A sneak peek to Flare™ V9: cutting edge science, integration and usability

The spring release of Flare, Cresset’s CADD solution for small molecule drug discovery, is packed with new and enhanced science and features, including a full integration of Spark™, Cresset’s bioisostere replacement solution, ensemble MM/GBSA on dynamics trajectories for binding affinity predictions, homology modeling, new features for Dynamics and Flare FEP calculations, and many others.

This sneak peek gives insight to just a few of the new features that will be accessible in Flare V9. The Cresset User Group Meeting on 18th and 19th June is the best way to hear about the latest developments at Cresset, including Flare, Spark, Ignite™ and invited presentations from customers: I encourage you to register at the earliest opportunity.

Spark integration in Flare

Our customers say Spark is the best scaffold hopping software they have ever used. Spark is well liked by both medicinal and computational chemists for the great results it generates and its simplicity of use. Still in very active development at Cresset, we recently released a new Spark V10.7.1 version to support the design of linkers for Target Protein Degraders and molecular glue targets.

What else could we do to make it even better? Making it available also in Flare was the intuitive way forward, so that you can benefit from its great results together with all the advantages that a Flare integration would offer, for example:

  • Accurate preparation of the experiment (starter ligand, water, receptor protein), particularly useful for advanced experiments such as ligand growing and joining, water replacement, growing with docking
  • A wide range of methods for refinement and post-processing, including docking and scoring, Cresset’s patented ligand-based alignment, Electrostatic Complementarity™, MM/GBSA and Flare FEP for prediction of binding affinity
  • A fully fledged API, as part of the Flare Python API
  • Full synergy between ligand-based and structure-based approaches

This integration was not without challenges. On one hand, we wanted to give Spark users access to the variety of methods and features which Flare can offer. On the other hand, we wanted to preserve the ease of use of Spark, which users universally indicate as one of its unique features.

The result was a two-year-long project, during which we carefully transferred the Spark wizards, (which are the key to Spark's minimal learning curve), the user-friendly interface for creating and updating Spark fragment databases, and the clustering algorithm, into the dedicated tab which you can see in Figure 1.

Figure 1 The Spark tab in Flare

Figure 1. The Spark tab in Flare.

The wizards are a streamlined version of the Spark wizards: Figure 2 shows the 'Scaffold Hopping or R-group replacement' wizard as an example. In the first panel (Figure 2 – left), circle the core of the molecule you want to replace. In the second panel (Figure 2 – middle), use the attachment point chooser (red box) to move across the different attachment points, and set the allowed atom types. The redesigned 'Reference & Protein' panel (Figure 2 – right) enables you to choose any reference molecules you want to use to guide the scaffold hopping experiment (these can be in the same role as the starter molecule, or in a different role) and whether to use a protein as an excluded volume or for docking.

Figure 2 The ‘Scaffold Hopping or R-group replacement’ wizard in Flare

Figure 2. The 'Scaffold Hopping or R-group replacement' wizard in Flare.

Finally, in the enhanced 'Database selection & Advanced options' panel (Figure 3) you can select the databases you want to use and start the search, set an experiment name and a role for the results, and choose whether to remove duplicate results from different Spark experiments.

Overall, a scaffold-hopping experiment can be started in just 4 clicks.

Figure 3 the enhanced ‘Database selection & Advanced options’ panel

Figure 3. In the enhanced 'Database selection & Advanced options' panel you can define an experiment name (which will be used to name the results, and optionally also as a Tag), define a role for saving the results, and remove duplicate Spark results from different Spark experiments saved into the same role.

One of the many advantages of running Spark experiments in Flare is also that you are no longer limited to one starter molecule, one protein, one set of results (in practice, one Spark experiment) per project. Rather, you can run multiple different experiments and keep the results nicely organized in different roles within the same Flare project.

Furthermore, all Spark features are now accessible from the Flare Python API (Figure 4). This makes it easy to set up custom workflows (for example, run a Spark search and automatically re-dock results, or automatically score them with MM/GBSA). Command-line scripts are also available as a drop-in replacement for existing Spark command-line tools.

Figure 4 All Spark features are accessible from the Flare Python API

Figure 4. All Spark features are accessible from the Flare Python API.

Ultimately however, the choice is yours. If you are a fan of the Spark GUI, you are more than welcome to keep using it: but if you enjoy Flare’s feature-rich GUI and Python API, the Spark integration was created for you. To further streamline productivity, Flare and Spark use the same fragment databases and license files, so you can seamlessly move between the two products when and where needed.

Preparing your biomolecules for further studies

An accurate and careful preparation of the biomolecular system under study is a key factor in the success of subsequent molecular modeling studies, from docking to Flare FEP calculations. In this release of Flare, we have introduced new options to make protein preparation more flexible and comprehensive.

For example, the chains of the multimeric, symmetrical structure shown in Figure 5 are glycosylated. If needed, you can use the new ‘Remove post-translational modifications’ option in the Protein Preparation dialogue to remove the glycosylation and prepare the protein for Dynamics or Flare FEP experiments using the AMBER force field. This new option will remove several types of post-translational modifications from relevant residues, including glycosylation, phosphorylation, methylation, acetylation and oxidation.

Figure 5 new ‘Create Biological Assemblies’ button in the Protein tab

Figure 5. Crystal structure of porcine surfactant protein D from PDB: 6BBD. Left: the biological assembly was recreated in Flare by pressing the new 'Create Biological Assemblies' button in the Protein tab. The protein chains are glycosylated. Right: the prepared protein with the post-translational modifications removed.

Additional fine-tuning of protein preparation is possible from the advanced options (Figure 6). New options give control over the changes in protonation and tautomeric state for protein residues and/or ligand and cofactors, as well as on Asn/Gln/His flips (light blue boxes). The 'Modify only picked atoms' option (dark blue box) is particularly useful when you want to cherry pick which residues (or ligand/cofactors) you want to prepare, for example after performing single-point mutations to a carefully prepared protein structure.

Figure 6 New advanced options for accurate protein preparation

Figure 6. New advanced options for accurate protein preparation.

JupyterLab integration

If you are a fan of Python, and enjoy working with the Flare Python API, in Flare V9 you can launch and configure a JupyterLab Notebook to use a Flare python kernel which enables the Notebook to interact and work with Flare (Figure 7). You can use the Notebook to write, save and run your own scripts: if you are a new user, then the Flare Cookbook is a useful starting point to familiarize yourself with the Flare Python API.

Flare V9 is fully integrated with JupyterLab Notebooks

Figure 7. Flare V9 is fully integrated with JupyterLab Notebooks: the new Flare python kernel enables the Notebook to interact and work with Flare.

Installing Flare Extensions made simple

Flare extensions are useful additions to Flare functionality, and over the years, we have made many available (see for example those in Figure 8). Custom Flare extensions are also actively developed by our customers, and then deployed across all users in their organization.

Figure 8. Some of the Cresset-developed Flare extensions

Figure 8. Some of the Cresset-developed Flare extensions.

An enhanced Extension Manager (Figure 9) now enables the seamless installation and update of Cresset or customer-created Python extensions with minimal user intervention. Extension packages can be installed manually by end users, or programmatically by IT, and it is possible to decide whether to update the extensions manually or automatically, whenever a new or updated extension package is available.

Figure 9. The Extension Manager in Flare

Figure 9. The Extension Manager enables the seamless installation and update of Cresset or proprietary Python extensions with minimal user intervention.

Stay tuned for the release

The Spring 2024 release of Flare, Cresset’s ligand-based and structure-based molecular modeling platform, is packed full of new science, new features, and usability improvements.

Stay tuned for our release announcement: and I look forward to presenting the new version of Flare at the Cresset UGM 2024.

To be among the first to try out the new features in Flare V9, register for an evaluation today.

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