The judicious application of ligand or binding efficiency metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Retrospective analysis of recently marketed oral drugs shows that they frequently have highly optimized ligand efficiency values for their targets. Optimizing ligand efficiency metrics based on both molecular mass and lipophilicity, when set in the context of the specific target, has the potential to ameliorate the inflation of these properties that has been observed in current medicinal chemistry practice, and to increase the quality of drug candidates.

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The role of ligand efficiency metrics in drug discovery

The coronavirus (CoV) spike (S) protein, involved in viral-host cell fusion, is the primary immunogenic target for virus neutralization and the current focus of many vaccine design efforts. The highly flexible S-protein, with its mobile domains, presents a moving target to the immune system. Here, to better understand S-protein mobility, we implemented a structure-based vector analysis of available β-CoV S-protein structures. Despite an overall similarity in domain organization, we found that S-proteins from different β-CoVs display distinct configurations. Based on this analysis, we developed two soluble ectodomain constructs for the SARS-CoV-2 S-protein, in which the highly immunogenic and mobile receptor binding domain (RBD) is either locked in the all-RBDs 'down' position or adopts 'up' state conformations more readily than the wild-type S-protein. These results demonstrate that the conformation of the S-protein can be controlled via rational design and can provide a framework for the development of engineered CoV S-proteins for vaccine applications.

/pdf/controlling-the-sars-cov-2-spike-glycoprotein-conformation-56eayewdoo.pdf

Controlling the SARS-CoV-2 spike glycoprotein conformation.

Docking is a computational technique that places a small molecule (ligand) in the binding site of its macromolecular target (receptor) and estimates its binding affinity. This review addresses methodological developments that have occurred in the docking field in 2009, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. These developments aim to address the main challenges of docking: receptor representation (such aspects as structural waters, side chain protonation, and, most of all, flexibility (from side chain rotation to domain movement)), ligand representation (protonation, tautomerism and stereoisomerism, and the effect of input conformation), as well as accounting for solvation and entropy of binding. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design. Copyright 2010 John Wiley & Sons, Ltd.

/pdf/challenges-and-advances-in-computational-docking-2009-in-15vsh0b2b1.pdf

Challenges and advances in computational docking: 2009 in review

Class A G-protein-coupled receptors (GPCRs) influence virtually every aspect of human physiology. Understanding receptor activation mechanism is critical for discovering novel therapeutics since about one-third of all marketed drugs target members of this family. GPCR activation is an allosteric process that couples agonist binding to G-protein recruitment, with the hallmark outward movement of transmembrane helix 6 (TM6). However, what leads to TM6 movement and the key residue level changes of this movement remain less well understood. Here, we report a framework to quantify conformational changes. By analyzing the conformational changes in 234 structures from 45 class A GPCRs, we discovered a common GPCR activation pathway comprising of 34 residue pairs and 35 residues. The pathway unifies previous findings into a common activation mechanism and strings together the scattered key motifs such as CWxP, DRY, Na+ pocket, NPxxY and PIF, thereby directly linking the bottom of ligand-binding pocket with G-protein coupling region. Site-directed mutagenesis experiments support this proposition and reveal that rational mutations of residues in this pathway can be used to obtain receptors that are constitutively active or inactive. The common activation pathway provides the mechanistic interpretation of constitutively activating, inactivating and disease mutations. As a module responsible for activation, the common pathway allows for decoupling of the evolution of the ligand binding site and G-protein-binding region. Such an architecture might have facilitated GPCRs to emerge as a highly successful family of proteins for signal transduction in nature.

Common activation mechanism of class A GPCRs.

https://www.cell.com/cell/pdf/S0092-8674(15)00685-6.pdf

Crystal Structure of Antagonist Bound Human Lysophosphatidic Acid Receptor 1.

Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity—the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking “hotspots,” or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.

/pdf/applying-physics-based-scoring-to-calculate-free-energies-of-1rqgjlqv28.pdf

Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.

We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 A for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation.

Fragment-based and de novo design strategies have been used in drug discovery for years. The methodologies for these strategies are typically discussed separately, yet the applications of these techniques overlap substantially. We present a review of various fragment-based discovery and de novo design protocols with an emphasis on successful applications in real-world drug discovery projects. Furthermore, we illustrate the strengths and weaknesses of the various approaches and discuss how one method can be used to complement another. We also discuss how the incorporation of experimental data as constraints in computational models can produce novel compounds that occupy unique areas in intellectual property (IP) space yet are biased toward the desired chemical property space. Finally, we present recent research results suggesting that computational tools applied to fragment-based discovery and de novo design can have a greater impact on the discovery process when coupled with the right experiments.

Computational Approaches for Fragment-Based and De Novo Design

Advances reported over the last few years and the increasing availability of protein crystal structure data have greatly improved structure-based druggability approaches. However, in practice, nearly all druggability estimation methods are applied to protein crystal structures as rigid proteins, with protein flexibility often not directly addressed. The inclusion of protein flexibility is important in correctly identifying the druggability of pockets that would be missed by methods based solely on the rigid crystal structure. These include cryptic pockets and flexible pockets often found at protein-protein interaction interfaces. Here, we apply an approach that uses protein modeling in concert with druggability estimation to account for light protein backbone movement and protein side-chain flexibility in protein binding sites. We assess the advantages and limitations of this approach on widely-used protein druggability sets. Applying the approach to all mammalian protein crystal structures in the PDB results in identification of 69 proteins with potential druggable cryptic pockets.

/pdf/structure-based-druggability-assessment-of-the-mammalian-1x09c6vokx.pdf

Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility.

We describe a novel method to develop energetically optimized, structure-based pharmacophores for use in rapid in silico screening. The method combines pharmacophore perception and database screening with protein ligand energetic terms computed by the Glide XP scoring function to rank the importance of pharmacophore features. We derive energy-optimized pharmacophore hypotheses for 30 pharmaceutically relevant crystal structures and screen a database to assess the enrichment of active compounds. The method is compared to three other approaches: (1) pharmacophore hypotheses derived from a systematic assessment of receptor ligand contacts, (2) Glide SP docking, and (3) 2D ligand fingerprint similarity. The method developed here shows better enrichments than the other three methods and yields a greater diversity of actives than the contact-based pharmacophores or the 2D ligand similarity. We then apply this method on fragment docking using fragment-specific settings aimed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. We show that the most energetically favourable pharmacophore sites are consistent with features from known tight binding compounds. The derived pharmacophore models are able to recover known active compounds from a database screen and retrieving diverse compounds that are not biased by the co-crystallized ligand.

/pdf/generation-of-structure-based-pharmacophores-using-energetic-1d02r69wjf.pdf

Kathryn Loving

Papers

Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation.

Computational Approaches for Fragment-Based and De Novo Design

Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility.

Generation of structure-based pharmacophores using energetic analysis – application on fragment docking