An adapted consensus protein design strategy for identifying globally optimal biotherapeutics.

Biotherapeutic optimization, whether to improve general properties or to engineer specific attributes, is a time-consuming process with uncertain outcomes. Conversely, Consensus Protein Design has been shown to be a viable approach to enhance protein stability while retaining function. In adapting this method for a more limited number of protein sequences, we studied 21 consensus single-point variants from eight publicly available CD3 binding sequences with high similarity but diverse biophysical and pharmacological properties. All single-point consensus variants retained CD3 binding and performed similarly in cell-based functional assays. Using Ridge regression analysis, we identified the variants and sequence positions with overall beneficial effects on developability attributes of the CD3 binders. A second round of sequence generation that combined these substitutions into a single molecule yielded a unique CD3 binder with globally optimized developability attributes. In this first application to therapeutic antibodies, adapted Consensus Protein Design was found to be highly beneficial within lead optimization, conserving resources and minimizing iterations. Future implementations of this general strategy may help accelerate drug discovery and improve success rates in bringing novel biotherapeutics to market.

ADMET Predictability at Boehringer Ingelheim: State-of-the-Art, and Do Bigger Datasets or Algorithms Make a Difference?

Computational methods assisting drug discovery and development are routine in the pharmaceutical industry. Digital recording of ADMET assays has provided a rich source of data for development of predictive models. Despite the accumulation of data and the public availability of advanced modeling algorithms, the utility of prediction in ADMET research is not clear. Here, we present a critical evaluation of the relationships between data volume, modeling algorithm, chemical representation and grouping, and temporal aspect (time sequence of assays) using an in-house ADMET database. We find no large difference in prediction algorithms nor any systemic and substantial gain from increasingly large datasets. Temporal-based data enlargement led to performance improvement in only in a limited number of assays, and with fractional improvement at best. Assays that are well-, intermediately-, or poorly-suited for ADMET predictions and reasons for such behavior are systematically identified, generating realistic expectations for areas in which computational models can be used to guide decision making in molecular design and development.

Evaluating the use of absolute binding free energy in the fragment optimisation process.

Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.

Hybrid Screening Approach for Very Small Fragments: X-ray and Computational Screening on FKBP51.

Fragment-based drug discovery (FBDD) permits efficient sampling of the vast chemical space for hit identification. Libraries are screened biophysically and fragment:protein co-structures are determined by X-ray crystallography. In parallel, computational methods can derive pharmacophore models or screen virtual libraries. We screened 15 very small fragments (VSFs) (HA ≤ 11) computationally, using site identification by ligand competitive saturation (SILCS), and experimentally, by X-ray crystallography, to map potential interaction sites on the FKBP51 FK1 domain. We identified three hot spots and obtained 6 X-ray co-structures, giving a hit rate of 40%. SILCS FragMaps overlapped with X-ray structures. The compounds had millimolar affinities as determined by 15N HSQC NMR. VSFs identified the same interactions as known FK1 binder and provide new chemical starting points. We propose a hybrid screening strategy starting with SILCS, followed by a pharmacophore-derived X-ray screen and 15N HSQC NMR based KD determination to rapidly identify hits and their binding poses.

BIreactive: A Machine-Learning Model to Estimate Covalent Warhead Reactivity.

In the past decade, the pharmaceutical industry has paid closer attention to covalent drugs. Differently from standard noncovalent drugs, these compounds can exhibit peculiar properties, such as higher potency or longer duration of target inhibition with a potentially lower dosage. These properties are mainly driven by the reactive functional group present in the compound, the so-called warhead that forms a covalent bond with a specific nucleophilic amino-acid on the target. In this work, we report the possibility to combine ab initio activation energies with machine-learning to estimate covalent compound intrinsic reactivity. The idea behind this approach is to have a precise estimation of the transition state barriers, and thus of the compound reactivity, but with the speed of a machine-learning algorithm. We call this method "BIreactive". Here, we demonstrate this approach on acrylamides and 2-chloroacetamides, two warhead classes that possess different reaction mechanisms. In combination with our recently implemented truncation algorithm, we also demonstrate the possibility to use BIreactive not only for fragments but also for lead-like molecules. The generic nature of this approach allows also the extension to several other warheads. The combination of these factors makes BIreactive a valuable tool for the covalent drug discovery process in a pharmaceutical context.

Fragment Binding Pose Predictions Using Unbiased Simulations and Markov-State Models.

Predicting the costructure of small-molecule ligands and their respective target proteins has been a long-standing problem in drug discovery. For weak binding compounds typically identified in fragment-based screening (FBS) campaigns, determination of the correct binding site and correct binding mode is usually done experimentally via X-ray crystallography. For many targets of pharmaceutical interest, however, establishing an X-ray system which allows for sufficient throughput to support a drug discovery project is not possible. In this case, exploration of fragment hits becomes a very laborious and consequently slow process with the generation of protein/ligand cocrystal structures as the bottleneck of the entire process. In this work, we introduce a computational method which is able to reliably predict binding sites and binding modes of fragment-like small molecules using solely the structure of the apoprotein and the ligand's chemical structure as input information. The method is based on molecular dynamics simulations and Markov-state models and can be run as a fully automated protocol requiring minimal human intervention. We describe the application of the method to a representative subset of different target classes and fragments from historical FBS efforts at Boehringer Ingelheim and discuss its potential integration into the overall fragment-based drug discovery workflow.

Second M3 muscarinic receptor binding site contributes to bronchoprotection by tiotropium.

BACKGROUND AND PURPOSE: The bronchodilator tiotropium binds not only to its main binding site on the M3 muscarinic receptor but also to an allosteric site. Here, we have investigated the functional relevance of this allosteric binding and the potential contribution of this behaviour to interactions with long-acting ß-adrenoceptor agonists, as combination therapy with anticholinergic agents and ß-adrenoceptor agonists improves lung function in chronic obstructive pulmonary disease. EXPERIMENTAL APPROACH: ACh, tiotropium, and atropine binding to M3 receptors were modelled using molecular dynamics simulations. Contractions of bovine and human tracheal smooth muscle strips were studied. KEY RESULTS: Molecular dynamics simulation revealed extracellular vestibule binding of tiotropium, and not atropine, to M3 receptors as a secondary low affinity binding site, preventing ACh entry into the orthosteric binding pocket. This resulted in a low (allosteric binding) and high (orthosteric binding) functional affinity of tiotropium in protecting against methacholine-induced contractions of airway smooth muscle, which was not observed for atropine and glycopyrrolate. Moreover, antagonism by tiotropium was insurmountable in nature. This behaviour facilitated functional interactions of tiotropium with the ß-agonist olodaterol, which synergistically enhanced bronchoprotective effects of tiotropium. This was not seen for glycopyrrolate and olodaterol or indacaterol but was mimicked by the interaction of tiotropium and forskolin, indicating no direct ß-adrenoceptor-M3 receptor crosstalk in this effect. CONCLUSIONS AND IMPLICATIONS: We propose that tiotropium has two binding sites at the M3 receptor that prevent ACh action, which, together with slow dissociation kinetics, may contribute to insurmountable antagonism and enhanced functional interactions with ß-adrenoceptor agonists.

Enhancing Drug Residence Time by Shielding of Intra-Protein Hydrogen Bonds: A Case Study on CCR2 Antagonists.

The target residence time (RT) for a given ligand is one of the important parameters that have to be optimized during drug design. It is well established that shielding the receptor-ligand hydrogen bond (H-bond) interactions from water has been one of the factors in increasing ligand RT. Building on this foundation, here we report that shielding an intra-protein H-bond, which confers rigidity to the binding pocket and which is not directly involved in drug-receptor interactions, can strongly influence RT for CCR2 antagonists. Based on our recently solved CCR2 structure with MK-0812 and molecular dynamics (MD) simulations, we show that the RT for this and structurally related ligands is directly dependent on the shielding of the Tyr120-Glu291 H-bond from the water. If solvated this H-bond is often broken, making the binding pocket flexible and leading to shorter RT.

Large scale relative protein ligand binding affinities using non-equilibrium alchemy.

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol-1, equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol-1. For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software.

Improved Solution-State Properties of Monoclonal Antibodies by Targeted Mutations.

Monoclonal antibody (mAb)-based therapeutics often require high-concentration formulations. Unfortunately, highly concentrated antibody solutions often have biophysical properties that are disadvantageous for therapeutic development, such as high viscosity, solubility limitations, precipitation issues, or liquid-liquid phase separation. In this work, we present a computational rational design principle for improving the thermodynamic stability of mAb solutions through targeted point mutations. Two publicly available IgG1 monoclonal antibodies that exhibit high viscosity at high concentrations were used as model systems. Guided by a computationally efficient approach that combines molecular dynamics simulations with three-dimensional reference interaction site model theory, point mutations of charged residues were introduced in the variable Fv regions in such a manner that the hydration free energy was optimized. Two selected point mutants were then produced by transient expression and characterized experimentally. Both engineered mAbs have reduced viscosity at high concentration, less negative second virial coefficient, and improved solubility compared to the respective wild-types. The results obtained with the suggested straightforward design principle underline the relevance of solvation effects for understanding, and ultimately optimizing, the properties of highly concentrated mAb solutions, with possible implications also for other biomolecular systems.

Prediction and Reduction of the Aggregation of Monoclonal Antibodies.

Protein aggregation remains a major area of focus in the production of monoclonal antibodies. Improving the intrinsic properties of antibodies can improve manufacturability, attrition rates, safety, formulation, titers, immunogenicity, and solubility. Here, we explore the potential of predicting and reducing the aggregation propensity of monoclonal antibodies, based on the identification of aggregation-prone regions and their contribution to the thermodynamic stability of the protein. Although aggregation-prone regions are thought to occur in the antigen binding region to drive hydrophobic binding with antigen, we were able to rationally design variants that display a marked decrease in aggregation propensity while retaining antigen binding through the introduction of artificial aggregation gatekeeper residues. The reduction in aggregation propensity was accompanied by an increase in expression titer, showing that reducing protein aggregation is beneficial throughout the development process. The data presented show that this approach can significantly reduce liabilities in novel therapeutic antibodies and proteins, leading to a more efficient path to clinical studies.

Structural Mechanisms of Voltage Sensing in G Protein-Coupled Receptors.

G-protein-coupled receptors (GPCRs) form the largest superfamily of membrane proteins and one-third of all drug targets in humans. A number of recent studies have reported evidence for substantial voltage regulation of GPCRs. However, the structural basis of GPCR voltage sensing has remained enigmatic. Here, we present atomistic simulations on the δ-opioid and M2 muscarinic receptors, which suggest a structural and mechanistic explanation for the observed voltage-induced functional effects. The simulations reveal that the position of an internal Na(+) ion, recently detected to bind to a highly conserved aqueous pocket in receptor crystal structures, strongly responds to voltage changes. The movements give rise to gating charges in excellent agreement with previous experimental recordings. Furthermore, free energy calculations show that these rearrangements of Na(+) can be induced by physiological membrane voltages. Due to its role in receptor function and signal bias, the repositioning of Na(+) has important general implications for signal transduction in GPCRs.

Accurate and Rigorous Prediction of the Changes in Protein Free Energies in a Large-Scale Mutation Scan.

The prediction of mutation-induced free-energy changes in protein thermostability or protein-protein binding is of particular interest in the fields of protein design, biotechnology, and bioengineering. Herein, we achieve remarkable accuracy in a scan of 762 mutations estimating changes in protein thermostability based on the first principles of statistical mechanics. The remaining error in the free-energy estimates appears to be due to three sources in approximately equal parts, namely sampling, force-field inaccuracies, and experimental uncertainty. We propose a consensus force-field approach, which, together with an increased sampling time, leads to a free-energy prediction accuracy that matches those reached in experiments. This versatile approach enables accurate free-energy estimates for diverse proteins, including the prediction of changes in the melting temperature of the membrane protein neurotensin receptor 1.

The impact of data integrity on decision making in early lead discovery.

Data driven decision making is a key element of today's pharmaceutical research, including early drug discovery. It comprises questions like which target to pursue, which chemical series to pursue, which compound to make next, or which compound to select for advanced profiling and promotion to pre-clinical development. In the following paper we will exemplify how data integrity, i.e. the context data is generated in and auxiliary information that is provided for individual result records, can influence decision making in early lead discovery programs. In addition we will describe some approaches which we pursue at Boehringer Ingelheim to reduce the risk for getting misguided.

Structure-guided residence time optimization of a dabigatran reversal agent.

Novel oral anticoagulants are effective and safe alternatives to vitamin-K antagonists for anticoagulation therapy. However, anticoagulation therapy in general is associated with an elevated risk of bleeding. Idarucizumab is a reversal agent for the direct thrombin inhibitor, dabigatran etexilate (Pradaxa®) and is currently in Phase 3 studies. Here, we report data on the antibody fragment aDabi-Fab2, a putative backup molecule for idarucizumab. Although aDabi-Fab2 completely reversed effects of dabigatran in a rat model in vivo, we observed significantly reduced duration of action compared to idarucizumab. Rational protein engineering, based on the X-ray structure of aDabi-Fab2, led to the identification of mutant Y103W. The mutant had optimized shape complementarity to dabigatran while maintaining an energetically favored hydrogen bond. It displayed increased affinity for dabigatran, mainly driven by a slower off-rate. Interestingly, the increased residence time translated into longer duration of action in vivo. It was thus possible to further enhance the efficacy of aDabi-Fab2 based on rational design, giving it the potential to serve as a back-up candidate for idarucizumab.

Boosting antibody developability through rational sequence optimization.

The application of monoclonal antibodies as commercial therapeutics poses substantial demands on stability and properties of an antibody. Therapeutic molecules that exhibit favorable properties increase the success rate in development. However, it is not yet fully understood how the protein sequences of an antibody translates into favorable in vitro molecule properties. In this work, computational design strategies based on heuristic sequence analysis were used to systematically modify an antibody that exhibited a tendency to precipitation in vitro. The resulting series of closely related antibodies showed improved stability as assessed by biophysical methods and long-term stability experiments. As a notable observation, expression levels also improved in comparison with the wild-type candidate. The methods employed to optimize the protein sequences, as well as the biophysical data used to determine the effect on stability under conditions commonly used in the formulation of therapeutic proteins, are described. Together, the experimental and computational data led to consistent conclusions regarding the effect of the introduced mutations. Our approach exemplifies how computational methods can be used to guide antibody optimization for increased stability.

What can we learn from molecular dynamics simulations for GPCR drug design?

Recent years have seen a tremendous progress in the elucidation of experimental structural information for G-protein coupled receptors (GPCRs). Although for the vast majority of pharmaceutically relevant GPCRs structural information is still accessible only by homology models the steadily increasing amount of structural information fosters the application of structure-based drug design tools for this important class of drug targets. In this article we focus on the application of molecular dynamics (MD) simulations in GPCR drug discovery programs. Typical application scenarios of MD simulations and their scope and limitations will be described on the basis of two selected case studies, namely the binding of small molecule antagonists to the human CC chemokine receptor 3 (CCR3) and a detailed investigation of the interplay between receptor dynamics and solvation for the binding of small molecules to the human muscarinic acetylcholine receptor 3 (hM3R).

pmx: Automated protein structure and topology generation for alchemical perturbations.

Computational protein design requires methods to accurately estimate free energy changes in protein stability or binding upon an amino acid mutation. From the different approaches available, molecular dynamics-based alchemical free energy calculations are unique in their accuracy and solid theoretical basis. The challenge in using these methods lies in the need to generate hybrid structures and topologies representing two physical states of a system. A custom made hybrid topology may prove useful for a particular mutation of interest, however, a high throughput mutation analysis calls for a more general approach. In this work, we present an automated procedure to generate hybrid structures and topologies for the amino acid mutations in all commonly used force fields. The described software is compatible with the Gromacs simulation package. The mutation libraries are readily supported for five force fields, namely Amber99SB, Amber99SB*-ILDN, OPLS-AA/L, Charmm22*, and Charmm36.

A designed conformational shift to control protein binding specificity.

In a conformational selection scenario, manipulating the populations of binding-competent states should be expected to affect protein binding. We demonstrate how in silico designed point mutations within the core of ubiquitin, remote from the binding interface, change the binding specificity by shifting the conformational equilibrium of the ground-state ensemble between open and closed substates that have a similar population in the wild-type protein. Binding affinities determined by NMR titration experiments agree with the predictions, thereby showing that, indeed, a shift in the conformational equilibrium enables us to alter ubiquitin's binding specificity and hence its function. Thus, we present a novel route towards designing specific binding by a conformational shift through exploiting the fact that conformational selection depends on the concentration of binding-competent substates.

Computational method to identify druggable binding sites that target protein-protein interactions.

Protein-protein interactions are implicated in the pathogenesis of many diseases and are therefore attractive but challenging targets for drug design. One of the challenges in development is the identification of potential druggable binding sites in protein interacting interfaces. Identification of interface surfaces can greatly aid rational drug design of small molecules inhibiting protein-protein interactions. In this work, starting from the structure of a free monomer, we have developed a ligand docking based method, called "FindBindSite" (FBS), to locate protein-protein interacting interface regions and potential druggable sites in this interface. FindBindSite utilizes the results from docking a small and diverse library of small molecules to the entire protein structure. By clustering regions with the highest docked ligand density from FBS, we have shown that these high ligand density regions strongly correlate with the known protein-protein interacting surfaces. We have further predicted potential druggable binding sites on the protein surface using FBS, with druggability being defined as the site with high density of ligands docked. FBS shows a hit rate of 71% with high confidence and 93% with lower confidence for the 41 proteins used for predicting druggable binding sites on the protein-protein interface. Mining the regions of lower ligand density that are contiguous with the high scoring high ligand density regions from FBS, we were able to map 70% of the protein-protein interacting surface in 24 out of 41 structures tested. We also observed that FBS has limited sensitivity to the size and nature of the small molecule library used for docking. The experimentally determined hotspot residues for each protein-protein complex cluster near the best scoring druggable binding sites identified by FBS. These results validate the ability of our technique to identify druggable sites within protein-protein interface regions that have the maximal possibility of interface disruption.