Identification of a phage display-derived peptide interacting with the N-terminal region of Factor VII activating protease (FSAP) enables characterization of zymogen activation.

Increased Factor VII activating protease (FSAP) activity has a protective effect in diverse disease conditions as inferred from studies in FSAP-/- mice and humans deficient in FSAP activity due to a single nucleotide polymorphism. The activation of FSAP zymogen in plasma is mediated by extracellular histones that are released during tissue injury or inflammation or by positively charged surfaces. However, it is not clear if this activation mechanism is specific and amenable to manipulation. Using a phage display approach we have identified a peptide, NNKC9/41, that activates pro-FSAP in plasma. Other commonly found zymogens in the plasma were not activated. Binding studies with FSAP domain deletion mutants indicate that the N-terminus of FSAP is the key interaction site of this peptide. Blocking the contact pathway of coagulation did not influence pro-FSAP activation by the peptide. In a monoclonal antibody screen, we identified MA-FSAP-38C7 that prevented the activation of pro-FSAP by the peptide. This antibody bound to the LESLDP sequence (amino acids 30-35) in the N-terminus of FSAP. The plasma clotting time was shortened by NNKC9/41 and this was reversed by MA-FSAP-38C7 demonstrating the utility of this peptide. Identification of this peptide, and the corresponding interaction site, provides proof of principle that it is possible to activate a single protease zymogen in blood in a specific manner. Peptide NNKC/41 will be useful as a tool to delineate the molecular mechanism of activation of pro-FSAP in more detail, elucidate its biological role.

Characterization of Composite Nano-Bioscaffolds Based on Collagen and Supercritical Fluids-Assisted Decellularized Fibrous Extracellular Matrix

Nano-bioscaffolds obtained from decellularized tissues have been employed in several medical applications. Nano-bioscaffolds could provide structural support for cell attachment and a suitable environment with sufficient porosity for cell growth and proliferation. In this study, a new combined method constitutes a decellularization protocol to remove the tissue and cellular molecules from porcine dermis for preparation of nano-bioscaffolds with fibrous extracellular matrix via pre- and post-treatment of supercritical fluids. The supercritical fluids-assisted nano-bioscaffolds were characterized by peptide identification, infrared spectrum of absorption, morphology, histological observations, DNA quantification, and hemocompatibility. Further, the resulting nano-bioscaffolds could be employed to obtain new cross-linked composite nano-bioscaffold containing collagen and acellular matrix.

Evolutionary Algorithms for Improving De Novo Peptide Sequencing

<p>De novo peptide sequencing algorithms have been developed for peptide identification in proteomics from tandem mass spectra (MS/MS), which can be used to identify and discover novel peptides and proteins that do not have a database available. Despite improvements in MS instrumentation and de novo sequencing methods, a significant number of CID MS/MS spectra still remain unassigned with the current algorithms, often leading to low confidence of peptide assignments to the spectra. Moreover, current algorithms often fail to construct the completely matched sequences, and produce partial matches. Therefore, identification of full-length peptides remains challenging. Another major challenge is the existence of noise in MS/MS spectra which makes the data highly imbalanced. Also missing peaks, caused by incomplete MS fragmentation makes it more difficult to infer a full-length peptide sequence. In addition, the large search space of all possible amino acid sequences for each spectrum leads to a high false discovery rate. This thesis focuses on improving the performance of current methods by developing new algorithms corresponding to three steps of preprocessing, sequence optimisation and post-processing using machine learning for more comprehensive interrogation of MS/MS datasets. From the machine learning point of view, the three steps can be addressed by solving different tasks such as classification, optimisation, and symbolic regression. Since Evolutionary Algorithms (EAs), as effective global search techniques, have shown promising results in solving these problems, this thesis investigates the capability of EAs in improving the de novo peptide sequencing. In the preprocessing step, this thesis proposes an effective GP-based method for classification of signal and noise peaks in highly imbalanced MS/MS spectra with the purpose of having a positive influence on the reliability of the peptide identification. The results show that the proposed algorithm is the most stable classification method across various noise ratios, outperforming six other benchmark classification algorithms. The experimental results show a significant improvement in high confidence peptide assignments to MS/MS spectra when the data is preprocessed by the proposed GP method. Moreover, the first multi-objective GP approach for classification of peaks in MS/MS data, aiming at maximising the accuracy of the minority class (signal peaks) and the accuracy of the majority class (noise peaks) is also proposed in this thesis. The results show that the multi-objective GP method outperforms the single objective GP algorithm and a popular multi-objective approach in terms of retaining more signal peaks and removing more noise peaks. The multi-objective GP approach significantly improved the reliability of peptide identification. This thesis proposes a GA-based method to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full-length sequences. The proposed GA method benefits the GA capability of searching a large search space of potential amino acid sequences to find the most likely full-length sequence. The experimental results show that the proposed method outperforms the most commonly used de novo sequencing method at both amino acid level and peptide level. This thesis also proposes a novel method for re-scoring and re-ranking the peptide spectrum matches (PSMs) from the result of de novo peptide sequencing, aiming at minimising the false discovery rate as a post-processing approach. The proposed GP method evolves the computer programs to perform regression and classification simultaneously in order to generate an effective scoring function for finding the correct PSMs from many incorrect ones. The results show that the new GP-based PSM scoring function significantly improves the identification of full-length peptides when it is used to post-process the de novo sequencing results.</p>

Evolutionary Algorithms for Improving De Novo Peptide Sequencing

<p>De novo peptide sequencing algorithms have been developed for peptide identification in proteomics from tandem mass spectra (MS/MS), which can be used to identify and discover novel peptides and proteins that do not have a database available. Despite improvements in MS instrumentation and de novo sequencing methods, a significant number of CID MS/MS spectra still remain unassigned with the current algorithms, often leading to low confidence of peptide assignments to the spectra. Moreover, current algorithms often fail to construct the completely matched sequences, and produce partial matches. Therefore, identification of full-length peptides remains challenging. Another major challenge is the existence of noise in MS/MS spectra which makes the data highly imbalanced. Also missing peaks, caused by incomplete MS fragmentation makes it more difficult to infer a full-length peptide sequence. In addition, the large search space of all possible amino acid sequences for each spectrum leads to a high false discovery rate. This thesis focuses on improving the performance of current methods by developing new algorithms corresponding to three steps of preprocessing, sequence optimisation and post-processing using machine learning for more comprehensive interrogation of MS/MS datasets. From the machine learning point of view, the three steps can be addressed by solving different tasks such as classification, optimisation, and symbolic regression. Since Evolutionary Algorithms (EAs), as effective global search techniques, have shown promising results in solving these problems, this thesis investigates the capability of EAs in improving the de novo peptide sequencing. In the preprocessing step, this thesis proposes an effective GP-based method for classification of signal and noise peaks in highly imbalanced MS/MS spectra with the purpose of having a positive influence on the reliability of the peptide identification. The results show that the proposed algorithm is the most stable classification method across various noise ratios, outperforming six other benchmark classification algorithms. The experimental results show a significant improvement in high confidence peptide assignments to MS/MS spectra when the data is preprocessed by the proposed GP method. Moreover, the first multi-objective GP approach for classification of peaks in MS/MS data, aiming at maximising the accuracy of the minority class (signal peaks) and the accuracy of the majority class (noise peaks) is also proposed in this thesis. The results show that the multi-objective GP method outperforms the single objective GP algorithm and a popular multi-objective approach in terms of retaining more signal peaks and removing more noise peaks. The multi-objective GP approach significantly improved the reliability of peptide identification. This thesis proposes a GA-based method to solve the complex optimisation task of de novo peptide sequencing, aiming at constructing full-length sequences. The proposed GA method benefits the GA capability of searching a large search space of potential amino acid sequences to find the most likely full-length sequence. The experimental results show that the proposed method outperforms the most commonly used de novo sequencing method at both amino acid level and peptide level. This thesis also proposes a novel method for re-scoring and re-ranking the peptide spectrum matches (PSMs) from the result of de novo peptide sequencing, aiming at minimising the false discovery rate as a post-processing approach. The proposed GP method evolves the computer programs to perform regression and classification simultaneously in order to generate an effective scoring function for finding the correct PSMs from many incorrect ones. The results show that the new GP-based PSM scoring function significantly improves the identification of full-length peptides when it is used to post-process the de novo sequencing results.</p>

PIPP: Improving peptide identity propagation using neural networks

Peptide identity propagation (PIP) can substantially reduce missing values in label-free mass spectrometry quantification by transferring peptides identified by tandem mass (MS/MS) spectra in one run to experimentally related runs where the peptides are not identified by MS/MS. The existing frameworks for matching identifications between runs perform peak tracing and propagation based on similarity of precursor features using only a limited number of dimensions available in MS1 data. These approaches do not produce accompanying confidence estimates and hence cannot filter probable false positive identity transfers. We introduce an embedding based PIP that uses a higher dimensional representation of MS1 measurements that is optimized to capture peptide identities using deep neural networks. We developed a propagation framework that works entirely on MaxQuant results. Current PIP workflows typically perform propagation mainly using two feature dimensions, and rely on deterministic tolerances for identification transfer. Our framework overcomes both these limitations while additionally assigning probabilities to each transferred identity. The proposed embedding approach enables quantification of the empirical false discovery rate (FDR) for peptide identification, while also increasing depth of coverage through coembedding the runs from the experiment with experimental libraries. In published datasets with technical and biological variability, we demonstrate that our method reduces missing values in MaxQuant results, maintains high quantification precision and accuracy, and low false transfer rate.

yHydra: Deep Learning enables an Ultra Fast Open Search by Jointly Embedding MS/MS Spectra and Peptides of Mass Spectrometry-based Proteomics

Mass spectrometry-based proteomics allows to study all proteins of a sample on a molecular level. The ever increasing complexity and amount of proteomics MS-data requires powerful and yet efficient computational and statistical analysis. In particular, most recent bottom-up MS-based proteomics studies consider either a diverse pool of post-translational modifications, employ large databases – as in metaproteomics or proteogenomics, contain multiple isoforms of proteins, include unspecific cleavage sites or even combinations thereof and thus face a computationally challenging situation regarding protein identification. In order to cope with resulting large search spaces, we present a deep learning approach that jointly embeds MS/MS spectra and peptides into the same vector space such that embeddings can be compared easily and interchangeable by using euclidean distances. In contrast to existing spectrum embedding techniques, ours are learned jointly with their respective peptides and thus remain meaningful. By visualizing the learned manifold of both spectrum and peptide embeddings in correspondence to their physicochemical properties our approach becomes easily interpretable. At the same time, our joint embeddings blur the lines between spectra and protein sequences, providing a powerful framework for peptide identification. In particular, we build an open search, which allows to search multiple ten-thousands of spectra against millions of peptides within seconds. yHydra achieves identification rates that are compatible with MSFragger. Due to the open search, delta masses are assigned to each identification which allows to unrestrictedly characterize post-translational modifications. Meaningful joint embeddings allow for faster open searches and generally make downstream analysis efficient and convenient for example for integration with other omics types.Availabilityupon [email protected]

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides

Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification using different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin on our employed benchmarks. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN. ACP-MHCNN is also publicly available as an online predictor at: https://anticancer.pythonanywhere.com/.

Improved peptide backbone fragmentation is the primary advantage of MS-cleavable crosslinkers

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

Antibody-guided design and identification of CD25-binding small antibody mimetics using mammalian cell surface display

Small antibody mimetics that contain high-affinity target-binding peptides can be lower cost alternatives to monoclonal antibodies (mAbs). We have recently developed a method to create small antibody mimetics called FLuctuation-regulated Affinity Proteins (FLAPs), which consist of a small protein scaffold with a structurally immobilized target-binding peptide. In this study, to further develop this method, we established a novel screening system for FLAPs called monoclonal antibody-guided peptide identification and engineering (MAGPIE), in which a mAb guides selection in two manners. First, antibody-guided design allows construction of a peptide library that is relatively small in size, but sufficient to identify high-affinity binders in a single selection round. Second, in antibody-guided screening, the fluorescently labeled mAb is used to select mammalian cells that display FLAP candidates with high affinity for the target using fluorescence-activated cell sorting. We demonstrate the reliability and efficacy of MAGPIE using daclizumab, a mAb against human interleukin-2 receptor alpha chain (CD25). Three FLAPs identified by MAGPIE bound CD25 with dissociation constants of approximately 30 nM as measured by biolayer interferometry without undergoing affinity maturation. MAGPIE can be broadly adapted to any mAb to develop small antibody mimetics.