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The adsorption of protein to nanoparticles plays an important role in toxicity, food science, pharmaceutics, and biomaterial science. Understanding how proteins bind to nanophase surfaces is instrumental for understanding and, ultimately, controlling nanoparticle (NP) biochemistry. Techniques probing the adsorption of proteins at NP interfaces exist; however, these methods have been unable to determine the orientation and folding of proteins at these interfaces. For the first time, we probe in situ with sum frequency scattering vibrational spectroscopy the orientation of model leucine-lysine (LK) peptides adsorbed to NPs. The results show that both α-helical and β-strand LK peptides bind the particles in an upright orientation, in contrast to the flat orientation of LKs binding to planar surfaces. The different binding geometry is explained by Coulombic forces between peptides across the particle volume.

Motivation In quantitative bottom-up mass spectrometry (MS)-based proteomics, the reliable estimation of protein concentration changes from peptide quantifications between different biological samples is essential. This estimation is not a single task but comprises the two processes of protein inference and protein abundance summarization. Furthermore, due to the high complexity of proteomics data and associated uncertainty about the performance of these processes, there is a demand for comprehensive visualization methods able to integrate protein with peptide quantitative data including their post-translational modifications. Hence, there is a lack of a suitable tool that provides post-identification quantitative analysis of proteins with simultaneous interactive visualization. Results In this article, we present VIQoR, a user-friendly web service that accepts peptide quantitative data of both labeled and label-free experiments and accomplishes the crucial components protein inference and summarization and interactive visualization modules, including the novel VIQoR plot. We implemented two different parsimonious algorithms to solve the protein inference problem, while protein summarization is facilitated by a well-established factor analysis algorithm called fast-FARMS followed by a weighted average summarization function that minimizes the effect of missing values. In addition, summarization is optimized by the so-called Global Correlation Indicator (GCI). We test the tool on three publicly available ground truth datasets and demonstrate the ability of the protein inference algorithms to handle shared peptides. We furthermore show that GCI increases the accuracy of the quantitative analysis in datasets with replicated design. Availability and implementation VIQoR is accessible at: http://computproteomics.bmb.sdu.dk/Apps/VIQoR/. The source code is available at: https://bitbucket.org/veitveit/viqor/. Supplementary information Supplementary data are available at Bioinformatics online.

Chiral communications exist in secondary structures of foldamers and copolymers via a network of noncovalent interactions within effective intermolecular force (IMF) range. It is not known whether long-range chiral communication exists between macromolecular tertiary structures such as peptide coiled-coils beyond the IMF distance. Harnessing the high sensitivity of single-molecule force spectroscopy, we investigate the chiral interaction between covalently linked DNA duplexes and peptide coiled-coils by evaluating the binding of a diastereomeric pair of three DNA-peptide conjugates. We find that right-handed DNA triple helices well accommodate peptide triple coiled-coils of the same handedness, but not with the left-handed coiled-coil stereoisomers. This chiral communication is effective in a range (, Chiral communication can propagate in secondary structures within the effective intermolecular force (IMF) range but it is not known whether long-range chiral communication exists between tertiary peptide structures. Here, the authors use single-molecule force spectroscopy to investigate chiral interaction between DNA duplexes/triplexes and peptide coiled-coils and demonstrate chiral communication beyond the IMF distance.

In the last decade, visible‐light photoredox catalysis has emerged as a powerful strategy to enable novel transformations in organic synthesis. Owing to mild reaction conditions (i.e., room temperature, use of visible light) and high functional‐group tolerance, photoredox catalysis could represent an ideal strategy for chemoselective biomolecule modification. Indeed, a recent trend in photoredox catalysis is its application to the development of novel methodologies for amino acid modification. Herein, an up‐to‐date overview of photocatalytic methodologies for the modification of single amino acids, peptides, and proteins is provided. The advantages offered by photoredox catalysis and its suitability in the development of novel biocompatible methodologies are described. In addition, a brief consideration of the current limitations of photocatalytic approaches, as well as future challenges to be addressed, are discussed.

International audience; Medicine formulations at the nanoscale, referred to as nanomedicines, have managed to overcome key challenges encountered during the development of new medical treatments and entered clinical practice, but considerable improvement in terms of local efficacy and reduced toxicity still need to be achieved. Currently, the fourth-generation of nanomedicines is being developed, employing biocompatible nanocarriers that are targeted, multifunctional, and stimuli-responsive. Proteins and polypeptides can fit the standards of an efficient nanovector because of their biodegradability, intrinsic bioactivity, chemical reactivity, stimuli-responsiveness, and ability to participate in complex supramolecular assemblies. These biomacromolecules can be obtained from natural resources, produced in heterologous hosts, or chemically synthesized, allowing for different designs to access suitable carriers for a variety of drugs. To enhance targeting or therapeutic functionality, additional chemical modifications can be applied. This review demonstrates the potential of polypeptide and protein materials for the design of drug delivery nanocarriers with a special focus on their preclinical evaluation in vitro and in vivo.

Elastin-like peptides are hydrophobic biopolymers that exhibit a reversible coacervation transition when the temperature is raised above a critical point. Here, we use a combination of linear infrared spectroscopy, two-dimensional infrared spectroscopy, and molecular dynamics simulations to study the structural dynamics of two elastin-like peptides. Specifically, we investigate the effect of the solvent environment and temperature on the structural dynamics of a short (5-residue) elastin-like peptide and of a long (450-residue) elastin-like peptide. We identify two vibrational energy transfer processes that take place within the amide I' band of both peptides. We observe that the rate constant of one of the exchange processes is strongly dependent on the solvent environment and argue that the coacervation transition is accompanied by a desolvation of the peptide backbone where up to 75% of the water molecules are displaced. We also study the spectral diffusion dynamics of the valine(1) residue that is present in both peptides. We find that these dynamics are relatively slow and indicative of an amide group that is shielded from the solvent. We conclude that the coacervation transition of elastin-like peptides is probably not associated with a conformational change involving this residue.

The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across ten newly generated as well as forty public HLA peptidomics datasets comprising more than 115,000 unique peptides, we show that we can rapidly and accurately identify many HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. Our approach recapitulates and refines known motifs for 43 of the most frequent alleles, uncovers new motifs for 9 alleles that up to now had less than five known ligands and provides a scalable framework to incorporate additional HLA peptidomics studies in the future. The refined motifs improve neo-antigen and cancer testis antigen predictions, indicating that unbiased HLA peptidomics data are ideal for in silico predictions of neo-antigens from tumor exome sequencing data. The new motifs further reveal distant modulation of the binding specificity at P2 for some HLA-I alleles by residues in the HLA-I binding site but outside of the B-pocket and we unravel the underlying mechanisms by protein structure analysis, mutagenesis and in vitro binding assays., Author summary Predicting the differences between cancer and normal cells that are visible to the immune system is of central importance for cancer immunotherapy. Here we introduce a novel computational framework to harness the wealth of data from in-depth HLA peptidomics studies, including ten novel high quality (

We have developed a new algorithm to identify proteins by means of peptide mass fingerprinting. Starting from the matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) spectra and environmental data such as species, isoelectric point and molecular weight, as well as chemical modifications or number of missed cleavages of a protein, the program performs a fully automated identification of the protein. The first step is a peak detection algorithm, which allows precise and fast determination of peptide masses, even if the peaks are of low intensity or they overlap. In the second step the masses and environmental data are used by the identification algorithm to search in protein sequence databases (SWISS-PROT and/or TrEMBL) for protein entries that match the input data. Consequently, a list of candidate proteins is selected from the database, and a score calculation provides a ranking according to the quality of the match. To define the most discriminating scoring calculation we analyzed the respective role of each parameter in two directions. The first one is based on filtering and exploratory effects, while the second direction focuses on the levels where the parameters intervene in the identification process. Thus, according to our analysis, all input parameters contribute to the score, however with different weights. Since it is difficult to estimate the weights in advance, they have been computed with a generic algorithm, using a training set of 91 protein spectra with their environmental data. We tested the resulting scoring calculation on a test set of ten proteins and compared the identification results with those of other peptide mass fingerprinting programs.

A novel strategy consisting of cleavable Isotope-Coded Affinity Tag (cICAT) combined with MASCOT Distiller was evaluated as a tool for the quantification of proteins in "abnormal" patient plasma, prepared by pooling samples from patients with acute stroke. Quantification of all light and heavy cICAT-labelled peptide ion pairs was obtained using MASCOT Distiller combined with a proprietary software. Peptides displaying differences were selected for identification by MS. These preliminary results show the promise of our approach to identify potential biomarkers.

FindPept (http://www.expasy.org/tools/findpept.html) is a software tool designed to identify the origin of peptide masses obtained by peptide mass fingerprinting which are not matched by existing protein identification tools. It identifies masses resulting from unspecific proteolytic cleavage, missed cleavage, protease autolysis or keratin contaminants. It also takes into account post-translational modifications derived from the annotation of the SWISS-PROT database or supplied by the user, and chemical modifications of peptides. Based on a number of experimental examples, we show that the commonly held rules for the specificity of tryptic cleavage are an oversimplification, mainly because of effects of neighboring residues, experimental conditions, and contaminants present in the enzyme sample.