BIreactive: Expanding the Scope of Reactivity Predictions to Propynamides.

We present the first comprehensive study on the prediction of reactivity for propynamides. Covalent inhibitors like propynamides often show improved potency, selectivity, and unique pharmacologic properties compared to their non-covalent counterparts. In order to achieve this, it is essential to tune the reactivity of the warhead. This study shows how three different in silico methods can predict the in vitro properties of propynamides, a covalent warhead class integrated into approved drugs on the market. Whereas the electrophilicity index is only applicable to individual subclasses of substitutions, adduct formation and transition state energies have a good predictability for the in vitro reactivity with glutathione (GSH). In summary, the reported methods are well suited to estimate the reactivity of propynamides. With this knowledge, the fine tuning of the reactivity is possible which leads to a speed up of the design process of covalent drugs.

Covalent inhibitor reactivity prediction by the electrophilicity index-in and out of scope.

Drug discovery is an expensive and time-consuming process. To make this process more efficient quantum chemistry methods can be employed. The electrophilicity index is one property that can be calculated by quantum chemistry methods, and if calculated correctly gives insight into the reactivity of covalent inhibitors. Herein we present the usage of the electrophilicity index on three common warheads, i.e., acrylamides, 2-chloroacetamides, and propargylamides. We thoroughly examine the properties of the electrophilicity index, show which pitfalls should be avoided, and what the requirements to successfully apply the electrophilicity index are.

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.

Small-Angle X-ray Scattering Curves of Detergent Micelles: Effects of Asymmetry, Shape Fluctuations, Disorder, and Atomic Details.

Small-angle X-ray scattering (SAXS) is a widely used experimental technique, providing structural and dynamic insight into soft-matter complexes and biomolecules under near-native conditions. However, interpreting the one-dimensional scattering profiles in terms of three-dimensional structures and ensembles remains challenging, partly because it is poorly understood how structural information is encoded along the measured scattering angle. We combined all-atom SAXS-restrained ensemble simulations, simplified continuum models, and SAXS experiments of a n-dodecyl-ß-d-maltoside (DDM) micelle to decipher the effects of model asymmetry, shape fluctuations, atomic disorder, and atomic details on SAXS curves. Upon interpreting the small-angle regime, we find remarkable agreement between (i) a two-component triaxial ellipsoid model fitted against the data and (ii) a SAXS-refined all-atom ensemble. However, continuum models fail at wider angles, even if they account for shape fluctuations, disorder, and asymmetry of the micelle. We conclude that modeling atomic details is mandatory for explaining SAXS curves at wider angles.

Interpreting SAXS/WAXS Data with Explicit-Solvent Simulations: A Practical Guide.

Small- and wide-angle X-ray scattering (SAXS/WAXS/SWAXS) have evolved to be accurate tools used to gain structural information of biomolecules in solution. However, the interpretation of SWAXS data remains challenging owing to the low information content of the data and scattering contributions from the solvent. In recent years, methods for the interpretation of SWAXS data based on explicit-solvent molecular dynamics (MD) simulations have become increasingly popular. The physicochemical information in the MD force fields complements the low-information SWAXS data, thereby greatly reducing the risk of overfitting, and the explicit-solvent models may accurately account for scattering contributions from the solvent. In this chapter, we provide a practical introduction to MD-based methods for the interpretation of SWAXS data. First, we present the back-calculation of a SWAXS curve from an MD trajectory as required to validate an MD simulation against experimental SWAXS data. Second, we present the structure refinement of an atomic model against SWAXS data using SAXS-driven MD simulations. Common technical problems together with appropriate solutions are discussed.

SAXS-Restrained Ensemble Simulations of Intrinsically Disordered Proteins with Commitment to the Principle of Maximum Entropy.

Intrinsically disordered proteins (IDPs) play key roles in biology and disease, rationalizing the wide interest in deriving accurate solution ensembles of IDPs. Molecular dynamics (MD) simulations of IDPs often suffer from force-field inaccuracies, suggesting that simulations must be complemented by experimental data to obtain physically correct ensembles. We present a method for integrating small-angle X-ray scattering (SAXS) data on-the-fly into MD simulations of disordered systems, with the aim to bias the conformational sampling toward agreement with ensemble-averaged SAXS data. By coupling a set of parallel replicas to the data and following the principle of maximum entropy, this method applies only a minimal bias. Using the RS peptide as a test case, we analyze the influence of (i) the number of parallel replicas, (ii) the scaling of the force constant for the SAXS-derived biasing energy with the number of parallel replicas, and (iii) the force field. The refined ensembles are cross-validated against experimental 3JHN-Hα couplings and further compared in terms of Cα distance maps and secondary structure content. Remarkably, we find that the applied force field only has a small influence on the SAXS-refined ensemble, suggesting that incorporating SAXS data into MD simulations may greatly reduce the force-field bias.