Dereplication and de novo sequencing of nonribosomal peptides.
Ontology highlight
ABSTRACT:
SUBMITTER: Ng J
PROVIDER: S-EPMC2754211 | biostudies-literature | 2009 Aug
REPOSITORIES: biostudies-literature
Publications
Dereplication and de novo sequencing of nonribosomal peptides.
Ng Julio J Bandeira Nuno N Liu Wei-Ting WT Ghassemian Majid M Simmons Thomas L TL Gerwick William H WH Linington Roger R Dorrestein Pieter C PC Pevzner Pavel A PA
Nature methods 20090713 8
Nonribosomal peptides (NRPs) are of great pharmacological importance, but there is currently no technology for high-throughput NRP 'dereplication' and sequencing. We used multistage mass spectrometry followed by spectral alignment algorithms for sequencing of cyclic NRPs. We also developed an algorithm for comparative NRP dereplication that establishes similarities between newly isolated and previously identified similar but nonidentical NRPs, substantially reducing dereplication efforts. ...[more]
Similar Datasets
Project description:Neoepitope peptides are newly formed antigens presented by major histocompatibility complex class I (MHC-I) on cell surfaces. The cells presenting neoepitope peptides are recognized and subsequently killed by cytotoxic T-cells. Immunopeptidomic approaches aim to characterize the peptide repertoire (including neoepitope) associated with the MHC-I molecules on the surface of tumor cells using proteomic technologies, providing critical information for designing effective immunotherapy strategies. We developed a novel constrained de novo sequencing algorithm to identify neo-epitope peptides from tandem mass spectra acquired in immunopeptidomic analyses. Our method incorporates prior probabilities to putative peptides according to position specific scoring matrices (PSSMs) representing the sequence preferences recognized by MHC-I molecules. We implemented a dynamic programming algorithm to determine the peptide sequences with an optimal posterior matching score for each given MS/MS spectrum. Similar to the de novo peptide sequencing, the dynamic programming algorithm allows an efficient searching in the entire peptide sequence space. On an LC-MS/MS dataset, we demonstrated the performance of our algorithm in detecting the neoepitope peptides bound by the HLA-C*0501 molecules that were superior to database search approaches and existing general purpose de novo peptide sequencing algorithms.
| S-EPMC6901291 | biostudies-literature
Project description: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 21 days. 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.
| S-EPMC8212187 | biostudies-literature
Project description:Full-length de novo sequencing of unknown proteins remains a challenging open problem. Traditional methods that sequence spectra individually are limited by short peptide length, incomplete peptide fragmentation, and ambiguous de novo interpretations. We address these issues by determining consensus sequences for assembled tandem mass (MS/MS) spectra from overlapping peptides (e.g., by using multiple enzymatic digests). We have combined electron-transfer dissociation (ETD) with collision-induced dissociation (CID) and higher-energy collision-induced dissociation (HCD) fragmentation methods to boost interpretation of long, highly charged peptides and take advantage of corroborating b/y/c/z ions in CID/HCD/ETD. Using these strategies, we show that triplet CID/HCD/ETD MS/MS spectra from overlapping peptides yield de novo sequences of average length 70 AA and as long as 200 AA at up to 99% sequencing accuracy.
| S-EPMC4591044 | biostudies-literature
Project description:Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes that have evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small-molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for accurate de novo design of conformationally restricted peptides, and the use of these methods to design 18-47 residue, disulfide-crosslinked peptides, a subset of which are heterochiral and/or N-C backbone-cyclized. Both genetically encodable and non-canonical peptides are exceptionally stable to thermal and chemical denaturation, and 12 experimentally determined X-ray and NMR structures are nearly identical to the computational design models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs.
| S-EPMC5161715 | biostudies-literature
Project description:De novo peptide sequencing by mass spectrometry (MS) can determine the amino acid sequence of an unknown peptide without reference to a protein database. MS-based de novo sequencing assumes special importance in focused studies of families of biologically active peptides and proteins, such as hormones, toxins, and antibodies, for which amino acid sequences may be difficult to obtain through genomic methods. These protein families often exhibit sequence homology or characteristic amino acid content; yet, current de novo sequencing approaches do not take advantage of this prior knowledge and, hence, search an unnecessarily large space of possible sequences. Here, we describe an algorithm for de novo sequencing that incorporates sequence constraints into the core graph algorithm and thereby reduces the search space by many orders of magnitude. We demonstrate our algorithm in a study of cysteine-rich toxins from two cone snail species (Conus textile and Conus stercusmuscarum) and report 13 de novo and about 60 total toxins.
| S-EPMC3412931 | biostudies-literature
Project description:Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutations or originate from unexpected processing of RNAs and proteins. De novo peptide sequencing approaches address this limitation but often suffer from low accuracy and require extensive validation by experts. Here, we develop SMSNet, a deep learning-based de novo peptide sequencing framework that achieves >95% amino acid accuracy while retaining good identification coverage. Applications of SMSNet on landmark proteomics and peptidomics studies reveal over 10,000 previously uncharacterized HLA antigens and phosphopeptides, and in conjunction with database-search methods, expand the coverage of peptide identification by almost 30%. The power to accurately identify new peptides of SMSNet would make it an invaluable tool for any future proteomics and peptidomics studies, including tumor neoantigen discovery, antibody sequencing, and proteome characterization of non-model organisms.
| S-EPMC6885704 | biostudies-literature
Project description:This work describes the de-novo design of peptides that inhibit a broad range of plant pathogens. Four structurally different groups of peptides were developed that differ in size and position of their charged and hydrophobic clusters and were assayed for their ability to inhibit bacterial growth and fungal spore germination. Several peptides are highly active at concentrations between 0,1 and 1 µg/ml against plant pathogenic bacteria, such as Pseudomonas syringae, Pectobacterium carotovorum, and Xanthomonas vesicatoria. Importantly, no hemolytic activity could be detected for these peptides at concentrations up to 200 µg/ml. Moreover, the peptides are also active after spraying on the plant surface demonstrating a possible way of application. In sum, our designed peptides represent new antimicrobial agents and with the increasing demand for antimicrobial compounds for production of "healthy" food, these peptides might serve as templates for novel antibacterial and antifungal agents.
| S-EPMC3741113 | biostudies-literature
Project description:The origin of novel genes and beneficial functions is of fundamental interest in evolutionary biology. New genes can originate from different mechanisms, including horizontal gene transfer, duplication-divergence, and de novo from noncoding DNA sequences. Comparative genomics has generated strong evidence for de novo emergence of genes in various organisms, but experimental demonstration of this process has been limited to localized randomization in preexisting structural scaffolds. This bypasses the basic requirement of de novo gene emergence, i.e., lack of an ancestral gene. We constructed highly diverse plasmid libraries encoding randomly generated open reading frames and expressed them in Escherichia coli to identify short peptides that could confer a beneficial and selectable phenotype in vivo (in a living cell). Selections on antibiotic-containing agar plates resulted in the identification of three peptides that increased aminoglycoside resistance up to 48-fold. Combining genetic and functional analyses, we show that the peptides are highly hydrophobic, and by inserting into the membrane, they reduce membrane potential, decrease aminoglycoside uptake, and thereby confer high-level resistance. This study demonstrates that randomized DNA sequences can encode peptides that confer selective benefits and illustrates how expression of random sequences could spark the origination of new genes. In addition, our results also show that this question can be addressed experimentally by expression of highly diverse sequence libraries and subsequent selection for specific functions, such as resistance to toxic compounds, the ability to rescue auxotrophic/temperature-sensitive mutants, and growth on normally nonused carbon sources, allowing the exploration of many different phenotypes.IMPORTANCEDe novo gene origination from nonfunctional DNA sequences was long assumed to be implausible. However, recent studies have shown that large fractions of genomic noncoding DNA are transcribed and translated, potentially generating new genes. Experimental validation of this process so far has been limited to comparative genomics, in vitro selections, or partial randomizations. Here, we describe selection of novel peptides in vivo using fully random synthetic expression libraries. The peptides confer aminoglycoside resistance by inserting into the bacterial membrane and thereby partly reducing membrane potential and decreasing drug uptake. Our results show that beneficial peptides can be selected from random sequence pools in vivo and support the idea that expression of noncoding sequences could spark the origination of new genes.
| S-EPMC6550523 | biostudies-literature
Project description:Computationally designed transmembrane α-helical peptides (CHAMP) have been used to compete for helix-helix interactions within the membrane, enabling the ability to probe the activation of the integrins αIIbβ3 and αvβ3. Here, this method is extended towards the design of CHAMP peptides that inhibit the association of the α5β1 transmembrane (TM) domains, targeting the Ala-X3-Gly motif within α5. Our previous design algorithm was performed alongside a new workflow implemented within the widely used Rosetta molecular modeling suite. Peptides from each computational approach activated integrin α5β1 but not αVβ3 in human endothelial cells. Two CHAMP peptides were shown to directly associate with an α5 TM domain peptide in detergent micelles to a similar degree as a β1 TM peptide does. By solution-state nuclear magnetic resonance, one of these CHAMP peptides was shown to bind primarily the integrin β1 TM domain, which itself has a Gly-X3-Gly motif. The second peptide associated modestly with both α5 and β1 constructs, with slight preference for α5. Although the design goal was not fully realized, this work characterizes novel CHAMP peptides activating α5β1 that can serve as useful reagents for probing integrin biology.
| S-EPMC6151875 | biostudies-literature
Project description:De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.
| S-EPMC5547637 | biostudies-literature