ABCD Flexsim-R: A new 3D descriptor for combinatorial library design and in-silico screening 2 nd Joint Sheffield Conference on Chemoinformatics: Computational Tools for Lead Discovery

ABCD Outline Introduction The Flexsim-R Methodology Validation Conclusion and Outlook

ABCD Introduction What is Flexsim-R? Flexsim-R calculates 3D descriptors for reagents, based on the virtual affinity fingerprint idea

ABCD Motivation to develop Flexsim-R Reagent-based descriptors are important for – combinatorial library design – virtual screening experiments – bioisosteric replacements – rational augmentation of inhouse reagent pool For large combinatorial libraries, product-based descriptor calculation is often not feasible -> possible solution: reagent-based product selection (e.g. by a GA) Descriptor calculation should be fast and automizable Descriptor should be related to experimental affinity data Encouragement by virtual affinity fingerprint methods

ABCD In-vitro Affinity Fingerprints Terrapin's Affinity Fingerprint Approach: (Kauvar et al., Chemistry & Biology, 1995, 2, ) Molecular similarity is defined by in-vitro binding patterns ("Affinity Fingerprints") of a ligand set (L) in reference binding assays (A) L1 L2 L3 L4 L5 L6 A1A2A3A4A5A6A7A8

ABCD Virtual Affinity Fingerprints (VAF) Terrapins in-vitro screening in diverse reference assays is simulated by Computational Docking into a reference panel of protein pockets (Docksim, Flexsim-X) by Computational Fitting onto a reference panel of small molecules (Flexsim-S) (Briem and Lessel, Perspectives in Drug Discovery and Design, 20 (2000) )

ABCD The Flexsim-R Method

ABCD The Flexsim-R Method Protein pocket Problems with Rgroups in conventional VAF approaches: Rgroups tend to be smaller than „drug-like“ molecules Alignment rule by common core attachment point gets lost Solution: Core-constrained multiple-site docking

ABCD The Flexsim-R Method Components of core-constrained multiple-site docking: 1. Rgroup Set 2. Common Core 3. Protein Binding Pockets

ABCD The Flexsim-R Method First step: Docking of common core group with FlexX Multiple (e.g. 50 best) solutions are stored RMS threshold can be applied to prevent clustering

ABCD Example: Thrombin active site with 50 best FlexX solutions of hydantoin (RMS threshold = 2.0) The Flexsim-R Method

ABCD The Flexsim-R Method Second step: Docking of core group + rgroup with FlexX Pre-stored core positions serve as reference FlexX scores are stored in descriptor matrix Core Pos Core Pos R1 R2 R3... Descriptor Matrix Protein pocket

ABCD The Flexsim-R Method

ABCD The Flexsim-R Method

ABCD The Flexsim-R Method

ABCD C1C2C3 Pocket 3 C1C2C3 Pocket 2 The Flexsim-R Method Multiple protein pockets-> Concatenated descriptor matrix R1 R2 R3... C1C2C3 Pocket 1

ABCD C1C2C3 X4 C1C2C3 X3 C1C2C3 X2 The Flexsim-R Method Multiple core attachment points -> Concatenated descriptor matrix R1 R2 R3... C1C2C3 X1

ABCD The Flexsim-R Method Example: Hydantoin Core 4 attachment points * 7 protein pockets * 50 FlexX solutions -> descriptor vector length = 1,400

ABCD The Flexsim-R Method Test set for method development and evaluation: Rgroups: 20 natural amino acids Core groups: 7 protein pockets: 1dwc, 1eed, 1pop, 2tsc, 3cla, 3dfr, 5ht2 (model)

ABCD Correlation Analysis Analyses were performed to check correlation between different protein pockets different cores different attachment points Analyses are based on euclidian distance matrices for all 190 pairwise amino acid vector combinations

ABCD Correlation Analysis Correlation matrix of protein pockets: (hydantoin core, all 4 attachment points)

ABCD Correlation Analysis Correlation matrix of core groups: (all 7 protein pockets, all attachment points)

ABCD Correlation Analysis Correlation matrix of attachment points: (hydantoin core, all 7 protein pockets)

ABCD Correlation Analysis Reduction of descriptor vector length (dimensionality) : no PCA was performed, since we want to get information about the most uncorrelated descriptor columns instead, an elimination method has been applied:  the complete pairwise correlation matrix is calculate  all pairs of columns with correlation coefficient (r) above a user- defined threshold (e.g. 0.7) are considered for elimination  from each correlating pair, that column is eliminated which can be better described by multiple linear regression of the remaining descriptors  resulting matrix doesn‘t contain pairs of columns with correlation coefficient above the threshold

ABCD Example: hydantoin core, all 7 proteins, all 4 attachment points Correlation Analysis Descriptor set 1 Descriptor set 2 Descriptor set 3

ABCD Correlation Analysis Thrombin with three most information-rich core positions

ABCD Descriptor Validation Five peptide datasets, taken from literature (Refs. in Matter, H., J. Peptide Res. 52 (1998) ) Product descriptors are generated by concatenation of respective reagent descriptors Validation by PLS Analysis leave-one-out (LOO) and leave-random-groups-out (LRGO) cross-validation

ABCD Descriptor Validation Datasets:

ABCD ACEBITBRAENKBR9 Leave - random-groups-out (LRGO) results: Descriptor Validation: Results

ABCD Summary Flexsim-R comprises a novel virtual affinity fingerprint method, which calculates meaningful 3D descriptors for reagents High correlation between different cores and attachment points For 3 out of 5 validation sets, significant cross-validated q 2 values could be obtained Rgroup alignment problem is tackled inherently Flexsim-R calculations are fast and can be automated easily: only clipped reagent structures are required core positions need to be calculated only once

ABCD Outlook More validation sets have to be tested (e.g. „real-life“ combichem dataset) Is there a set of descriptors, which works well for different datasets? Integration in Boehringer Ingelheim library design and virtual screening workflow

ABCD Acknowledgements Alexander Weber (Boehringer Ingelheim/University of Marburg) Andreas Teckentrup (Boehringer Ingelheim) Hans Matter (Aventis) BMBF for financial support