Biomolecular Interaction Analysis of Gestrinone-anti-Gestrinone Using Arrays of High Aspect Ratio SU-8 Nanopillars

In this paper, label-free biosensing for antibody screening by periodic lattices of high-aspect ratio SU-8 nano-pillars (BICELLs) is presented. As a demonstration, the determination of anti-gestrinone antibodies from whole rabbit serum is carried out, and for the first time, the dissociation constant (KD = 6 nM) of antigen-antibody recognition process is calculated using this sensing system. After gestrinone antigen immobilization on the BICELLs, the immunorecognition was performed. The cells were interrogated vertically by using micron spot size Fourier transform visible and IR spectrometry (FT-VIS-IR), and the dip wavenumber shift was monitored. The biosensing assay exhibited good reproducibility and sensitivity (LOD = 0.75 ng/mL).Ortega Higueruelo, FJ.; Bañuls Polo, MJ.; Sanza, FJ.; Casquel, R.; Laguna, MF.; Holgado, M.; López-Romero, D.... (2012). Biomolecular Interaction Analysis of Gestrinone-anti-Gestrinone Using Arrays of High Aspect Ratio SU-8 Nanopillars. 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Dr. PIAS: an integrative system for assessing the druggability of protein-protein interactions

<p>Abstract</p> <p>Background</p> <p>The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine.</p> <p>Results</p> <p>To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS). Dr. PIAS has a meta-database that stores various types of information (tertiary structures, drugs/chemicals, and biological functions associated with PPIs) retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM). Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV) proteins identified to date.</p> <p>Conclusions</p> <p>The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data.</p

The Overlap of Small Molecule and Protein Binding Sites within Families of Protein Structures

Protein–protein interactions are challenging targets for modulation by small molecules. Here, we propose an approach that harnesses the increasing structural coverage of protein complexes to identify small molecules that may target protein interactions. Specifically, we identify ligand and protein binding sites that overlap upon alignment of homologous proteins. Of the 2,619 protein structure families observed to bind proteins, 1,028 also bind small molecules (250–1000 Da), and 197 exhibit a statistically significant (p<0.01) overlap between ligand and protein binding positions. These “bi-functional positions”, which bind both ligands and proteins, are particularly enriched in tyrosine and tryptophan residues, similar to “energetic hotspots” described previously, and are significantly less conserved than mono-functional and solvent exposed positions. Homology transfer identifies ligands whose binding sites overlap at least 20% of the protein interface for 35% of domain–domain and 45% of domain–peptide mediated interactions. The analysis recovered known small-molecule modulators of protein interactions as well as predicted new interaction targets based on the sequence similarity of ligand binding sites. We illustrate the predictive utility of the method by suggesting structural mechanisms for the effects of sanglifehrin A on HIV virion production, bepridil on the cellular entry of anthrax edema factor, and fusicoccin on vertebrate developmental pathways. The results, available at http://pibase.janelia.org, represent a comprehensive collection of structurally characterized modulators of protein interactions, and suggest that homologous structures are a useful resource for the rational design of interaction modulators

Small molecules, big targets: drug discovery faces the protein-protein interaction challenge.

Protein-protein interactions (PPIs) are of pivotal importance in the regulation of biological systems and are consequently implicated in the development of disease states. Recent work has begun to show that, with the right tools, certain classes of PPI can yield to the efforts of medicinal chemists to develop inhibitors, and the first PPI inhibitors have reached clinical development. In this Review, we describe the research leading to these breakthroughs and highlight the existence of groups of structurally related PPIs within the PPI target class. For each of these groups, we use examples of successful discovery efforts to illustrate the research strategies that have proved most useful.JS, DES and ARB thank the Wellcome Trust for funding.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nrd.2016.2

The role of ligand efficiency metrics in drug discovery

The judicious application of ligand or binding efficiencies, which quantify the molecular properties required to gain binding affinity for a drug target, is gaining traction in the selection and optimisation of fragments, hits, and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimised ligand efficiency values for their target. Optimising ligand efficiencies based on both molecular size and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the molecular inflation that pervades current practice in medicinal chemistry, and to increase the developability of drug candidates

Coexistence of megabenthic assemblages and artisanal fishers: The case of Cap de Creus Marine Protected Area (North-Western Mediterranean Sea)

11 pages, 5 figures, 4 tables, supplementary data https://doi.org/10.1016/j.marenvres.2023.106211.-- Data availability: Data will be made available on requestArtisanal fisheries, although considered less harmful, can still endanger marine ecosystems, especially in areas with long-standing tradition. In Cap de Creus, where artisanal fisheries has likely occurred for centuries, the status of benthic communities in fishing grounds was poorly understood. Through collaboration with local fishers, the benthic assemblages in three artisanal fishing grounds within Cap de Creus Marine Protected Area (MPA) were studied. Using video transects recorded by a remotely operated vehicle (ROV), the diversity and distribution of species were analysed in relation to substrate type, slope, and depth. The study also assessed the impacts on these communities by examining marine litter, lost fishing gear, and the condition of gorgonian populations. The findings identified three megabenthic assemblages and revealed higher fishing pressure and impact in the Maça d’Oros area, likely due to multiple fishing guilds converging. However, the study demonstrated lower impact in MPAs compared to unprotected Mediterranean areas, highlighting the importance of coastal managementThis work was performed under the MitiCap and ResCap projects, which are funded by the Fundación Biodiversidad [Biodiversity Foundation] of the Ministerio para la Transición Ecológica [Spanish Ministry for Ecological Transition], through the Pleamar Program, co-funded by the European Maritime and Fisheries Fund. In addition, the authors affiliated to the Institut de Ciències del Mar [Institute of Marine Sciences] had the institutional support of the “Severo Ochoa Centre of Excellence” accreditation (CEX 2019-000928-S)Peer reviewe

Characterisation of inverse agonism of the orphan-G protein-coupled receptor GPR52 by cannabinoid ligands Cannabidiol and O-1918

The identification of cannabinoid ligands Cannabidiol and O-1918 as inverse agonists of the orphan receptor GPR52 is reported. Detailed characterisation of GPR52 pharmacology and modelling of the proposed receptor interaction is described. The identification of a novel and further CNS pharmacology for the polypharmacological agent and marketed drug Cannabidiol is noteworthy

Hotspots API: A Python Package for the Detection of Small Molecule Binding Hotspots and Application to Structure-Based Drug Design.

Methods that survey protein surfaces for binding hotspots can help to evaluate target tractability and guide exploration of potential ligand binding regions. Fragment Hotspot Maps builds upon interaction data mined from the CSD (Cambridge Structural Database) and exploits the idea of identifying hotspots using small chemical fragments, which is now widely used to design new drug leads. Prior to this publication, Fragment Hotspot Maps was only publicly available through a web application. To increase the accessibility of this algorithm we present the Hotspots API (application programming interface), a toolkit that offers programmatic access to the core Fragment Hotspot Maps algorithm, thereby facilitating the interpretation and application of the analysis. To demonstrate the package's utility, we present a workflow which automatically derives protein hydrogen-bond constraints for molecular docking with GOLD. The Hotspots API is available from https://github.com/prcurran/hotspots under the MIT license and is dependent upon the commercial CSD Python API