5SE1

CRYSTAL STRUCTURE OF HUMAN PHOSPHODIESTERASE 10 IN COMPLEX WITH c1(nn(cc1NC(c2nc(ccc2Nc3cncnc3)C4CC4)=O)C)C(=O)N(C)C, micromolar IC50=0.031527

Summary for 5SE1

• Entry DOI: 10.2210/pdb5se1/pdb
• Group deposition: Exploiting Structural Data for Improved On-Target Prediction in Lead Optimization (G_1002226)
• Descriptor: cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A, ZINC ION, MAGNESIUM ION, ... (5 entities in total)
• Functional Keywords: phosphodiesterase, pde10, hydrolase, schizophrenia, hydrolase-hydrolase inhibitor complex, hydrolase/hydrolase inhibitor
• Biological source: Homo sapiens (human)
• Total number of polymer chains: 1
• Total formula weight: 39909.36

Authors

Joseph, C.,Rodriguez-Sarmiento, R.M.,Benz, J.,Schlatter, D.,Rudolph, M.G. (deposition date: 2022-01-21, release date: 2022-10-12, Last modification date: 2024-10-16)

Primary citation

Tosstorff, A.,Rudolph, M.G.,Cole, J.C.,Reutlinger, M.,Kramer, C.,Schaffhauser, H.,Nilly, A.,Flohr, A.,Kuhn, B.
A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios.
J.Comput.Aided Mol.Des., 36:753-765, 2022

PubMed Abstract: We release a new, high quality data set of 1162 PDE10A inhibitors with experimentally determined binding affinities together with 77 PDE10A X-ray co-crystal structures from a Roche legacy project. This data set is used to compare the performance of different 2D- and 3D-machine learning (ML) as well as empirical scoring functions for predicting binding affinities with high throughput. We simulate use cases that are relevant in the lead optimization phase of early drug discovery. ML methods perform well at interpolation, but poorly in extrapolation scenarios-which are most relevant to a real-world application. Moreover, we find that investing into the docking workflow for binding pose generation using multi-template docking is rewarded with an improved scoring performance. A combination of 2D-ML and 3D scoring using a modified piecewise linear potential shows best overall performance, combining information on the protein environment with learning from existing SAR data.

PubMed: 36153472
DOI: 10.1007/s10822-022-00478-x

Experimental method

X-RAY DIFFRACTION (2.3 Å)