Solvated interaction energy: from small-molecule to antibody drug design

Scoring functions are ubiquitous in structure-based drug design as an aid to predicting binding modes and estimating binding affinities. Ideally, a scoring function should be broadly applicable, obviating the need to recalibrate and refit its parameters for every new target and class of ligands. Traditionally, drugs have been small molecules, but in recent years biologics, particularly antibodies, have become an increasingly important if not dominant class of therapeutics. This makes the goal of having a transferable scoring function, i.e., one that spans the range of small-molecule to protein ligands, even more challenging. One such broadly applicable scoring function is the Solvated Interaction Energy (SIE), which has been developed and applied in our lab for the last 15 years, leading to several important applications. This physics-based method arose from efforts to understand the physics governing binding events, with particular care given to the role played by solvation. SIE has been used by us and many independent labs worldwide for virtual screening and discovery of novel small-molecule binders or optimization of known drugs. Moreover, without any retraining, it is found to be transferrable to predictions of antibody-antigen relative binding affinities and as accurate as functions trained on protein-protein binding affinities. SIE has been incorporated in conjunction with other scoring functions into ADAPT (Assisted Design of Antibody and Protein Therapeutics), our platform for affinity modulation of antibodies. Application of ADAPT resulted in the optimization of several antibodies with 10-to-100-fold improvements in binding affinity. Further applications included broadening the specificity of a single-domain antibody to be cross-reactive with virus variants of both SARS-CoV-1 and SARS-CoV-2, and the design of safer antibodies by engineering of a pH switch to make them more selective towards acidic tumors while sparing normal tissues at physiological pH

Exploring rigid-backbone protein docking in biologics discovery: a test using the DARPin scaffold

Accurate protein-protein docking remains challenging, especially for artificial biologics not coevolved naturally against their protein targets, like antibodies and other engineered scaffolds. We previously developed ProPOSE, an exhaustive docker with full atomistic details, which delivers cutting-edge performance by allowing side-chain rearrangements upon docking. However, extensive protein backbone flexibility limits its practical applicability as indicated by unbound docking tests. To explore the usefulness of ProPOSE on systems with limited backbone flexibility, here we tested the engineered scaffold DARPin, which is characterized by its relatively rigid protein backbone. A prospective screening campaign was undertaken, in which sequence-diversified DARPins were docked and ranked against a directed epitope on the target protein BCL-W. In this proof-of-concept study, only a relatively small set of 2,213 diverse DARPin interfaces were selected for docking from the huge theoretical library from mutating 18 amino-acid positions. A computational selection protocol was then applied for enrichment of binders based on normalized computed binding scores and frequency of binding modes against the predefined epitope. The top-ranked 18 designed DARPin interfaces were selected for experimental validation. Three designs exhibited binding affinities to BCL-W in the nanomolar range comparable to control interfaces adopted from known DARPin binders. This result is encouraging for future screening and engineering campaigns of DARPins and possibly other similarly rigid scaffolds against targeted protein epitopes. Method limitations are discussed and directions for future refinements are proposed

TRAF6 and IRF7 Control HIV Replication in Macrophages

The innate immune system recognizes virus infection and evokes antiviral responses which include producing type I interferons (IFNs). The induction of IFN provides a crucial mechanism of antiviral defense by upregulating interferon-stimulated genes (ISGs) that restrict viral replication. ISGs inhibit the replication of many viruses by acting at different steps of their viral cycle. Specifically, IFN treatment prior to in vitro human immunodeficiency virus (HIV) infection stops or significantly delays HIV-1 production indicating that potent inhibitory factors are generated. We report that HIV-1 infection of primary human macrophages decreases tumor necrosis factor receptor-associated factor 6 (TRAF6) and virus-induced signaling adaptor (VISA) expression, which are both components of the IFN signaling pathway controlling viral replication. Knocking down the expression of TRAF6 in macrophages increased HIV-1 replication and augmented the expression of IRF7 but not IRF3. Suppressing VISA had no impact on viral replication. Overexpression of IRF7 resulted in enhanced viral replication while knocking down IRF7 expression in macrophages significantly reduced viral output. These findings are the first demonstration that TRAF6 can regulate HIV-1 production and furthermore that expression of IRF7 promotes HIV-1 replication

Theory and application of medium to high throughput prediction method techniques for asymmetric catalyst design

With the use of computational methods in the field of drug design becoming ever more prevalent, there is pressure to port these technologies to other fields. One of the fields ripe for application of computational drug design techniques; specifically virtual screening and computer-aided molecular design, is the design and synthesis of asymmetric catalysts. Such methods could either guide the selection of the optimal catalyst(s) for a given reaction and a given substrate or provide an enriched selection of highly efficient asymmetric catalysts which enable the synthetic chemists to focus on the most promising candidates. This would in turn provide savings in time and reduce the costs associated with the synthesis and evaluation of large libraries of molecules. However, to be applicable to the evaluation of a large number of potential catalysts, speed is of utmost importance. This impetus has led to the development of medium to high throughput virtual screening (HTVS) methods for asymmetric catalyst development or assessment, although a very few applications have been reported. These methods typically fall into four classes: methods combining quantum mechanics and molecular mechanics (QM/MM), pure molecular mechanics-based methods \u2013 a class which can be subdivided into static and dynamic transition state modeling \u2013 and lastly quantitative structure selectivity relationship methods (QSSR). This review will cover specific methods within these classes and their application to selected reactions.Peer reviewed: YesNRC publication: Ye

Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2

Abstract Predicting the structure of antibody-antigen complexes has tremendous value in biomedical research but unfortunately suffers from a poor performance in real-life applications. AlphaFold2 (AF2) has provided renewed hope for improvements in the field of protein–protein docking but has shown limited success against antibody-antigen complexes due to the lack of co-evolutionary constraints. In this study, we used physics-based protein docking methods for building decoy sets consisting of low-energy docking solutions that were either geometrically close to the native structure (positives) or not (negatives). The docking models were then fed into AF2 to assess their confidence with a novel composite score based on normalized pLDDT and pTMscore metrics after AF2 structural refinement. We show benefits of the AF2 composite score for rescoring docking poses both in terms of (1) classification of positives/negatives and of (2) success rates with particular emphasis on early enrichment. Docking models of at least medium quality present in the decoy set, but not necessarily highly ranked by docking methods, benefitted most from AF2 rescoring by experiencing large advances towards the top of the reranked list of models. These improvements, obtained without any calibration or novel methodologies, led to a notable level of performance in antibody-antigen unbound docking that was never achieved previously

Rapid prediction of solvation free energy. 2. The first-shell hydration (FiSH) continuum model

Local ordering of water in the first hydration shell around a solute is different from isotropic bulk water. This leads to effects that are captured by explicit solvation models and missed by continuum solvation models which replace the explicit waters with a continuous medium. In this paper, we introduce the First-Shell Hydration (FiSH) model as a first attempt to introduce first-shell effects within a continuum solvation framework. One such effect is charge asymmetry, which is captured by a modified electrostatic term within the FiSH model by introducing a nonlinear correction of atomic Born radii based on the induced surface charge density. A hybrid van der Waals formulation consisting of two continuum zones has been implemented. A shell of water restricted to and uniformly distributed over the solvent-accessible surface (SAS) represents the first solvation shell. A second region starting one solvent diameter away from the SAS is treated as bulk water with a uniform density function. Both the electrostatic and van der Waals terms of the FiSH model have been calibrated against linear interaction energy (LIE) data from molecular dynamics simulations. Extensive testing of the FiSH model was carried out on large hydration data sets including both simple compounds and drug-like molecules. The FiSH model accurately reproduces contributing terms, absolute predictions relative to experimental hydration free energies, and functional class trends of LIE MD simulations. Overall, the implementation of the FiSH model achieves a very acceptable performance and transferability improving over previously developed solvation models, while being complemented by a sound physical foundation.Peer reviewed: YesNRC publication: Ye

Rapid Prediction of Solvation Free Energy. 1. An Extensive Test of Linear Interaction Energy (LIE)

The present study provides a comprehensive systematic analysis on the applicability of the linear interaction energy (LIE) approximation to the prediction of gas-to-water transfer (hydration) free energy. The study is based on molecular dynamics simulations in explicit solvent for an extensive and diverse hydration data set comprising 564 neutral compounds with measured hydration free energies, including a \u201ctraditional\u201d data set and the more challenging drug-like SAMPL1 data set. A highly correlative LIE model was achieved without empirical scaling of the solute-solvent interaction energy terms along with a cavity term calibrated to the experiment. This model was particularly accurate for the \u201ctraditional\u201d data set and of acceptable accuracy for the SAMPL1 data set, with mean-unsigned-errors below 1 kcal/mol and slightly above 2 kcal/mol, respectively. We have analyzed the sensitivity of the LIE model to several parameters such as continuum correction terms applied outside the explicit water shell, the impact of various charging methods, the applicability of single-conformer representation of the solute, and the inclusion of internal energy terms. The parameters with the greatest sensitivity are the charging methods used, with AM1BCC-SP (without AM1 geometry optimization) charges favored over AM1BCC-OPT and RESP charges. The inclusion of the change in intramolecular van der Waals and electrostatic energies between the solution and gas phases can also lead to improved prediction accuracies. Functional group based error analysis identified several chemical classes as minor outliers with systematic errors. A direct comparison of the LIE and free energy perturbation (FEP) approaches using the same force field and charging method shows that the LIE approximation is at least as accurate as the FEP approach with a reduction of computing time by at least 1 order of magnitude.Peer reviewed: YesNRC publication: Ye

An accurate TMT-based approach to quantify and model lysine susceptibility to conjugation via N-hydroxysuccinimide esters in a monoclonal antibody

Abstract Conjugation of small molecules to proteins through N-hydroxysuccinimide (NHS) esters results in a random distribution of small molecules on lysine residues and the protein N-terminus. While mass spectrometry methods have improved characterization of these protein conjugates, it remains a challenge to quantify the occupancy at individual sites of conjugation. Here, we present a method using Tandem Mass Tags (TMT) that enabled the accurate and sensitive quantification of occupancy at individual conjugation sites in the NIST monoclonal antibody. At conjugation levels relevant to antibody drug conjugates in the clinic, site occupancy data was obtained for 37 individual sites, with average site occupancy data across 2 adjacent lysines obtained for an additional 12 sites. Thus, altogether, a measure of site occupancy was obtained for 98% of the available primary amines. We further showed that removal of the Fc-glycan on the NIST mAb increased conjugation at two specific sites in the heavy chain, demonstrating the utility of this method to identify changes in the susceptibility of individual sites to conjugation. This improved site occupancy data allowed calibration of a bi-parametric linear model for predicting the susceptibility of individual lysines to conjugation from 3D-structure based on their solvent exposures and ionization constants. Trained against the experimental data for lysines from the Fab fragment, the model provided accurate predictions of occupancies at lysine sites from the Fc region and the protein N-terminus (R2 = 0.76). This predictive model will enable improved engineering of antibodies for optimal labeling with fluorophores, toxins, or crosslinkers