Browse
Submit Data
Databases
API
Help

Dataset Information

49 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

In silico peptide-directed ligand design complements experimental peptide-directed binding for protein-protein interaction modulator discovery.

ABSTRACT: Using the protein-protein interaction of Mcl-1/Noxa, two methods for efficient modulator discovery are directly compared. In silico peptide-directed ligand design is evaluated against experimental peptide-directed binding, allowing for the discovery of two new inhibitors of Mcl-1/Noxa with cellular activity. In silico peptide-directed ligand design demonstrates an in vitro hit rate of 80% (IC₅₀ < 100 μM). The two rapid and efficient methods demonstrate complementary features for protein-protein interaction modulator discovery.

SUBMITTER: Howell LA

PROVIDER: S-EPMC8341744 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

In silico peptide-directed ligand design complements experimental peptide-directed binding for protein-protein interaction modulator discovery.

Howell Lesley Ann LA Beekman Andrew Michael AM

RSC chemical biology 20201119 1

Using the protein-protein interaction of Mcl-1/Noxa, two methods for efficient modulator discovery are directly compared. In silico peptide-directed ligand design is evaluated against experimental peptide-directed binding, allowing for the discovery of two new inhibitors of Mcl-1/Noxa with cellular activity. In silico peptide-directed ligand design demonstrates an in vitro hit rate of 80% (IC50 < 100 μM). The two rapid and efficient methods demonstrate complementa ...[more]

PMID: 34458784

Similar Datasets

Identification of selective protein-protein interaction inhibitors using efficient in silico peptide-directed ligand design.

Project description:The development of protein-protein interaction (PPI) inhibitors with therapeutic value is of increasing importance as the first clinical agent has now been approved, but PPIs remain difficult targets for the development of small molecule ligands. This article describes a highly efficient approach to the development of inhibitors of the p53/hDMX or hDM2 interaction that involves the design of small molecules in silico based upon a peptide/protein structure. The process for molecule design, starting from a virtual library of just over 1200 fragments, led to the eventual synthesis of twenty compounds, of which ten bound to either hDM2, hDMX or both in in vitro binding assays. This 50% success rate is extremely efficient compared to traditional high throughput screening. The identification of two selective hDMX inhibitors from twenty compounds highlights this efficiency as, to date, only two other hDMX-selective agents exist in the literature. Preliminary biological studies show that 20% of the compounds identified have cellular activity and activate downstream pathways associated with p53 activation.

| S-EPMC6482886 | biostudies-literature

Site-directed ligand discovery.

Project description:We report a strategy (called "tethering") to discover low molecular weight ligands ( approximately 250 Da) that bind weakly to targeted sites on proteins through an intermediary disulfide tether. A native or engineered cysteine in a protein is allowed to react reversibly with a small library of disulfide-containing molecules ( approximately 1,200 compounds) at concentrations typically used in drug screening (10 to 200 microM). The cysteine-captured ligands, which are readily identified by MS, are among the most stable complexes, even though in the absence of the covalent tether the ligands may bind very weakly. This method was applied to generate a potent inhibitor for thymidylate synthase, an essential enzyme in pyrimidine metabolism with therapeutic applications in cancer and infectious diseases. The affinity of the untethered ligand (K(i) approximately 1 mM) was improved 3,000-fold by synthesis of a small set of analogs with the aid of crystallographic structures of the tethered complex. Such site-directed ligand discovery allows one to nucleate drug design from a spatially targeted lead fragment.

| S-EPMC16870 | biostudies-literature

In silico analysis and experimental verification of OSR1 kinase - Peptide interaction.

Project description:The oxidative-stress-responsive kinase 1 (OSR1) and the STE20/SPS1-related proline/alanine-rich kinase (SPAK) are key enzymes in a signaling cascade regulating the activity of Na(+)-K(+)-2Cl(-) cotransporters (NKCC1-2) and Na(+)-Cl(-) cotransporter (NCC). Both kinases have a conserved carboxyl-terminal (CCT) domain, which recognizes a unique peptide motif present in OSR1- and SPAK-activating kinases (with-no-lysine kinase 1 (WNK1) and WNK4) as well as their substrates (NKCC1, NKCC2, and NCC). Utilizing various modalities of the Rosetta Molecular Modeling Software Suite including flexible peptide docking and protein design, we comprehensively explored the sequence space recognized by the CCT domain. Specifically, we studied single residue mutations as well as complete unbiased designs of a hexapeptide substrate. The computational study started from a crystal structure of the CCT domain of OSR1 in complex with a hexapeptide derived from WNK4. Point mutations predicted to be favorable include Arg to His or Trp substitutions at position 2 and a Phe to Tyr substitution at position 3 of the hexapeptide. In addition, de novo design yielded two peptides predicted to bind to the CCT domain: FRFQVT and TRFDVT. These results, which indicate a little bit more freedom in the composition of the peptide, were confirmed through the use of yeast two-hybrid screening.

| S-EPMC4082687 | biostudies-literature

MIEC-SVM: automated pipeline for protein peptide/ligand interaction prediction.

Project description:MotivationMIEC-SVM is a structure-based method for predicting protein recognition specificity. Here, we present an automated MIEC-SVM pipeline providing an integrated and user-friendly workflow for construction and application of the MIEC-SVM models. This pipeline can handle standard amino acids and those with post-translational modifications (PTMs) or small molecules. Moreover, multi-threading and support to Sun Grid Engine (SGE) are implemented to significantly boost the computational efficiency.Availability and implementationThe program is available at http://wanglab.ucsd.edu/MIEC-SVM CONTACT: : wei-wang@ucsd.eduSupplementary informationSupplementary data available at Bioinformatics online.

| S-EPMC4907390 | biostudies-literature

Identification of β-strand mediated protein-protein interaction inhibitors using ligand-directed fragment ligation.

Project description:β-Strand mediated protein-protein interactions (PPIs) represent underexploited targets for chemical probe development despite representing a significant proportion of known and therapeutically relevant PPI targets. β-Strand mimicry is challenging given that both amino acid side-chains and backbone hydrogen-bonds are typically required for molecular recognition, yet these are oriented along perpendicular vectors. This paper describes an alternative approach, using GKAP/SHANK1 PDZ as a model and dynamic ligation screening to identify small-molecule replacements for tranches of peptide sequence. A peptide truncation of GKAP functionalized at the N- and C-termini with acylhydrazone groups was used as an anchor. Reversible acylhydrazone bond exchange with a library of aldehyde fragments in the presence of the protein as template and in situ screening using a fluorescence anisotropy (FA) assay identified peptide hybrid hits with comparable affinity to the GKAP peptide binding sequence. Identified hits were validated using FA, ITC, NMR and X-ray crystallography to confirm selective inhibition of the target PDZ-mediated PPI and mode of binding. These analyses together with molecular dynamics simulations demonstrated the ligands make transient interactions with an unoccupied basic patch through electrostatic interactions, establishing proof-of-concept that this unbiased approach to ligand discovery represents a powerful addition to the armory of tools that can be used to identify PPI modulators.

| S-EPMC8179271 | biostudies-literature

Project description:The expansion of protein-ligand annotation databases has enabled large-scale networking of proteins by ligand similarity. These ligand-based protein networks, which implicitly predict the ability of neighboring proteins to bind related ligands, may complement biologically-oriented gene networks, which are used to predict functional or disease relevance. To quantify the degree to which such ligand-based protein associations might complement functional genomic associations, including sequence similarity, physical protein-protein interactions, co-expression, and disease gene annotations, we calculated a network based on the Similarity Ensemble Approach (SEA: sea.docking.org), where protein neighbors reflect the similarity of their ligands. We also measured the similarity with functional genomic networks over a common set of 1,131 genes, and found that the networks had only small overlaps, which were significant only due to the large scale of the data. Consistent with the view that the networks contain different information, combining them substantially improved Molecular Function prediction within GO (from AUROC~0.63-0.75 for the individual data modalities to AUROC~0.8 in the aggregate). We investigated the boost in guilt-by-association gene function prediction when the networks are combined and describe underlying properties that can be further exploited.

| S-EPMC4965129 | biostudies-literature

Aptamer-Protein Structures Guide In Silico and Experimental Discovery of Aptamer-Short Peptide Recognition Complexes or Aptamer-Amino Acid Cluster Complexes.

Project description:A method to computationally and experimentally identify aptamers against short peptides or amino acid clusters is introduced. The method involves the selection of a well-defined protein aptamer complex and the extraction of the peptide sequence participating in the binding of the protein to the aptamer. The subsequent fragmentation of the peptide sequence into short peptides and the in silico docking-guided identification of affinity complexes between the miniaturized peptides and the antiprotein aptamer, followed by experimental validation of the binding features of the short peptides with the antiprotein aptamers, leads to the identification of new short peptide-aptamer complexes. This is exemplified with the identification of the pentapeptide RYERN as the scaffold that binds thrombin to the DNA thrombin aptamer (DNA TA). In silico docking studies followed by microscale thermophoresis (MST) experiments demonstrate that the miniaturized tripeptides RYE, YER, and ERN reveal selective binding affinities toward the DNA TA. In addition, docking and MST experiments show that the ribonucleotide-translated RNA TA shows related binding affinities of YER to the DNA TA. Most importantly, we demonstrate that the separated amino acids Y/E/R assemble as a three amino acid cluster on the DNA TA and RNA TA aptamers in spatial configurations similar to the tripeptide YER on the respective aptamers. The clustering phenomenon is selective for the YER tripeptide system. The method to identify binding affinities of miniaturized peptides to known antiprotein aptamers and the specific clustering of single amino acids on the aptamers is further demonstrated by in silico and experimental identification of the binding of the tripeptide RET and the selective clustering of the separated amino acids R/E/T onto a derivative of the AS1411 aptamer against the nucleolin receptor protein.

| S-EPMC9661473 | biostudies-literature

Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation.

Project description:Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.

| S-EPMC10883280 | biostudies-literature

Protein-protein interaction and in silico mutagenesis studies on IL17A and its peptide inhibitor.

Project description:Protein-protein interactions of Interleukin-17 (IL17) play vital role in the autoimmune and inflammatory diseases, such as rheumatoid arthritis, multiple sclerosis, and psoriasis. Potent therapeutics for these diseases could be developed by blocking or modulating these interactions through biologics, peptide inhibitors and small molecule inhibitors. Unlike biologics, peptide inhibitors are cost effective and can be orally available. Peptide inhibitors do not require a binding groove as that of small molecules either. Therefore, crystal structure of IL17A in complex with a high affinity peptide inhibitor (HAP) (1-IHVTIPADLWDWIN-14) is investigated with an aim to find hot spots that could improve its potency. An in silico mutagenesis strategy was implemented using FoldX PSSM to scan for positions tolerant to amino acid substitution. Three positions T4, A7, and N14 showed improved stability when mutated with 'F/M/Y', 'P' and 'F/M/Y', respectively. A set of 31 mutant peptides are designed through combinations of these tolerant mutations using Build Model application of FoldX. Binding affinity and interactions of 31 peptides are assessed through protein-peptide docking and binding free energy calculations. Two peptides namely, P1 ("1-IHVTIPPDLWDWIY-14") and P2 ("1-IHVMIPPDLWDWIF-14") showed better binding affinity to IL17A dimerization site compared to HAP. Interactions of P1, P2 and HAP are also analyzed through 100 ns molecular dynamics simulations using GROMACS v5.0. The results revealed that the P2 peptide likely to offer better potency compared to HAP and P1. Therefore, the P2 peptide can be synthesized to develop oral therapies for autoimmune and inflammatory diseases with further experimental evaluations.Supplementary informationThe online version contains supplementary material available at 10.1007/s13205-021-02856-y.

| S-EPMC8167077 | biostudies-literature

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Project description:Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

| S-EPMC1978274 | biostudies-other