Best of Both Worlds: Combining Pharma Data and State of the Art Modeling Technology To Improve <i>in Silico</i> p<i>K</i>_a Prediction

In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new <i>in silico</i> p<i>K</i>_a prediction tool with outstanding prediction quality. An existing p<i>K</i>_a prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of ∼14,000 literature p<i>K</i>_a values (∼11,000 compounds, representing literature chemical space) and ∼19,500 p<i>K</i>_a values experimentally determined at Bayer Pharma (∼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of ∼31,000 new p<i>K</i>_a values measured at Bayer. For the largest and most difficult test set with >16,000 p<i>K</i>_a values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (<i>R</i>²) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and <i>R</i>² = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new p<i>K</i>_a prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists

Three-dimensional Structure of the NLRP7 Pyrin Domain: INSIGHT INTO PYRIN-PYRIN-MEDIATED EFFECTOR DOMAIN SIGNALING IN INNATE IMMUNITY*

The innate immune system provides an initial line of defense against infection. Nucleotide-binding domain- and leucine-rich repeat-containing protein (NLR or (NOD-like)) receptors play a critical role in the innate immune response by surveying the cytoplasm for traces of intracellular invaders and endogenous stress signals. NLRs themselves are multi-domain proteins. Their N-terminal effector domains (typically a pyrin or caspase activation and recruitment domain) are responsible for driving downstream signaling and initiating the formation of inflammasomes, multi-component complexes necessary for cytokine activation. However, the currently available structures of NLR effector domains have not yet revealed the mechanism of their differential modes of interaction. Here, we report the structure and dynamics of the N-terminal pyrin domain of NLRP7 (NLRP7 PYD) obtained by NMR spectroscopy. The NLRP7 PYD adopts a six-α-helix bundle death domain fold. A comparison of conformational and dynamics features of the NLRP7 PYD with other PYDs showed distinct differences for helix α3 and loop α2-α3, which, in NLRP7, is stabilized by a strong hydrophobic cluster. Moreover, the NLRP7 and NLRP1 PYDs have different electrostatic surfaces. This is significant, because death domain signaling is driven by electrostatic contacts and stabilized by hydrophobic interactions. Thus, these results provide new insights into NLRP signaling and provide a first molecular understanding of inflammasome formation