Photo-Cross-Linked Small-Molecule Microarrays as Chemical Genomic Tools for Dissecting Protein–Ligand Interactions

We compiled a list of 11 016 unique structures of small-molecule ligands bound to proteins representing 750 873 protein–ligand atomic interactions, and analyzed the frequency, geometry and the impact of each interaction type. The most frequent ligand–protein atom pairs can be clustered into seven interaction types.

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Small molecule-dependent genetic selection in stochastic nanodroplets as a means of detecting protein-ligand interactions on a large scale

Chemistry & Biology ◽

10.1016/s1074-5521(97)90304-5 ◽

1997 ◽

Vol 4 (12) ◽

pp. 961-968 ◽

Cited By ~ 41

Author(s):

Allen Borchardt ◽

Stephen D. Liberles ◽

Stephen R. Biggar ◽

Gerald R. Crabtree ◽

Stuart L. Schreiber

Keyword(s):

Small Molecule ◽

Large Scale ◽

Genetic Selection ◽

Protein Ligand Interactions ◽

Ligand Interactions

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MALDI-TOF-Based Affinity Selection Mass Spectrometry for Automated Screening of Protein–Ligand Interactions at High Throughput

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555220959266 ◽

2020 ◽

Vol 26 (1) ◽

pp. 44-57

Author(s):

Roman P. Simon ◽

Martin Winter ◽

Carola Kleiner ◽

Lucie Wehrle ◽

Michael Karnath ◽

...

Keyword(s):

Mass Spectrometry ◽

Small Molecule ◽

High Throughput ◽

Binding Assay ◽

Size Exclusion ◽

Affinity Selection ◽

Maldi Tof ◽

Protein Ligand Interactions ◽

Small Molecule Ligands ◽

Ligand Interactions

Demonstration of in vitro target engagement for small-molecule ligands by measuring binding to a molecular target is an established approach in early drug discovery and a pivotal step in high-throughput screening (HTS)-based compound triaging. We describe the setup, evaluation, and application of a ligand binding assay platform combining automated affinity selection (AS)-based sample preparation and label-free matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) analysis. The platform enables mass spectrometry (MS)-based HTS for small-molecule target interactions from single-compound incubation mixtures and is embedded into a regular assay automation environment. Efficient separation of target–ligand complexes is achieved by in-plate size exclusion chromatography (SEC), and small-molecule ligands are subsequently identified by MALDI-TOF analysis. In contrast to alternative HTS-capable binding assay formats, MALDI-TOF AS-MS is capable of identifying orthosteric and allosteric ligands, as shown for the model system protein tyrosine phosphatase 1B (PTP1B), irrespective of protein function. Furthermore, determining relative binding affinities (RBAs) enabled ligand ranking in accordance with functional inhibition and reference data for PTP1B and a number of diverse protein targets. Finally, we present a validation screen of more than 23,000 compounds within 24 h, demonstrating the general applicability of the platform for the HTS-compatible assessment of protein–ligand interactions.

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Development of a Protein-Ligand Extended Connectivity (PLEC) Fingerprint and Its Application for Binding Affinity Predictions.

10.26434/chemrxiv.5928406.v1 ◽

2018 ◽

Author(s):

Maciej Wójcikowski ◽

Michał Kukiełka ◽

Marta Stepniewska-Dziubinska ◽

Pawel Siedlecki

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Linear Model ◽

Binding Affinity ◽

Small Molecule ◽

3D Structure ◽

Protein Ligand Interactions ◽

Core Set ◽

Ligand Interactions ◽

Extended Connectivity

<div>Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of fingerprints, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions. Here, we present a Protein-Ligand Extended Connectivity (PLEC) fingerprint that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC fingerprints were used to construct different machine learning (ML) models tailored for predicting protein-ligand affinities (pK<sub>i/d</sub>). Even the simplest linear model built on the PLEC fingerprint achieved R<sub>p</sub>=0.83 on the PDBbind v2016 "core set”, demonstrating its descriptive power. The PLEC fingerprint has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt).</div>

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Development of a Protein-Ligand Extended Connectivity (PLEC) Fingerprint and Its Application for Binding Affinity Predictions.

10.26434/chemrxiv.5928406 ◽

2018 ◽

Author(s):

Maciej Wójcikowski ◽

Michał Kukiełka ◽

Marta Stepniewska-Dziubinska ◽

Pawel Siedlecki

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Linear Model ◽

Binding Affinity ◽

Small Molecule ◽

3D Structure ◽

Protein Ligand Interactions ◽

Core Set ◽

Ligand Interactions ◽

Extended Connectivity

<div>Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of fingerprints, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions. Here, we present a Protein-Ligand Extended Connectivity (PLEC) fingerprint that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC fingerprints were used to construct different machine learning (ML) models tailored for predicting protein-ligand affinities (pK<sub>i/d</sub>). Even the simplest linear model built on the PLEC fingerprint achieved R<sub>p</sub>=0.83 on the PDBbind v2016 "core set”, demonstrating its descriptive power. The PLEC fingerprint has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt).</div>

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Faculty Opinions recommendation of A small molecule microarray platform to select RNA internal loop-ligand interactions.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1095990.553569 ◽

2007 ◽

Author(s):

Janet Morrow

Keyword(s):

Small Molecule ◽

Microarray Platform ◽

Internal Loop ◽

Ligand Interactions ◽

Rna Internal Loop

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19F-NMR in Target-based Drug Discovery

Current Medicinal Chemistry ◽

10.2174/0929867326666190610160534 ◽

2019 ◽

Vol 26 (26) ◽

pp. 4964-4983 ◽

Cited By ~ 7

Author(s):

CongBao Kang

Keyword(s):

Drug Discovery ◽

Conformational Changes ◽

Protein Structures ◽

19F Nmr ◽

Binding Affinities ◽

Protein Ligand Interactions ◽

Compound Libraries ◽

Ligand Interactions ◽

Reference Ligand ◽

Hit Identification

Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR can play important roles in fragment- based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorinecontaining compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and is particularly useful for screening competitive compounds when a reference ligand is available.

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