Using Python in Flare to find common contacts

In a recent blog post Pat Walters nicely used the structures of Viagra and Cialis when bound to PDE5 to argue that scaffold hopping between these two drugs was not a task that could be performed easily. He used Python to demonstrate that each drug interacted with siginificantly different parts of the protein and that they only shared interactions with 4 residues. Inspired by this, I sought (with the help of Paolo Tosco) to implement Pat’s code in Flare.

Paolo has been working on the implementation of a Jupyter notebook within Python (see his post here) and this provides the ideal environment to implement and discuss code to explore the common and specific interactions of Cialis and Viagra with PDE5. The notebook contents are shown in the iframe below.

As you see the output (last line) is the same as originally reported. However, with the addition of the Flare interface we are able to create a nice visual representation of the results, rendering the common and ligand specific residues differently. The script takes around a 30 seconds to run:

If you would like to learn more about Flare and using Python to customize, script or automate common actions or you would like to try the code out for yourself then please contact us. The current range of Python extensions for Flare are avaiable from our GitLab repository.

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.

Flare: Accessible structure-based design

Modern structure-based design encompasses hundreds of methods, advanced algorithms and diverse biological targets. Cresset has a long-standing reputation for easy to use applications in the ligand-based design sphere. In deciding to bring Flare™, a new structure-based design application, to the market we created a challenge – can structure-based design be made simple?

A balance of usability and flexibility

Usability and flexibility are such fundamental features of well-designed software that they are often only noticed when they are absent. Usability reduces frustration, reduces training overhead, and makes it easier to access the full potential of the software. However, users also want the flexibility to tweak experiments, perform complex workflows and customise their applications to work the way they do. These key ‘unspoken’ features have been part of the design process in building Flare from the very start.

Focus on design

Critical to any analysis is the use of your conclusions to change the future. In structure-based design this means using information on how ligands bind to proteins to influence the design of the next ligand. In Flare we have put ligands at the heart of the application. They are stored in their own table and have a dedicated tab menu. The table layout enables physico-chemical property data to be stored alongside each ligand or calculated for every new design. It enables you to organize your compounds (sorting ligands based on their properties) or split ligands into different groups (roles) so that you can break down larger datasets into manageable chunks.

Designing new ligands is easy using the simple ‘Edit a copy’ feature. This brings up the molecular editor where the ligand can be improved in the protein active site and in reference to other ligands. The combination makes it easy to design in 3D, gaining all the productivity benefits that this brings. Sequential edits give an iterative process where each new design can be analysed and used as the basis for the next design.

Accessible methods

Analysis of existing or newly designed ligands requires complex methods. From docking to a detailed energetic prediction of binding, structure-based design methods are all complex. The challenge here is to present complex methods in an accessible way that enables expert users to modify key parameters but is not daunting to the regular user. In Flare a standardised layout is used for all calculation dialogues and provide default settings that work well in most cases. Experts have the choice to take the defaults or progress to the Advanced Options to change the parameters to meet their needs.

Great pictures

Creating great pictures is central to structure-based design. It seems like no J. Med. Chem. Article is complete without at least one protein-ligand picture. Creating pictures in Flare is easy with control over every aspect of the 3D display straight from the Home tab menu. Add to these the ability to control the clipping planes of surfaces independently of atoms and bonds and control over the picture resolution and you have all the elements you need to make stunning pictures. Whether for internal presentations, print articles or large posters, Flare delivers the quality of picture that you need.

Free evaluation of Flare

The feedback that we have received on the usability of Flare has been very positive. In the next release, you will see even more usability features such as storyboards, improved ligand selection and enhancements to the drag and drop features. We want Flare to be the best structure-based design application you use, so share your experience with us.

Request a free evaluation of Flare to see for yourself just how accessible structure-based design is for computational, medicinal and synthetic chemists.

New Spark reagent databases: eMolecules’ Tiers 1, 2, and 3

Each month we release updated Spark databases derived from eMolecules’ building blocks. These have proved very popular with our customers. This month a small change is being made to the databases in that we now only include reagents that are in eMolecules’ Tiers 1, 2, and 3. These correspond to the most accessible reagents and should be a good source of inspiration for R-group design experiments in Spark.

Why the change?

The number of reagents that are now listed as available has grown significantly. In the last couple of months we have been processing around 650,000 reagents but this month that number is close to 1.1 million. Unfortunately the majority of this increase is in eMolecules’ Tier 4 category with availability in the multiple-weeks time frame. We felt that these additional reagents were largely noise in the majority of Spark experiments. As a result we have slimmed the downloads, search times and results by only including Tiers 1, 2 and 3. These still encompass 295,000 reagents and hence provide you with an excellent source of readily available R-group bioisosteres.

If you are interested in the Tier 4 reagents, please contact Cresset support to discuss the options.

Installing the Spark reagent databases is easy using the built in Spark database update facility.

Tversky similarity in field-based virtual screening

In the releases of Blaze V10.3 and Forge V10.5 we introduced new similarity metrics alongside the new capabilities to manually weight the similarity function using pharmacophore constraints. With the introduction of Tanimoto and particularly Tversky measures of similarity, a new range of experiments are available to you that help you tailor the results you get. In this post I will use the Tversky similarity to perform substructure and superstructure type searches using Blaze. These new options are also available in Forge.


Figure 1: Blaze results can be tailored to generate the type of results that interest you, from substructure like to pure chemotype switching or super-structure like.

Similarity in Blaze

Blaze uses the field point patterns of molecules combined with their shape to align and score a ‘database’ of molecules against a ‘reference’ or ‘query’ that is usually a known active. In this context the default Dice similarity has worked well. It returns active molecules that are similar in size to the query, but is not too size-dependent allowing Blaze to find hits that are smaller than the reference. In most cases this is exactly what you want – a ligand the same size or smaller than the reference that maintains most of the potential sites of interaction. The scoring algorithm could be altered to generate more substructure like or more superstructure like results. However, this was complex to set up and sub-optimal in performance. In Blaze V10.3 the new Tversky similarity makes these searches more accessible. A look at the average MW of the first 100 compounds returned using the standard Dice and the new Tversky options highlights the difference:

Table of average MW of first 100 compounds returned using different similarity metrics. Database of 35283 positively charged Chembl compounds with 5-30 heavy atoms on Blaze demo server. Query MW: 319. Database average MW: 318

Dice Tanimoto Tversky, α 0.05 Tversky, α 0.95
314 313 192 363

 

Substructure searches with Blaze

The Tversky metric has two parameters, α and β. Using the Tversky similarity option in Blaze, and setting α to 0.05 and β 0.95, results in a substructure-like search. In fact, we don’t deal with structures so this actually equates to a ‘sub-field’ search. It returns molecules that contain a field pattern that is contained within the query – i.e. field fragments of the query. This is useful where you have a large known active but want to screen or design a fragment library of smaller molecules that match parts of the query.


Figure 2: Search query and 3 selected results (ranks 3, 5, 11) from a sub-field search using the A2C active from the Fragment hopping with Blaze case study. Each result includes some features of the search query but also omits at least one functional group.

Superstructure searches with Blaze

Setting a Tversky similarity with α at 0.95 and β at 0.05 generates a ‘super-field’ search. That is, molecules that contain a field pattern similar to the query are scored highly whether or not they have additional field points. This is useful for growing hits from a fragment screen or in other situations where you do not want to penalize results for having additional functionality to the query. As hits could contain the query at any position and any orientation, this option works particularly well when combined with field, pharmacophore or excluded volume constraints. For example, using an excluded volume will direct the results towards the available space around the query. Equally, using field constraints or the new pharmacophore constraints will ensure that results contain the interactions that you know to be important.

Figure 3: Search query and 3 selected results (ranks 2, 4, 6) from a super-field search using A2C active from the Fragment hopping with Blaze case study and an expanded database to include larger fragments. Each result contains a similar field pattern to the query plus additional features or functional groups.

Tanimoto similarity in place of Dice

In addition to Tversky, the new versions of Blaze and Forge offer the opportunity to change from the default Dice similarity to Tanimoto. This will make a difference to how the individual elements of the score are combined, resulting in a small change in the order that molecules are returned in a virtual screening experiment, but the two experiments are highly correlated. The effect is somewhat complicated to describe and hence will be explored in a future post.

Figure 4: Plot of rank returned using Tanimoto similarity vs Dice similarity for ~10,600 compounds. The results are highly correlated with r2 0.96.

Conclusion

The new similarity metrics increase the range of experiments that can be easily performed within Blaze. Using the new metrics in Forge enables refinement or enhancement of Blaze results using the same metrics. Sub-field and super-field searches in particular should prove useful for fragment-based discovery.

If you would like to try the Blaze interface, or study the effects of the new similarity metrics, then signup for a Blaze demo server account.

To try Blaze on your datasets or your projects, request a full evaluation.

Torch V10.5 release includes new science and improved workflows

Now released and available for download, V10.5 of TorchTM brings new science, improved design workflows, an updated GUI and is recommended for all users.

Highlights

New pharmacophore constraints give you another way to bias the alignment towards the results that you expect. This new science has applicability to design, ligand alignment and virtual screening. Available pharmacophore types include H-bond donors and acceptors, metal chelators, and covalent centers.

The design workflow has been improved by including and updating physico-chemical properties in the editor as you design. You now get immediate feedback on how your design fits with critical physio-chemical property profiles and for predicted activity (through Forge QSAR models) through inclusion of the radial plot in the editor.

Lastly we have updated the GUI styling and improved usability throughout the application to streamline your molecule design process.

Enhanced design workflow

Design is central to Torch. In this version we have taken a look at the workflow and come up with significant enhancements. You told us how much you liked the immediate feedback for scoring of molecules against the reference and against QSAR models developed in Forge, but wanted us to extend this to physico-chemical properties. To satisfy your request we have introduced the radial plot into the editor enabling rapid, visual feedback of the fit of physico-chemical properties to a project profile as you draw your new designs. This will significantly help you to design molecules that have the properties that you want without having to spend time parsing large amounts of numerical data or having to exit the editor when you think that you have a good design.

To further enhance and smooth your design experience in Torch, we have added the capability to snapshot designs into the main project without leaving the editor. The new ‘Save a copy’ button stores your molecule directly into the project with current molecule title and any notes that you have made. The new workflow enables greater granularity in the deisgn process, capturing more designs and ensuring that no good idea or inspirational moment is lost.

The last stage for any design is to communicate it to others. In the previous version of Torch we introduced Storyboards to enable you to capture particular 3D views for later recall. In V10.5 we have signicantly improved storyboards to better serve your communication. All storyboard images are now stamped with a time and date, can be given a title and annotated with detailed notes to enable others to understand the story, whether or not you are there to talk it through.

Pharmacophore constraints

We know that our approach often gives superior results to other methods when aligning diverse and congeneric series but there are times when you want more control to weight the alignment towards a particular interaction. This has always been possible in field space, but you wanted the ability to more tightly control the type of atoms that are aligned. In this version we have added pharmacophore constraints into the alignment. This option enhances the already present field and excluded volume constraints such that you can specify that a particular pharmacophoric atom type must be in a specific location in the aligned molecules or the score is penalised. The result is significantly higher control over the alignments. Forge V10.5 has more on this exciting feature and how it affects the alignments, while the recent Blaze V10.3 announcement describes the effect on virtual screening performance.

Improved substructure alignments

Our field based alignments give an excellent view of how ligands compare from a potential binding interactions’s point of view. The results compare favorably with structure-based approaches such as docking. However, when looking at activity cliffs, and particularly the underlying causes of the change in activity, a more ligand centric alignment often gives better results. For that reason we introduced the option to align using substructure in previous versions of the software. Torch V10.5 revisits that algorithm, making a number of improvements behind the scenes to deliver the results that you expect even more frequently. This is the heart of Torch and we are delighted to release an improvement to what was already very good.

General improvements

Alongside the specific workflow and scientific improvements we have introduced a number of enhancements to the Torch interface. These include new options for protein ribbon display, improved measurement and protein-ligand contact display, an improved grid view function, improved support for stereo, tagging of molecules directly from the 3D window, updated and clearer icons, and completely new widget for adding constraints.

Upgrade to Torch V10.5

Upgrade at your earliest convenience to benefit from the many new and improved features in this release.

Evaluate Torch

If you are not currently a Torch customer, download a free evaluation.

Blaze V10.3 released for even better virtual screening

The latest version of BlazeTM, our virtual screening platform is now available. V10.3 introduces pharmacophore constraints to enable you to find the best possible new hits and leads. Alongside pharmacophore constraints, we’ve added additional similarity metrics and updated the user interface.

Figure 1: Blaze has a new look that includes WebGL views of the search molecules

New pharmacophore constraints

In previous versions of Blaze we have enabled the setting of Field constraints. These work by down-weighting any result that did not have a specific electrostatic or hydrophobic field at a location you specify. V10.3 enhances this capability by giving you the ability to add a specific atom as a pharmacophoric feature that must be matched by an atom of a similar type in the results. The effect of this is to provide you with a mechanism for ensuring that the results that you get from your virtual screening experiment fit with the known SAR or with your expectations. For example, using pharmacophore constraints you can ensure that all results retrieved from a virtual screen for new kinase hinge binders have the donor-acceptor-donor pharmacophore motif. This differs from field constraints by the severity of the match required. Using field constraints a donor can match other motifs that also express positive electrostatics – such as electron deficient aromatic C-Hs where a pharmacophore feature would only match hydrogen atoms attached to heteroatoms.

Figure 2: (a) Ligand from PDB 4Z3V with field and pharmacophore constraints added. (b) Active BTK inhibitor that satisfies both constraints. Note that the aromatic hydrogens match the field constraint but would not have matched a pharmacophore constraint placed on the indazole NH.

 

We tested the new pharmacophore constraints using a selection of kinase targets taken from the DUD dataset. We applied constraints to the hinge binding motif of each query molecule and studied the retrieval rates. Overall we found an average improvement of around 0.13 in ROC-AUC across the tested targets which represents a reasonable gain given the deficiencies in the dataset.

The ability to constrain result molecules to those that fit a specific pharmacophoric feature is very powerful. However, we advise caution – there are many known actives that do not necessarily contain a specific pharmacophore. This is highlighted in the BTK example above but can also be seen in kinases. For example, the CDK2 ligand from PDB 2uzl, although less active than some chemotypes, lacks any of the classic pharmacophore features associated with hinge binding and hence would not be retrieved by a query with constraints on these features.

 


Figure 3: Overlay of equivalent C-alpha atoms of PDB 2uzl (ligand in brown) and PDB 3c6o (ligand in pink). The 2uzl ligand lacks all of the classic pharmacophoric hinge binding motifs.

 

Beyond standard pharmacophores

Perhaps the most interesting aspect of the new pharmacophore constraints is in the application to virtual screening for covalent inhibitors. These enable you to specify that the retrieved molecules must contain a electrophillic center at same position as in your query. This works in exactly the same way as the traditional H-bond donor, H-bond acceptor type of pharmacophore constraint. In the ligand alignment algorithm we downweight any alignment where an electrophile is not overlaid with the constrained atom. This could be especially useful when screening large virtual libraries or other custom collections where the standard filters for screening collections are not appropriate. As well as electrophiles, we have an definition for metal binding warheads which, again, should help find a richer set of compounds for wet-screening than was previously possible.

Updated look and feel

The Blaze interface has a new crisp look that emphasizes the easy-to-use nature of the web interface. Unlike other virtual screening algorithms, Blaze is a complete system that enables easy compound and collection management combined with user and project based permissions. All of this is accessed through a web interface that has a wizard approach to experimental setup.

Figure 4: The New Blaze interface is cleaner with color coordinated help and prompts.

 

The web interface is not the only way to use Blaze. Our desktop applications Forge and Torch use Blaze’s REST API to submit searches and retrieve results giving you access to the power of Blaze from your desktop. However, the REST API can be incorporated into virtually any other application and we provide Pipeline pilot protocols, and example KNIME workflows to show how to search and manage compounds from these workflow solutions.

All the new science released in Blaze V10.3, described above, is available through the REST API.

Try Blaze V10.3

See the new interface, and try out the new science for yourself, by signing up for our Blaze demo server. Blaze is available as software for installation on your internal cluster, as an Amazon Machine Image that will run within your Amazon deployment or for rental on a per project basis using our Blaze Cloud installation. Contact us for more information.

Launch of Flare

Flare™ 1.0 is released and available for evaluation! Flare is designed to bring you new insights for structure-based design in a modern, easy to use interface that provides a framework for future growth. Flare combines the best of Cresset research with cutting edge methods from academia and selected commercial partners to give you a deeper understanding of protein-ligand complexes that will inform and improve new molecule design.

The Flare GUI includes ligand and protein windows that enable you to create and browse through the structures that are important to you.

New methods for understanding your protein-ligand system

Key new technology available in Flare 1.0:

  • Visualize the electrostatics of the protein active site using protein interaction potentials
  • Calculate the positions and stability of water in apo and liganded proteins using 3D-RISM
  • Understand the energetics of ligand binding using the WaterSwap technique.

Protein active site electrostatics, visualized through protein interaction potentials clearly indicate areas of favorable ligand binding such as the electron rich pyrrolo-pyrimidine hinge binding motif in this PERK kinase inhibitor (PDB 4G31).

Robust enabling capabilities

Robust enabling capabilities support the new technology in Flare, providing you with:

  • Protein preparation
  • Ligand docking
  • Minimization using the XED force field.

Docking experiments in Flare are easily configured using one of the preset settings or can be customized with advanced options.

Intuitive  GUI

Flare has a logical menu structure using the ‘tabbed’ menu system to provide functionality that is easy to find and use. We’ve extended the approach to experiment setup that we have developed in our ligand-based tools to enable you to rapidly start a new experiment with a set of reliable default parameters or customize and save your own for future use.

The tabbed menu structure enables rapid identification of the functionality that you desire. For example the View tab contains functions related to the 3D view of the molecules such as the options to enable full screen mode or stereo mode

Try Flare for new insights

Flare is a new generation of structure-based design applications designed to give you new insights into your small molecule discovery project.

Evaluate Flare today.

Flare release imminent

New insights for structure-based design, thanks to our testers

With the release of Flare imminent, I would like to thank all our dedicated alpha and beta testers for their time and patience. Your work has been invaluable to directing the final stages of development and smoothing out workflows before the full release.

Throughout the development of Flare we have worked closely with users to ensure that we concentrate on the capabilities that are most important to you. We trust that Flare will add great value to your work, repaying the time and effort you have put in to its development.

Flare is a new product for us, moving into new scientific space, and has been many years in the making. Extensive scientific testing and benchmarking have been carried out on our own in-house methods and on those we have brought in from our scientific partners. We are completely committed to giving you the best science in the most usable form to push your projects forward and to fit in with your workflows.

The finishing touches are now being completed in advance of release which is scheduled for next week.

Request an evaluation of Flare.