No abstract

We present a general approach to transform between molecular potential functions during free energy calculations using a variance minimized linear basis functional form. This approach splits the potential energy function into a sum of pairs of basis functions, which depend on coordinates, and 'alchemical' switches, which depend only on the coupling variable. The power of this approach is that, first, the calculation of the coupling parameter dependent terms is removed from inner loop force calculation routines, second, the flexibility in specifying basis functions and alchemical switches allows users to choose transformation pathways that maximize statistical efficiency, and third, it is possible to predict entirely in postprocessing, without any additional energy evaluations, the thermodynamic properties along any alchemical path with moderate overlap from an initial simulation that uses the same basis functions. This allows construction of optimized, minimum variance alchemical switches from a single simulation with fixed basis functions and trial alchemical switching functions. We describe how to construct these linear basis potentials for real molecular systems of different sizes and shapes, considering particularly the problems of eliminating singularities and minimizing variance of particle removal in dense fluids. The statistical error in free energy calculations using the optimized basis functions is lower than standard soft core models, and approach the minimum variance possible over all pair potentials. We recommend an optimized set of basis functions and alchemical switches for standard molecular free energy calculations.

The MUSASHI (MSI) family of RNA binding proteins (MSI1 and MSI2) contribute to a wide spectrum of cancers including acute myeloid leukemia. We find that the small molecule Ro 08–2750 (Ro) binds directly and selectively to MSI2 and competes for its RNA binding in biochemical assays. Ro treatment in mouse and human myeloid leukemia cells results in an increase in differentiation and apoptosis, inhibition of known MSI-targets, and a shared global gene expression signature similar to shRNA depletion of MSI2. Ro demonstrates in vivo inhibition of c-MYC and reduces disease burden in a murine AML leukemia model. Thus, we identify a small molecule that targets MSI’s oncogenic activity. Our study provides a framework for targeting RNA binding proteins in cancer.

We extend our previous linear basis function approach for alchemical free energy calculations to the insertion and deletion of charged particles in dense fluids. We compute a near optimal statistical path to introduce Coulombic interactions into various molecules in solution and find that this near optimal path is only marginally more efficient than simple linear coupling of electrostatics in all cases where a repulsive core is already present. We also explore the order in which nonbonded forces are coupled to the environment in alchemical transformations. We test two sets of Lennard-Jones basis functions, a Weeks-Chandler-Andersen (WCA) and a 12-6 decomposition of the repulsive and attractive forces turned on in sequence along with changes in charge, to determine a statistically optimized order in which forces should be coupled. The WCA decomposition has lower statistical uncertainty as coupling the attractive r(-6) basis function contributes non-negligible statistical error. In all cases, the charge should be coupled only after the repulsive core is fully coupled, and the WCA attractive portion can be coupled at any stage without significantly changing the efficiency. The statistical uncertainty of two of the basis function approaches with charged particles is nearly identical to the soft core approach for decoupling electrostatics, though the correlation times for sampling are often longer for a soft core electrostatics approach than the basis function approach. The basis function approach for introducing or removing molecules or functional groups thus represents a useful alternative to the soft core approach with a number of clear computational advantages.

The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python‐based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI‐hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force‐field fitting, and more through a Python interface. Facile, user‐friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at https://qcarchive.molssi.org. The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCArchive Infrastructure is open‐source (BSD‐3C), code repositories can be found at https://github.com/MolSSI, and releases can be downloaded via PyPI and Conda. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry Data Science > Computer Algorithms and Programming