2 nanowires for their photocatalytic decomposition as well as for LDI-TOF mass spectrometry as a solid-matrix.

Proteome-Wide Structural Biology: An Emerging Field for the Structural Analysis of Proteins on the Proteomic Scale.

Over the past decade, a suite of new mass-spectrometry-based proteomics methods has been developed that now enables the conformational properties of proteins and protein-ligand complexes to be studied in complex biological mixtures, from cell lysates to intact cells. Highlighted here are seven of the techniques in this new toolbox. These techniques include chemical cross-linking (XL-MS), hydroxyl radical footprinting (HRF), Drug Affinity Responsive Target Stability (DARTS), Limited Proteolysis (LiP), Pulse Proteolysis (PP), Stability of Proteins from Rates of Oxidation (SPROX), and Thermal Proteome Profiling (TPP). The above techniques all rely on conventional bottom-up proteomics strategies for peptide sequencing and protein identification. However, they have required the development of unconventional proteomic data analysis strategies. Discussed here are the current technical challenges associated with these different data analysis strategies as well as the relative analytical capabilities of the different techniques. The new biophysical capabilities that the above techniques bring to bear on proteomic research are also highlighted in the context of several different application areas in which these techniques have been used, including the study of protein ligand binding interactions (e.g., protein target discovery studies and protein interaction network analyses) and the characterization of biological states.

Paving the way to single-molecule protein sequencing.

Proteins are major building blocks of life. The protein content of a cell and an organism provides key information for the understanding of biological processes and disease. Despite the importance of protein analysis, only a handful of techniques are available to determine protein sequences, and these methods face limitations, for example, requiring a sizable amount of sample. Single-molecule techniques would revolutionize proteomics research, providing ultimate sensitivity for the detection of low-abundance proteins and the realization of single-cell proteomics. In recent years, novel single-molecule protein sequencing schemes that use fluorescence, tunnelling currents and nanopores have been proposed. Here, we present a review of these approaches, together with the first experimental efforts towards their realization. We discuss their advantages and drawbacks, and present our perspective on the development of single-molecule protein sequencing techniques.

Protein sequencing via nanopore based devices: a nanofluidics perspective.

Proteins perform a huge number of central functions in living organisms, thus all the new techniques allowing their precise, fast and accurate characterization at single-molecule level certainly represent a burst in proteomics with important biomedical impact. In this review, we describe the recent progresses in the developing of nanopore based devices for protein sequencing. We start with a critical analysis of the main technical requirements for nanopore protein sequencing, summarizing some ideas and methodologies that have recently appeared in the literature. In the last sections, we focus on the physical modelling of the transport phenomena occurring in nanopore based devices. The multiscale nature of the problem is discussed and, in this respect, some of the main possible computational approaches are illustrated.

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence-Function Space and Genome Context to Discover Novel Functions.

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of "genomic enzymology" web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assist the discovery novel metabolic pathways: (1) Enzyme Function Initiative-Enzyme Similarity Tool (EFI-EST) for generating sequence similarity networks to visualize and analyze sequence-function space in protein families and (2) Enzyme Function Initiative-Genome Neighborhood Tool (EFI-GNT) for generating genome neighborhood networks to visualize and analyze the genome context in microbial and fungal genomes. Both tools have been adapted to other applications to facilitate target selection for enzyme discovery and functional characterization. As the natural products community has demonstrated, the enzymology community needs to embrace the essential role of web tools that allow the protein and genome sequence databases to be leveraged for novel insights into enzymological problems.

Front-End Electron Transfer Dissociation Coupled to a 21 Tesla FT-ICR Mass Spectrometer for Intact Protein Sequence Analysis.

High resolution mass spectrometry is a key technology for in-depth protein characterization. High-field Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) enables high-level interrogation of intact proteins in the most detail to date. However, an appropriate complement of fragmentation technologies must be paired with FTMS to provide comprehensive sequence coverage, as well as characterization of sequence variants, and post-translational modifications. Here we describe the integration of front-end electron transfer dissociation (FETD) with a custom-built 21 tesla FT-ICR mass spectrometer, which yields unprecedented sequence coverage for proteins ranging from 2.8 to 29 kDa, without the need for extensive spectral averaging (e.g., ~60% sequence coverage for apo-myoglobin with four averaged acquisitions). The system is equipped with a multipole storage device separate from the ETD reaction device, which allows accumulation of multiple ETD fragment ion fills. Consequently, an optimally large product ion population is accumulated prior to transfer to the ICR cell for mass analysis, which improves mass spectral signal-to-noise ratio, dynamic range, and scan rate. We find a linear relationship between protein molecular weight and minimum number of ETD reaction fills to achieve optimum sequence coverage, thereby enabling more efficient use of instrument data acquisition time. Finally, real-time scaling of the number of ETD reactions fills during method-based acquisition is shown, and the implications for LC-MS/MS top-down analysis are discussed. Graphical Abstract ᅟ.

Activated Ion-Electron Transfer Dissociation Enables Comprehensive Top-Down Protein Fragmentation.

Here we report the first demonstration of near-complete sequence coverage of intact proteins using activated ion-electron transfer dissociation (AI-ETD), a method that leverages concurrent infrared photoactivation to enhance electron-driven dissociation. AI-ETD produces mainly c/z-type product ions and provides comprehensive (77-97%) protein sequence coverage, outperforming HCD, ETD, and EThcD for all proteins investigated. AI-ETD also maintains this performance across precursor ion charge states, mitigating charge-state dependence that limits traditional approaches.

TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics.

Next-generation mass spectrometric (MS) techniques such as SWATH-MS have substantially increased the throughput and reproducibility of proteomic analysis, but ensuring consistent quantification of thousands of peptide analytes across multiple liquid chromatography-tandem MS (LC-MS/MS) runs remains a challenging and laborious manual process. To produce highly consistent and quantitatively accurate proteomics data matrices in an automated fashion, we developed TRIC (http://proteomics.ethz.ch/tric/), a software tool that utilizes fragment-ion data to perform cross-run alignment, consistent peak-picking and quantification for high-throughput targeted proteomics. TRIC reduced the identification error compared to a state-of-the-art SWATH-MS analysis without alignment by more than threefold at constant recall while correcting for highly nonlinear chromatographic effects. On a pulsed-SILAC experiment performed on human induced pluripotent stem cells, TRIC was able to automatically align and quantify thousands of light and heavy isotopic peak groups. Thus, TRIC fills a gap in the pipeline for automated analysis of massively parallel targeted proteomics data sets.

MetazSecKB: the human and animal secretome and subcellular proteome knowledgebase.

The subcellular location of a protein is a key factor in determining the molecular function of the protein in an organism. MetazSecKB is a secretome and subcellular proteome knowledgebase specifically designed for metazoan, i.e. human and animals. The protein sequence data, consisting of over 4 million entries with 121 species having a complete proteome, were retrieved from UniProtKB. Protein subcellular locations including secreted and 15 other subcellular locations were assigned based on either curated experimental evidence or prediction using seven computational tools. The protein or subcellular proteome data can be searched and downloaded using several different types of identifiers, gene name or keyword(s), and species. BLAST search and community annotation of subcellular locations are also supported. Our primary analysis revealed that the proteome sizes, secretome sizes and other subcellular proteome sizes vary tremendously in different animal species. The proportions of secretomes vary from 3 to 22% (average 8%) in metazoa species. The proportions of other major subcellular proteomes ranged approximately 21-43% (average 31%) in cytoplasm, 20-37% (average 30%) in nucleus, 3-19% (average 12%) as plasma membrane proteins and 3-9% (average 6%) in mitochondria. We also compared the protein families in secretomes of different primates. The Gene Ontology and protein family domain analysis of human secreted proteins revealed that these proteins play important roles in regulation of human structure development, signal transduction, immune systems and many other biological processes. Database URL: http://proteomics.ysu.edu/secretomes/animal/index.php.

Spatial resolution in Raman spectroscopy.

This article is intended to set the scope of the meeting, in particular, the high spatial resolution section.

A simplified method for peptide de novo sequencing using (18)O labeling.

Incorporation of an (18)O atom into a peptide C-terminus by proteolytic cleavage in the presence of H2(18)O is one of the most effective ways of enhancing tandem mass spectrometry (MS/MS)-based de novo sequencing. Incorporation is usually accomplished by procedures including vacuum-assisted drying of tryptic peptides extracted from gels, their subsequent reconstitution in a H2(16)O/H2(18)O mixture and re-treatment with trypsin. In the present work, we propose a simplified procedure for (18)O incorporation into tryptic peptides by adding H2(18)O and trypsin to the original digest solution. In comparison to published methods, the proposed protocol for peptide de novo sequencing brings significant advantages in analysis and workflow with no deterioration in method performance. We show that labeling by this simplified method leads to a highlighting of the y-ion fragment series in the peptide matrix-assisted laser desorption/ionization (MALDI)- MS/MS data, which facilitates MS/MS data interpretation. We also prove that eliminating acid extraction of peptides from gels does not result in a decrease in sequence coverage or a qualitative loss of particular peptides detectable by MALDI-MS. The method was examined by MALDI-MS/MS on bovine serum albumin and recombinant histidine kinase CKI1 from Arabidopsis thaliana, and was verified by de novo sequencing of tryptic peptides originating from Apodemus sylvaticus salivary proteins.

Discrimination of leucine and isoleucine in peptides sequencing with Orbitrap Fusion mass spectrometer.

An efficient approach to easy and reliable differentiation between isomeric leucine and isoleucine in peptide sequencing utilizes multistage electron transfer dissociation and higher energy collision activated dissociation in the Orbitrap Fusion mass spectrometer. The MS(3) method involves production and isolation of primary odd-electron z(•) ions, followed by radical site initiation of their fragmentation with formation of w-ions, characteristic of the isomeric amino acid residues. Six natural nontryptic peptides isolated from the secretion of frog Rana ridibunda were studied. Their lengths were in the range between 15 and 37 amino acids and the number of targeted isomeric (Leu/Ile) residues varied between 1 and 7. The experiments were successful in all 22 cases of Leu/Ile residues, leaving no doubts in identification. The method is extremely selective as the targeted w-ions appear to be the most intense in the spectra. The proposed approach may be incorporated into shotgun proteomics algorithms and allows for the development of an exclusively mass spectrometric method for automated complete de novo sequencing of various peptides and proteins.

Selection of relevant features from amino acids enables development of robust classifiers.

Machine learning (ML) has been extensively applied to develop models and to understand high-throughput data of biological processes. However, new ML models, trained with novel experimental results, are required to build regularly for more precise predictions. ML methods can build models from numeric data, whereas biological data are generally textual (DNA, protein sequences) or images and needs feature calculation algorithms to generate quantitative features. Programming skills along with domain knowledge are required to develop these algorithms. Therefore, the process of knowledge discovery through ML is decelerated due to lack of generic tools to construct features and to build models directly from the data. Hence, we developed a schema that calculates about 5,000 features, selects relevant features and develops protein classifiers from the training data. To demonstrate the general applicability and robustness of our method, fungal adhesins and nuclear receptor proteins were used for building classifiers which outperformed existing classifiers when tested on independent data. Next, we built a classifier for mitochondrial proteins of Plasmodium falciparum which causes human malaria because the latest corresponding classifiers are not publically accessible. Our classifier attained 98.18 % accuracy and 0.95 Matthews correlation coefficient by fivefold cross-validation and outperformed existing classifiers on independent test set. We implemented this schema as user-friendly and open source application Pro-Gyan ( http://code.google.com/p/pro-gyan/ ), to build and share executable classifiers without programming knowledge.

Accelerating in silico research with workflows: a lesson in Simplicity.

Bioinformatics is the application of computer science and related disciplines to the field of molecular biology. While there are currently several web based and desktop tools available for biologists to perform routine bioinformatics tasks, these tools often require users to manually and repeatedly co-ordinate multiple applications before reaching a result. In an effort to reduce time and error, workflow tools have been developed to automate these tasks. However, many of these tools require expert knowledge of the techniques and supporting databases which more often than not lies outside the scope of most biologists. Herein, we describe the development of sequence information management platform (Simplicity), a workflow-based bioinformatics management tool, which allows non-bioinformaticians to rapidly annotate large amounts of DNA and protein sequence data.