Energy-entropy prediction of octanol–water logP of SAMPL7 N-acyl sulfonamide bioisosters

From Springer Nature via Jisc Publications RouterHistory: received 2021-03-04, accepted 2021-06-17, registration 2021-06-18, pub-print 2021-07, pub-electronic 2021-07-10, online 2021-07-10Publication status: PublishedFunder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/L015218/1, EP/N025105/1Abstract: Partition coefficients quantify a molecule’s distribution between two immiscible liquid phases. While there are many methods to compute them, there is not yet a method based on the free energy of each system in terms of energy and entropy, where entropy depends on the probability distribution of all quantum states of the system. Here we test a method in this class called Energy Entropy Multiscale Cell Correlation (EE-MCC) for the calculation of octanol–water logP values for 22 N-acyl sulfonamides in the SAMPL7 Physical Properties Challenge (Statistical Assessment of the Modelling of Proteins and Ligands). EE-MCC logP values have a mean error of 1.8 logP units versus experiment and a standard error of the mean of 1.0 logP units for three separate calculations. These errors are primarily due to getting sufficiently converged energies to give accurate differences of large numbers, particularly for the large-molecule solvent octanol. However, this is also an issue for entropy, and approximations in the force field and MCC theory also contribute to the error. Unique to MCC is that it explains the entropy contributions over all the degrees of freedom of all molecules in the system. A gain in orientational entropy of water is the main favourable entropic contribution, supported by small gains in solute vibrational and orientational entropy but offset by unfavourable changes in the orientational entropy of octanol, the vibrational entropy of both solvents, and the positional and conformational entropy of the solute

Thermodynamic Origin of Differential Excipient-Lysozyme Interactions

From Frontiers via Jisc Publications RouterHistory: collection 2021, received 2021-03-31, accepted 2021-05-25, epub 2021-06-11Publication status: PublishedUnderstanding the intricate interplay of interactions between proteins, excipients, ions and water is important to achieve the effective purification and stable formulation of protein therapeutics. The free energy of lysozyme interacting with two kinds of polyanionic excipients, citrate and tripolyphosphate, together with sodium chloride and TRIS-buffer, are analysed in multiple-walker metadynamics simulations to understand why tripolyphosphate causes lysozyme to precipitate but citrate does not. The resulting multiscale decomposition of energy and entropy components for water, sodium chloride, excipients and lysozyme reveals that lysozyme is more stabilised by the interaction of tripolyphosphate with basic residues. This is accompanied by more sodium ions being released into solution from tripolyphosphate than for citrate, whilst the latter instead has more water molecules released into solution. Even though lysozyme aggregation is not directly probed in this study, these different mechanisms are suspected to drive the cross-linking between lysozyme molecules with vacant basic residues, ultimately leading to precipitation

Thermodynamic Origin of Differential Excipient-Lysozyme Interactions

Understanding the intricate interplay of interactions between proteins, excipients, ions and water is important to achieve the effective purification and stable formulation of protein therapeutics. The free energy of lysozyme interacting with two kinds of polyanionic excipients, citrate and tripolyphosphate, together with sodium chloride and TRIS-buffer, are analysed in multiple-walker metadynamics simulations to understand why tripolyphosphate causes lysozyme to precipitate but citrate does not. The resulting multiscale decomposition of energy and entropy components for water, sodium chloride, excipients and lysozyme reveals that lysozyme is more stabilised by the interaction of tripolyphosphate with basic residues. This is accompanied by more sodium ions being released into solution from tripolyphosphate than for citrate, whilst the latter instead has more water molecules released into solution. Even though lysozyme aggregation is not directly probed in this study, these different mechanisms are suspected to drive the cross-linking between lysozyme molecules with vacant basic residues, ultimately leading to precipitation

rMD17-aq dataset

<h2>The rMD17-aq dataset:</h2><h3>Citation:</h3><p>Jas Kalayan, Ismaeel Ramzan, Christopher D. WIlliams, Neil A. Burton and Richard A. Bryce "A neural network potential based on pairwise resolved atomic forces and energies", publication TBC</p><h3>Description:</h3><p>QM/MM aqueous simulations of the 10 molecules from the original MD17 dataset by Chmiela et al. (and revised dataset by Christensen et al.) were performed surrounded by 400 SPC/E water molecules. Each simulation was performed for 100~ps at 500K temperature and 1 atm pressure. The solute conformations sampled from the QM/MM simulations performed with CP2K are used to recalculate forces and energies of each conformation in Gaussian with a denser integral grid to effectively remove numerical noise.</p><p>We also include an 11th molecule of a higher energy conformer of salicylic acid (directory name: salicylic_high_energy_conformer) in addition to the lower energy conformer sampled in the MD17 dataset.</p><p>For each molecule (excluding all surrounding water molecules), this dataset contains the nuclear charges, coordinates (Angstrom), forces (kcal/mol/Ang), energies (kcal/mol/Ang) and partial atomic charges (atomic units) in space separated formats outputted from the numpy savetxt function.</p><h3>The data:</h3><p>The files in each molecule directory are:</p><p><i>'nuclear_charges.txt'</i> : The nuclear charges for each atom in a molecule.</p><p><i>'coords.txt'</i>  : The Cartesian coordinates for each atom in a conformation (Angstrom units)</p><p><i>'energies.txt' </i> : The total energy of each conformation (kcal/mol units)</p><p><i>'forces.txt'</i>  : The Cartesian forces for each atom in a conformation (kcal/mol/Angstrom units)</p><p><i>'charges.txt'</i>  : The partial ElectroStatic Potential (ESP) atomic charges (atomic units)</p><p><i>'molecules.prmtop'</i> : The Amber formatted topology file containing the MM parameters for water molecules (solute MM parameters are not used)</p><p><i>'minimised.rst.pdb' </i>: The initial coordinates of a minimised system used to perform QM/MM simulations in CP2K</p><h3>The input data:</h3><p>The input files to perform simulations and single point energy calculations are provided in the '_cp2k_gaussian_example_inputs' directory. These files are:</p><p><i>'cp2k-qmmm-example.inp'</i> : input file for the QM/MM simulations performed with CP2K. The number of QM atom kinds are replaced with placeholders CCC, OOO, HHH, NNN for the number of carbon, oxygen, hydrogen and nitrogen atoms respectively in a solute molecule. The system dimensions placeholder XXYYZZ can be replaced with the BOX_DIMENSIONS in the molecules.prmtop file.</p><p><i>'def2-svp.1.cp2k'</i> : the basis set used in QM/MM simulations</p><p><i>'gaussain_input.com'</i>: an example of a Gaussian input file for single point energy calculations for aspirin.</p&gt

Energy-Entropy Prediction of Octanol-Water LogP of SAMPL7 N-Acyl Sulfonamide Bioisosters

Partition coefficients quantify a molecule’s distribution between two immiscible liquid phases. While there are many methods to compute them, there is not yet a method based on the free energy of each system in terms of energy and entropy, where entropy depends on the probability distribution of all quantum states of the system. Here we test a method in this class called Energy Entropy Multiscale Cell Correlation (EE-MCC) for the calculation of octanol–water logP values for 22 N-acyl sulfonamides in the SAMPL7 Physical Properties Challenge (Statistical Assessment of the Modelling of Proteins and Ligands). EE-MCC logP values have a mean error of 1.8 logP units versus experiment and a standard error of the mean of 1.0 logP units for three separate calculations. These errors are primarily due to getting sufficiently converged energies to give accurate differences of large numbers, particularly for the large-molecule solvent octanol. However, this is also an issue for entropy, and approximations in the force field and MCC theory also contribute to the error. Unique to MCC is that it explains the entropy contributions over all the degrees of freedom of all molecules in the system. A gain in orientational entropy of water is the main favourable entropic contribution, supported by small gains in solute vibrational and orientational entropy but offset by unfavourable changes in the orientational entropy of octanol, the vibrational entropy of both solvents, and the positional and conformational entropy of the solute. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10822-021-00401-w

Total free energy analysis of fully hydrated proteins

The total free energy of a hydrated biomolecule and its corresponding decomposition of energy and entropy provides detailed information about regions of thermodynamic stability or instability. The free energies of four hydrated globular proteins with different net charges are calculated from a molecular dynamics simulation, with the energy coming from the system Hamiltonian and entropy using multiscale cell correlation. Water is found to be most stable around anionic residues, intermediate around cationic and polar residues, and least stable near hydrophobic residues, especially when more buried, with stability displaying moderate entropy-enthalpy compensation. Conversely, anionic residues in the proteins are energetically destabilized relative to singly solvated amino acids, while trends for other residues are less clear-cut. Almost all residues lose intraresidue entropy when in the protein, enthalpy changes are negative on average but may be positive or negative, and the resulting overall stability is moderate for some proteins and negligible for others. The free energy of water around single amino acids is found to closely match existing hydrophobicity scales. Regarding the effect of secondary structure, water is slightly more stable around loops, of intermediate stability around β strands and turns, and least stable around helices. An interesting asymmetry observed is that cationic residues stabilize a residue when bonded to its N-terminal side but destabilize it when on the C-terminal side, with a weaker reversed trend for anionic residues.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/175503/1/prot26411.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175503/2/prot26411_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175503/3/prot26411-sup-0001-Supinfo.pd