In silico peptide-directed ligand design complements experimental peptide-directed binding for protein-protein interaction modulator discovery.
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ABSTRACT:
SUBMITTER: Howell LA
PROVIDER: S-EPMC8341744 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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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.
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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.
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Project description:How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. Here, we present OPEX, an optimal experimental design method to identify informative omics experiments for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's cross-behavior potential, when exposed to novel biocide and antibiotic combinations, led to accelerated knowledge discovery with predictive models that are more accurate while needing 44% fewer data to train. Selecting experiments favoring broader exploration followed by fine-tuning emerged as the optimal strategy. This led to the discovery of 29 cross-protection and 4 cross-vulnerability conditions, with further validation revealing the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to automate omics data collection for training accurate predictive models, evidence-driven decision making and accelerated knowledge discovery in life sciences.
2020-07-09 | GSE144604 | GEO
Project description:Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis does not provide complete solution of AD due to multifactorial nature of the disease and one target one drug fails to provide better treatment against AD. Moreover, currently available treatments are limited and most of the upcoming treatments under clinical trials are based on modulating single target. So, the current AD drug discovery research is shifting towards a new approach for a better solution that simultaneously modulates more than one targets in the neurodegenerative cascade. This can be achieved by network pharmacology, multi-modal therapies, multifaceted, and/or the more recently proposed term "multi-targeted designed drugs". Drug discovery project is a tedious, costly and long-term project. Moreover, multi-target AD drug discovery added extra challenges such as the good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off-target side effect and crossing of the blood-brain barrier. These hurdles may be addressed by insilico methods for an efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here, we are summarizing some of the most prominent and computationally explored single targets against AD and further, we discussed a successful example of dual or multiple inhibitors for same targets. Moreover, we focused on ligand and structure-based computational approach to design MTDL against AD. However, it is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy is useful in future MTDLs drug discovery alone or in combination with a fragment-based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery.
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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.
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Project description:BACKGROUND:A rapid phage display method for the elucidation of cognate peptide specific ligand for receptors is described. The approach may be readily integrated into the interface of genomic and proteomic studies to identify biologically relevant ligands. METHODS:A gene fragment library from influenza coat protein haemagglutinin (HA) gene was constructed by treating HA cDNA with DNAse I to create 50-100 bp fragments. These fragments were cloned into plasmid pORFES IV and in-frame inserts were selected. These in-frame fragment inserts were subsequently cloned into a filamentous phage display vector JC-M13-88 for surface display as fusions to a synthetic copy of gene VIII. Two well characterized antibodies, mAb 12CA5 and pAb 07431, directed against distinct known regions of HA were used to pan the library. RESULTS:Two linear epitopes, HA peptide 112-126 and 162-173, recognized by mAb 12CA5 and pAb 07431, respectively, were identified as the cognate epitopes. CONCLUSION:This approach is a useful alternative to conventional methods such as screening of overlapping synthetic peptide libraries or gene fragment expression libraries when searching for precise peptide protein interactions, and may be applied to functional proteomics.
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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.
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Project description:Cysteine-reactive small molecules are used as chemical probes of biological systems and as medicines. Identifying high-quality covalent ligands requires comprehensive kinetic analysis to distinguish selective binders from pan-reactive compounds. Quantitative irreversible tethering (qIT), a general method for screening cysteine-reactive small molecules based upon the maximization of kinetic selectivity, is described. This method was applied prospectively to discover covalent fragments that target the clinically important cell cycle regulator Cdk2. Crystal structures of the inhibitor complexes validate the approach and guide further optimization. The power of this technique is highlighted by the identification of a Cdk2-selective allosteric (type IV) kinase inhibitor whose novel mode-of-action could be exploited therapeutically.
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Project description:Cyclotides are plant peptides comprising a circular backbone and three conserved disulfide bonds that confer them with exceptional stability. They were originally discovered in Oldenlandia affinis based on their use in traditional African medicine to accelerate labor. Recently, cyclotides have been identified in numerous plant species of the coffee, violet, cucurbit, pea, potato, and grass families. Their unique structural topology, high stability, and tolerance to sequence variation make them promising templates for the development of peptide-based pharmaceuticals. However, the mechanisms underlying their biological activities remain largely unknown; specifically, a receptor for a native cyclotide has not been reported hitherto. Using bioactivity-guided fractionation of an herbal peptide extract known to indigenous healers as "kalata-kalata," the cyclotide kalata B7 was found to induce strong contractility on human uterine smooth muscle cells. Radioligand displacement and second messenger-based reporter assays confirmed the oxytocin and vasopressin V1a receptors, members of the G protein-coupled receptor family, as molecular targets for this cyclotide. Furthermore, we show that cyclotides can serve as templates for the design of selective G protein-coupled receptor ligands by generating an oxytocin-like peptide with nanomolar affinity. This nonapeptide elicited dose-dependent contractions on human myometrium. These observations provide a proof of concept for the development of cyclotide-based peptide ligands.
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