Browsing by Subject "Molecular dynamics simulation"
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Item Addressing Intersite Coupling Unlocks Large Combinatorial Chemical Spaces for Alchemical Free Energy Methods(American Chemical Society, 2022) Hayes, Ryan L.; Vilseck, Jonah Z.; Brooks, Charles L., III.; Biochemistry and Molecular Biology, School of MedicineAlchemical free energy methods are playing a growing role in molecular design, both for computer-aided drug design of small molecules and for computational protein design. Multisite λ dynamics (MSλD) is a uniquely scalable alchemical free energy method that enables more efficient exploration of combinatorial alchemical spaces encountered in molecular design, but simulations have typically been limited to a few hundred ligands or sequences. Here, we focus on coupling between sites to enable scaling to larger alchemical spaces. We first discuss updates to the biasing potentials that facilitate MSλD sampling to include coupling terms and show that this can provide more thorough sampling of alchemical states. We then harness coupling between sites by developing a new free energy estimator based on the Potts models underlying direct coupling analysis, a method for predicting contacts from sequence coevolution, and find it yields more accurate free energies than previous estimators. The sampling requirements of the Potts model estimator scale with the square of the number of sites, a substantial improvement over the exponential scaling of the standard estimator. This opens up exploration of much larger alchemical spaces with MSλD for molecular design.Item CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed(American Chemical Society, 2024) Hwang, Wonmuk; Austin, Steven L.; Blondel, Arnaud; Boittier, Eric D.; Boresch, Stefan; Buck, Matthias; Buckner, Joshua; Caflisch, Amedeo; Chang, Hao-Ting; Cheng, Xi; Choi, Yeol Kyo; Chu, Jhih-Wei; Crowley, Michael F.; Cui, Qiang; Damjanovic, Ana; Deng, Yuqing; Devereux, Mike; Ding, Xinqiang; Feig, Michael F.; Gao, Jiali; Glowacki, David R.; Gonzales, James E., II; Hamaneh, Mehdi Bagerhi; Harder, Edward D.; Hayes, Ryan L.; Huang, Jing; Huang, Yandong; Hudson, Phillip S.; Im, Wonpil; Islam, Shahidul M.; Jiang, Wei; Jones, Michael R.; Käser, Silvan; Kearns, Fiona L.; Kern, Nathan R.; Klauda, Jeffery B.; Lazaridis, Themis; Lee, Jinhyuk; Lemkul, Justin A.; Liu, Xiaorong; Luo, Yun; MacKerell, Alexander D., Jr.; Major, Dan T.; Meuwly, Markus; Nam, Kwangho; Nilsson, Lennart; Ovchinnikov, Victor; Paci, Emanuele; Park, Soohyung; Pastor, Richard W.; Pittman, Amanda R.; Post, Carol Beth; Prasad, Samarjeet; Pu, Jingzhi; Qi, Yifei; Rathinavelan, Thenmalarchelvi; Roe, Daniel R.; Roux, Benoit; Rowley, Christopher N.; Shen, Jana; Simmonett, Andrew C.; Sodt, Alexander J.; Töpfer, Kai; Upadhyay, Meenu; van der Vaart, Arjan; Vazquez-Salazar, Luis Itza; Venable, Richard M.; Warrensford, Luke C.; Woodcock, H. Lee; Wu, Yujin; Brooks, Charles L., III; Brooks, Bernard R.; Karplus, Martin; Chemistry and Chemical Biology, School of ScienceSince its inception nearly a half century ago, CHARMM has been playing a central role in computational biochemistry and biophysics. Commensurate with the developments in experimental research and advances in computer hardware, the range of methods and applicability of CHARMM have also grown. This review summarizes major developments that occurred after 2009 when the last review of CHARMM was published. They include the following: new faster simulation engines, accessible user interfaces for convenient workflows, and a vast array of simulation and analysis methods that encompass quantum mechanical, atomistic, and coarse-grained levels, as well as extensive coverage of force fields. In addition to providing the current snapshot of the CHARMM development, this review may serve as a starting point for exploring relevant theories and computational methods for tackling contemporary and emerging problems in biomolecular systems. CHARMM is freely available for academic and nonprofit research at https://academiccharmm.org/program.Item Crystal Packing Reveals a Potential Autoinhibited KRAS Dimer Interface and a Strategy for Small-Molecule Inhibition of RAS Signaling(American Chemical Society, 2023) Brenner, Robert J.; Landgraf, Alexander D.; Bum-Erdene, Khuchtumur; Gonzalez-Gutierrez, Giovanni; Meroueh, Samy O.; Biochemistry and Molecular Biology, School of MedicineKRAS GTPases harbor oncogenic mutations in more than 25% of human tumors. KRAS is considered to be largely undruggable due to the lack of a suitable small-molecule binding site. Here, we report a unique crystal structure of His-tagged KRASG12D that reveals a remarkable conformational change. The Switch I loop of one His-KRASG12D structure extends into the Switch I/II pocket of another His-KRASG12D in an adjacent unit cell to create an elaborate interface that is reminiscent of high-affinity protein-protein complexes. We explore the contributions of amino acids at this interface using alanine-scanning studies with alchemical free energy perturbation calculations based on explicit-solvent molecular dynamics simulations. Several interface amino acids were found to be hot spots as they contributed more than 1.5 kcal/mol to the protein-protein interaction. Computational analysis of the complex revealed the presence of two large binding pockets that possess physicochemical features typically found in pockets considered druggable. Small-molecule binding to these pockets may stabilize this autoinhibited structure of KRAS if it exists in cells to provide a new strategy to inhibit RAS signaling.Item Facilitating Ab Initio QM/MM Free Energy Simulations by Gaussian Process Regression with Derivative Observations(Royal Society of Chemistry, 2022-10-27) Snyder, Ryan; Kim, Bryant; Pan, Xiaoliang; Shao, Yihan; Pu, Jingzhi; Chemistry and Chemical Biology, School of ScienceIn combined quantum mechanical and molecular mechanical (QM/MM) free energy simulations, how to synthesize the accuracy of ab initio (AI) methods with the speed of semiempirical (SE) methods for a cost-effective QM treatment remains a long-standing challenge. In this work, we present a machine-learning-facilitated method for obtaining AI/MM-quality free energy profiles through efficient SE/MM simulations. In particular, we use Gaussian process regression (GPR) to learn the energy and force corrections needed for SE/MM to match with AI/MM results during molecular dynamics simulations. Force matching is enabled in our model by including energy derivatives into the observational targets through the extended-kernel formalism. We demonstrate the effectiveness of this method on the solution-phase SN2 Menshutkin reaction using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. Trained on only 80 configurations sampled along the minimum free energy path (MFEP), the resulting GPR model reduces the average energy error in AM1/MM from 18.2 to 5.8 kcal mol-1 for the 4000-sample testing set with the average force error on the QM atoms decreased from 14.6 to 3.7 kcal mol-1 Å-1. Free energy sampling with the GPR corrections applied (AM1-GPR/MM) produces a free energy barrier of 14.4 kcal mol-1 and a reaction free energy of -34.1 kcal mol-1, in closer agreement with the AI/MM benchmarks and experimental results.Item How to Sample Dozens of Substitutions per Site with λ Dynamics(American Chemical Society, 2024) Hayes, Ryan L.; Cervantes, Luis F.; Abad Santos, Justin Cruz; Samadi, Amirmasoud; Vilseck, Jonah Z.; Brooks, Charles L., III; Biochemistry and Molecular Biology, School of MedicineAlchemical free energy methods are useful in computer-aided drug design and computational protein design because they provide rigorous statistical mechanics-based estimates of free energy differences from molecular dynamics simulations. λ dynamics is a free energy method with the ability to characterize combinatorial chemical spaces spanning thousands of related systems within a single simulation, which gives it a distinct advantage over other alchemical free energy methods that are mostly limited to pairwise comparisons. Recently developed methods have improved the scalability of λ dynamics to perturbations at many sites; however, the size of chemical space that can be explored at each individual site has previously been limited to fewer than ten substituents. As the number of substituents increases, the volume of alchemical space corresponding to nonphysical alchemical intermediates grows exponentially relative to the size corresponding to the physical states of interest. Beyond nine substituents, λ dynamics simulations become lost in an alchemical morass of intermediate states. In this work, we introduce new biasing potentials that circumvent excessive sampling of intermediate states by favoring sampling of physical end points relative to alchemical intermediates. Additionally, we present a more scalable adaptive landscape flattening algorithm for these larger alchemical spaces. Finally, we show that this potential enables more efficient sampling in both protein and drug design test systems with up to 24 substituents per site, enabling, for the first time, simultaneous simulation of all 20 amino acids.Item The Molecular Structure of Sphingomyelin in Fluid Phase Bilayers Determined by the Joint Analysis of Small-Angle Neutron and X-ray Scattering Data(American Chemical Society, 2020-06-25) Doktorova, Milka; Kučerka, Norbert; Kinnun, Jacob J.; Pan, Jianjun; Marquardt, Drew; Scott, Haden L.; Venable, Richard M.; Pastor, Richard W.; Wassall, Stephen R.; Katsaras, John; Heberle, Frederick A.; Physics, School of ScienceWe have determined the fluid bilayer structure of palmitoyl sphingomyelin (PSM) and stearoyl sphingomyelin (SSM) by simultaneously analyzing small-angle neutron and X-ray scattering data. Using a newly developed scattering density profile (SDP) model for sphingomyelin lipids, we report structural parameters including the area per lipid, total bilayer thickness, and hydrocarbon thickness, in addition to lipid volumes determined by densitometry. Unconstrained all-atom simulations of PSM bilayers at 55 °C using the C36 CHARMM force field produced a lipid area of 56 Å2, a value that is 10% lower than the one determined experimentally by SDP analysis (61.9 Å2). Furthermore, scattering form factors calculated from the unconstrained simulations were in poor agreement with experimental form factors, even though segmental order parameter (SCD) profiles calculated from the simulations were in relatively good agreement with SCD profiles obtained from NMR experiments. Conversely, constrained area simulations at 61.9 Å2 resulted in good agreement between the simulation and experimental scattering form factors, but not with SCD profiles from NMR. We discuss possible reasons for the discrepancies between these two types of data that are frequently used as validation metrics for molecular dynamics force fields.Item Optimizing Multisite λ-Dynamics Throughput with Charge Renormalization(American Chemical Society, 2022) Vilseck, Jonah Z.; Cervantes, Luis F.; Hayes, Ryan L.; Brooks, Charles L., III.; Biochemistry and Molecular Biology, School of MedicineWith the ability to sample combinations of alchemical perturbations at multiple sites off a small molecule core, multisite λ-dynamics (MSλD) has become an attractive alternative to conventional alchemical free energy methods for exploring large combinatorial chemical spaces. However, current software implementations dictate that combinatorial sampling with MSλD must be performed with a multiple topology model (MTM), which is nontrivial to create by hand, especially for a series of ligand analogues which may have diverse functional groups attached. This work introduces an automated workflow, referred to as msld_py_prep, to assist in the creation of a MTM for use with MSλD. One approach for partitioning partial atomic charges between ligands to create a MTM, called charge renormalization, is also presented and rigorously evaluated. We find that msld_py_prep greatly accelerates the preparation of MSλD ready-to-use files and that charge renormalization can provide a successful approach for MTM generation, as long as bookending calculations are applied to correct small differences introduced by charge renormalization. Charge renormalization also facilitates the use of many different force field parameters with MSλD, broadening the applicability of MSλD for computer-aided drug design.Item Predicted Structure and Functions of the Prototypic Alphaherpesvirus Herpes Simplex Virus Type-1 UL37 Tegument Protein(MDPI, 2022-10-04) Collantes, Therese Marie A.; Clark, Carolyn M.; Musarrat, Farhana; Jambunathan, Nithya; Jois, Seetharama; Kousoulas, Konstantin G.; Medicine, School of MedicineThe alphaherpesvirus UL37 tegument protein is a highly conserved, multi-functional protein. Mutagenesis analysis delineated the UL37 domains necessary for retrograde transport and viral replication. Specifically, the amino-terminal 480 amino acids are dispensable for virus replication in epithelial cell culture, but it is unknown whether this amino-terminal deletion affects UL37 structure and intracellular transport in epithelial cells and neurons. To investigate the structure and function of UL37, we utilized multiple computational approaches to predict and characterize the secondary and tertiary structure and other functional features. The structure of HSV-1 UL37 and Δ481N were deduced using publicly available predictive algorithms. The predicted model of HSV-1 UL37 is a stable, multi-functional, globular monomer, rich in alpha helices, with unfolded regions within the linker and the C-tail domains. The highly flexible C-tail contains predicted binding sites to the dynein intermediate chain, as well as DNA and RNA. Predicted interactions with the cytoplasmic surface of the lipid membrane suggest UL37 is a peripheral membrane protein. The Δ481N truncation did not alter the predicted structure of the UL37 C-terminus protein and its predicted interaction with dynein. We validated these models by examining the replication kinetics and transport of the Δ481N virus toward the nuclei of infected epithelial and neuronal cells. The Δ481N virus had substantial defects in virus spread; however, it exhibited no apparent defects in virus entry and intracellular transport. Using computational analyses, we identified several key features of UL37, particularly the flexible unstructured tail; we then demonstrated that the UL37 C-terminus alone is sufficient to effectively transport the virus towards the nucleus of infected epithelial and neuronal cells.