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Research in the Brenk lab

The overall research goal of the Brenk group is to improve methods used for structure-based drug design and to apply these methods to design inhibitors for enzymes with biological relevance. A key point in our research is the interplay of theoretical and experimental methods.

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Currently, we are working on the following topics:

1. RNA-ligand interactions

We are developing methods to predict the binding modes and affinities of RNA-ligand complexes. Further, we are exploiting these methods to discover ligands for riboswitches which are novel targets for antibiotics.

Related News pages:

Selected publications:

  1. Rekand IH, Brenk R. Ligand design for riboswitches, an emerging target class for novel antibiotics. Future Med Chem. 2017 Sep;9(14):1649-1662. [Pubmed | DOI | Post-print version]
  1. Wehler, T.; Brenk, R. Structure-Based Discovery of Small Molecules Binding to RNA. In RNA Therapeutics; Topics in Medicinal Chemistry; Springer, 2017; pp 47–77. [DOI]
  1. Daldrop P, Brenk R. Structure-Based Virtual Screening for the Identification of RNA-Binding Ligands. Methods Mol Biol 1103, 127-39 (2014). [Pubmed | DOI]
  1. Daldrop P, Reyes FE, Robinson DA, Hammond CM, Lilley DM, Batey RT, Brenk R. Novel Ligands for a Purine Riboswitch iscovered by RNA-Ligand Docking. Chem Biol 18 (3), 324-35 (2011). [Pubmed | DOI]

2. A better understanding of protein-ligand interactions

Ideally, we should be able to predict the binding affinity of a ligand based on the structure of the protein-ligand complex. However, we are still far away from reaching this goal. Therefore, we are using a combination of biophysical and computational methods to improve our understanding of protein-ligand interactions. A particular focus in this context is to better undertand the driving forces of selective inhibiton in highly conserved binding sites. In addition, we are developing methods to predict how likely it is to to find a drug-like ligand for a given binding site and which physico-chemical properties this ligand will likely have. Further, we are exploiting the vast data about protein-ligand complexes to develop scoring functions to predict the binding affinities of ligands to their targets. For this task, we are using modern machine learning methods.

Related News pages:

Selected publications:

  1. Kersten C, Fleischer E, Kehrein J, Borek C, Jaenicke E, Sotriffer C, Brenk R. How To Design Selective Ligands for Highly Conserved Binding Sites: A Case Study Using N-Myristoyltransferases as a Model System. J Med Chem. J Med Chem. 2020 Mar 12;63(5):2095-2113 [Pubmed | DOI]
  1. Sarkar A, Brenk R. To Hit or Not to Hit, That Is the Question - Genome-wide Structure-Based Druggability Predictions for Pseudomonas aeruginosa Proteins. PLoS One 10 (9), e0137279 (2015). [Pubmed | DOI]
  1. Krasowski A, Muthas D, Sarkar A, Schmitt S, Brenk R. DrugPred: A Structure-Based Approach To Predict Protein Druggability Developed Using an Extensive Nonredundant Data Set. J Chem Inf Model 51 (11), 2829-42 (2011). [Pubmed | DOI]

    3. Structure-based ligand design

    In collaboration with other labs we are working on the structure-based design of ligands for a variety of targets. A partical focus on this work is the discovery for starting points for new antibiotics.

    Related News pages:

    Selected publications:

    1. Klein R, Cendron L, Montanari M, Bellio P, Celenza G, Maso L, Tondi D, Brenk R. Targeting the Class A Carbapenemase GES-5 via Virtual Screening. Biomolecules. 2020 Feb 14;10(2). pii: E304.[Pubmed | DOI]
    1. Klein R, Linciano P, Celenza G, Bellio P, Papaioannou S, Blazquez J, Cendron L, Brenk R, Tondi D. In silico identification and experimental validation of hits active against KPC-2 β-lactamase. PLoS One. 2018 Nov 29;13(11):e0203241. [Pubmed | DOI | bioRxiv]
    1. Urich R, Wishart G, Kiczun M, Richters A, Tidten-Luksch N, Rauh D, Sherborne B, Wyatt PG, Brenk R. De novo design of protein kinase inhibitors by in silico identification of hinge region-binding fragments. ACS Chem Biol 8 (5), 1044-52 (2013). [Pubmed | DOI]
    1. Frearson JA, Brand S, McElroy SP, Cleghorn LA, Smid O, Stojanovski L, Price HP, Guther ML, Torrie LS, Robinson DA, Hallyburton I, Mpamhanga CP, Brannigan JA, Wilkinson AJ, Hodgkinson M, Hui R, Qiu W, Raimi OG, van Aalten DM, Brenk R, Gilbert IH, Read KD, Fairlamb AH, Ferguson MA, Smith DF, Wyatt PG. N-myristoyltransferase inhibitors as new leads to treat sleeping sickness. Nature 464 (7289), 728-32 (2010). [Pubmed | DOI | F1000 recommended]