Application Guide

Automated De Novo Design Workflow with Physics-Based Scoring Function for Fast Lead

Identification and Optimization

Hongwei HuangAmit Kulkarni

We prepared a protocol through Pipeline Pilot with existing Discovery Studio 1.7 components, which can quickly generate new ligand molecules by fusing suggested fragments to a scaffold, followed by further refinement, and physics-based scoring (MM-GBSA) using CHARMm for ranking.

Introduction

Structure-Based Design (SBD) allows us to simulate screening before performing experimental assays, which helps researchers prioritize lead candidates before synthesis or purchase. Many studies with SBD focus on identifying new scaffolds, navigating around existing patents, or simply modeling analogs for medicinal chemistry1. In most cases, experimental data shows that these analogs are in such a similar series that their scaffold is positioned very similarly in the receptor binding site. This means a full docking experiment is often unnecessary. In these instances, developing a fast and easy way to enumerate the fragment-scaffold combination becomes more critical. Furthermore, the workflow can be customized to incorporate further refinement and accurate physics-based scoring in an automated manner.

Method

DS AutoLudi is a de novo design tool that suggests modifications and additions to a ligand scaffold within a target binding site in order to enhance binding affinity to the receptor2. In “Combinatorial mode”, DS AutoLudi will enumerate all fitted fragment combinations at the specified scaffold link sites (see Fig.1).

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Fig. 1 Scheme of AutoLudi Combinatorial mode

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The fragments are derived from a specified Ludi link library, which is generated by Genfra, a program that will process an sd file of compounds into a Ludi library, automatically generating Ludi labels for each entry. The ligands are fitted according to the Ludi-generated interaction map and the fragments are scored using one of the Ludi energy estimates. In the next step, a suggested fragment is automatically fused onto the scaffold to form a new ligand molecule.

The new ligand can be further refined by CHARMm minimization and scored with a variety of scoring functions (LigScore, PLP, Jain, etc) for ranking [�]. For this study, the docked ligand poses were scored with the more accurate and physics-based Molecular Mechanics-Generalized Born with Surface Area (MM-GBSA) method in DS CHARMm, which approximates the binding free energy (as shown in Fig.2)4.

All of the relevant components used in this project, are available with Discovery Studio 1.7 and Pipeline Pilot. The components were accessed and further customized through the Pipeline Pilot client.

Result & Discussion

We designed a single workflow (fig. �) that combines the following steps:

• fragment library preparation• de novo design• CHARMm refinement • MM-GBSA scoring with CHARMm

Fig. 2 Workflow of MM-GBSA by CHARMm

Fig. 3 Workflow (right) and relevant protocol (left) composed of de novo design and CHARMm programs

The automated protocol prepared in Pipeline Pilot contains three major pipelines:

Automatically converts a de novo (Ludi) link library from a fragment sd file using Genfra (link site needs to be specified as a dummy atom X in the sd file). Prior to this step, multiple �D conformations are generated to account for the flexibility of larger fragments (Figure 4).

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Fig. 4 Pipeline to generate Ludi link library

Automatically grows scaffold and generates new ligand molecules by fusing the suggested fragments to the scaffold using DS AutoLudi (three AutoLudi modes are included, but this study is focused only on Combinatorial mode by specifying the link point on scaffold) (Figure 5)

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Fig. 5 Pipeline to grow scaffold by fusing fragments

Lastly, a customized parameter explorer for the protocol was created (figure 7). The only inputs required to run the protocol are the receptor, the scaffold (with specified R group) and the fragment library (with link site information).

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Fig. 7 Protocol parameter table in DS1.7 (required inputs is circled in red)

Refines new ligand molecules with DS CHARMm (multiple minimization steps included) followed by binding energy calculation using MM-GBSA or MM-PBSA. (Figure 6)

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Fig. 6 Pipeline for Binding Energy calculation

A validation study was carried out to examine the accuracy of the automated protocol created in Pipeline Pilot using a Src Tyrosine Kinase system [5]. This study only aimed to examine each individual link point (R) in the scaffold (see Fig. 8).

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Fig. 9 Superposition of retrieved active ligands (yellow) over X-ray ligands of Src Tyrosine kinase

Fig. 8 Scaffold and all fragments of Src Tyrosine Kinase inhibitors

The Src Tyrosine Kinase, 2BDF.pdb was used as receptor. The purine core from the X-ray ligand in the complex was extracted and used as scaffold. A total of 12 substitutions for R1, R2, and R� were prepared and saved as a fragment file in sd format. The DS AutoLudi parameters were biased to obtain only the most active fragments on each link point. As a result, all of the R1 fragments were retrieved, while only 40% of the R� fragments were found in the final hit list. As shown in figure 9, the poses of the DS AutoLudi-generated ligands were fairly consistent with the X-ray ligands.

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Fig. 10 All hits from 112 fragments were displayed in binding site with H-bonds monitored (green dot line). They were scored and ranked using MM-GBSA scoring. The bottom plot shows the calculated ΔG bind by MM-GBSA versus the experimental ΔG bind for the active Src Tyrosine Kinase inhibitors.

To examine the sensitivity of the scoring functions, a randomization experiment was performed in order to determine whether the most active fragments could be identified from a pool of randomly generated fragments. Twelve fragments of Src Tyrosine Kinase inhibitors were mixed together with 100 randomly selected fragments from Accelrys’ default Ludi link library (MW<150). All fragments were tested at the R1 link point using the same protocol, proceeding through the growth of the scaffold, followed by CHARMm refinement and MM-GBSA scoring. A total of 1� hits were obtained with corresponding Binding Energy estimates in the output sd file. All active R1 fragments were ranked in the top 5, which is in a good agreement with experimental IC50 data (see Fig. 10). It was found that MM-GBSA scoring performed much better than other scoring functions for ranking, which were examined in a separate scoring experiment.

DS AutoLudi parameters can be adjusted to explore more candidates through examining additional fragment-scaffold combinations. However, this identified only one of the active fragments at R2. One reason for this might be the lack of Ludi interaction site information along this link site. Furthermore, this link side is also exposed to solvent. We are currently working on a new workflow/protocol, which could work better on the R2 and R� link sites on the scaffold of the Src tyrosine kinase inhibitor.

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Ji, H., Zhang, et al., “Structure-Based de Novo Design, Synthesis, and Biological Evaluation of Non-Azole Inhibitors Specific for Lanosterol 14r-Demethylase of Fungi,” J. Med. Chem., 2003, 46, 474-485.H.-J. Böhm,“The Computer Program Ludi:A New Method for the De Novo Design of Enzyme Inhibitors,” J. Comp. Aided Molec. Design, 1992. , 6, 61-78,. Brooks et al., “CHARMM: A program for macromolecular energy, minimization, and dynamics calculations,” J. Comp. Chem., 1983, 4, 187-217.Massova, I., et al., “Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding,” Perspectives in Drug Discovery and Design, 2000, 18, 11�–1�5.Dalgarno, D., et al., “Structural Basis of Src Kinase Inhibition with a New Class of Potent and Selective Trisubstituted Purine-Based Compounds.” Chem. Biol. Drug Design, 2006, 67, 46-57.

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References

Conclusion

This workflow/protocol was generated for performing “all-in-one” de novo design, including Ludi link library generation, growth of scaffold, CHARMm refinement and scoring. We are working on a new workflow/protocol that could produce a fast enumeration of analogs, followed by core-constrained refinement and MM-GBSA scoring.