Available Datasets


HSP90 - SPR

Introduction

Please download data file here: HSP90 - SPR (uploaded on 2020-01-30).

Binding kinetics data set for HSP90 inhibitors

Author: Daria Kokh (daria.kokh@h-its.org)

The Excel file contains a set of kinetic rate constants and SMILE strings for more than 140 small molecule inhibitors of  the N-terminal domain of  heat shock protein 90 (HSP90).
Date were obtained by SPR measurements (using a single protocol) as a part of the K4DD project and were reported in the four publications listed below.

 

Kinetic rate constants for 110 HSP90 inhibitors: dissociation rate constants are plotted vs association rate constants. Compounds with three main binding core fragments are coloured in cyan, magenta, and orange; the rest of compounds are shown in black
Fig. 1: Kinetic rate constants for approx. 140  HSP90 inhibitors: dissociation rate constants are plotted vs association rate constants. Compounds with three main binding core fragments are coloured in cyan, magenta, and orange; the rest of compounds are shown in black.

Fig. 2: Dissociation rates are plotted against the number of heavy atoms (representing molecular size); compounds bound to different HSP90 binding site conformations (see Ref.1) are indicated by different colours. The relation between dissociation rate and molecular weight is discussed in Ref. 2.

 
 

Fig. 3: (A-D): 2D images of compounds (separated into three largest groups by the core fragments: resorcinol, indazole, quinazoline and one group of compounds with various cores).

 

Data related to this data set:

  1. Chao Xu, Xianglei Zhang, Lianghao Zhao, Gennady M. Verkhivker, and Fang Bai: Accurate Characterization of Binding Kinetics and Allosteric Mechanisms for the HSP90 Chaperone Inhibitors Using AI-Augmented Integrative Biophysical Studies. JACS Au 2024, XX:XXXX-XXXX. 

Data were collected from the following publications:

  1. Kokh DB, Amaral M, Bomke J, Grädler U, Musil D, Buchstaller HP, Dreyer MK, Frech M, Lowinski M, Vallee F, et al.: Estimation of Drug-Target Residence Times by τ-Random Acceleration Molecular Dynamics Simulations. J Chem Theory Comput 2018, 14:3859–3869. (see method description 'τ-RAMD' in kbbox)
  2. Kokh Daria B., Kaufmann Tom, Kister Bastian, Wade Rebecca C.,  Machine Learning Analysis of τRAMD Trajectories to Decipher Molecular Determinants of Drug-Target Residence Times, Front. Mol. Biosci. (2019) 6,1-17 (see method description 'τ-RAMD' in kbbox)
  3. Schuetz, D. A.; Richter, L.; Amaral, M.; Grandits, M.; Grädler, U.; Musil, D.; Buchstaller, H.-P.; Eggenweiler, H.-M.; Frech, M.; Ecker, G. F. Ligand Desolvation Steers on-Rate and Impacts Drug Residence Time of Heat Shock Protein 90 (Hsp90) Inhibitors. J. Med. Chem. 2018, 61, 4397–4411.​ (see method description 'steered molecular dynamics' in kbbox)
  4. Schuetz DA, Bernetti M, Bertazzo M, Musil D, Eggenweiler HMH-M, Recanatini M, Masetti M, Ecker GF, Cavalli A: Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics. J Chem Inf Model 2019, 59:535–549.​ (see method description 'smoothed or scaled molecular dynamics' in kbbox)
  5. Wolf S, Amaral M, Lowinski M, Vallée F, Musil D, Güldenhaupt J, Dreyer MK, Bomke J, Frech M, Schlitter J, et al.: Estimation of Protein-Ligand Unbinding Kinetics Using Non-Equilibrium Targeted Molecular Dynamics Simulations. J Chem Inf Model 2019, 59:5135–5147. (see methods description 'targeted molecular dynamics' in kbbox)

K4DD project was supported by EU/EFPIA Innovative Medicines Initiative (IMI) Joint Undertaking, K4DD (grant no. 115366)​