Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Automated affinity selection for rapid discovery of peptide binders.

ABSTRACT: In-solution affinity selection (AS) of large synthetic peptide libraries affords identification of binders to protein targets through access to an expanded chemical space. Standard affinity selection methods, however, can be time-consuming, low-throughput, or provide hits that display low selectivity to the target. Here we report an automated bio-layer interferometry (BLI)-assisted affinity selection platform. When coupled with tandem mass spectrometry (MS), this method enables both rapid de novo discovery and affinity maturation of known peptide binders with high selectivity. The BLI-assisted AS-MS technology also features real-time monitoring of the peptide binding during the library selection process, a feature unattainable by current selection approaches. We show the utility of the BLI AS-MS platform toward rapid identification of novel nanomolar (dissociation constant, K _D < 50 nM) non-canonical binders to the leukemia-associated oncogenic protein menin. To our knowledge, this is the first application of BLI to the affinity selection of synthetic peptide libraries. We believe our approach can significantly accelerate the use of synthetic peptidomimetic libraries in drug discovery.

SUBMITTER: Zhang G

PROVIDER: S-EPMC8372318 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Ultra-large chemical libraries for the discovery of high-affinity peptide binders.

Project description:High-diversity genetically-encoded combinatorial libraries (108-1013 members) are a rich source of peptide-based binding molecules, identified by affinity selection. Synthetic libraries can access broader chemical space, but typically examine only ~ 106 compounds by screening. Here we show that in-solution affinity selection can be interfaced with nano-liquid chromatography-tandem mass spectrometry peptide sequencing to identify binders from fully randomized synthetic libraries of 108 members-a 100-fold gain in diversity over standard practice. To validate this approach, we show that binders to a monoclonal antibody are identified in proportion to library diversity, as diversity is increased from 106-108. These results are then applied to the discovery of p53-like binders to MDM2, and to a family of 3-19 nM-affinity, α/β-peptide-based binders to 14-3-3. An X-ray structure of one of these binders in complex with 14-3-3σ is determined, illustrating the role of β-amino acids in facilitating a key binding contact.

| S-EPMC7311396 | biostudies-literature

Rapid discovery of cyclic peptide protein aggregation inhibitors through continuous selection HTS data

Project description:We report a new platform for the rapid phenotypic selection of protein aggregation inhibitors from genetically encoded cyclic peptide libraries in E. coli based on phage-assisted continuous evolution (PACE). Here, we developed a new PACE-compatible selection for protein aggregation inhibition and employed it to identify cyclic peptides that suppress amyloid-β42 (Aβ42) and human islet amyloid polypeptide (hIAPP) aggregation. Additionally, we integrated a negative selection that removes false positives and off-target hits, significantly improving cyclic peptide selectivity. We show that selected inhibitors are active when chemically re-synthesized in in vitro assays. Our platform provides a powerful new approach for the rapid discovery of cyclic peptide inhibitors of protein aggregation and may serve as the basis for the future evolution of cyclic peptides with a broad spectrum of inhibitory activities. Data deposited here are HTS data critical to the conclusions of this study.

2024-10-25 | GSE279094 | GEO

<i>De Novo</i> Discovery of High-Affinity Peptide Binders for the SARS-CoV-2 Spike Protein.

Project description:The β-coronavirus SARS-CoV-2 has caused a global pandemic. Affinity reagents targeting the SARS-CoV-2 spike protein are of interest for the development of therapeutics and diagnostics. We used affinity selection-mass spectrometry for the rapid discovery of synthetic high-affinity peptide binders for the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. From library screening with 800 million synthetic peptides, we identified three sequences with nanomolar affinities (dissociation constants K d = 80-970 nM) for RBD and selectivity over human serum proteins. Nanomolar RBD concentrations in a biological matrix could be detected using the biotinylated lead peptide in ELISA format. These peptides do not compete for ACE2 binding, and their site of interaction on the SARS-CoV-2-spike-RBD might be unrelated to the ACE2 binding site, making them potential orthogonal reagents for sandwich immunoassays. These findings serve as a starting point for the development of SARS-CoV-2 diagnostics or conjugates for virus-directed delivery of therapeutics.

| S-EPMC7755081 | biostudies-literature

In vitro affinity screening of protein and peptide binders by megavalent bead surface display.

Project description:The advent of protein display systems has provided access to tailor-made protein binders by directed evolution. We introduce a new in vitro display system, bead surface display (BeSD), in which a gene is mounted on a bead via strong non-covalent (streptavidin/biotin) interactions and the corresponding protein is displayed via a covalent thioether bond on the DNA. In contrast to previous monovalent or low-copy bead display systems, multiple copies of the DNA and the protein or peptide of interest are displayed in defined quantities (up to 10(6) of each), so that flow cytometry can be used to obtain a measure of binding affinity. The utility of the BeSD in directed evolution is validated by library selections of randomized peptide sequences for binding to the anti-hemagglutinin (HA) antibody that proceed with enrichments in excess of 10(3) and lead to the isolation of high-affinity HA-tags within one round of flow cytometric screening. On-bead K(d) measurements suggest that the selected tags have affinities in the low nanomolar range. In contrast to other display systems (such as ribosome, mRNA and phage display) that are limited to affinity panning selections, BeSD possesses the ability to screen and rank binders by their affinity in vitro, a feature that hitherto has been exclusive to in vivo multivalent cell display systems (such as yeast display).

| S-EPMC3785251 | biostudies-literature

A mix-and-read drop-based in vitro two-hybrid method for screening high-affinity peptide binders.

Project description:Drop-based microfluidics have recently become a novel tool by providing a stable linkage between phenotype and genotype for high throughput screening. However, use of drop-based microfluidics for screening high-affinity peptide binders has not been demonstrated due to the lack of a sensitive functional assay that can detect single DNA molecules in drops. To address this sensitivity issue, we introduced in vitro two-hybrid system (IVT2H) into microfluidic drops and developed a streamlined mix-and-read drop-IVT2H method to screen a random DNA library. Drop-IVT2H was based on the correlation between the binding affinity of two interacting protein domains and transcriptional activation of a fluorescent reporter. A DNA library encoding potential peptide binders was encapsulated with IVT2H such that single DNA molecules were distributed in individual drops. We validated drop-IVT2H by screening a three-random-residue library derived from a high-affinity MDM2 inhibitor PMI. The current drop-IVT2H platform is ideally suited for affinity screening of small-to-medium-sized libraries (10(3)-10(6)). It can obtain hits within a single day while consuming minimal amounts of reagents. Drop-IVT2H simplifies and accelerates the drop-based microfluidics workflow for screening random DNA libraries, and represents a novel alternative method for protein engineering and in vitro directed protein evolution.

| S-EPMC4778045 | biostudies-literature

Rapid <i>in silico</i> Design of Potential Cyclic Peptide Binders Targeting Protein-Protein Interfaces.

Project description:Rational design of specific inhibitors of protein-protein interactions is desirable for drug design to control cellular signal transduction but also for studying protein-protein interaction networks. We have developed a rapid computational approach to rationally design cyclic peptides that potentially bind at desired regions of the interface of protein-protein complexes. The methodology is based on comparing the protein backbone structure of short peptide segments (epitopes) at the protein-protein interface with a collection of cyclic peptide backbone structures. A cyclic peptide that matches the backbone structure of the segment is used as a template for a binder by adapting the amino acid side chains to the side chains found in the target complex. For a small library of cyclic peptides with known high resolution structures we found for the majority (~82%) of 154 protein-protein complexes at least one very well fitting match for a cyclic peptide template to a protein-protein interface segment. The majority of the constructed protein-cyclic peptide complexes was very stable during Molecular Dynamics simulations and showed an interaction energy score that was typically more favorable compared to interaction scores of typical peptide-protein complexes. Our cPEPmatch approach could be a promising approach for rapid suggestion of cyclic peptide binders that could be tested experimentally and further improved by chemical modification.

| S-EPMC7578414 | biostudies-literature

Rapid discovery of cyclic peptide protein aggregation inhibitors through continuous selection HTS data

Project description:Rapid discovery of cyclic peptide protein aggregation inhibitors through continuous selection HTS data

| PRJNA1170358 | ENA

Just Add Data: automated predictive modeling for knowledge discovery and feature selection.

Project description:Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio (JADBio), an AutoML platform applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBio focuses on knowledge discovery by performing feature selection and identifying the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome or phenotype of interest. It also returns a palette of useful information for interpretation, clinical use of the models, and decision making. JADBio is qualitatively and quantitatively compared against Hyper-Parameter Optimization Machine Learning libraries. Results show that in typical omics dataset analysis, JADBio manages to identify signatures comprising of just a handful of features while maintaining competitive predictive performance and accurate out-of-sample performance estimation.

| S-EPMC9203777 | biostudies-literature

Quantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Design.

Project description:The computational identification of peptides that can bind the major histocompatibility complex (MHC) with high affinity is an essential step in developing personal immunotherapies and vaccines. We introduce PUFFIN, a deep residual network-based computational approach that quantifies uncertainty in peptide-MHC affinity prediction that arises from observational noise and the lack of relevant training examples. With PUFFIN's uncertainty metrics, we define binding likelihood, the probability a peptide binds to a given MHC allele at a specified affinity threshold. Compared to affinity point estimates, we find that binding likelihood correlates better with the observed affinity and reduces false positives in high-affinity peptide design. When applied to examine an existing peptide vaccine, PUFFIN identifies an alternative vaccine formulation with higher binding likelihood. PUFFIN is freely available for download at http://github.com/gifford-lab/PUFFIN.

| S-EPMC6715517 | biostudies-literature

Mimicking the Nucleosomal Context in Peptide-Based Binders of a H3K36me Reader Increases Binding Affinity While Altering the Binding Mode.

Project description:Targeting of proteins in the histone modification machinery has emerged as a promising new direction to fight disease. The search for compounds that inhibit proteins that readout histone modification has led to several new epigenetic drugs, mostly for proteins involved in recognition of acetylated lysines. However, this approach proved to be a challenging task for methyllysine readers, which typically feature shallow binding pockets. Moreover, reader proteins of trimethyllysine K36 on the histone H3 (H3K36me3) not only bind the methyllysine but also the nucleosomal DNA. Here, we sought to find peptide-based binders of H3K36me3 reader PSIP1, which relies on DNA interactions to tightly bind H3K36me3 modified nucleosomes. We designed several peptides that mimic the nucleosomal context of H3K36me3 recognition by including negatively charged Glu-rich regions. Using a detailed NMR analysis, we find that addition of negative charges boosts binding affinity up to 50-fold while decreasing binding to the trimethyllysine binding pocket. Since screening and selection of compounds for reader domains is typically based solely on affinity measurements due to their lack of enzymatic activity, our case highlights the need to carefully control for the binding mode, in particular for the challenging case of H3K36me3 readers.

| S-EPMC7662849 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data