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 third-order density-functional tight-binding. In our earlier study [Inakollu and Yu, "A systematic benchmarking of computational vibrational spectroscopy with DFTB3: Normal mode analysis and fast Fourier transform dipole autocorrelation function," J. Comput. Chem. 39, 2067 (2018)] of deprotonated serine, we observed a significant difference in the vibrational spectra with the classical MD simulations compared to the infrared multiple photon dissociation spectra. It was postulated that this is due to neglecting the nuclear quantum effects (NQEs). In this work, NQEs are considered in spectral calculation using the TRPMD simulations. With the help of potential of mean force calculations, the conformational space of deprotonated serine is analyzed and used to understand the difference in the spectra of classical MD and TRPMD simulations at 298.15 and 100 K. The high-frequency vibrational bands in the spectra are characterized using Fourier transform localized vibrational mode (FT-νN AC) 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-νN AC and the interatomic distance histograms, the high-frequency vibrational bands are discussed in detail.

A systematic benchmarking of computational vibrational spectroscopy with DFTB3: Normal mode analysis and fast Fourier transform dipole autocorrelation function.

Computational vibrational spectroscopy serves as an important tool in the interpretation of experimental infrared (IR) spectra. In this article, we present a systematic benchmarking study of DFTB3 with two different computational vibrational spectroscopic methods, based on either normal mode analysis (NMA) or fast Fourier transform dipole autocorrelation function (FT-DAC). The results were compared with experimental data and theoretical calculations with B3LYP/cc-pVTZ. The empirical scaling factors for DFTB3/NMA, DFTB3-freq/NMA, and DFTB3/FT-DAC methods are 0.9993, 1.0059, and 0.9982, respectively. We also demonstrate the significance of anharmonicity and conformational sampling in vibrational spectroscopic calculations on flexible molecules. As expected, DFTB3/FT-DAC predicted the anharmonic vibrational peaks more accurately than DFTB3/NMA and NMA spectra are highly dependent on the initial structures. The potential limitations of DFTB3 for vibrational spectroscopic calculations and the challenges in assigning the FT-DAC spectral peaks were noted. DFTB3/FT-DAC is expected to serve as a promising technique in computational spectroscopy in complex biomolecular systems. © 2018 Wiley Periodicals, Inc.

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 (Z-score) can significantly improve virtual screening enrichments over any of the single screening methods. We show that an augmented Z-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.