Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins.

Funder: ExscientiaFunder: Diamond Light SourceFunder: Kungliga Tekniska HoegskolanFunder: Chinese Center for Disease Control and PreventionFunder: European Federation of Pharmaceutical Industries and AssociationsFunder: European CommissionFunder: Kennedy Trust for Rheumatology ResearchFunder: Ontario Institute for Cancer ResearchFunder: Royal Institution for the Advancement of Learning McGill UniversityFunder: UCBSelectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family

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

SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids

This work was supported by grants of the German Research Foundation (DFG: KR 4073/11-1; SFBTRR219, 322900939; and CRU344, 428857858, and CRU5011 InteraKD 445703531), a grant of the European Research Council (ERC-StG 677448), the Federal Ministry of Research and Education (BMBF NUM-COVID19, Organo-Strat 01KX2021), the Dutch Kidney Foundation (DKF) TASK FORCE consortium (CP1805), the Else Kroener Fresenius Foundation (2017_A144), and the ERA-CVD MENDAGE consortium (BMBF 01KL1907) all to R.K.; DFG (CRU 344, Z to I.G.C and CRU344 P2 to R.K.S.); and the BMBF eMed Consortium Fibromap (to V.G.P, R.K., R.K.S., and I.G.C.). R.K.S received support from the KWF Kankerbestrijding (11031/2017–1, Bas Mulder Award) and a grant by the ERC (deFiber; ERC-StG 757339). J.J. is supported by the Netherlands Organisation for Scientific Research (NWO Veni grant no: 091 501 61 81 01 36) and the DKF (grant no. 19OK005). B.S. is supported by the DKF (grant: 14A3D104) and the NWO (VIDI grant: 016.156.363). R.P.V.R. and G.J.O. are supported by the NWO VICI (grant: 16.VICI.170.090). P.B. is supported by the BMBF (DEFEAT PANDEMIcs, 01KX2021), the Federal Ministry of Health (German Registry for COVID-19 Autopsies-DeRegCOVID, www.DeRegCOVID.ukaachen.de; ZMVI1-2520COR201), and the German Research Foundation (DFG; SFB/TRR219 Project-IDs 322900939 and 454024652). S.D. received DFG support (DJ100/1-1) as well as support from VGP and TBH (SFB1192). M.d.B,R.R., N.S., and A.A. are supported by an ERC Advanced Investigator grant (H2020-ERC-2017-ADV-788982-COLMIN) to N.S. A.A. is supported by the NWO (VI.Veni.192.094). We thank Saskia de Wildt, Jeanne Pertijs (Radboudumc, Department of Pharmacology), and Robert M. Verdijk (Erasmus Medical Center, Department of Pathology) for providing tissue controls (Erasmus MC Tissue Bank) and Christian Drosten (Charite´ Universitatsmedizin Berlin, Institute of € Virology) and Bart Haagmans (Erasmus Medical Center, Rotterdam) for providing the SARS-CoV-2 isolate. We thank Kioa L. Wijnsma (Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Amalia Children’s Hospital, Radboud University Medical Center) for support with statistical analysis regarding the COVID-19 patient cohort.Peer reviewedPublisher PD