SAMPL7 physical property prediction from EC-RISM theory

Inspired by the successful application of the embedded cluster reference interaction site model (EC-RISM), a combination of quantum–mechanical calculations with three-dimensional RISM theory to predict Gibbs energies of species in solution within the SAMPL6.1 (acidity constants, pKa) and SAMPL6.2 (octanol–water partition coefficients, log P) the methodology was applied to the recent SAMPL7 physical property challenge on aqueous pKa and octanol–water log P values. Not part of the challenge but provided by the organizers, we also computed distribution coefficients log D7.4 from predicted pKa and log P data. While macroscopic pKa predictions compared very favorably with experimental data (root mean square error, RMSE 0.72 pK units), the performance of the log P model (RMSE 1.84) fell behind expectations from the SAMPL6.2 challenge, leading to reasonable log D7.4 predictions (RMSE 1.69) from combining the independent calculations. In the post-submission phase, conformations generated by different methodology yielded results that did not significantly improve the original predictions. While overall satisfactory compared to previous log D challenges, the predicted data suggest that further effort is needed for optimizing the robustness of the partition coefficient model within EC-RISM calculations and for shaping the agreement between experimental conditions and the corresponding model description

The SAMPL6 challenge on predicting octanol–water partition coefficients from EC-RISM theory

Results are reported for octanol–water partition coefficients (log P) of the neutral states of drug-like molecules provided during the SAMPL6 (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenge from applying the “embedded cluster reference interaction site model” (EC-RISM) as a solvation model for quantum-chemical calculations. Following the strategy outlined during earlier SAMPL challenges we first train 1- and 2-parameter water-free (“dry”) and water-saturated (“wet”) models for n-octanol solvation Gibbs energies with respect to experimental values from the “Minnesota Solvation Database” (MNSOL), yielding a root mean square error (RMSE) of 1.5 kcal mol−1 for the best-performing 2-parameter wet model, while the optimal water model developed for the pKa part of the SAMPL6 challenge is kept unchanged (RMSE 1.6 kcal mol−1 for neutral compounds from a model trained on both neutral and ionic species). Applying these models to the blind prediction set yields a log P RMSE of less than 0.5 for our best model (2-parameters, wet). Further analysis of our results reveals that a single compound is responsible for most of the error, SM15, without which the RMSE drops to 0.2. Since this is the only compound in the challenge dataset with a hydroxyl group we investigate other alcohols for which Gibbs energy of solvation data for both water and n-octanol are available in the MNSOL database to demonstrate a systematic cause of error and to discuss strategies for improvement

Quantum–mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?

Joint academic–industrial projects supporting drug discovery are frequently pursued to deploy and benchmark cutting-edge methodical developments from academia in a real-world industrial environment at different scales. The dimensionality of tasks ranges from small molecule physicochemical property assessment over protein–ligand interaction up to statistical analyses of biological data. This way, method development and usability both benefit from insights gained at both ends, when predictiveness and readiness of novel approaches are confirmed, but the pharmaceutical drug makers get early access to novel tools for the quality of drug products and benefit of patients. Quantum–mechanical and simulation methods particularly fall into this group of methods, as they require skills and expense in their development but also significant resources in their application, thus are comparatively slowly dripping into the realm of industrial use. Nevertheless, these physics-based methods are becoming more and more useful. Starting with a general overview of these and in particular quantum–mechanical methods for drug discovery we review a decade-long and ongoing collaboration between Sanofi and the Kast group focused on the application of the embedded cluster reference interaction site model (EC-RISM), a solvation model for quantum chemistry, to study small molecule chemistry in the context of joint participation in several SAMPL (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenges. Starting with early application to tautomer equilibria in water (SAMPL2) the methodology was further developed to allow for challenge contributions related to predictions of distribution coefficients (SAMPL5) and acidity constants (SAMPL6) over the years. Particular emphasis is put on a frequently overlooked aspect of measuring the quality of models, namely the retrospective analysis of earlier datasets and predictions in light of more recent and advanced developments. We therefore demonstrate the performance of the current methodical state of the art as developed and optimized for the SAMPL6 pKa and octanol–water log P challenges when re-applied to the earlier SAMPL5 cyclohexane-water log D and SAMPL2 tautomer equilibria datasets. Systematic improvement is not consistently found throughout despite the similarity of the problem class, i.e. protonation reactions and phase distribution. Hence, it is possible to learn about hidden bias in model assessment, as results derived from more elaborate methods do not necessarily improve quantitative agreement. This indicates the role of chance or coincidence for model development on the one hand which allows for the identification of systematic error and opportunities toward improvement and reveals possible sources of experimental uncertainty on the other. These insights are particularly useful for further academia–industry collaborations, as both partners are then enabled to optimize both the computational and experimental settings for data generation

Chemically stabilized DNA barcodes for DNA-encoded chemistry

DNA-encoded compound libraries are a widely used small molecule screening technology. One important aim in library design is the coverage of chemical space through structurally diverse molecules. Yet, the chemical reactivity of native DNA barcodes limits the toolbox of reactions for library design. Substituting the chemically vulnerable purines by 7-deazaadenine, which exhibits tautomerization stability similar to natural adenine with respect to the formation of stable Watson–Crick pairs, yielded ligation-competent, amplifiable, and readable DNA barcodes for encoded chemistry with enhanced stability against protic acid- and metal ion-promoted depurination. The barcode stability allowed for straightforward translation of 16 exemplary reactions that included isocyanide multicomponent reactions, acid-promoted Pictet–Spengler and Biginelli reactions, and metal-promoted pyrazole syntheses on controlled pore glass-coupled barcodes for diverse DEL design. The Boc protective group of reaction products offered a convenient handle for encoded compound purification

Minimal Viral Potassium Channels for Studying Protein/Lipid Interaction

n/

Identification of Intrahelical Bifurcated H‑Bonds as a New Type of Gate in K+ Channels

Gating of ion channels is based on structural transitions between open and closed states. To uncover the chemical basis of individual gates, we performed a comparative experimental and computational analysis between two K+ channels, KcvS and KcvNTS. These small viral encoded K+ channel proteins, with a monomer size of only 82 amino acids, resemble the pore module of all complex K+ channels in terms of structure and function. Even though both proteins share about 90% amino acid sequence identity, they exhibit different open probabilities with ca. 90% in KcvNTS and 40% in KcvS. Single channel analysis, mutational studies and molecular dynamics simulations show that the difference in open probability is caused by one long closed state in KcvS. This state is structurally created in the tetrameric channel by a transient, Ser mediated, intrahelical hydrogen bond. The resulting kink in the inner transmembrane domain swings the aromatic rings from downstream Phes in the cavity of the channel, which blocks ion flux. The frequent occurrence of Ser or Thr based helical kinks in membrane proteins suggests that a similar mechanism could also occur in the gating of other ion channels. Includes Supporting Informatio

Identification of Intrahelical Bifurcated H‑Bonds as a New Type of Gate in K+ Channels

Gating of ion channels is based on structural transitions between open and closed states. To uncover the chemical basis of individual gates, we performed a comparative experimental and computational analysis between two K+ channels, KcvS and KcvNTS. These small viral encoded K+ channel proteins, with a monomer size of only 82 amino acids, resemble the pore module of all complex K+ channels in terms of structure and function. Even though both proteins share about 90% amino acid sequence identity, they exhibit different open probabilities with ca. 90% in KcvNTS and 40% in KcvS. Single channel analysis, mutational studies and molecular dynamics simulations show that the difference in open probability is caused by one long closed state in KcvS. This state is structurally created in the tetrameric channel by a transient, Ser mediated, intrahelical hydrogen bond. The resulting kink in the inner transmembrane domain swings the aromatic rings from downstream Phes in the cavity of the channel, which blocks ion flux. The frequent occurrence of Ser or Thr based helical kinks in membrane proteins suggests that a similar mechanism could also occur in the gating of other ion channels. Includes Supporting Informatio

Relevance of Lysine Snorkeling in the Outer Transmembrane Domain of Small Viral Potassium Ion Channels

Transmembrane domains (TMDs) are often flanked by Lys or Arg because they keep their aliphatic parts in the bilayer and their charged groups in the polar interface. Here we examine the relevance of this so-called “snorkeling” of a cationic amino acid, which is conserved in the outer TMD of small viral K+ channels. Experimentally, snorkeling activity is not mandatory for KcvPBCV-1 because K29 can be replaced by most of the natural amino acids without any corruption of function. Two similar channels, KcvATCV-1 and KcvMT325, lack a cytosolic N-terminus, and neutralization of their equivalent cationic amino acids inhibits their function. To understand the variable importance of the cationic amino acids, we reanalyzed molecular dynamics simulations of KcvPBCV-1 and N-terminally truncated mutants; the truncated mutants mimic KcvATCV-1 and KcvMT325. Structures were analyzed with respect to membrane positioning in relation to the orientation of K29. The results indicate that the architecture of the protein (including the selectivity filter) is only weakly dependent on TMD length and protonation of K29. The penetration depth of Lys in a given protonation state is independent of the TMD architecture, which leads to a distortion of shorter proteins. The data imply that snorkeling can be important for K+ channels; however, its significance depends on the architecture of the entire TMD. The observation that the most severe N-terminal truncation causes the outer TMD to move toward the cytosolic side suggests that snorkeling becomes more relevant if TMDs are not stabilized in the membrane by other domains