De Novo Peptide Research Articles

The Hunt Lab Guide to De Novo Peptide Sequence Analysis by Tandem Mass Spectrometry

The Hunt Lab Guide to De Novo Peptide Sequence Analysis by Tandem Mass Spectrometry

NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing

De novo peptide sequencing is one of the most fundamental research areas in mass spectrometry (MS) based proteomics. Many methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) of de novo peptide-spectrum matches (PSMs). Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species), and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide sequencing methods on target-decoy spectra and to estimate and validate their FDRs. Our FDR estimation provides valuable information to assess the reliability of new peptides identified by de novo sequencing tools, especially when no ground-truth information is available to evaluate their accuracy. The FDR estimation can also be used to evaluate the capability of de novo peptide sequencing tools to distinguish between de novo PSMs and random matches. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide sequencing methods, and how their performances depend on specific applications and the types of data.

Identification of a De Novo Peptide against Palmitoyl Acyltransferase 6 to Block Survivability and Infectivity of Leishmania donovani.

Palmitoylation is an essential post-translational modification in Leishmania donovani, catalyzed by enzymes called palmitoyl acyl transferases (PATs) and has an essential role in virulence. Due to the toxicity and promiscuity of known PAT inhibitors, identification of new molecules is needed. Herein, we identified a specific novel de novo peptide inhibitor, PS1, against the PAT6 Leishmania donovani palmitoyl acyl transferase (LdPAT6). To demonstrate specific inhibition of LdPAT6 by PS1, we employed a bacterial orthologue system and metabolic labeling-coupled click chemistry where both LdPAT6 and PS1 were coexpressed and displayed palmitoylation suppression. Furthermore, strong binding of the LdPAT6-DHHC domain with PS1 was observed through analysis using microscale thermophoresis, ELISA, and dot blot assay. PS1 specific to LdPAT6 showed significant growth inhibition in promastigotes and amastigotes by expressing low cytokines levels and invasion. This study reveals discovery of a novel de novo peptide against LdPAT6-DHHC which has potential to block survivability and infectivity of L. donovani.

PXg: Comprehensive Identification of Noncanonical MHC-I–Associated Peptides From De Novo Peptide Sequencing Using RNA-Seq Reads

Discovering noncanonical peptides has been a common application of proteogenomics. Recent studies suggest that certain noncanonical peptides, known as noncanonical major histocompatibility complex-I (MHC-I)-associated peptides (ncMAPs), that bind to MHC-I may make good immunotherapeutic targets. De novo peptide sequencing is a great way to find ncMAPs since it can detect peptide sequences from their tandem mass spectra without using any sequence databases. However, this strategy has not been widely applied for ncMAP identification because there is not a good way to estimate its false-positive rates. In order to completely and accurately identify immunopeptides using de novo peptide sequencing, we describe a unique pipeline called proteomics X genomics. In contrast to current pipelines, it makes use of genomic data, RNA-Seq abundance and sequencing quality, in addition to proteomic features to increase the sensitivity and specificity of peptide identification. We show that the peptide-spectrum match quality and genetic traits have a clear relationship, showing that they can be utilized to evaluate peptide-spectrum matches. From 10 samples, we found 24,449 canonical MHC-I–associated peptides and 956 ncMAPs by using a target-decoy competition. Three hundred eighty-seven ncMAPs and 1611 canonical MHC-I–associated peptides were new identifications that had not yet been published. We discovered 11 ncMAPs produced from a squirrel monkey retrovirus in human cell lines in addition to the two ncMAPs originating from a complementarity determining region 3 in an antibody thanks to the unrestricted search space assumed by de novo sequencing. These entirely new identifications show that proteomics X genomics can make the most of de novo peptide sequencing’s advantages and its potential use in the search for new immunotherapeutic targets.

PepDRED: De Novo Peptide Design with Strong Binding Affinity for Target Protein.

De novo design of peptides that bind specifically to functional proteins is beneficial for diagnostics and therapeutics. However, complex permutations and combinations of amino acids pose significant challenges to the rational design of peptides with desirable stability and affinity. Herein, we develop a computational-based evolution method, namely, peptidomimetics-driven recognition elements design (PepDRED), to derive hemoglobin-inspired peptidomimetics. PepDRED mimics the natural evolutionism pipeline to generate stable apovariant (AVs) structures for wild-type counterparts via automated point mutations and validates their efficiency through free binding energy analysis and per residue energy decomposition analysis. For application demonstration, we applied PepDRED to design de novo peptides to bind FhuA, a typical TonB-dependent transporter (TBDT). TBDTs are Gram-negative bacterial outer membrane proteins responsible for iron transport and vital for bacterial resistance. PepDRED generated a pool of AVs and proceeded to reach an optimized peptide, AV440, with a remarkable binding affinity of -21 kcal/mol. AV440 is ∼2.5-fold stronger than the existing FhuA inhibitor Microcin J25. Network energy analysis further unveils that incorporating methionine (M42) in the N-terminal region significantly enhances inter-residue contacts and binding affinity. PepDRED offers a prompt and efficient in silico approach to develop potent peptide candidates for target proteins.

Denovo-GCN: De Novo Peptide Sequencing by Graph Convolutional Neural Networks

The de novo peptide-sequencing method can be used to directly infer the peptide sequence from a tandem mass spectrum. It has the advantage of not relying on protein databases and plays a key role in the determination of the protein sequences of unknown species, monoclonal antibodies, and cancer neoantigens. In this paper, we propose a method based on graph convolutional neural networks and convolutional neural networks, Denovo-GCN, for de novo peptide sequencing. We constructed an undirected graph based on the mass difference between the spectral peaks in a tandem mass spectrum. The features of the nodes on the spectrum graph, which represent the spectral peaks, were the matching information of the peptide sequence and the mass spectrum. Next, the Denovo-GCN used CNN to extract the features of the nodes. The correlation between the nodes was represented by an adjacency matrix, which aggregated the features of neighboring nodes. Denovo-GCN provides a complete end-to-end training and prediction framework to sequence patterns of peptides. Our experiments on various data sets from different species show that Denovo-GCN outperforms DeepNovo with a relative improvement of 13.7–25.5% in terms of the peptide-level recall.

Direct Identification of Urinary Tract Pathogens by MALDI-TOF/TOF Analysis and De Novo Peptide Sequencing.

For mass spectrometry-based diagnostics of microorganisms, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used to identify urinary tract pathogens. However, it requires a lengthy culture step for accurate pathogen identification, and is limited by a relatively small number of available species in peptide spectral libraries (≤3329). Here, we propose a method for pathogen identification that overcomes the above limitations, and utilizes the MALDI-TOF/TOF MS instrument. Tandem mass spectra of the analyzed peptides were obtained by chemically activated fragmentation, which allowed mass spectrometry analysis in negative and positive ion modes. Peptide sequences were elucidated de novo, and aligned with the non-redundant National Center for Biotechnology Information Reference Sequence Database (NCBInr). For data analysis, we developed a custom program package that predicted peptide sequences from the negative and positive MS/MS spectra. The main advantage of this method over a conventional MALDI-TOF MS peptide analysis is identification in less than 24 h without a cultivation step. Compared to the limited identification with peptide spectra libraries, the NCBI database derived from genome sequencing currently contains 20,917 bacterial species, and is constantly expanding. This paper presents an accurate method that is used to identify pathogens grown on agar plates, and those isolated directly from urine samples, with high accuracy.

De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update.

Nowadays, machine learning and deep learning approaches are widely utilized for generative chemistry and computer-aided drug design and discovery such as de novo peptide and protein design, where target-specific peptide-based/protein-based therapeutics have been suggested to cause fewer adverse effects than the traditional small-molecule drugs. In light of current advancements in deep learning techniques, generative adversarial network (GAN) algorithms are being leveraged to a wide variety of applications in the process of generative chemistry and computer-aided drug design and discovery. In this review, we focus on the up-to-date developments for de novo peptide and protein design research using GAN algorithms in the interdisciplinary fields of generative chemistry, machine learning, deep learning, and computer-aided drug design and discovery. First, we present various studies that investigate GAN algorithms to fulfill the task of de novo peptide and protein design in the drug development pipeline. In addition, we summarize the drawbacks with respect to the previous studies in de novo peptide and protein design using GAN algorithms. Finally, we depict a discussion of open challenges and emerging problems for future research.

The peptide secreted at the water to land transition in a model amphibian has antioxidant effects.

In addition to the morphophysiological changes experienced by amphibians during metamorphosis, they must also deal with a different set of environmental constraints when they shift from the water to the land. We found that Pithecopus azureus secretes a single peptide ([M + H]+ = 658.38 Da) at the developmental stage that precedes the onset of terrestrial behaviour. De novo peptide and cDNA sequencing revealed that the peptide, named PaT-2, is expressed in tandem and is a member of the tryptophyllins family. In silico studies allowed us to identify the position of reactive sites and infer possible antioxidant mechanisms of the compounds. Cell-based assays confirmed the predicted antioxidant activity in mammalian microglia and neuroblast cells. The potential neuroprotective effect of PaT-2 was further corroborated in FRET-based live cell imaging assays, where the peptide prevented lipopolysaccharide-induced ROS production and glutamate release in human microglia. In summary, PaT-2 is the first peptide expressed during the ontogeny of P. azureus, right before the metamorphosing froglet leaves the aquatic environment to occupy terrestrial habitats. The antioxidant activity of PaT-2, predicted by in silico analyses and confirmed by cell-based assays, might be relevant for the protection of the skin of P. azureus adults against increased O2 levels and UV exposure on land compared with aquatic environments.

Toward Molecular Cooperation by De Novo Peptides.

Theoretical models of the chemical origins of life depend on self-replication or autocatalysis, processes that arise from molecular interactions, recruitment, and cooperation. Such models often lack details about the molecules and reactions involved, giving little guidance to those seeking to detect signs of interaction, recruitment, or cooperation in the laboratory. Here, we develop minimal mathematical models of reactions involving specific chemical entities: amino acids and their condensation reactions to form de novo peptides. Reactions between two amino acids form a dipeptide product, which enriches linearly in time; subsequent recruitment of such products to form longer peptides exhibit super-linear growth. Such recruitment can be reciprocated: a peptide contributes to and benefits from the formation of one or more other peptides; in this manner, peptides can cooperate and thereby exhibit autocatalytic or exponential growth. We have started to test these predictions by quantitative analysis of de novo peptide synthesis conducted by wet-dry cycling of a five-amino acid mixture over 21days. Using high-performance liquid chromatography, we tracked abundance changes for >60 unique peptide species. Some species were highly transient, with the emergence of up to 17 new species and the extinction of nine species between samplings, while other species persisted across many cycles. Of the persisting species, most exhibited super-linear growth, a sign of recruitment anticipated by our models. This work shows how mathematical modeling and quantitative analysis of kinetic data can guide the search for prebiotic chemistries that have the potential to cooperate and replicate.

Deep Novo A+: Improving the Deep Learning Model for De Novo Peptide Sequencing with Additional Ion Types and Validation Set

Background: De novo peptide sequencing is one of the key technologies in proteomics, which can extract peptide sequences directly from tandem mass spectrometry (MS/MS) spectra without any protein databases. Since the accuracy and efficiency of de novo peptide sequencing can be affected by the quality of the MS/MS data, the DeepNovo method using deep learning for de novo peptide sequencing is introduced, which outperforms the other state-of-the-art de novo sequencing methods. Objective: For superior performance and better generalization ability, additional ion types of spectra should be considered and the model of DeepNovo should be adaptive. Methods: Two improvements are introduced in the DeepNovo A+ method: a_ions are added in the spectral analysis, and the validation set is used to automatically determine the number of training epochs. Results: Experiments show that compared to the DeepNovo method, the DeepNovo A+ method can consistently improve the accuracy of de novo sequencing under different conditions. Conclusion: By adding a_ions and using the validation set, the performance of de novo sequencing can be improved effectively.

A De novo Peptide from a High Throughput Peptide Library Blocks Myosin A -MTIP Complex Formation in Plasmodium falciparum.

Apicomplexan parasites, through their motor machinery, produce the required propulsive force critical for host cell-entry. The conserved components of this so-called glideosome machinery are myosin A and myosin A Tail Interacting Protein (MTIP). MTIP tethers myosin A to the inner membrane complex of the parasite through 20 amino acid-long C-terminal end of myosin A that makes direct contacts with MTIP, allowing the invasion of Plasmodium falciparum in erythrocytes. Here, we discovered through screening a peptide library, a de-novo peptide ZA1 that binds the myosin A tail domain. We demonstrated that ZA1 bound strongly to myosin A tail and was able to disrupt the native myosin A tail MTIP complex both in vitro and in vivo. We then showed that a shortened peptide derived from ZA1, named ZA1S, was able to bind myosin A and block parasite invasion. Overall, our study identified a novel anti-malarial peptide that could be used in combination with other antimalarials for blocking the invasion of Plasmodium falciparum.

Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design.

A growing body of evidence now suggests that artificial intelligence and machine learning techniques can serve as an indispensable foundation for the process of drug design and discovery. In light of latest advancements in computing technologies, deep learning algorithms are being created during the development of clinically useful drugs for treatment of a number of diseases. In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks. Firstly, we review drug design and discovery studies that leverage various GAN techniques to assess one main application such as molecular de novo design in drug design and discovery. In addition, we describe various GAN models to fulfill the dimension reduction task of single-cell data in the preclinical stage of the drug development pipeline. Furthermore, we depict several studies in de novo peptide and protein design using GAN frameworks. Moreover, we outline the limitations in regard to the previous drug design and discovery studies using GAN models. Finally, we present a discussion of directions and challenges for future research.

De Novo Peptide Sequencing Reveals Many Cyclopeptides in the Human Gut and Other Environments.

Cyclic and branch cyclic peptides (cyclopeptides) represent a class of bioactive natural products that include many antibiotics and anti-tumor compounds. Despite the recent advances in metabolomics analysis, still little is known about the cyclopeptides in the human gut and their possible interactions due to a lack of computational analysis pipelines that are applicable to such compounds. Here, we introduce CycloNovo, an algorithm for automated de novo cyclopeptide analysis and sequencing that employs de Bruijn graphs, the workhorse of DNA sequencing algorithms, to identify cyclopeptides in spectral datasets. CycloNovo reconstructed 32 previously unreported cyclopeptides (to the best of our knowledge) in the human gut and reported over a hundred cyclopeptides in other environments represented by various spectra on Global Natural Products Social Molecular Network (GNPS). https://github.com/bbehsaz/cyclonovo.

Computational Investigation of the Dissociation Pathways of Peptides

Our ability to determine the sequence of amino acids is essential to our understanding of the underlying chemical processes dictating the nature of proteins. De-novo peptide sequencing carried out with collision-induced dissociation (CID) and tandem mass spectrometry (MS/MS), with subsequent database matching is able to accurately identify proteome and peptidome sequences. One aspect of our principal objective is to use molecular dynamics simulations to gain a better understanding of the peptide fragmentation mechanisms occurring in the dissociation process, in particular, the behavior of the mobility and migration patterns of the transferring proton.

PSite: Amino Acid Confidence Evaluation for Quality Control of De Novo Peptide Sequencing and Modification Site Localization.

MS-based de novo peptide sequencing has been improved remarkably with significant development of mass-spectrometry and computational approaches but still lacks quality-control methods. Here we proposed a novel algorithm pSite to evaluate the confidence of each amino acid rather than the full-length peptides obtained by de novo peptide sequencing. A semi-supervised learning approach was used to discriminate correct amino acids from random one; then, an expectation-maximization algorithm was used to adaptively control the false amino-acid rate (FAR). On three test data sets, pSite recalled 86% more amino acids on average than PEAKS at the FAR of 5%. pSite also performed superiorly on the modification site localization problem, which is essentially a special case of amino acid confidence evaluation. On three phosphopeptide data sets, at the false localization rate of 1%, the average recall of pSite was 91% while those of Ascore and phosphoRS were 64 and 63%, respectively. pSite covered 98% of Ascore and phosphoRS results and contributed 21% more phosphorylation sites. Further analyses show that the use of distinct fragmentation features in high-resolution MS/MS spectra, such as neutral loss ions, played an important role in improving the precision of pSite. In summary, the effective and universal model together with the extensive use of spectral information makes pSite an excellent quality control tool for both de novo peptide sequencing and modification site localization.

Combining De Novo Peptide Sequencing Algorithms, A Synergistic Approach to Boost Both Identifications and Confidence in Bottom-up Proteomics.

Complex mass spectrometry based proteomics data sets are mostly analyzed by protein database searches. While this approach performs considerably well for sequenced organisms, direct inference of peptide sequences from tandem mass spectra, i.e., de novo peptide sequencing, oftentimes is the only way to obtain information when protein databases are absent. However, available algorithms suffer from drawbacks such as lack of validation and often high rates of false positive hits (FP). Here we present a simple method of combining results from commonly available de novo peptide sequencing algorithms, which in conjunction with minor tweaks in data acquisition ensues lower empirical FDR compared to the analysis using single algorithms. Results were validated using state-of-the art database search algorithms as well specifically synthesized reference peptides. Thus, we could increase the number of PSMs meeting a stringent FDR of 5% more than 3-fold compared to the single best de novo sequencing algorithm alone, accounting for an average of 11 120 PSMs (combined) instead of 3476 PSMs (alone) in triplicate 2 h LC-MS runs of tryptic HeLa digestion.

Open-pNovo: De Novo Peptide Sequencing with Thousands of Protein Modifications.

De novo peptide sequencing has improved remarkably, but sequencing full-length peptides with unexpected modifications is still a challenging problem. Here we present an open de novo sequencing tool, Open-pNovo, for de novo sequencing of peptides with arbitrary types of modifications. Although the search space increases by ∼300 times, Open-pNovo is close to or even ∼10-times faster than the other three proposed algorithms. Furthermore, considering top-1 candidates on three MS/MS data sets, Open-pNovo can recall over 90% of the results obtained by any one traditional algorithm and report 5-87% more peptides, including 14-250% more modified peptides. On a high-quality simulated data set, ∼85% peptides with arbitrary modifications can be recalled by Open-pNovo, while hardly any results can be recalled by others. In summary, Open-pNovo is an excellent tool for open de novo sequencing and has great potential for discovering unexpected modifications in the real biological applications.

Recent Developments in Computational Methods for De Novo Peptide Sequencing from Tandem Mass Spectrometry (MS/MS).

Tandem mass spectrometry (MS/MS) has emerged as a major technology for peptide sequencing. Typically, there are three kinds of methods for the peptide sequencing: database searching, peptide tagging, and de novo sequencing. De novo sequencing has drawn increasing attention because of its independence from existing protein databases and potential for identifying new proteins, proteins resulting from mutations, proteins with unexpected modifications and so on. Recently, with the improvements in the accuracy of MS/MS and development of alternative fragmentation modes of MS/MS, many new de novo sequencing methods have been formulated. This paper reviews these recently developed sequencing methods including those for alternative MS/MS spectra. The paper first introduces background knowledge on peptide sequencing and mass spectrometry, and then reviews de novo peptide sequencing methods for traditional CID spectra. After that, it focuses on the recent development of de novo methods for alternative MS/MS spectra. In addition, methods using multiple spectra from the same peptide are surveyed. Finally, conclusions and some directions of future work are discussed.

Isotopic Differentiation Protocol, a Selective Extraction of C- and N-Terminal Ions in ESI-Ms/Ms De Novo Peptide Sequencing

Simple and selective extraction method of C- and N-terminal ions in ESI-MS/MS analyses, named isotopic differentiation protocol (IDP), was developed. It takes advantage of the mass-difference between the isotopologues after methyl and deuterated methyl ester formations at the C-terminal carboxylic acid or acetamide and deuterated acetamide formations at the N-terminal amines. This method distinguishes the N-terminal and C-terminal ions in principle to facilitate the de novo peptide sequencing. An application with non-ribosomal depsipeptide plipastatin revealed that a pair of the peptide and the corresponding methyl ester at the C-terminus was also applicable to IDP method giving spectrum with enough quality. Facilitating software named “MSMS Search Tool” was also developed.