Review of 255th American Chemical Society Meeting

As a first time ACS delegate and speaker, and first time visitor to New Orleans, these were pretty awesome firsts! And for different reasons both eye opening. The cacophony of night life and live music on Bourbon Street contrasted with the splendour of Royal Street and Decatur Street architecture and many excellent cafés and restaurants. This, together with the hefty pace of an almost overwhelming barrage of scientific presentations, posters and exhibitors, was both exhilarating and exhausting.

Relaxing in my hotel room, as the sun glittered off the wide Mississippi below, I could reflect on the week’s events. Here are some of the highlights for me.

Cresset science

We presented in the CINF fragrances, food and cheminformatics session, work we have done to classify odorants using shape and electrostatics generated using the XED force field 1. The less intuitive relationships between structure and olfaction, which are often not correctly attributable using 2D similarity techniques, are adequately described by a 3D field and shape treatment.

In COMP, our poster on the addition of user defined pharmacophoric features to Blaze 2 (giving an additional way for users to influence the results beyond pure electrostatics and shape) received a lot of interest – particularly in the pros and cons of doing so. These new features were released in Blaze V10.3.

Fragrances, Food & Cheminformatics

One key issue in flavour & fragrance science is the absence of a really robust biological readout for quantifying odour perception. Jack Bicker (IFF) 3 and Joel Mainland (Monell Chemical Senses Center) 4 both discussed the ability to predict the intensity of odorants, and to reconstruct something akin to a full dose response curve, by manipulating ‘odour perception data’ collated from human panelists. Jack covered a range of predictive modelling techniques used for analysing both property and structure data for odorants. Joel Mainland’s remaining time focused on the results of the ‘Dream challenge’ which provided details of sensory data on a series of diverse odorants and invited ‘participants’ to predict the variety of odorant properties from structure alone.  Surprisingly, this appears to have been most successful via recursive partitioning techniques.

GPCRs revival

Over the whole ACS there was a revival of interest in GPCRs as targets including Merck presenting on CBR1 5, CCR2 6, GPR40 7 8. All very much on the back of advances in obtaining x-ray crystallography for this important family of proteins.

Oliceridine as an agonist of the μ-Opioid receptor, a brand new drug for quite an established target, was presented by Trevena 9. One of the key issues with the use of agonists of this receptor (e.g., morphine), which behave pharmacologically as analgesics, is an associated respiratory depression effect. Trevena developed their medicine specifically to avoid this side effect and were able to do so by optimizing the drug specifically for the beta arrestin signalling pathway.


Figure 1: Oliceridine in the context of μ-Opioid receptor PDB: 5c1m using Forge to align it to the original bound ligand from this PDB structure.
Similarly, this notion resurfaced during Merck 5 and Lilly 6 talks on the effect of glucose lowering for diabetes via engagement with the GPR40 receptor. Again, biased signalling was associated with achieving a beneficial outcome. Lilly described the observed relationship between off rate and signalling bias – suggesting that increasing ligand residency time was associated with an increased engagement of the beta arrestin pathway. However, in this case the relationship between SAR and the pharmacology is complicated further by the observation of two distinct allosteric binding locations for the GPCR ligands.


Figure 2: Merck’s two allosteric ligands bound into the GPR40 receptor PDB: 5tzy with the ligands shown in space fill (top structure magenta and bottom green).
Thus, the portfolio of allosteric binding sites for GPCRs is duly expanding (numbers in parenthesis); with B2AR (2) GPR40 (2), CCR9 (1), ACM1 (1) all showing allosteric sites including a number where protein lipid interfacial contacts are made with the ligands.

New targets

New targets (to me at least) described in the various medicinal chemistry talks include, to mention a few: APOBEC3 10, VMAT2 11, HPGDS 12, Ketohexokinase 13, SH2P 14 and CRB 15. Also, in particular, the DUBS and their corresponding ligase counterparts, which together control ubiquitin mediated signalling, received lots of attention. For instance, a fascinating talk from Michael Clague at Liverpool University’s Cellular and Molecular Physiology Department 16, inferred that USP30 has a potential role in neurodegenerative disease. USP30 is involved in regulating mitochondrial autophagy via modulation of the extent of ubiquitin labelling of external membrane proteins on mitochondria.

First time disclosures I noted were:

 

AstraZeneca’s AZD0364 as a potent, selective orally dosed ERK inhibitor for anti-cancer therapy 17:

GDC-0927; an estrogen receptor degrader from Genentec for breast cancer 18:

Two BET inhibitors from Incyte INCB057643 and INCB054329, showing anti-proliferative activities against hematologic malignancies and solid tumors in preclinical models, currently entered into human clinical trials 19:

An RSV (Respiratory Syncytial virus) fusion protein inhibitor RV521 from Reviral 20:

VNRX-5133, a broad spectrum beta-lactamase inhibitor that is active at the standard enzymes including metallo-enzymes NDM and VIM associated with ‘super-bugs’, from Venorx 21 ; showing the wide scope of the medicinal chemistry efforts against a variety of disease targets for both human and animal health and made for a very interesting and informative week:

References

  1. CINF 24: Using molecular fields to understand molecular determinants of the olfaction process
  2. COMP 192: Adding pharmacophores to shape and electrostatics – too much of a good thing?
  3. CINF 26: QSAR/QSPR models to support fragrance ingredient molecular design
  4. CINF 23: Predicting human olfactory perception from molecular structure
  5. COMP 413: Exploring the structural features of peripherally-restricted CB1 receptor antagonists and inverse agonists
  6. COMP 366: Use of modeling and crystallography to understand CCR2 antagonist SAR and receptor binding
  7. COMP 365: Targeting GPR40 for diabetes: A multipronged approach
  8. MEDI 307: Advances in understanding the relationship of pharmacology to GPCR structure and function: A GPR40 journey from the lab to diabetic patients
  9. MEDI 226: Discovery of oliceridine (TRV130), a novel G protein biased ligand at the µ-opioid receptor, for the management of moderate to severe acute pain
  10. MEDI 258: Discovery of small molecule and nucleic acid inhibitors of APOBEC3 deaminases
  11. MEDI 280: Discovery and characterization of ingrezza (valbenazine): A VMAT2 inhibitor approved for the treatment of tardive dyskinesia
  12. MEDI 274: Orally-active inhibitors of H-PGDS for the treatment of asthma, allergic rhinitis and chronic obstructive pulmonary disease
  13. MEDI 275: Optimization of the chemical matter and synthesis leading to a ketohexokinase inhibitor clinical candidate
  14. MEDI 277: RMC-4550: An allosteric inhibitor optimized for in vivo studies of SHP2
  15. MEDI 279: Rapid development of potent and selective CBP inhibitors: The impact of a tetrahydroquinoline LPF binder
  16. MEDI 233: Target selection and small molecule development in the DUB space
  17. MEDI 293: Discovery of AZD0364, a potent and selective oral inhibitor of ERK1/2 that is efficacious in both monotherapy and combination therapy in models of NSCLC
  18. MEDI 294: GDC-0927, a selective estrogen receptor degrader and full antagonist for ER+ breast cancer
  19. MEDI 306: Invention of INCB054329 and INCB057643, two potent and selective BET inhibitors in clinical trials for oncology
  20. MEDI 308: Design, identification and clinical progression of RV521, an inhibitor of respiratory syncytial virus fusion
  21. MEDI 309: Discovery of VNRX-5133: A broad-spectrum serine- and metallo-beta-lactamase inhibitor (BLI) for carbapenem-resistant bacterial infections (“superbugs”)

Resurrection of the covalent inhibitor?

There has been a revival of interest in covalent inhibitors in recent years. This has culminated in a growing list of diverse proteins that have been successfully targeted, that are outside the well-trodden protease area.1-4

An older non-protease example is Clopidogrel. This P2Y12 inhibitor requires activation in vivo (through oxidative metabolism) to generate the electrophile intermediate (sulfenic acid) that forms a covalent disulphide bridge to a cysteine residue in the target GPCR. Unravelling the true mechanism of this compound led to second generation inhibitors which further exploit the covalent mode of action5.

Potential benefits

One more recent, and well documented, non-protease example is in the field of oncology, with the drug Ibrutinib. The covalent binding of the inhibitor to the target kinase BTK demonstrates multiple benefits:

  • It removes competition with the endogenous ligand, as is true for other covalent inhibitors, but in this case is more significant as concentration of competing cellular ATP is in the millimolar range.
  • High efficacy, in contrast to some conventional inhibitors, gained specifically by using this covalent inhibitor design.


Figure 1: Ibrutinb in BTK PDB: 5P9J showing Cys-481 spheres near with the covalent linkage visualized using Flare a new structure-based design application from Cresset.6
From a safety point of view, a method of achieving high efficiency/potency of any drug should translate into a lower dose that is ideally below any potential toxicity threshold (very few drugs are given at =< 10mg per day!). Similarly, prolonged duration of action, a consequence of the covalent inhibition, can result in less frequent dosage regimes that could be very beneficial – depending on the exact pharmacology.

So why have drug discovery companies not adopted this approach more broadly?

Mainly, because this is not a panacea – some proteins are more quickly processed, turned over or are mutated. The BTK case itself is an unfortunate example of the latter; the resistance mutation C481S produces a protein that cannot undergo the reaction with the inhibitor and ultimately limits its utility in patients. What may have more generally hampered covalent inhibition strategies in the past, observed particularly in the field of protease inhibitor design, was the high electrophilicity and poor selectivity. The very justifiable fear of off-target activity and threat of unresolvable toxicity issues, has contributed to the adoption of a dogma which still significantly disfavors the approach across the industry. The lack of a wider uptake of the approach since then has been exacerbated, perhaps, by the industrialization of experimental design and/or the lack of attention to the kinetic behavior of these systems. One size doesn’t really fit all!

Computational chemistry is critical for modern covalent inhibitor design strategies

An appreciation of the kinetics of molecular recognition and its impact on drug efficacy (Figure 2) has had a resurgence; together with the introduction of biophysical techniques, such as Surface Plasmon Resonance (SPR), which allow the routine resolution of otherwise painfully elusive kinetic parameters such as kon/koff.


Figure 2: Schematic of the covalent inhibition process (ki/Ki equivalent to classical Michaelis Menten enzymic parameters kcat/Km), transition state energy diagram for the covalent transformation.
 

The observed rate of inhibition for a covalent inhibitor is effectively dependent on the ratio of the rate constant for the chemical transformation of the inhibitor (ki), relative to the rate of unbinding (koff). We can introduce a level of catalysis in this system if we design appropriate geometries that lower the energy of (i.e. favor) the E : I complex7. If we are talking about hand-crafting optimum molecular geometries, then we are usually thinking about utilising computational chemistry as a technique.

Advances in the covalent inhibition area have clearly arisen from a systematic investigation into the modulation of the intrinsic reactivity of these electrophilic ligands. Covalent inhibitor design, in its more current form, now reduces reliance on the Ki, and drives optimization through binding (i.e., reducing koff) rather than reactivity. This more thoughtful approach is an essential step towards maximizing selectivity and minimizing off target activity. In parallel with the ‘much needed’ experimental investigations, a range of computational techniques are required for modern covalent inhibitor design strategies. These include:

Ligand-based design

  • Virtual calculations defining reactivity and ranking of electrophiles.

Structure-based design

  • Optimizing the molecular recognition toward the target enzyme by careful design of the ligand functionality through steric and electrostatic control.
  • Optimizing placement of an appropriate electrophile on the inhibitor in relation to an accessible, non-conserved nucleophile on the protein. Selectivity of the covalent bonding step is governed by the choice of a suitable electrophile and proximity to the nucleophile. Optimizing the reactivity of the electrophile for a covalent inhibitor requires a balance between achieving a sufficient high rate of reaction with the selected nucleophile on the target protein as well as minimal rate of reaction with off target nucleophiles.

We anticipate that this approach will prove increasingly useful, particularly for otherwise intractable biological targets involving large and complex protein-protein contacts.

Add direction and insight to your projects

Cresset Discovery Services’ experienced scientists work alongside your chemists to solve problems, provide fresh ideas, remove roadblocks and add direction and insight to your projects.8 They offer expert molecular design capabilities and use cutting-edge technologies to provide support for numerous client projects, across a range of disciplines.

Free confidential discussion

To find out how Cresset Discovery Services can help you reach your next milestone faster and more cost effectively, contact us for a free confidential discussion.

References and links

  1. Bauer R.A., Covalent inhibitors in drug discovery: from accidental discoveries to avoided liabilities and designed therapies, Drug Discov. Today, 2015, 20 (9), 1061-1073.
  2. Baillies T.A., Targeted Covalent Inhibitors for Drug Design, Angew. Chem. Int. Ed., 2016, 55, 13408-13421.
  3. De Cesco S., Kurian J. Dufresne C., Mittermaier A.K., Moitessier N., Covalent inhibitors design and discovery, Eur. J. Med. Chem., 2017, 138, 96-114.
  4. Lagoutte R., Patouret R., Winssinger N., Covalent inhibitors: an opportunity for rational target selectivity, Curr. Opin. Chem. Biol., 2017, 39, 54-63.
  5. Exploring synthetically accessible alternatives to P2Y12 antagonists using electrostatics and shape.
  6. https://www.cresset-group.com/flare/
  7. See ‘Hammond postulate‘ for an exothermic reaction.
  8. https://www.cresset-group.com/discovery-services/

Computational approaches to ion channel drug discovery

In October, I presented Computational approaches to ion channel drug discovery at the ‘USA Integrated Drug Discovery Spotlight Event: Overcoming Challenges in Ion Channel Drug Discovery and Safety’ hosted by Concept Life Sciences and Metrion Biosciences.

Abstract

Structure-based molecular modeling approaches are probably the most acceptable form of computationally driven rational design deemed useful by the medicinal chemistry community for drug discovery. For unliganded projects, invariably, medicinal chemistry is a painstakingly slow process.

Ion channels are inherently difficult protein targets to tackle as very few have been crystalized. Strangely, it seems that the greater the knowledge we accumulate – the deeper the complexity.

Alternative approaches e.g., chemogenomics and good ligand centric approaches, are vital to squeeze out all valuable insights from this ‘costly’ SAR data.

Cresset has a reputation for producing powerful software for ligand-centric molecular modeling. Analyses involving molecular 3D shape and accurate electrostatics, via Cresset’s proprietary multipole force field (XED), allow fast and informative interrogation of SAR in the absence of structure data.

I describe the current diversity of the ion channel target space and highlight some of the issues faced. Flare expands Cresset’s product portfolio into structure-based molecular design, bringing our electrostatic workflows into the protein realm.  Electrostatics are fundamental to ion channel function. These techniques are highly likely to prove very useful in the ion channel drug discovery arena, as more appropriate techniques for providing structure data become available e.g., CryoEM.

I make the case for information based strategies and ligand centric techniques, particularly computational workflows used by Cresset Discovery Services for customer projects, which pit the efficiency of rationality against blind trial and error.

Flare enhances the Cresset Discovery Services toolbox

One of the advantages of working for Cresset Discovery Service is that I have access to software that is under development. I’ve therefore been working successfully with Flare, our newly-released structure-based modeling application, on client discovery projects for some time and I’m delighted that it is now available to a wider market.

Processing structures to extract discovery information

So, what does Flare offer Cresset Discovery Services that it didn’t have before? Forge is the comprehensive workbench for ligand-centric workflows and I use it as the main workhouse for ligand work. However, more and more drug discovery projects are now structure enabled.

In fact, the increasingly routine use of X-rays and Cryo-EM means that projects generally have access to lots of structures, not just one. Processing these in order to extract useful information is often a key step for us on client projects. For this structure enabled work, Flare has become my Forge equivalent – my main workbench for structure-based work.

Using Flare

The Flare GUI is intuitive and easy to navigate, yet already has significant, powerful and unique functionality. From a practical point of view, Flare is stable on loading 20 or more X-ray structures, and doesn’t slow down (unlike some other tools) or glitch. These structures can be automatically aligned by sequence (Cobalt) and superimposed at the click of button.

Protein preparation is automated. Water analysis via 3D-RISM allows exploration of key water molecules around ligands and binding pockets. Of course, Cresset’s own electrostatic fields can now be visualized on selected protein surfaces, in addition to ligands.

Lead Finder is a Flare plug-in that gives the capability to dock ligands and ultimately conduct high throughput structure-based virtual screening as a complementary addition to Cresset’s state of the art ligand centric-virtual screening service using Blaze.

Zika virus protease PDB : 5H6V and a covalent inhibitor. Protein positive fields (left), negative fields (middle) and ligand fields (right).

Flare is focused on ligand design

The addition of Flare to the Cresset Discovery Services toolbox enhances our client offering with a new degree of confidence for structure-based work. Flare has been developed based on input from scientists in industry and academia so that it is relevant to real discovery workflows. My own work on client projects also contributed to the design of Flare, and I know from personal experience that it is ideally suited to the needs of computational chemists.

The upshot of this is that Flare has been created with ligand design as a central task. This means that even the most computationally intensive tasks are tailored to the effect that they have on the molecules that you make. This really is the great power of computational chemistry; informing and enhancing the discovery of the molecules that matter to your project, using the best computational methods at our disposal. Flare greatly eases that task and is a great addition to our toolbox.

A single force field across ligands and proteins

However, what really puts Flare apart from other structure-based methods is the seamless use of the XED forcefield between ligands and proteins. This means that I can make calculations on structure-ligand interactions based on a continuous force field, and make meaningful comparisons at all stages of work.

Ever since I joined Cresset in 2012, customers have been asking me, ‘Why not apply the XED force field to proteins?’ I’m delighted that this question has finally been answered with the release of Flare.

Contact us for a free confidential discussion about how cutting edge ligand and structure-based methods can transform your discovery.

In silico methods to streamline optimization

It’s a long journey from hit to lead, and the path is called optimization.

Your discovery program started with some promising leads from a high throughput screen. The biology has been done and the chemistry has shown that there is an effect on the biochemical assays. You have a series of hit molecules, a set of required parameters, thousands of data points, a team of chemists, a budget and a deadline. Now the optimization work begins.

In silico methods streamline the optimization process by giving you more understanding of your target and your hits, and by making it easier to manage your data. There are methods to:

  • Identify any gaps in your data so that you can decide what further work needs to be done. The more you know, the greater choice of intelligent steps you have.
  • Make sure you have the best possible understanding of your molecule-target interaction, giving you as many optimization options as possible.
  • Balance diverse properties. Visualization and multi-parameter optimization tools can transform your ability to understand the impact of changes across different compounds.
  • Stay in the active window as you make minor changes. Building an activity pharmacophore helps you to understand how far you can take the changes.
  • Escape any liability from toxicity or pre-existing patents. Fragment replacement methods can be invaluable for moving to new areas of chemical space and adding new ideas and directions to your research.

Are there gaps in your data?

The optimization decisions you can make depend on the data you already have.

Activity Atlas, a component of Forge, summarizes the SAR for a series into a 3D model that can help you find any gaps in your data. You can calculate:

  • Activity Cliff Summary: What do the activity cliffs tell us about the SAR?
  • Average of actives: What do active molecules have in common?
  • Regions explored: Where have I been? For a new molecule, would making it increase our understanding? This analysis also calculates a novelty score for each molecule.

This approach is also helpful in looking at toxicity and other liabilities. For example, you may be optimizing a molecule that was identified from a screen. It is active, but has some undesirable chemistry. If you can understand as much as possible about the SAR, electrostatics and shape you are more likely to discover a better way to escape a liability. The more you know, the greater choice of intelligent steps you have.


Figure 1: Activity Atlas condenses your structure-activity data into highly visual 3D maps that inform the design and optimization of new compounds.

Understand the ligand-protein interaction

Understanding how your hits interact with the target helps you to optimize the affinity of your compound. In structure-enabled projects Flare, our new structure-based design application, can be used to analyze the protein-ligand system, calculate the energetics of ligand binding and analyze the water stability and energetics.

Without knowledge of the target structure, you may need to go back and deduce ligand-protein interactions from your hit so that you have a clearer understanding of the binding mechanism. This knowledge makes it far easier to optimize the affinity of your compound.

You get an extra level of insight to this process with Cresset techniques. Our electrostatic, hydrophobic and shape based analyses make it clear which chemical changes can have the largest biological impact.


Figure 2: Flare GUI.

Visualization tools help with multi-parameter optimization

Compounds that come out of the screening process generally have weaker potency than is required, so one of the first tasks is to increase the activity. This is usually done by making small changes to the molecule and testing their effect. However, there is a range of properties besides activity that need to be optimized.

Firstly, drug molecules need to be stable and small. Larger molecules are more likely to have off-target effects and have more problems travelling through membranes. They are also likely to have more complicated chemistry, making them harder and more expensive to synthesize. One of the key steps in optimization is to retain as many components as are required to make the compound active, but no more. The difficulty is that when you change one property you tend to change others.

Computational visualization for multi-parameter optimization shows you how the changes you are making affect other molecular properties. For example, you may want to simultaneously optimize the polar surface area and the LogP. The Torch and Forge radial plots can be set up to define acceptable project ranges for project data and in silico–calculable properties.

 

Contact Cresset Discovery Services for a confidential discussion about how we can streamline your optimization.

Molecular design towards Protein-Protein Interaction inhibitors

In December 2016 I attended the SCI Protein-Protein Interaction symposium. Armed with Cresset’s powerful ligand centric molecular modeling suite Forge, and an embryonic version of our new structure-based design application, Flare, I was keen to see what could usefully be done with PPI’s.

Prof. Richard Baylis (University of Leeds, UK) presented new data on the interaction of N-MYC with Aurora A. N-MYC is a disordered multi-domain protein with a host of interaction partners. Dysregulation of N-MYC has been linked to a range of cancers. N-MYC is short lived in-vivo and its usual fate is to be ubiquitinylated and degraded. Binding with Aurora A protects N-MYC from this process allowing its various tumorogenic affects to persist. The Baylis group provided the first x-ray evidence showing how N-MYC interacts at an allosteric site of Aurora A which stabilises an active conformation of the Kinase (figure 1).


Figure 1: Aurora A kinase with N-MYC – light green (left), and detail of the N-MYC short helical domain 74-89 (right).
Baylis suggested that DFG-out inhibitors of Aurora A provide distortions of the kinase that would prevent MYC binding, conversely, inhibition with ATP competitive inhibitors would not. Evidence of potential beneficial effects of the former type of kinase inhibitor, but not the latter, may be explained by this fact and led to the suggestion that this may be an effective therapeutic strategy for some types of cancer such as neuroblastoma.

An alternative computational strategy, which occurred to Cresset at the time, was to employ a structure-based approach; to furnish molecular designs that could directly prevent this protein-protein interaction. For this purpose, an initial analysis of the surface interaction, including both electrostatic and lipophilic hot-spots, would be vital.

During the talk, I used Flare to quickly download the relevant PDB file (5G1X) and to load the protein coordinates directly into the application. An automated protein prep protocol (build-model) was used to refine the pdb structure before generating the surface interaction maps, using Cressets proprietary XED force field (figure 2).


Figure 2: (A) Positive protein electrostatic isopotential surface of Aurora (left), negative protein electrostatic isopotential surface (center), and neutral isopotential surface with some key residues of N-MYC (right).
These isopotential maps show discrete positive (red), negative (blue) and neutral (yellow) surface regions that represent key interactions sites between N-MYC and Aurora A which allowed the assignment of the N-MYC residues on which to focus. The N-MYC protein was similarly used to generate and visualise the complimentary fields – as the other component half of the PPI (figure 3).


Figure 3: Negative protein electrostatic isopotential surface of N-MYC short helical region (left), and positive protein electrostatic isopotential surface of the same (right).
In keeping with other known PPI’s such as the MDM2 system, in the short helical domain (N-MYC 74-89) residues Met81 and Trp77 were identified as key lipophilic contacts. Much of the rest of the helix is largely for structural integrity and for stabilising solvent except for the NH of Trp77 and Glu84, which provide additional polar contacts, the latter capping an adjacent helix from Aurora. Further along the N-MYC peptide, towards the N-terminus, Pro74-Pro75 motif (figure 1) marks a change in sec. structure leading to another lipophilic contact Val61 and another polar contact Ser64 (not shown).

We can exploit this information to generate chemical starting points, once each important set of residues is identified and mapped. Thus, from the 3D shape and detailed electrostatic information we can conduct de-novo design experiments to furnish ideas for synthesis, or use virtual screening (Blaze) to search for commercial compounds to purchase and test.

Since the distance between the two main hot spot regions was not ideal (27 Ang. Val61 to Trp77) and although linking them might have been possible using a fragment linking or growing technique e.g., using Spark (Using Cresset’s Spark to grow and link distant fragment hits with sensible chemistry), we chose to tackle them independently with a de-novo design technique. I used the key residues Pro75, Trp77, Glu80, Met81 and Glu84 from the short helical domain as a molecular reference. We used this reference to score our molecular ideas against, and to optimize them via iterative ‘molecular design > alignment > scoring’ cycles in Forge. This powerful technique scores 3D shape, electrostatics and protein steric clashes whilst simultaneously calculating and/or filtering in-silico physiochemical properties. This method as described is limited only by the imagination of the user. In conjunction with Spark as the idea generator however, the limit is set only by the availability of appropriate fragments in the Spark databases – which is a substantial resource.

Later, when we returned, we also ran a virtual screening test on this system using the Blaze demo server. Results of this quick virtual screen against a sub-set of the ChEMBL database are shown below (figure 4).


Figure 4: Forge ‘tile view‘ of example diverse 2D output results of the virtual screen using the Blaze Demo server against a sub-set of ChEMBL (left) and 3D alignments of two of these (pink and green sticks) against the reference N-MYC peptide (blue lines) bound to Aurora A (Forge screenshot).
Although some of the Blaze examples retrieved were interesting, very good considering that this was a very small set <200k compound DB, it appears that good shape score and field score were not generally observed simultaneously. The ‘new’ addition of ‘pharmacophoric atom features in Blaze’ ensured we retrieved some of the key contacts such as the indole H-bond. However, we felt that design was probably the best way to address achieving the precise set of contacts we were looking to mimic.  Afterwards, I expanded on the ‘initial’ de-novo design ideas and provided around 20 further designs which had more reasonable properties and synthetic tractability (figure 5).

A powerful combination of cutting edge ligand and structure-based modeling

Figure 5: Flare screenshot of the structure of an initial idea (left) superimposed on N-MYC hot spot residues, plus its calculated properties, and (right) a space filling model of a further example with superior properties, improved fit, better synthetic tractability and … an IP position.Although this is only a thought experiment (until the point at which any of these molecular designs are synthesized and tested) this illustrates how the powerful combination of both ligand centric and structure-based techniques in Flare, Forge, and perhaps also Spark, could be used to generate specific ideas that address the types of challenges presented by PPI’s or fragment enabled drug discovery projects. This is not untypical, in terms of a portfolio of tasks we might suggest to Cresset Discovery Services clients.

Download an evaluation of Flare, Forge, Spark and Blaze, or contact us to find out how Cresset Discovery Services can enhance your project with insightful and creative delivery of powerful molecular modeling.

Help with writing your grant application

Don’t wait until you have funding to talk to Cresset Discovery Services about working together. We can help before you even start writing your grant application.

Our experience in helping write grant applications for academic and government funding shows that working together at the very start of a project reaps rewards. Being involved from the beginning gives all parties the flexibility to see exactly how and when we can make the maximum contribution to your research.

The University of Newcastle and Sygnature Discovery engaged our services from the earliest stages of an MRC funding application: this three-way collaboration involved writing the successful funding proposal together, ensuring the project used the strengths of each collaborator in the most efficient way.

When it comes to writing your application, we can give you as much help as you want. At a very minimum you will receive detailed scientific input, with descriptions of our methods, deliverables and estimates of time and cost.

Cost and benefit analysis of our methods compared to alternative approaches can also be provided. For example, virtual screening can be an incredibly cost effective way of finding a chemical starting point. We recently carried out ligand-protein docking for a customer as a virtual screen of available library compounds, leading to the selection and purchase of a small sub-set of the available compounds. Cresset Discovery Services helped the customer make an estimated five-fold saving over traditional HTS screening approaches without the pre-selection of likely hits.

Milestones, deliverables and reporting are part and parcel of grant funding. We will work with you on these, and of course we are happy to receive staged payments.

If you prefer, we can also contribute to the writing of the whole proposal. We can even work as a project manager to other collaborators. For example, we can manage your procurement process and work with CROs to outsource assays.

It can often help to have a third party, such as Cresset Discovery Services, bring expertise to your grant application. It will certainly reduce your workload!

Flexibility is key in how you engage our services. Contact us to find out how we can help with your next grant application.

Water stability is key to designing novel patentable chemistry

An analysis of the water stability and positions in a ligand-protein complex informed the design of novel ligands for a customer target. This work led to new active chemistry that the customer went on to patent.

A Cresset Discovery Services customer had identified a novel target with a natural ligand and were looking for new chemistry that would be active at the target site. Our scientists carried out an initial project to learn more about the protein-ligand system. The Cresset field approach, used to analyse the structure and interactions, gave the customer valuable insights into the active features of the ligand.

The customer used this information to develop analogous synthetic compounds and example molecules. They asked us to work with them again to computationally align the example molecules and prioritize them for synthesis.

We carried out an initial alignment and then modeled the system in detail. It appeared that part of the molecule that was important for the interaction was not making any contact with the protein.

The PDB had some crystal structures of related proteins, but not of the target of interest. We studied the available protein data to learn as much as possible about the binding pocket, paying particular attention to the positions and stability of the water molecules. This led to us putting forward the hypothesis that an important part of the ligand interaction included the stabilization of water.

Based on this hypothesis we prioritized the molecules that bridged the observed gap between the natural ligand and the target while also stabilizing the free waters.

Water analysis was carried out by manually superimposing multiple crystal structures, viewing the crystallographic waters that clustered together, and mapping on their temperature factors. This process allowed us to determine the importance of each water molecule in the solvation sphere around the ligand and protein pocket. With the advent of the new 3D-RISM method in Flare a similar computational work-flow can be accessed which is far more efficient for this type of analysis. This is a more systematic approach which enables us to calculate the position and stability of all water molecules around a proposed ligand in a binding pocket. Moreover, as it does this without the need for any crystallographic water data, this is far more useful as well as convenient. Ultimately, this data can be used to assess or compare ligands in terms of how well they might stabilize essential water.

Based on our equivalent ‘hands-on’ analysis, we worked with the customer to choose the best candidates for synthesis. These newly-designed ligands resulted in new active chemistry for the customer that was valuable enough for them to patent.


The position and energetics of water molecules in and around the active site is of crucial importance when designing novel ligands. Knowing which water molecules are energetically favorable can give valuable insights into the best positions for ligand molecules. 3D-RISM analysis is one of the methods available in Flare for structure-based drug design.

Move from hit to lead

A long-standing customer had a hit series with good activity but poor properties. Cresset Discovery Services worked closely with the customer to formulate a plan of action to optimize the compound properties while maintaining potency.

Cresset Discovery Services has worked closely with a customer on a target. We ran virtual screens, aligned literature chemotypes and proprietary chemotypes in order to arrive at a robust binding hypothesis and a field hypothesis. This enabled the customer to find a new chemotype that had good activity at the target. However, the hit series had poor properties. They needed to optimize the compounds, potentially sacrificing some potency while balancing this against improving the properties of the molecules.

The target in question required lipophilic molecules, so the set of compounds had reasonably high lipophilicity which can be a liability in drug development. The compounds also had high protein binding and, we suspected, high clearance.

 
Figure 1: Forge radial plot. The selected ‘highlighted’ set (Figure 3). Compounds with better properties give a larger area in the radial plot.

 
Figure 2: Forge graphical radial plot parameters. For each property a function is used to describe perfect, acceptable and unacceptable values. Perfect values are plotted at the edge of the radial plot and unacceptable plotted at the center. A single ‘Radial Plot Score’ is created to represent the fit of a compound to the chosen set as a function of parameters using the specified weighting scheme.

 
Figure 3: Forge property plots. Radial plot scores as a MPO method: Compounds with a radial plot score greater than the chosen cut-off (bottom left) were selected and hence are highlighted all the plots. These compounds have a good balance of activity and other physicochemical properties.

The best course of action for optimization was to search for compounds that were reasonably active but far more polar than the bulk of the molecules in the compound set. We designed suitable variants of these candidates and checked them using the alignment model in field space that we had developed earlier in the project. New combinations of functional groups on the core were selected to address the overall lipophilicity whilst maintaining the essential interaction features. This solution had the potential to address both the protein binding and clearance issues.

Over the course of the following weeks the customer worked through these ideas with a high degree of success. We collaborated closely with the customer throughout the project. They shared not only their activity data, but also their property data. We assessed both the activity and property landscape, and refined the suggested sets with the aid of multi-parameter optimization, which enabled us to suggest which compounds they should make and progress to help to get them past the hurdles they were experiencing. The customer has now arrived at a set of compounds with far better properties without a significant loss of potency.

The customer has retained our services to help them to optimize the potencies of the back-up series, which may help them to choose which chemotypes to progress next.

Contact us to see how we can help you with similar projects.

Conduct ligand-protein docking

A long-standing customer of Cresset Discovery Services asked us to identify new compounds that could be active at their protein target. We conducted ligand-protein docking to narrow down their 50k compound library to the best 1.5k compounds. The cost of the consulting project plus the chemistry for 1.5k compounds was about 20% of what it would have cost to buy and screen the entire 50k library.

Ligand-protein docking can be an excellent way to build up knowledge about the binding pocket. It can also form the basis for a virtual screen to identify new active compounds.

Cresset Discovery Services had been working with this customer on a particular ligand for some time, but there was very little information available about the protein target. There were homologues in the literature, but they were distantly related and nothing very similar had been crystallized.

Detailed preparatory work to model the protein active site

It was necessary to do a lot of modeling work to build up the relationship between the human target and the distantly related proteins available from the literature. We built sequence alignments and compared them, enabling us to build up 3D models of the target and its interaction with the ligand.

Some mutagenesis data was available on the known ligands, so we were able to use this to refine the 3D models and check that the correct residues were in the right places on the active site. This enabled us to define the active site for the ligands. We went on to calculate the energies for the protein-ligand interactions to make sure we had identified poses that made sense.

This was a complex system that required a great deal of protein preparation. This preparatory work was essential for successful docking and required expert knowledge, experience and skill.

Docking and virtual screening using different scenarios

At the end of this process we had a good model of the protein-ligand system. The next step was to remove the ligand and carry out docking.

Docking was first tested on the molecules that were known to bind to the target. This resulted in excellent retrieval rates, showing that the model would also be able to retrieve new compounds.

There were a number of different binding sites on the protein so we decided to carry out the virtual screening using different scenarios for the protein. We:

  • Kept the ligand intact in the binding site
  • Removed the ligand completely
  • Looked at partly bound situations and un-bound situations for each of the binding sites.

The customer provided us with a set of 50k ligands and we docked each of these against the binding pockets. A docking scoring system was used to rank the top 2k compounds from each of the screens.

Analyzing the results and compiling a purchasing list

The top 2k compounds from the four screens were analysed in detail. We visualized every one of the top 2k compounds and looked at each of the docking poses. The docking gave us good geometries for the ligands and we used Cresset software to check that the electrostatics made sense. Any compounds that were unlikely to bind well were rejected.

A final, ranked list was provided to the customer with a very high degree of confidence that it included compounds that were active at the protein target. They were able to procure about 75% of the compounds from the hit list, giving them a final set of 1.5k compounds to test.

An incredible saving in time and money

Carrying out virtual screening to focus the library in this way represented an incredible saving in time and money for our customer. The alternative approach would have been to buy and test the whole 50k compound set. Not only would the customer have needed to purchase all of the compounds, but also shipped them, stored them, plated them, screened them, and then they would still have to analyse the results.

The estimated cost of doing this for all 50k compounds would have been about five times the cost of the combined tasks of the Cresset Discovery Services project plus buying and testing 1.5k compounds.

Cost of

{buying and testing 50k compounds}

=  5 X

Cost of

{Cresset Discovery Services project + buying and testing 1.5k hit list}

Contact us to find out how we can add value to your project.

 

 

 

 

 

Dr Martin Slater, Director of Consulting Services