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.
- Applying the XED molecular mechanics force field to the binding mechanism of GPCRs
- 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)
- 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
- 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