Investigating the SAR of XIAP ligands with Electrostatic Complementarity maps and scores


Electrostatic Complementarity™ maps implemented in Flare™,1 Cresset’s structure-based design application, were used to investigate the protein-ligand electrostatic interactions and the Structure-Activity Relationship (SAR) of a small set of inhibitors of the X-linked IAP (XIAP)-caspase protein-protein interaction. A good correlation was also obtained between XIAP-BIR3 affinity and the Electrostatic Complementarity scores for the same data set.


Inhibitor of apoptosis proteins (IAPs) are key regulators of antiapoptotic and pro-survival signaling pathways.2-4 Their deregulation occurs in various cancers and is associated with tumor growth, resistance to treatment and poor prognosis. This makes them an attractive target for anticancer drug discovery.5-7  The best characterized IAP, X-linked IAP (XIAP), exerts its antiapoptotic activity by binding and inactivation of caspases 3, 7, and 9 via its BIR domains. Disruption of the protein-protein interaction (PPI) between XIAP-BIR domains and caspases via small molecules is a promising strategy to inhibit XIAP. However, drugging PPIs can be particularly challenging due to their unusual binding interfaces, which are unlike classical binding sites generally flat and large.8

A recent paper from Astex9 reports that the XIAP-BIR3 activity of the small dataset of antagonists in Table 1 is increased by the introduction of electron-withdrawing substituents on the indoline ring, and shows a nice correlation between the XIAP-BIR3 pIC50 and Hammett’s σp.

In this case study, we used the Electrostatic Complementarity maps available in Flare to investigate the protein-ligand electrostatic interactions and the SAR of the molecules in Table 1. Electrostatic Complementarity scores calculated with Flare were used to quantitatively model XIAP-BIR3 pIC50.

Table 1. XIAP-BIR3 affinity of C-6 substituted indolines.8


Protein preparation

The 5C7A ligand-protein complex was downloaded from the Protein Data Bank into Flare and prepared using the Build Model10 tool from BioMolTech,11 to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes and assign optimal protonation states to the protein structure. Any truncated protein chain was capped as part of protein preparation. The binding site was visually inspected to check for correct protonation states of ligands and amino acid side chains and re-optimize water orientations of suboptimal water hydrogen bonding networks. We chose to keep only water molecules in and close to the binding site that have at least 2 hydrogen bonding contacts to the protein or at least 1 hydrogen bond to ligand and protein for electrostatic complementarity calculations. As many of the modeled binding modes (e.g., compounds 9, 11, 15, 16) clash with the flexible side chain of Lys297 (Figure 1), the side chain atoms were minimized with the XED force field12 for each ligand. The resulting receptors were used to compute the electrostatic complementarity of the respective compounds.

Figure 1. The PDB: 5C7A ligand-protein complex.

Data set construction

The compounds in Table 1 were drawn using the molecule editor in Flare, starting from the crystal structure of the ligand in PDB:5C7A (compound 7 in Table 1). The 11 compounds were then aligned in Forge13 to the 5C7A ligand, using a Maximum Common Substructure alignment to minimize the conformational noise in the common indoline-piperazine scaffold.

Electrostatic Complementarity surfaces and scores

Electrostatic Complementarity maps and scoring functions are an extension of Flare’s Protein Interaction Potentials based on Cresset’s polarizable XED force field. In contrast to classical force fields that rely on atom-centered charges, XED enables description of anisotropic charge distribution around atoms which is usually only possible with ab initio approaches. Polarization effects and description of atomic charge anisotropy are especially useful for computing electrostatic properties of aromatic or unsaturated hydrocarbons, sp2 hybridized oxygen atoms, sp or sp2 hybridized nitrogen atoms, and aromatic halogens (sigma hole of Cl, Br, and I).14-16

To calculate the Electrostatic Complementarity map for a ligand towards a protein of interest, the solvent-accessible surface is first placed over the ligand. A calculation of electrostatic potentials due to the ligand and the protein is then carried out at each vertex on the surface.

These potentials are then scaled, added together, and normalized to yield the Electrostatic Complementarity score. Perfect electrostatic complementarity means that at each vertex point the ligand electrostatic potential value is paired with a protein electrostatic potential value of the same magnitude with reverse sign. Regions of the ligand surface where there is electrostatic complementarity with the protein are colored green, while the regions where there is a electrostatic clash are colored red. A more detailed description of the electrostatic potential and complementarity methodology will be presented elsewhere.17

The Electrostatic Complementarity scores quantify the ligand-protein electrostatic complementarity with three different metrics suitable for diverse protein-ligand scenarios.

The first computed score (‘Complementarity’) is the normalized surface integral of the complementarity score over the surface of the ligand (effectively the average value of that score over the surface of the ligand).

The other two scores (‘Complementarity r’ and ‘Complementarity rho’) are the Pearson’s correlation coefficient and the Spearman rank correlation coefficient, respectively, which are computed on the raw ligand and protein electrostatic potentials sampled on the surface vertices.

All three measures range from 1 (perfect complementarity) to -1 (perfect clash) but have different characteristics. The Complementarity score includes some compensation for desolvation effects, and so may be more robust when these are significant. The Pearson and Spearman correlation coefficients can provide a better indication of ligand activity in some cases, but are more susceptible to noise (r more than rho). The Spearman’s rho number is more robust against background electric fields, which may be useful if the computed protein electric potential is being biased by a large net charge on the protein.

The calculation is fast and predictive: scoring a hundred ligands normally takes less than a couple of minutes on an average laptop and gives important insights into protein-ligand electrostatics, which typically correlate with compound activity.

Mapping the electrostatics of the XIAP active site

The Electrostatic Complementarity map of compound 7 in the XIAP active site (PDB: 5C7A, Figure 2 – left) shows a strong electrostatic clash (red) in the region above the indoline ring. This is caused by an area of negative electrostatic potential in the protein’s active site, generated by the backbone carbonyl of Gly306 and the phenolic oxygen of Tyr324 (Figure 2 – middle), clashing with the negative electrostatic field associated with the indoline ring (Figure 2 – right). A less pronounced electrostatic clash can be seen between the positive electrostatic field of the protonated side chain of Lys297 (Figure 2 – middle) and the positive electrostatic field of the sigma hydrogens of the indoline ring (Figure 2 – right).

According to this map (and in agreement with the reported correlation8), electron-withdrawing substituents which make the indoline ring less electron-rich are expected to increase XIAP binding. Substituents associated with a more negative (or less positive) electrostatic field, favoring the interaction with the protonated side chain of Lys297, should also be beneficial.

Figure 2. Left: Electrostatic Complementarity map for the PDB:5C7A ligand (green: good complementarity; red: electrostatic clash). Middle: protein electrostatic potential map for PDB:5C7A (red: positive; cyan: negative). Right: ligand fields for the ligand in PDB:5C7A (red: positive; cyan: negative.

Electrostatic Complementarity and XIAP SAR

Figure 3 shows the Electrostatic Complementarity maps for the compounds in Table 1, shown in order of increasing XIAP-BIR3 activity from left to right.

A clear trend can be observed as we move from the electron-donating substituents (-NH2, -OMe), to the electron-withdrawing substituents -F, -Cl, -SO2Me. These make the indoline ring less electron-rich, reducing the clash with the negative electrostatic of the XIAP active site.

Figure 3. Electrostatic complementarity maps for some of the ligands in Table 1 (green: good complementarity; red: electrostatic clash).

 The substituents for the three most potent compounds are also associated with a negative ligand field of their own (Figure 4), favoring the interaction with the protonated side chain of Lys297, according to our initial hypothesis.

Figure 4. Negative ligand fields (cyan) for compounds 17, 15 and 16.

 These qualitative observations are confirmed by the nice correlation (r2 = 0.671) between XIAP-BIR3 pIC50 and the ‘Complementarity rho’ score shown in Figure 5.

Figure 5. Plot of XIAP-BIR3 pIC50 versus Complementarity rho.

Electrostatic Complementarity scores and MW

We monitored the correlation between MW and XIAP-BIR3 affinity/Complementarity rho to verify whether the Electrostatic Complementarity scores provide information which goes beyond the use of simple physico-chemical descriptors for drug design.

The correlation between MW and XIAP-BIR3 pIC50 (r2 = 0.613, Figure 6 – left), would possibly point towards a space filling effect as the simplest explanation of the changes in XIAP affinity in this data set.

However, the low correlation between Complementarity rho and MW (Figure 6 – right) confirms that the Electrostatic Complementarity scores are size independent.

Using the Electrostatic Complementarity scores for quantitative SAR modeling, therefore, generates trends completely independent from size effects.

Furthermore, Electrostatic Complementarity maps provide visual insight into ligand-protein binding and SAR which cannot be derived from traditional, simple physico-chemical descriptors such as MW and Hammett’s σp, thus providing invaluable information for drug design.

Figure 6. Left: Plot of XIAP-BIR3 pIC50 versus MW. Right: Plot of Complementarity rho versus MW.


Application of Electrostatic Complementarity to a reported XIAP-BIR3 data set showed that our method can detect and quantify electrostatic differences in XIAP ligands that cause changes in bioactivity. Electrostatic Complementarity scores and maps in Flare V2, based on Cresset’s polarizable XED force field, provide rapid activity prediction with visual feedback on new molecule designs. They provide useful information for understanding ligand binding and SAR and can be used for rapidly ranking of new molecule designs.

References and Links

  2. Salvesen, G. S. et al., Rev. Mol. Cell Biol. 2002, 3 (6), 401-10
  3. Gyrd-Hansen et al., Nat. Cancer 2010, 10 (8), 561-74
  4. Silke, J. et al., Cold Spring Harbor Perspect. Biol. 2013, 5 (2), a008730
  5. I et al., Clin. Cancer Res. 2004, 10 (11), 3737-3744
  6. Mizutani, Y. et al., Int. J. Oncol. 2007, 30 (4), 919-925
  7. Fulda S. et al., Nat. Rev. Drug Discovery 2012, 11 (2), 109 -124
  8. Arkin, M. R. et al., Chem. Biol. 2014, 21 (9), 1102-1114
  9. Chessari, G. et al., J. Med. 2015, 58 (16), 6574-6588
  10. V. Stroganov et al., Proteins 2011, 79 (9), 2693-2710
  14. Vinter, J. G., Comput. Aided Mol. Des. 1994, 8 (6), 653–668
  15. Vinter, J. G., Comput. Aided Mol. Des. 1996, 10 (5), 417–426
  16. Chessari, G. et al., Chem. Eur. J. 2002, 8 (13), 2860–2867
  17. Bauer, M. R. & Mackey, M. D. et al., manuscript in preparation

Rapid and accessible in silico macrocycle design


Macrocyclization of pharmaceutical compounds plays an increasing role in drug discovery. Macrocycles can provide several advantages such as favorable drug-like properties, increased selectivity and improved binding affinity.

The assessment of potential macrocyclizations is a challenging computational problem. Linkers should be synthetically feasible, not too flexible, have a sensible conformation and be pharmacophorically compatible with the active site. Few computational strategies are available for this task, so most design is done ad hoc.

Here we present a modification of our existing bioisostere searching tool, Spark, to this problem. Traditional bioisostere searches specify a fragment to replace in the starting ligand, and look for similar fragments in a database. Instead, Spark assesses bioisosteric replacements in product space, which allows more complex experiments. A modification of the Spark scoring method to include similarity to other ligands known to bind in the region of the linker, as well as constraints from the protein active site and known pharmacophoric requirements, allows sensible ranking of potential linkers.

Here we present case studies of designing macrocyclization strategies for BRD4 and for Orexin 2. The Spark algorithms enable a rapid assessment of the ideal linker length and suggested chemistry for each cyclization option.

See the ‘Rapid and accessible in silico macrocycle design’ presentation Rob Scoffin presented in the COMP Division at the 256th ACS National Meeting.

Outstanding new 3D graphics in Spark 10.5.5

A new patch level release of Spark™, our scaffold hopping and bioisostere replacement application, is now available for download by all Spark users. Spark 10.5.5 includes considerable improvements to the look and feel, rendering and performance of the graphics of the 3D window.

Figure 1. Improved 3D graphics in Spark 10.5.5.

Spark 10.5.5 also includes a small number of additional improvements and bug fixes:

  • Improved support for the configuration of proxy servers
  • Improved Spark start up times when using databases sitting in a remote location on a slow connection
  • Improved support for high-dpi displays
  • Fixed issue which caused the effect of the application of pharmacophore constraints to be overestimated in some circumstances
  • Fixed issue on macOS which prevented to dock back in the desired position any dock window moved outside of the main Spark interface
  • Fixed rare issue in the wizard where in some occasions the desired hydrogen atom could not be picked for replacement.

Download Spark 10.5.5

To ensure you benefit from the improved 3D graphics, and other improvements and bug fixes, keep an eye out for an email with download links and upgrade Spark at your earliest convenience.

If you are not currently a Spark customer, please request a free evaluation.

Contact us if you have queries.

Launch of Cresset Python extensions for Flare

We have launched three new repositories for Python scripts that extend the functionality of FlareTM, our structure-based design platform. These new repositories are available to all Flare users free of charge.

Last month, Paolo Tosco, explained the advantages and opportunities offered by the Flare Python API to computational chemists and developers.

But what if you are not familiar with Python scripting, and you just want to use one of the scripts developed by us, which we showed at the Cresset User Group Meeting 2018? Or if you would like to run Flare tasks from the command line? Or maybe you know Python well, but could benefit from some scripting examples, just to get yourself started with the Flare Python API. These new repositories provide the solutions. They are:

  • Flare Python extensions – Cresset written scripts that extend the functionality of Flare
  • Flare Python pyflare – Command line scripts that use pyflare to create command line workflows
  • Flare Python developers – Example scripts that can be used by developers as templates to write their own extensions.

Below I will discuss the different types of scripts and show you some interesting examples of additional functions you can add to Flare through scripting.

Flare API extensions: Use the power of Python within the Flare GUI

Download the Flare API extensions.

What do we mean by ‘extensions’? These are a collection of Python scripts which add powerful new functionality to Flare. After installing them, a new ribbon tab called ‘Extensions’ will be added to the Flare GUI, containing buttons to access this new functionality.

Figure 1. The ‘Extensions’ tab in Flare 2.0.

As you hover with the mouse over each of the buttons, a tooltip will appear providing a short explanation of the extension’s function.

For example, the ‘Ramachandran Plot’ extension will show the Ramachandran plot for the protein of interest.

Figure 2. Ramachandran plot for PDB:5C7A calculated with the ‘Ramachandran Plot’ extension.

Another nice snippet of extension functionality is the ‘Show in RCSB’ addition to the Proteins table context menu.

Figure 3. Choosing the ‘Show in RCSB’ extension from the context menu opens the PDB:5C7A entry in the RCSB.

If you are used to the highly interactive environment of Jupyter® notebooks, then you should definitely install the ‘Python QtConsole’ extension, which adds a Jupyter QtConsole dock to the built-in Python Interpreter and Python Console docks. The Python QtConsole provides all the nifty Jupyter features, i.e., TAB completion, auto-indentation, syntax highlighting, context help, inline graphics, and more.

Figure 4. The highly interactive environment provided by the Python QtConsole.

Finally, there is a whole group of Cresset extensions dedicated to making Flare communicate with other Cresset products. For example, choosing the ‘Align’ extension will enable you to run a Forge alignment for the ligands in your Flare project, without leaving Flare. You will need a Forge license for this to work; click here if you wish to request a free evaluation of Forge.

Figure 5. The ‘Align’ extension.

pyflare scripts: run Flare from the command line

These scripts allow all the main Flare functions to be accessed through the pyflare command line Python interpreter.

Figure 6. Running a Flare Python script outside Flare using the pyflare interpreter.

This is useful when you need to carry out a completely automated task, for example an overnight preparation of a panel of proteins followed by docking of several ligand series, distributing it on a cluster via a queueing system for maximum performance.

Download pyflare scripts to:

  • Dock ligands to a protein using the Lead Finder™ algorithm
  • Prepare your protein
  • Calculate Electrostatic Complementarity™ scores
  • Minimize the protein active site
  • Run a 3D-RISM analysis
  • Run a WaterSwap analysis
  • Calculate and export protein field surfaces.

Scripting examples for developers

This GitLab repository contains a few interesting scripting examples to help Python developers get started with writing their own extensions and scripts with the Flare API.

Give it a try

These examples can be downloaded for free from GitLab by all Flare customers, clicking on the links above and following the download instructions.

If you questions about the use of Python extensions for Flare, feel free to contact Cresset support.

Request a free evaluation of Flare.

Flare API: A new playground for computational chemists and developers

Flare 2.0 comes with a full-featured Python API which enables both high-level access to scientific functionality and low-level access to the graphical user interface and internal processes. In addition to Flare methods, you will have access to RDKit, NumPy, SciPy, and to virtually any other Python module that can be pip-installed. RDKit molecules are treated just as native Flare molecules, and can be loaded interchangeably to Flare’s ligand table. This means that you can generate a virtual 2D library using RDKit reaction SMARTS and then turn it into a 3D library of field-enabled Cresset molecules without ever leaving the Flare environment.

Additionally, if you realize that you often use a certain Python script as part of your Flare workflow, you may decide to associate it to a custom control in the Flare ribbon user interface, be it a button, a menu, or a complex dialog with signals and callback functions (Figure 1).

Figure 1. An example of the custom controls that can be added to the Flare ribbon.
We have done our best to write a clear and easy-to-browse HTML documentation, but even more importantly we have come up with a number of sample scripts which cover a variety of use cases, in order to get you started as quickly as possible.

Python commands can be loaded and executed in Flare in a number of different ways, ranging from highest automation to highest interactivity.

For example, when you need to carry out a completely automated task, such as overnight preparation of a panel of proteins followed by docking of several ligand series, the most convenient option is to write a Python script that runs outside the Flare GUI, such that it can be distributed on a cluster via a queueing system for maximum performance. This use case is covered by the pyflare binary, which is effectively a Python interpreter just as the familiar python binary, slightly modified to play well with the rest of the Flare ecosystem (Figure 2).

Figure 2. Running a Flare Python script outside Flare using the pyflare interpreter.
If your goal is to run a simple Python one-liner to, for example, list all cysteine residues in a protein chain or to print the distance between a ligand atom and a couple of key residues in the active site, the embedded Python Console is probably the simplest and leanest option – run the command and examine its output in the text port (Figure 3).

Figure 3. Entering simple one-liners in the Python Console widget.
If you believe you will need to go through some iterations of a code-run-test-debug workflow on a somewhat more complex script, you will most likely choose the embedded Python Interpreter widget, which allows you to load a script, interactively edit it inside Flare and then save your modifications (Figure 4). Both the Python Console and the Interpreter come with a multi-tabbed interface that makes it possible to work on multiple Python snippets at the same time.

Figure 4. Loading and editing a script in the Python Interpreter widget.
If you are used to the highly interactive environment of Jupyter notebooks, then you are going to love the Python QtConsole embedded in Flare. This widget provides all the nifty Jupyter features, i.e., TAB completion, auto-indentation, syntax highlighting, context help, inline graphics, and more (Figure 5). Type Python commands, examine molecules, draw plots – all in the same window.

Figure 5. Visualizing text output and inline graphics in the Python QtConsole widget.
If you would rather prefer to use Jupyter Notebooks in the traditional way, i.e., multiple tabbed sessions inside your favorite browser, that’s entirely possible, too. You will even be able to look at ligands and proteins in 3D inside your notebook, including Cresset surfaces and field points (Figure 6).

Figure 6. Running a Flare workflow inside a Jupyter Notebook outside the Flare GUI application.
In summary, programmatic access to Flare methods is very appealing to computational chemists looking for ways to automate tasks or run complex workflows on multiple datasets. On the other hand, the possibility to extend the core functionality as well as the graphical user interface with own methods makes Flare the ideal playground for Python developers.

For academic researchers, Flare is an opportunity to make their science more broadly accessible through integration into a user-friendly environment, while corporate users will appreciate the possibility to augment Flare capabilities with in-house REST services and tools. The possibility to code in Python taking advantage of the highly interactive experience provided by the Jupyter QtConsole and Notebook is a further incentive to make Flare the environment of choice for computational chemistry workflows.

Request a free evaluation of Flare.

Flare™ V2 released: Introducing the new science and functionality of Cresset’s structure-based design application

Version 2 of Flare™, our application for fresh insights into structure-based design, is now available. I will briefly introduce the new science and functionality included in this version, which will be presented in full at the Cresset User Group Meeting on June 21-22.

Predicting activity using Electrostatic Complementarity™: You asked, we listened

Since the initial release of Flare, you repeatedly asked us to develop a smart way of quantifying the complementarity of ligand vs. protein electrostatics and suggested that this would be a rapid method for prioritizing new molecule designs.

Your requests have led to the introduction of Electrostatic Complementarity (EC) scores and maps in Flare V2, based on Cresset’s polarizable XED force field. These provide rapid activity prediction with visual feedback on new molecule designs, and prove invaluable for understanding ligand binding, structure-activity relationships and the ranking new molecule designs.

Figure 1. Left: the less active XIAP analog is less complementary (red region) to the PDB: 5C7D binding site than the more active XIAP analog in the centre (green region).  Right: the Electrostatic Complementarity score is highly correlated with the experimental activity of XIAP analogs.

Electrostatic Complementarity scores quantify the ligand-protein electrostatic complementarity with three different metrics suitable for diverse protein-ligand scenarios. The calculation is fast and predictive: scoring a hundred ligands normally takes less than a couple of minutes on an average laptop and gives good correlation with activity in the majority of cases.

Electrostatic Complementarity maps are based on a calculation of electrostatic potentials for the ligand and the protein on the surface of the ligand. These potentials are then added together, normalized and scaled. Regions of the ligand surface where there is perfect electrostatic complementarity with the protein are colored green, while the regions where there is a perfect electrostatic clash are colored red.

Enhanced protein surface coloring

Faster and improved surface generation code, and new protein surface coloring options to give you more insights into protein-ligand interactions and support molecule design, are also included in this release.

Figure 2. Left: protein electrostatic potential map for PDB: 5HLW (red: positive; cyan: negative). Middle: ligand fields for the ligand in PDB: 5HLW (red: positive; cyan: negative). Right: electrostatic complementarity map for PDB: 5HLW (green: perfect complementarity; red: perfect clash).

Coloring the protein surface according to the Wimley-White [1] residue hydrophobicity value is an excellent way of visualizing hydrophobic areas of the protein active site.

Figure 3. The surface of the binding side of PDB: 5HLW is colored by Wimley-White residue hydrophobicity from yellow (hydrophobic) to blue (hydrophilic).

Ensemble docking

Rapidly and easily dock your ligands in a single experiment to multiple protein conformations using ensemble docking. Results are saved as a list of docked poses for the ligands included in the study, each associated to a specific protein conformation.

Figure 4. Results of an ensemble docking experiment in Flare. Each pose is associated with a specific protein conformation, making browsing of results easy.

Enhanced ligand design functionality

Significant improvements have been made to the ligand functionality in Flare V2.

Radial plots and Multi-Parameter Scoring

Radial plots are useful to gain immediate visual feedback about how each ligand matches the ideal physico-chemical profile for your project. To support Multi-Parameter Scoring, radial plot properties are weighted and combined into a single score that represents the fit of the ligand to the ideal physico-chemical profile. The radial plot score can then be used to filter or sort the ligands.

Good Middling Poor
Figure 5. Radial plots and radial plot scores for ligand showing a match to the ideal physical-chemical profile ranging from good (left) to poor (right).


Flare V2 enables the definitions of filters (Figure 6) to show only the ligands that conform to a desired set of rules. This includes filtering on numerical values, text data, Boolean values, tags, ligand structure using either a SMARTS string or a substructure sketched into the Flare Molecule Editor.

Figure 6: The Filters window in Flare V2.


Use the Storyboard window to capture and replay scenes recording all details from the 3D window. Each scene can be easily annotated and recalled when needed.

Figure 7: The Storyboard in Flare V2 showing four scenes and their titles together with notes about each scene.

Flare Python® API

The new Python API lets you create your own workflows, automate your common tasks, expand Flare with Python modules and add custom controls. It gives full access to all of Flare’s capabilities, including the RDKit cheminformatics toolkit.

Flare can be upgraded with Python modules for graphing statistics, Jupyter® notebook integration and much more.

Flare V2 makes advanced structure-based design techniques, such as Electrostatic Complementarity, multiparametric scoring and Python scripting, accessible through an intuitive GUI.

See the benefits Flare V2 can bring to your project

With over 200 new or improved features, Flare V2 is built to make structure-based design easy and accessible while incorporating cutting edge scientific methods. I encourage you to upgrade your version of Flare at your earliest convenience.

If you are not currently a Flare customer, please request a free evaluation.

Contact us if you have queries relating to this release.


  1. Wimley WC & White SH (1996). Nature Struct. Biol. 3:842-848.


A sneak peek into Flare V2: A major advancement for structure-based design with Flare

Flare V2 is in the final rounds of testing, which means the release announcement is imminent. Ahead of the user group meeting, where we will be presenting this major advancement, this post takes a sneak peek at some of the new features in this version.

New coloring options

Completely rewritten surface generation code results in faster and better surfaces with quality options built in to the surface creation dialog. This is combined with new coloring options for new surfaces to give you more insights into your proteins and ligands.

Figure 1: (a) New surface coloring options in Flare V2, and (b) PDB code 4MBS with a hydrophobic surface colored yellow (hydrophobic) to blue (hydrophilic).

Improved Z-clipping

Making pictures is key to communicating your insights on protein-ligand binding. Flare V2 has major improvements to the Z-clipping to enable you to get the view that you want. In addition, to apply a specific clipping plane to an individual surface, you now have the option to exclude ligands from the clip altogether. This option makes a significant impact on pictures of binding sites that are completely buried.

Figure 2: PDB 1IKW showing the ability to selectively clip proteins. (a) Ligand clipping often makes it difficult to get the picture you want whereas (b) disabled ligand clipping in Flare V2 gives you more options to communicate key insights.

Figure 3: Flare V2 gives the option to clip individual surfaces independently of other objects. Here a clipping plane is added only to the electrostatic surface enabling the visualization of protein residues that are above the ligand in combination with a surface.

Other features contribute to a major advancement for Flare

The new protein surfaces are complemented by new options for ligand surfaces, the new storyboard panel to capture and replay key 3D insights and many new features for ligands. Taken together with the Python API this release of Flare is a major advancement in this innovative new application for structure-based design.

Find out more and get hands-on

Register for the up-coming user group meeting to find out more about Flare V2, network with existing users and receive free training at one of the hands-on workshops.

Request an evaluation.

In the Cresset lab: Molecular design re-imagined

Molecule design is a central task in drug discovery. It is both personal and collaborative, easy to do in 2D (on the fume hood or using a drawing application) but more productive when combined with the 3D environment of the chosen target. We have been thinking about how best to provide you with a molecule design application that satisfies all these requirements. Whilst Torch has many favorable attributes that make it a popular choice, it cannot satisfy the two key features that you request of it – to be able to sketch in 2D in a popular drawing application, and to work in a collaborative environment. Project TorchWeb is now underway to satisfy these requests. This project will deliver our next generation of molecule design application and, as the name suggests, will be entirely web based.

We have recently completed a proof of concept application, part funded by a UK government grant, and are delighted with the two key technologies it has at its heart – the ability to work exclusively in 2D and yet have the 3D context of your design immediately available, and the option to share your work ‘live’ with one or many collaborators.

TorchWeb reimagines the molecular design process within a web browser. Using 2D and 3D representations of the molecule together with Cresset’s electrostatic descriptors gives an detailed view of your new design.

Thinking in 2D and 3D simultaneously

As chemists we are taught to synthesize molecules using 2D representations. This makes our life simplier, condensing complex situations down to a 2D language that we use to think about and communicate our ideas. However, molecules are 3D and exert their effects in 3D. Why do we not link these two using the computer to extrapolate our ideas from 2D to 3D automatically? This is the philosophy behind design in TorchWeb.

Changes in the 2D window are automatically interpreted into 3D.

Collaboration as standard

Modern drug discovery teams are often geographically diverse, both within a single company and across multiple organisations. In TorchWeb we have developed a collaboration layer that enables you to invite other users to share your current design environment. We aim to remove artificial barriers to working with others in your team, catalyzing new ideas, stimulating new thoughts and focusing on current challenges.

Sharing a session gives a collaboration platform where ideas can flow between users without restrictions.

Improved decision making at every stage

Once released we hope that TorchWeb will enable medicinal chemists to make the best, most informed decision at every stage of the discovery process. In part this is due to the core Cresset technologies but also because of the web based nature of the application. Central to the design of TorchWeb is the ability to extend the application through plugins or custom windows that can provide real time feedback on new designs at the moment of conception. This will allow you to bring all of the insights available from your existing QSAR and QSPR methods into the design process.

Alpha and beta testing coming soon

We are actively developing TorchWeb and are aiming for launch in 2019. As with our desktop applications, we are keen to gain feedback from our customers to ensure you receive a product that works for you. Whilst it is not ready for testing just yet, we will announce alpha and beta test programs in the coming months. In the meantime, if you would like to know more about this exciting new application then please do not hesitate to get in touch.

Hear about TorchWeb in more detail

See a demonstration and hear more about the core algorithms behind TorchWeb – register for The Cresset User Group Meeting on June 21 – 22, 2018.

A sneak peek into Flare V2: Python API and new science

Less than a year ago we released Flare for structure-based design. At the time, we promised you that a Python API would be central to future of this novel application. In keeping with that promise, Flare V2 will include a new Flare Python API, new science and significant improvements to functionality and usability.

Let’s have a quick look at the Flare Python API and what it can do for you.

What is the Flare Python API?

The Flare Python API enables Flare functionality to be accessed from Python and for you to customize the Flare interface. Python scripts can be run from the Flare graphical user interface (GUI) or by the command line pyFlare.

To access the power of Python from the Flare GUI we created a dedicated new tabbed menu named ‘Python’ (Figure 1).

Figure 1: The Python tabbed menu in Flare.
From the Python menu you can:

  • Run simple, one line scripts from the Python console
  • Write, load, run and save one or more scripts using the Python Interpreter
  • Manage the Python scripts using the Manager and Log buttons
  • Access the built-in Python documentation.

As you create your own scripts, you can choose to add them to Flare as new ‘buttons’ either in a new dedicated tab or in an existing tab.

How can Python scripts written with the Flare API make my project life easier?

Automate routine workflow

With the Flare Python API, you can automate routine workflows which you normally carry out for every project. For example, the ‘Prep_n_align’ script (Figure 2) starts from a Flare project where you have imported your protein structures, sequence aligns and superimposes all proteins to the first, then prepares the proteins and extracts the ligands, and finally adds a surface to the active site and focuses the view on the ligands. It also creates a button in a new ‘My Tab’ ribbon so that it takes just a click to launch the script whenever you need it.

Figure 2: A Python script to automate protein preparation, alignment and display.

Add functionality to your Flare project

Importing standard Python libraries will significantly enhance the capabilities of Flare. For example, using the matplotlib library, you can easily write a script which will create an x/y plot from data columns in your Flare project.

Make your workflows talk to Flare

Alongside the GUI interface to the Python API is a new command line binary ‘pyFlare’. This will give you access to all Flare methods directly from the command line enabling you to automate and script your workflows.

Sounds great, but what about the science?

It’s not like Cresset to forget about the science and Flare V2 makes no exception. Stay tuned for the release announcement next month to find out more about ensemble docking, electrostatic complementarity, new protein and ligand surfaces, and lots of improvements to functionality and usability.

Hands-on training

To learn more about Flare and the other Cresset applications, register for one of the hands-on workshops which will be held as part The Cresset User Group Meeting on June 21 – 22, 2018.

Flexible academic licensing

Are you a:

  • PhD student or postdoctoral researcher
  • Course tutor
  • Principal investigator
  • Department head?

Did you know that:

  • You can access Cresset ligand-based desktop applications through a variety of flexible licensing options
  • If you publish work which used Cresset technology, and you cite the applications used, we will promote your work through our website and newsletter?

Apply Cresset to your research

Academic customers tell us that our applications help them communicate ideas, give new insight, and are easy to learn and use. Below are just a few examples of how you can apply Cresset technology to your research.

Active design using electrostatics

Torch’s ligand-centric view enables 3D design whether or not you have a protein crystal structure. It makes it easy for you to focus on the designs that work and have good physicochemical properties.

Electrostatic and shape descriptors provide a rich informed view to help you understand the effects of chemical changes and eliminate designs that are unlikely to be active.

Figure 1: A change on one side of the molecule can often influence a distal region, especially if the systems are electronically linked through π-systems. This simple change has multiple effects due to the increased electron-withdrawing character of the new heteroatom and the addition of an aromatic ring: (A) the boundary of negative electrostatic potential extends further; (B) the shape and size of the negative π-cloud is significantly altered; (C) the size and extents of the positively charged aromatic edge are increased; and (D) there is a small increase in the positive potential associated with the aromatic hydrogens of the pyrimidine nucleus at the other end of the molecule.

Find and understand activity and selectivity cliffs in your SAR

Activity Miner, a component of Torch and Forge, helps you find and understand critical regions in complex SAR. Using the concepts of activity cliffs and matched molecular pairs, you can link activity changes to electrostatic and shape changes. Since selectivity is often as important as activity, Activity Miner makes it easy for you to compare multiple end points. The design process in Torch enables you to apply this knowledge to progress your project.

Activity Miner top pairs
Figure 2: Pinpoint the most significant changes to your molecules using the sortable top pairs table. Find critical points in the SAR and understand how they relate to changes in physicochemical properties.

Powerful models to interpret your data

Forge uses the Cresset patented ligand alignment algorithm to generate realistic, interpretable relationships between your molecules. It includes an impressive range of SAR models that combine robust analysis with customizable parameters, ease of use and intuitive visualization. For SAR analysis, there is no need to look any further than Forge.

Figure 3: QSAR models in Forge decipher complex SAR and inform the design of new molecules.

New SAR insights form novel methods

Figure 4: Activity Atlas is a novel, qualitative method that generates three distinct maps of the electrostatic, shape and hydrophobic properties around your molecules. It can be used with small or large data sets and is particularly useful for projects where traditional 3D-QSAR approaches fail.

Find biologically equivalent alternatives to escape IP and toxicity traps

Customers tell us that Spark is the best scaffold hopping and bioisostere replacement tool they have ever used. The easy to use interface quickly generates a range of novel molecules from an initial structure. Profiling and scoring help you choose the most innovative and tractable leads with the properties you need.

Figure 5: Spark workflow.

Request an academic license


Molecular visualization makes it easier to communicate ideas

“Working with Cresset tools has helped me generate new ideas for my projects in various disease areas. The molecular visualization has made it easier to communicate my ideas to my experimental collaborators, both chemists and biologists.”

New insight

“Cresset’s software gives new insight to projects I’ve been working on for the past three to four years. I specifically use it to see how proteins, DNA and molecules interact and bind to each other. If we can design drug molecules that will bind to DNA the same way proteins do, we can open up whole new lines of therapy. Cresset is supportive of academic research and they’ve been wonderful to work with to get everything up and running.”

Easy to learn and use

“Our students who use the Cresset systems in their projects tend to gain an affinity with a number of med-chem concepts far earlier than those who do strictly organic projects, for example using Spark to identify new frameworks that possess favorable properties and which can then be synthesized in the lab via a novel reaction. Compounds can be tracked within a TorchLite template and the student can invoke field patterns and orbital coefficients to explain changes to NMR spectra.”

 “My students and I are very grateful for providing us with the educational license and thus an opportunity to explore Cresset’s software in our lectures. Cresset provides marvelous software, easy to learn, easy to use, pleasant looking and its structure is logical and educational. The program manuals are great.”

 “Considering the absence of an experimental structure, the field based concept was a perfect match for our work. It helped us obtain bioactive conformations of ligands based on a validated pharmacophore. The software provided by Cresset is user easy and user friendly, creative and flexible for a new starter to an experienced researcher to use.”

“Visualizing the inhibitor/substrate binding site of protein crystal structure in Cresset using field points calculated by XED force field is very informative. The protocols for all the modules in Cresset are very quick and easy to use. Forge and Spark are excellent programs for LBDD. The radial plots obtained from alignment methods implemented in Forge provide a visual inspection of results and could be effectively used for simultaneously comparing any number of physical properties for the compounds in the dataset. I strongly believe that Cresset software is an important inclusion in the spectrum of software programs used for Computer Aided Drug Discovery paradigm.”