Modeling the intricacies of molecular recognition: Make ‘smart antibodies’ into biologics

Antibodies are fantastically versatile molecular recognition engines, capable of creating artificial enzymes in response to recognized interactions. Similarly, enzymes depend on molecular recognition for catalysis. Cresset Discovery Services has a depth of experience in modeling molecular recognition scenarios, stretching back to the early nineties.

Nature’s molecular recognition engines

In 1992 I was about to start a PhD on ‘catalytic antibodies’, investigating the amazingly versatile ability of the immune system to prepare artificial enzymes. It all sounded fascinating, and still does. Alas, rather than building antibodies, the path of my PhD altered, resulting in me developing my organo-phosphorus chemistry skills and producing ‘transition-state inhibitors’ of beta lactamase enzymes instead.

However, I retained a keen interest in molecular recognition and a familiarity with antibodies as a protein class and this has proved useful for our work at Cresset Discovery Services today. Indeed, it wasn’t until much, much, later that immunoglobulins would reveal their impact as important future medicines and of course this was way before the multi-billion-dollar blockbuster TNFa targeting biologic Adalibumab (Humira) (Figure 1, right).

Immunoglobulins are biologically evolved to recognize an enormously variable patch of molecular surface using a single protein architecture; an arrangement of two polypeptide chains (heavy and light) each bearing three hyper variable loops.

Figure 1 illustrates the diversity of recognition possible in antibodies, as modeled in Flare™, ranging from small molecules (sulfathiazole) to big molecules (Fullerene C60)1 and peptides (TNFa)2.

Figure 1: Left: an example of a fullerene recognizing mouse antibody. Middle:another mouse antibody recognizing a sulphonamide drug, Sulfathiazole. Right: the human engineered antibody Adalibumab (green) with TNFa. Heavy chain (crimson) variable loops (magenta), Light chain (grey) variable loops (black). All modeled in Flare using PDB codes: 5CP3, 1EMT and 3WD5.

Similarly, enzymes also use molecular recognition to great advantage via a diverse array of protein architectures. In contrast to antibodies, they specifically recognize molecular surfaces that represent a transition state of a chemical transformation. Lowering the energy of the transition state through binding allows catalysis to happen in enzymes.Figure 1: Left: an example of a fullerene recognizing mouse antibody. Middle:another mouse antibody recognizing a sulphonamide drug, Sulfathiazole. Right: the human engineered antibody Adalibumab (green) with TNFa. Heavy chain (crimson) variable loops (magenta), Light chain (grey) variable loops (black). All modeled in Flare using PDB codes: 5CP3, 1EMT and 3WD5.

The main premise of catalytic antibodies is to elicit their production in response to a transition state that can be mimicked by a ligand structure, which in theory then reproduces enzymic behaviour in the resultant protein. This pursuit simultaneously probes our fundamental knowledge of molecular recognition and of enzyme catalysis.

A catalytic antibody that cleaves cocaine

In 2006 workers at Scripps published work on a catalytic antibody3 that cleaves cocaine at the benzoyl ester. The antibody was elicited using an aryl phosphonate ester – a mimic of the carboxyl ester hydrolysis transition state.

The insights gained through the X-ray structures that were solved for this system were remarkable. Multiple components of the reaction coordinate are shown to exploit common recognition patterns, whilst the different geometries are accommodated by both residue and backbone movements in a dynamic process (Arg and Tyr movements in Figure 2).

This is an excellent model system. It shows how some interactions are very favourable e.g., the cation-pi interaction of the tropane is very solid across the structures, whilst others e.g., the ester, are variable. It also demonstrates that proteins, and in particular enzymes, ‘breathe’ – they are not statues (Figure 2).

Figure 2: The Scripps catalytic antibody in the different protein conformations that recognize the substrate and the transition state for cocaine ester hydrolysis. This shows highly mobile Arg (magenta) and Tyr (cyan) residues and the ligand (cocaine hydrolysis reaction coordinate – light to dark green). All modeled in Flare using PDB codes: 2AJU, 2AJV, 2AJZ, 2AJX, 2AJY and 2AJS.

All molecular recognition is not equal

One very interesting facet of molecular recognition demonstrated by antibody antigen interactions is that these interactions are not all equal. In fact, there are lesser and greater interactions – the latter of which can predominate in driving antibody production. Preferred interactions (or ‘hot spots’) can be visualised using various techniques (e.g., as demonstrated using the previous cocaine system modeling in Flare shown in Figure 3).

Figure 3: The Scripps catalytic antibody conformer ligand interaction surfaces. All modeled in Flare using PDB codes: 2AJV and 2AJX. This shows highly mobile surface changes required to squash the substrate into the transition state geometry (this time with the phosphono mimetic) and the hydrophobic hot spot surface (yellow) calculated by Flare.

Antibodies are produced via natural selection processes as a cellular response to a presented antigen. Some parts of the antigen are better than others at eliciting antibody responses, since they may involve preferred interactions, and so the resultant antibody may not bind in a therapeutically useful way.

Modeling and design of these interactions (i.e., via antigen engineering) becomes a useful task, since leaving it to nature can divert us from our intended goals.

Cresset Discovery Services work on biologics

While we are actively working and delivering on projects involving biologics, client confidentiality means that it remains a challenge to describe details of work done for commercial clients. We can, however, speak more generally and we have recently delivered on projects to:

  • Predict / design mutants that prevent binding
  • Model strategic glycan positioning at unhelpful yet potently antigenic surfaces
  • Model / characterize the observed binding order of an antigen – receptor series.

Read more about Cresset’s capabilities in biologics: Modeling ‘big’: Applying the XED force field to biologics.

Biologics remain a very important class of protein targets for disease therapeutics which we intend to continue to support through our innovative modeling services.

Free confidential discussion

Find out how we can accelerate your project by requesting a free confidential discussion.


  1. X-ray crystal structure of an anti-Buckminsterfullerene antibody Fab fragment: Biomolecular recognition of C60, Braden*, B. C., Goldbaum F. A., Chen B., Kirschner A. N., Wilson S. R. and Erlanger B. F. PNAS 97, no. 22, 12193–12197, 2000.
  2. Comparison of the Inhibition Mechanisms of Adalimumab and Infliximab in Treating Tumor Necrosis Factor a-Associated Diseases from a Molecular View, Shi Hu, Shuaiyi Liang, Huaizu Guo, Dapeng Zhang, Hui Li, Xiaoze Wang, Weili Yang, Weizhu Qian, Sheng Hou, Hao Wang§, Yajun Guo and Zhiyong Lou, Journal of Biological Chemistry, 288, 38, pp. 27059 –27067, 2013
  3. Complete Reaction Cycle of a Cocaine Catalytic Antibody at Atomic Resolution, Zhu X., Dickerson T. J., Rogers C. J., Kaufmann G. F., Mee J. M., McKenzie K. M., Janda K. D. and Wilson I. A. Structure 14, 205–216, 2006.

Review of the XXV EFMC International Symposium on Medicinal Chemistry 2018

The September meeting of EFMC-IMCS was in the picturesque city of Ljubljana, nestled amongst the surrounding mountains, with its many bridges, hilltop castle and numerous riverside eateries. The good food, wines and beers alone would have made the visit worthwhile, but there was more than enough excellent science to keep us in the conference centre.

With three separate scientific tracks: Therapeutic Areas, Technologies and Chemical Biology, it was impossible for the 800 plus delegates to see everything (much like an ACS meeting), but the busy schedule meant that there was something for everybody.

A cracking start

Fittingly, the opening on Sunday included a fascinating talk by the Slovenian academic and Zika virus expert Prof. Tatjana Avsic-Zupanc (University of Ljubljana) who was the first to characterize the link between Zika infection and foetal abnormalities and to identify a causative molecular target: PrM protein mutant S139N.

Bayard Huck (Merck Biopharm) presented his interpretation of their strategy towards the evolution of the medicinal chemist, a technology and multi-discipline embracing ‘Versatilist’, the scientist equivalent of a ‘Swiss army knife’. Bayard went on to describe virtual screening (one of Cresset Discovery Services’ areas of expertize) as vital for increasing cost-effectiveness. He also described the current mania with all things AI as being at the peak of inflated expectations in the Gartner hype cycle, although he noted that useful applications are emerging from the hype. Bayard elaborated further on the nature of workspace and how the removal of ‘silos’ is essential for effective innovation to occur between scientists, which sounded remarkably familiar to me; I realised later that it was the open structure of the Cresset office I was thinking of.

Prof. Christa Müller’s (University of Bonn) talk about purines: P2X, P2Y, P1 and P0 related receptors and enzymes, and Prof. Peter Seeberger’s (Max Planck Institute of Colloids and Interfaces) talk about the development and use of a remarkable, state-of-the-art automated glycan synthesiser for synthetic vaccine discovery got the conference off to a cracking start.

First disclosures, GPCRs and other highlights

There were several themes running through the conference including: immuno-oncology, a real opportunity to tackle cancer, targeted degradation as a completely new therapeutic paradigm and the emergence of various AI strategies. Marwin Segler (Benevolent AI) showed how deep learning could be used for evaluation of millions of possible synthetic routes to a novel compound. First time disclosures are shown in table 1.

Table 1: 2D structures of first disclosures.​

Entry Description Structure
1 Risdiplam (RG7916); developed as a splicing modifier for the treatment of spinal muscular atrophy (SMA), from Hoffmann-La Roche and PTC therapeutics
2 Highly selective covalent BTK inhibitor LOU064 from Novartis  
3 Potent Novel Oral sCG Activtor from Bayer: Bay1101042
4 PDE9 selective inhibitor BI409306 by Boehringer Ingelheim for CNS disorders  


Risdiplam has the electrostatic hallmarks of an RNA/DNA nucleotide base interactor (a relatively electron deficient heterocycle which is then more suited to face on face interaction with nucleotide bases) in a similar manner to the fluoroquinolone antibiotics e.g., grepafloxacin (Figure 1).

Figure 1. Risdiplam structure (left) with electrostatic surface (red = positive and Blue = negative) and that of Grepafloxin (right).

GPCR talks that caught my eye are captured in table 2 and include: A MgluR2 ligand talk by Jose Cid (Jannsen), a GabbaB ligand talk by Sean Turner (AbbVie). Also, a very nice talk by Anne Valade (UCB) describing deuterium exchange as a method for identifying the allosteric ligand binding site for the dopamine D1 receptor. Finally, a first disclosure from Stefan Bäurle (Bayer) on their collaboration with Evotec.

Table 2: GPCR ligand structures.

Entry Description Hit Optimized lead
5 Metabotropic glutamate receptor 2 from Janssen
6 GabbaB positive allosteric modulators from AbbVie
7 Bradykinin B1 antagonist first disclosure Bay-840 from Bayer-Evotec

Other ligands captured from talks that were interesting are shown in table 3. Of particular interest was the superb talk by Allan Jordan, Cancer Research UK, which possibly wins the prize for the ugliest looking validated hit.

Table 3: Ligands from other diverse talks of note.

Entry Description Hit Optimized lead
8 David Tully from Novartis described their FXR agonist for NASH: Tropifexor
9 Anna Miggalautsch Graz University  Adipose trigliceride lipase – mouse active Atglistatin
10 Paul Fish from UCL Notum inhibitor for AD
11 Allan Jordan NMT1 inhibitor CRUK
12 Rainer Machauer disclosure on CNP-520 a new Bace2 inhibitor from Novartis
13 Juraj Velcicky Gamma Secretase-like membrane bound peptidase SPPL2A developed from Gamma Secretase SAR, for Endometriosis


Overall it was a successful, enjoyable and informative EFMC-ISMC symposium.

Modeling ‘big’: Applying the XED force field to biologics

Cresset is well known for powerful and accurate ligand-centric modeling, and Flare has established our methods for protein-ligand interactions. Work on GPCR modeling and viruses demonstrates the effectiveness and potential of Cresset technology for protein-protein interactions. Here I discuss the successes and challenges of modeling ‘big’ – applying Cresset’s XED force field to biologics.

Adventures in protein modeling: GPCRs

In 2014 Dr Andy Vinter, Cresset founder, reported on GPCR modeling exercises using the XED force field1, where ligand poses were exhaustively explored together with full complex minimizations to provide qualitative or quantitative analyses with binding estimates for agonist v antagonists. Although this was a huge modeling challenge, the approach provided fascinating new insights into GPCR behaviour that are in keeping with more recent literature. In particular, Brian Kobilka (joint winner of the 2012 Nobel Chemistry Prize) published a paper in 2016 showcasing the use of specific nanobody binding to the intracellular side of the GPCR to probe the long-range influence of ligands at the extracellular side2. He provided evidence supporting the hypothesis that GPCRs are likely partitioned between different states by differential stabilization of the full complexes in response to ligands. Our modeling findings concur in that the subtleties of these interactions extend beyond direct local binding interaction events and are propagated at distance across the full protein complex.

A matter of scale

Long-distance effects are not unusual in the realm of protein-protein interactions yet are beyond the scope of traditional molecular mechanics – from an accuracy point of view. From a sheer scaling point of view, the number of atoms involved means they are also beyond the scope of quantum mechanics. QMMM methods are also sometimes a poor compromise as these are discontinuous and focus on the local binding event.

Interestingly, this is where the XED force field has a nice sweet spot; accuracy approaching that of QM, but speed and the ability to map larger numbers of atoms >30,000, which is highly appropriate for the analysis of protein ligand and protein-protein systems. We can do this accurately and consistently through deployment of careful protein preparation and minimization workflows on protein systems.

Example: Influenza virus

The Centre for Pathogen Evolution at The University of Cambridge3 is involved in mapping data ultimately for the potential prediction of vaccine escape mutations of the influenza virus.

Hemagglutinin virus protein is the receptor that recognises mammalian cell surface glycans as an essential route to host cell entry. The ability of the virus to recognise sialic acid containing glycans is essential to this process and residues that contribute to its recognition represent those which are consequently difficult to mutate without compromising the virus. Antibodies which are directed to this site (Figure 1 left and middle) are less likely to suffer from viable virus mutations than others 4,5 (Figure 1 right).

Figure 1: Left: Influenza H3N2 hemagglutinin with the electrostatics of the core recognition element sialic acid from PDB 5VTQ. Middle: overlapping monoclonal antibody (blue tube) recognition site with electrostatics from key residues from PDB 2VIR. Right: a non-overlapping monoclonal antibody from PDB 5W42.

Optimizing biologics using 3D electrostatic shape and complementarity

In vogue, directed degradation mechanisms (PROTACS)/antibodies/vaccines, i.e., biologics, are example therapeutic paradigms which involve subclasses of these protein–protein interactions rather than the classical small molecule drug – protein target interactions. Modeling them is a significant challenge faced by many organizations charged with producing an array of diversely targeted therapeutics, because it is where a lot of what remains (the ‘higher hanging fruit’) happens to be.

The biologics industry may have slightly different criteria for cycling through an optimization, but ultimately similar schemes to those operating in the pharmaceutical industry still apply. There is an equivalent of traditional medicinal chemistry drug discovery workflows – involving SAR analysis, design, synthesis and test cycles. For antibodies, as for small molecules, target affinity, solubility, aggregation are key initial concerns. Mouse to human transformation is a uniquely biologics issue (unless we are talking in vivo models) as is the means of controlling SAR. For proteins it is all in the manipulation of the amino acid sequence, protein loop conformational preference, by single or multiple residue mutation. Incidentally, conventional sequence similarity metrics are not a useful measure of a residues relative potential for interaction with ligands or proteins in active sites (despite often being the tool of choice for analyzing protein data), as they were derived from natural mutation propensity and that consequence on maintenance of protein architecture.

Ultimately, the mechanism of target engagement, the molecular recognition event, is through electrostatic and shape complementarity and is fundamentally the same 3D phenomenon that applies to small molecules. Cresset scientists have an outstanding track record of working on electrostatic and shape complementarity and have successfully applied these skills to protein-protein interactions.

In the last 12 months, Cresset Discovery Services has completed work on viral vaccine modeling and biologics modeling which have proved highly useful for clients. We matched observed binding events with calculated binding enthalpy trends and predicting a-priori the observed pattern of protein binding or unwanted peptide binding suppression. This has been done using WT or mutant proteins that we have successfully taken through analysis, modeling/design and client testing cycles. As you would expect, client confidentiality prevents us disclosing further details, but contact us for a free confidential discussion.


  1. Applying the XED molecular mechanics force field to the binding mechanism of GPCRs
  2. Allosteric nanobodies reveal the dynamic range and diverse mechanisms of G-protein-coupled receptor activation, Kobilka et al, Naturevolume 535, pages448–452 (21 July 2016)
  4. Substitutions Near the Receptor Binding Site Determine Major Antigenic Change During Influenza Virus Evolution, David F. Burke, Derek J. Smith et al, Science 22 Nov 2013:
    342, Issue 6161, pp. 976-979
  5. Diversity of Functionally Permissive Sequences in the Receptor-Binding Site of Influenza Hemagglutinin, Nicholas C. Wu Jia Xie Tianqing Zheng, Corwin M. Nycholat, Geramie Grande, James C. Paulson Richard A. Lerner and Ian A. Wilson, Host & Microbe 21, 742–753, June 14, 2017

Parkinson’s Virtual Biotech secures further funding for novel gene transcription modulators project

Following excellent progress in this drug discovery project that we announced in March, I am delighted that our unique software, virtual screening capabilities and highly experienced team have contributed to Parkinson’s UK securing further funding for the novel gene transcription modulators collaboration we are working on along with Selcia.

We look forward to continuing to deliver molecular modeling support for the next phase of this important project.

Review of 8th RSC / SCI symposium on kinase inhibitor design: Towards new frontiers

As kinases have become a well-established class of therapeutic targets, it’s probably fitting that the theme of the RSC kinase symposium this year was relatively conservative, although a reasonably diverse selection of kinases (table 1) and therapeutic areas (table 2) were covered. It is remarkable that so many ATP pocket blocking inhibitors (i.e., most examples described this year) have translated to clinical candidates and drugs on the market, despite the fact that kinase biology, particularly the role and mechanism of kinase modulation by multiple protein complex formation as a prerequisite for signalling, is not very well understood.

Table 1: Kinase targets 2018 symposium.

Targets Number Kinase
Lipid kinases 4 PI2K, PI3K, PI4K, ATM
Serine Threonine 10 MPS1 (TTK), ERK, PKC, AMPK, CDK, LRRK2, MLK, TTBK1, PINK1, mTORC
Tyrosine 3 ALK, EGFR, TRK


Table 2: Kinase therapeutic areas 2018 symposium.

Therapy Number
Oncology 9
Diabetes 1
Transplant rejection 1
Tropical disease 1


The talk by Craig Malcolm (Promega UK Ltd) perfectly illustrated the lack of understanding of which processes involving kinases are actually operating in cells i.e., those which may, or may not, be therapeutically relevant and accessible. He described an interesting ‘different take’ on the standard kinase biochemical assay by using florescent kinase inhibitor probes as a technique to assay kinase activity in the ‘live’ cellular context.

Lipid kinases

The talks by Klaus Okkenhaug (University of Cambridge and Babraham Institute, UK), and Roger Williams (MRC Laboratory of Molecular Biology, UK), offered in depth exploration of the intricacies of the lipid kinase family and the effect of the wider molecular and cellular context of these kinases on the therapeutic outcome.

Klaus described how diverse this family is firstly with the PI3K delta inhibitor Idelalisib (1), which in combination therapy with biologic Rituximab for Chronic Lymphocytic Leukemia (CLL), serves to draw cells out of the lymph nodes so that they can be targeted by antibody treatment. The role of PI3K delta in immuno-oncology was evident in combination with CSF1 as this synergizes knocking out tumor cell protection. Finally, Leniosilib (2), is an anti-inflammatory agent through selective PI3K delta inhibition.

In his talk, Roger described the VPS34 system and how the nature of multi-protein architecture governs specific membrane interactions and thus activation of this complex assembly.

Figure 1: Left – VPS34 (human PI3K) with inhibitor (PDB code 4PH4); center – yeast VPS34 / VPS15 complex (PDB code 5KC2); right – the larger yeast complex (PDB code 5DFZ) showing the context of PI3K catalytic domain (in grey across the structures).

In addition, James Reuberson (UCB, UK) went on to describe a PI4K 3-beta inhibitor for immune suppression (3) and Emily Hannan talked about Genentec’s selective PI3K alpha inhibitor and degrader (4) for breast cancer. The latter molecule stimulates removal of PI3K, presumably by stabilizing a conformation that can be more readily ubiquitinated.

Kinase activators

Exceptions to the general rule of ‘ATP site binding inhibition’ were two talks describing the discovery and development of kinase ‘activators’ of AMPK (5) and then PINK1 (6). AMPK is an unusual kinase with an allosteric modulatory site above the ATP binding catalytic site. PINK-1 is also an unusual kinase which has extended loops around the catalytic domain responsible for binding and phosphorylating ubiquitin. PINK-1 interacts directly with an E3-ligase at the surface of mitochondria and modulates mitochondrial autophagy. Youcef Mehellou, (Cardiff School of Pharmacy and Pharmaceutical Sciences, UK) described the use of novel ATP mimetic prodrugs which activate a form of PINK-1 that is mutated in Parkinson’s disease.

Kinase inhibitors for the CNS

Unsurprisingly, most talks focused on oncology as the major topic. Another sub-theme was accessing the CNS with kinase inhibitors for brain tumors or with CNS penetrating ligands for psychiatry or neurodegenerative diseases. Compound 6  is an example of a CNS penetrating pro-drug. Most other examples focused on optimizing inhibitors either by  multiple parameter optimization of calculated properties, or using experimental measurements such as compound measurement in brain slice homogenate as a component of their KPU metric. These include: a TTBK1 inhibitor from Lundbeck (7) for Alzheimer’s, the ATM inhibitor from AZ (8) for Glioblastoma, and the pan TRK inhibitor from Chugai (9) for brain metastasis. Note the absence of the classic donor-acceptor-donor hinge interaction motif.

Figure 2: Lundbeck TTBK1 inhibitor (7) for Alzheimer’s disease (left – PDB code 4NFN) shows non-classical hinge interactions which are more evident using Cresset electrostatic field visualization (right) in Flare (cyan: negative potential; red: positive potential).



Overall the symposium was interesting and informative.

Contact us for a free confidential discussion to see how Cresset Discovery Services experts can advance your project.

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:


  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.
  7. See ‘Hammond postulate‘ for an exothermic reaction.

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.


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.