MM-PBSA is a post-processing end-state method to calculate free energies of molecules in solution. MMPBSA.py is a program written in Python for streamlining end-state free energy calculations using ensembles derived from molecular dynamics (MD) or Monte Carlo (MC) simulations. Several implicit solvation models are available with MMPBSA.py, including the Poisson-Boltzmann Model, the Generalized Born Model, and the Reference Interaction Site Model. Vibrational frequencies may be calculated using normal mode or quasi-harmonic analysis to approximate the solute entropy. Specific interactions can also be dissected using free energy decomposition or alanine scanning. A parallel implementation significantly speeds up the calculation by dividing frames evenly across available processors. MMPBSA.py is an efficient, user-friendly program with the flexibility to accommodate the needs of users performing end-state free energy calculations. The source code can be downloaded at http://ambermd.org/ with AmberTools, released under the GNU General Public License.
Detailed knowledge of how molecules recognize interaction partners and of the conformational preferences of biomacromolecules is pivotal for understanding biochemical processes. Such knowledge also provides the foundation for the design of novel molecules, as undertaken in pharmaceutical research. Computer-based free energy calculations enable a detailed investigation of the energetic factors that are responsible for molecular stability or binding affinity. The Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach is an efficient method for the calculation of free energies of diverse molecular systems. Here we describe the concepts of this approach and outline the practical proceeding. Furthermore we give an overview of the wide spectrum of problems that have been addressed with this method and of successful analyses carried out, thereby focussing on ambitious and recent studies. Limits of the approach in terms of accuracy and applicability are discussed. Despite these limitations MM-PBSA is a method with great potential that allows comparative free energy analyses for various molecular systems at low computational cost.
We report a consistent set of AMBER force-field parameters for the most common phosphorylated amino acids, phosphoserine, phosphothreonine, phosphotyrosine, and phosphohistidine in different protonation states. The calculation of atomic charges followed the original restrained electrostatic potential fitting procedure used to determine the charges for the parm94/99 parameter set, taking alpha-helical and beta-strand conformations of the corresponding ACE-/NME-capped model peptide backbone into account. Missing force-field parameters were taken directly from the general AMBER force field (gaff) and the parm99 data set with minor modifications, or were newly generated based on ab initio calculations for model systems. Final parameters were validated by geometry optimizations and molecular-dynamics simulations. Template libraries for the phosphorylated amino acids in Leap format and corresponding frcmod parameter files are made available. [Figure: see text].
Correctly ranking compounds according to their computed relative binding affinities will be of great value for decision making in the lead optimization phase of industrial drug discovery. However, the performance of existing computationally demanding binding free energy calculation methods in this context is largely unknown. We analyzed the performance of the molecular mechanics continuum solvent, the linear interaction energy (LIE), and the thermodynamic integration (TI) approach for three sets of compounds from industrial lead optimization projects. The data sets pose challenges typical for this early stage of drug discovery. None of the methods was sufficiently predictive when applied out of the box without considering these challenges. Detailed investigations of failures revealed critical points that are essential for good binding free energy predictions. When data set-specific features were considered accordingly, predictions valuable for lead optimization could be obtained for all approaches but LIE. Our findings lead to clear recommendations for when to use which of the above approaches. Our findings also stress the important role of expert knowledge in this process, not least for estimating the accuracy of prediction results by TI, using indicators such as the size and chemical structure of exchanged groups and the statistical error in the predictions. Such knowledge will be invaluable when it comes to the question which of the TI results can be trusted for decision making.
Visceral leishmaniasis causes considerable mortality and morbidity in many parts of the world. There is an urgent need for the development of new, effective treatments for this disease. Here we describe the development of an anti-leishmanial drug-like chemical series based on a pyrazolopyrimidine scaffold. The leading compound from this series (7, DDD853651/GSK3186899) is efficacious in a mouse model of visceral leishmaniasis, has suitable physicochemical, pharmacokinetic and toxicological properties for further development, and has been declared a preclinical candidate. Detailed mode-of-action studies indicate that compounds from this series act principally by inhibiting the parasite cdc-2-related kinase 12 (CRK12), thus defining a druggable target for visceral leishmaniasis.
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