Crystal lattice energy is a key property affecting the ease of processing pharmaceutical materials during manufacturing, as well as product performance. We present an extensive comparison of 324 force-field protocols for calculating the lattice energies of single component, organic molecular crystals (further restricted to Z′ less than or equal to one), corresponding to a wide variety of force-fields (DREIDING, Universal, CVFF, PCFF, COMPASS, COM-PASSII), optimization routines, and other variations, which could be implemented as part of an automated workflow using the industry standard Materials Studio software. All calculations were validated using a large new dataset (SUB-BIG), which we make publicly available. This dataset comprises public domain sublimation data, from which estimated experimental lattice energies were derived, linked to 235 molecular crystals. Analysis of pharmaceutical relevance was performed according to two distinct methods based upon (A) public and (B) proprietary data. These identified overlapping subsets of SUB-BIG comprising (A) 172 and (B) 63 crystals, of putative pharmaceutical relevance, respectively. We recommend a protocol based on the COMPASSII force field for lattice energy calculations of general organic or pharmaceutically relevant molecular crystals. This protocol was the most highly ranked prior to subsetting and was either the top ranking or amongst the top 15 protocols (top 5%) following subsetting of the dataset according to putative pharmaceutical relevance. Further analysis identified scenarios where the lattice energies calculated using the recommended force-field protocol should either be disregarded (values greater than or equal to zero and/or the messages generated by the automated workflow indicate extraneous atoms were added to the unit cell) or treated cautiously (values less than or equal to −249 kJ/mol), as they are likely to be inaccurate. Application of the recommended force-field protocol, coupled with these heuristic filtering criteria, achieved an root mean-squared error (RMSE) around 17 kJ/mol (mean absolute deviation (MAD) around 11 kJ/mol, Spearman's rank correlation coefficient of 0.88) across all 226 SUB-BIG structures retained after removing calculation failures and applying the filtering criteria. Across these 226 structures, the estimated experimental lattice energies ranged from −60 to −269 kJ/mol, with a standard deviation around 29 kJ/mol. The performance of the recommended protocol on pharmaceutically relevant crystals could be somewhat reduced, with an RMSE around 20 kJ/ mol (MAD around 13 kJ/mol, Spearman's rank correlation coefficient of 0.76) obtained on 62 structures retained following filtering according to pharmaceutical relevance method B, for which the distribution of experimental values was similar. For a diverse set of 17 SUB-BIG entries, deemed pharmaceutically relevant according to method B, this recommended force-field protocol was compared to dispersion corrected density functional theory (DFT) calculations (PBE + TS). These calc...

Chaperones are fundamental to regulating the heat shock response, mediating protein recovery from thermal‐induced misfolding and aggregation. Using the QconCAT strategy and selected reaction monitoring (SRM) for absolute protein quantification, we have determined copy per cell values for 49 key chaperones in Saccharomyces cerevisiae under conditions of normal growth and heat shock. This work extends a previous chemostat quantification study by including up to five Q‐peptides per protein to improve confidence in protein quantification. In contrast to the global proteome profile of S. cerevisiae in response to heat shock, which remains largely unchanged as determined by label‐free quantification, many of the chaperones are upregulated with an average two‐fold increase in protein abundance. Interestingly, eight of the significantly upregulated chaperones are direct gene targets of heat shock transcription factor‐1. By performing absolute quantification of chaperones under heat stress for the first time, we were able to evaluate the individual protein‐level response. Furthermore, this SRM data was used to calibrate label‐free quantification values for the proteome in absolute terms, thus improving relative quantification between the two conditions. This study significantly enhances the largely transcriptomic data available in the field and illustrates a more nuanced response at the protein level.