Chapin E. Cavender

Partial atomic charges are a fundamental component underlying classical molecular simulations, but assigning charges remains a computational bottleneck; many common methods rely on quantum mechanical calculations that scale poorly with molecular size and are sensitive to the choice of conformer geometry. We introduce Open Force Field (OpenFF) AshGC, a new graph convolutional neural network charge model, as well as the Sage 2.3.0 small molecule force field for drug-like molecules parametrized to be consistent with AshGC. AshGC is designed to efficiently produce conformer-independent charges of semiempirical quality at linear cost for molecules of all sizes, from small molecules to macromolecules. AshGC largely generates charges within the range of other accepted AM1-BCC backends such as OpenEye's oequacpac and AmberTools' sqm, deviating most in smaller molecules between 4 and 9 heavy atoms, in negatively charged molecules, and areas of chemistry underrepresented in the training set, such as particular sulfur- and phosphorus-containing functional groups. We further present the development and performance of Sage 2.3.0, which has both Lennard-Jones and valence parameters that are retrained to be consistent with neural network charges for the first time. Benchmarks spanning gas-phase geometry optimization through protein-ligand binding free energies show Sage 2.3.0 performs comparably to earlier Sage releases, with modest improvements in condensed-phase properties and a slight decrease in nonaqueous solvation free energy accuracy. As with other OpenFF force fields, Sage 2.3.0 was validated in protein-ligand benchmarks to be compatible with Amber protein force fields. All data are publicly available, along with scripts and environments for reproducing the training and benchmarking of AshGC and Sage 2.3.0 at https://github.com/openforcefield/ashgc-v1.0-fit and https://github.com/openforcefield/ash-sage-rc2 respectively.

We report a new charge model and a new general small molecule force field. Here, we address the development and benchmarking of both the Open Force Field (OpenFF) AshGC charge model, as well as the Sage 2.3.0 small molecule force field for drug-like molecules. AshGC is a graph neural network-based method for efficient assignment of charges to both small and large molecules, designed to produce charges of semi- empirical quality at low cost. We describe the model, its architecture, featurization, training process, and performance. AshGC largely generates charges within the range of other accepted AM1-BCC backends such as OpenEye’s oequacpac and AmberTools’ sqm, but we note areas of poor performance such as in smaller molecules and in charged molecules, as well as areas of chemistry underrepresented in the training set. We fur- ther describe the development and performance of Sage 2.3.0, where Lennard-Jones and valence parameters are re-trained with neural network charges for the first time. Our benchmark suite of common force field tasks ranges from optimizing molecular structures in vacuum to protein-ligand binding free energies. Overall, Sage 2.3.0 performs comparably to previous Sage force fields, although we report minor improvements in the prediction of condensed phase properties and a minor decrease in performance in the prediction of non-aqueous solvation free energies. As with other OpenFF force fields, Sage 2.3.0 was validated in protein-ligand benchmarks to be compatible with Amber protein force fields. All data are publicly available, along with scripts, environments, and complete details for reproducing the training and benchmarking of AshGC and Sage 2.3.0 at https://github.com/openforcefield/ashgc-v1.0-fit and github.com/openforcefield/ash-sage-rc2 respectively.

This review article provides an overview of structurally oriented experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography. We discuss what the observables are, what they tell us about structure and dynamics, what makes them useful for assessing force field accuracy, and how they can be connected to molecular dynamics simulations carried out using the force field one wishes to benchmark. We also touch on best practices for setup and analysis of benchmark simulations. We hope this article will be particularly useful to computational researchers and trainees who develop, benchmark, or use protein force fields or machine learning models that generate protein ensembles.

Symmetry-adapted perturbation theory (SAPT) is an ab initio approach that directly computes noncovalent interaction energies in terms of electrostatics, exchange repulsion, induction/polarization, and London dispersion components. Due to its high computational scaling, routine applications of even the lowest order of SAPT are typically limited to a few hundred atoms. To address this limitation, we report here the addition of electrostatic embedding to the SAPT (EE-SAPT) and ISAPT (EE-ISAPT) methods. We illustrate the embedding scheme using water trimer as a prototype example. Then, we show that EE-SAPT/EE-ISAPT can be applied for efficiently and accurately computing noncovalent interactions in large systems, including solvated dimers and protein–ligand systems. In the latter application, particular care must be taken to properly handle the quantum mechanics/molecular mechanics boundary when it cuts covalent bonds. We investigate various schemes for handling charges near this boundary and demonstrate which are most effective in the context of charge-embedded SAPT.

A generalized and extensible machine-learned molecular mechanics force field trained on over 1.1 million QC data applicable for drug discovery applications. Figure reproduced from the arXiv:201001196 preprint under the arXiv non-exclusive license.

Riboswitches are structured non-coding RNAs often located upstream of essential genes in bacterial messenger RNAs. Such RNAs regulate expression of downstream genes by recognizing a specific cellular effector. Although nearly 50 riboswitch classes are known, only a handful recognize multiple effectors. Here, we report the 2.60-Å resolution co-crystal structure of a class I type I preQ₁-sensing riboswitch that reveals two effectors stacked atop one another in a single binding pocket. These effectors bind with positive cooperativity in vitro and both molecules are necessary for gene regulation in bacterial cells. Stacked effector recognition appears to be a hallmark of the largest subgroup of preQ₁ riboswitches, including those from pathogens such as Neisseria gonorrhoeae. We postulate that binding to stacked effectors arose in the RNA World to closely position two substrates for RNA-mediated catalysis. These findings expand known effector recognition capabilities of riboswitches and have implications for antimicrobial development.

The essential pre-mRNA splicing factor U2AF2 (also called U2AF ⁶⁵ ) identifies polypyrimidine (Py) tract signals of nascent transcripts, despite length and sequence variations. Previous studies have shown that the U2AF2 RNA recognition motifs (RRM1 and RRM2) preferentially bind uridine-rich RNAs. Nonetheless, the specificity of the RRM1/RRM2 interface for the central Py tract nucleotide has yet to be investigated. We addressed this question by determining crystal structures of U2AF2 bound to a cytidine, guanosine, or adenosine at the central position of the Py tract, and compared U2AF2-bound uridine structures. Local movements of the RNA site accommodated the different nucleotides, whereas the polypeptide backbone remained similar among the structures. Accordingly, molecular dynamics simulations revealed flexible conformations of the central, U2AF2-bound nucleotide. The RNA binding affinities and splicing efficiencies of structure-guided mutants demonstrated that U2AF2 tolerates nucleotide substitutions at the central position of the Py tract. Moreover, enhanced crosslinking and immunoprecipitation of endogenous U2AF2 in human erythroleukemia cells showed uridine-sensitive binding sites with lower sequence conservation at the central nucleotide positions of otherwise uridine-rich, U2AF2-bound splice sites. Altogether, these results highlight the importance of RNA flexibility for protein recognition and take a step towards relating splice site motifs to pre-mRNA splicing efficiencies.

Riboswitches are structured RNA motifs that recognize metabolites to alter the conformations of downstream sequences, leading to gene regulation. To investigate this molecular framework, we determined crystal structures of a preQ₁-I riboswitch in effector-free and bound states at 2.00 Å and 2.65 Å-resolution. Both pseudoknots exhibited the elusive L2 loop, which displayed distinct conformations. Conversely, the Shine-Dalgarno sequence (SDS) in the S2 helix of each structure remained unbroken. The expectation that the effector-free state should expose the SDS prompted us to conduct solution experiments to delineate environmental changes to specific nucleobases in response to preQ₁. We then used nudged elastic band computational methods to derive conformational-change pathways linking the crystallographically-determined effector-free and bound-state structures. Pathways featured: (i) unstacking and unpairing of L2 and S2 nucleobases without preQ₁—exposing the SDS for translation and (ii) stacking and pairing L2 and S2 nucleobases with preQ₁—sequestering the SDS. Our results reveal how preQ₁ binding reorganizes L2 into a nucleobase-stacking spine that sequesters the SDS, linking effector recognition to biological function. The generality of stacking spines as conduits for effector-dependent, interdomain communication is discussed in light of their existence in adenine riboswitches, as well as the turnip yellow mosaic virus ribosome sensor.

Natural and lab-evolved proteins often recognize their RNA partners with exquisite affinity. Structural analysis of such complexes can offer valuable insight into sequence-selective recognition that can be exploited to alter biological function. Here, we describe the structure of a lab-evolved RNA recognition motif (RRM) bound to the HIV-1 trans-activation response (TAR) RNA element at 1.80 Å-resolution. The complex reveals a trio of arginines in an evolved β2–β3 loop penetrating deeply into the major groove to read conserved guanines while simultaneously forming cation-π and salt-bridge contacts. The observation that the evolved RRM engages TAR within a double-stranded stem is atypical compared to most RRMs. Mutagenesis, thermodynamic analysis and molecular dynamics validate the atypical binding mode and quantify molecular contributions that support the exceptionally tight binding of the TAR-protein complex (K_D,App of 2.5 ± 0.1 nM). These findings led to the hypothesis that the β2–β3 loop can function as a standalone TAR-recognition module. Indeed, short constrained peptides comprising the β2–β3 loop still bind TAR (K_D,App of 1.8 ± 0.5 μM) and significantly weaken TAR-dependent transcription. Our results provide a detailed understanding of TAR molecular recognition and reveal that a lab-evolved protein can be reduced to a minimal RNA-binding peptide.