Comparative Studies of IR Spectra of Deprotonated Serine with Classical and Thermostated Ring Polymer Molecular Dynamics Simulations

Here we report the vibrational spectra of deprotonated serine calculated from the classical molecular dynamics (MD) simulations and thermostated ring-polymer molecular dynamics (TRPMD) simulation with DFTB3. In our earlier study1 of deprotonated serine, we observed a significant difference in the vibrational spectra with the classical MD simulations compared to the infrared multiple photon dissociation (IRMPD) spectra. It was postulated that this is due to neglecting the nuclear quantum effects (NQEs). In this work, NQEs are considered in the spectral calculation using the TRPMD simulations. With the help of potential of mean force (PMF) calculations, the conformational space of deprotonated serine is analysed and used to understand the difference in the spectra of classical MD and TRPMD simulations at 298.15 K and 100 K. The high-frequency vibrational bands in the spectra are characterised using Fourier transform localised vibrational mode (FT-νNAC) and interatomic distance histograms. At room temperature, the quantum effects are less significant, and the free energy profiles in the classical MD and the TRPMD simulations are very similar. However, the hydrogen bond between the hydroxyl-carboxyl bond is slightly stronger in TRPMD simulations. At 100 K, the quantum effects are more prominent, especially in the 2600-3600 cm−1, and the free energy profile slightly differs between the classical MD and TRPMD simulations. Using the FT-νNAC and the interatomic distance histograms, the high-frequency vibrational bands are discussed in detail

Boosting Virtual Screening Enrichments with Data Fusion: Coalescing Hits from Two-Dimensional Fingerprints, Shape, and Docking

Virtual screening is an effective way to find hits in drug discovery, with approaches ranging from fast information-based similarity methods to more computationally intensive physics-based docking methods. However, the best approach to use for a given project is not clear in advance of the screen. In this work, we show that combining results from multiple methods using a standard score (<i>Z</i>-score) can significantly improve virtual screening enrichments over any of the single screening methods. We show that an augmented <i>Z</i>-score, which considers the best two out of three scores for a given compound, outperforms previously published data fusion algorithms. We use three different virtual screening methods (two-dimensional (2D) fingerprint similarity, shape-based similarity, and docking) and study two different databases (DUD and MDDR). The average enrichment in the top 1% was improved by 9% for DUD and 25% for the MDDR, compared with the top individual method. Improvements of 22% for DUD and 43% for MDDR are seen over the average of the three individual methods. Statistics are presented that show a high significance associated with the findings in this work