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1. A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs

   https://doi.org/10.3390/molecules24112097  

   Plante, Ambrose; Shore, Derek M.; Morra, Giulia; Khelashvili, George; Weinstein, Harel (June 2019, Molecules)

   G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity. 

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2. Elucidating the Structure of the Eu‐EDTA Complex in Solution at Various Protonation States

   https://doi.org/10.1002/ejic.202400042  

   Licup, Gerra_L; Summers, Thomas_J; Sobrinho, Josiane_A; de_Bettencourt‐Dias, Ana; Cantu, David_C (April 2024, European Journal of Inorganic Chemistry)

   Abstract Ethylenediaminetetraacetic acid (EDTA), which has two amine and four carboxylate protonation sites, forms stable complexes with lanthanide ions. This work analyzes the coordination structure, in atomic resolution, of the Eu³⁺ion complexed with EDTA in all its protonation states in aqueous solution. Eu‐EDTA complexes were modeled using classical molecular dynamics (MD) simulations using force field parameters optimized with ab initio molecular dynamics (AIMD) simulations. Structures from the MD simulations were used to predict extended X‐ray absorption fine structure (EXAFS) spectra and compared with EXAFS measurements of the Eu³⁺aqua ion and Eu‐EDTA complexes at pH 3 and 11. This work details how Eu‐EDTA complex coordination structures change with increasing protonation of the EDTA ligand in the complex, from the tightly bound unprotonated complex to the unbinding of the fully protonated EDTA ligand from the Eu³⁺ion as both become solvated by water. Agreement between predicted and measured EXAFS spectra supports the findings from simulation. 

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3. Orientation and dynamics of Cu ²⁺ based DNA labels from force field parameterized MD elucidates the relationship between EPR distance constraints and DNA backbone distances

   https://doi.org/10.1039/d0cp05016d  

   Ghosh, Shreya; Casto, Joshua; Bogetti, Xiaowei; Arora, Charu; Wang, Junmei; Saxena, Sunil (December 2020, Physical Chemistry Chemical Physics)

   null (Ed.)

   Pulsed electron paramagnetic resonance (EPR) based distance measurements using the recently developed Cu 2+ -DPA label present a promising strategy for measuring DNA backbone distance constraints. Herein we develop force field parameters for Cu 2+ -DPA in order to understand the features of this label at an atomic level. We perform molecular dynamics (MD) simulations using the force field parameters of Cu 2+ -DPA on four different DNA duplexes. The distance between the Cu 2+ centers, extracted from the 2 μs MD trajectories, agrees well with the experimental distance for all the duplexes. Further analyses of the trajectory provide insight into the orientation of the Cu 2+ -DPA inside the duplex that leads to such agreement with experiments. The MD results also illustrate the ability of the Cu 2+ -DPA to report on the DNA backbone distance constraints. Furthermore, measurement of fluctuations of individual residues showed that the flexibility of Cu 2+ -DPA in a DNA depends on the position of the label in the duplex, and a 2 μs MD simulation is not sufficient to fully capture the experimental distribution in some cases. Finally, the MD trajectories were utilized to understand the key aspects of the double electron electron resonance (DEER) results. The lack of orientational selectivity effects of the Cu 2+ -DPA at Q-band frequency is rationalized in terms of fluctuations in the Cu 2+ coordination environment and rotameric fluctuations of the label linker. Overall, a combination of EPR and MD simulations based on the Cu 2+ -DPA labelling strategy can contribute towards understanding changes in DNA backbone conformations during protein–DNA interactions. 

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4. Molecular Dynamics Simulations on Relaxed Reduced-DimensionalPotential Energy Surfaces

   Lui, C. (April 2019, The journal of physical chemistry. A.)

   Molecular dynamics (MD) simulations with full-dimensional potential energy surfaces (PESs) obtained from high-level ab initio calculations are frequently used to model reaction dynamics of small molecules (i.e., molecules with up to 10 atoms). Construction of full-dimensional PESs for larger molecules is, however, not feasible since the number of ab initio calculations required grows rapidly with the increase of dimension. Only a small number of coordinates are often essential for describing the reactivity of even very large systems, and reduced-dimensional PESs with these coordinates can be built for reaction dynamics studies. While analytical methods based on transition-state theory framework are well established for analyzing the reduced-dimensionalPESs, MD simulation algorithms capable of generating trajectories on such surfaces are more rare. In this work, we present a new MD implementation that utilizes the relaxed reduced-dimensional PES for standard micro canonical (NVE) and canonical (NVT) MD simulations.The method is applied to the pyramidal inversion of a NH3molecule. The results from the MD simulations on a reduced, three-dimensional PES are validated against the ab initio MD simulations, as well as MD simulations on full-dimensional PES and experimental data. 

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5. Ligand-Dependent Conformational Transitions in Molecular Dynamics Trajectories of GPCRs Revealed by a New Machine Learning Rare Event Detection Protocol

   https://doi.org/10.3390/molecules26103059  

   Plante, Ambrose; Weinstein, Harel (May 2021, Molecules)

   null (Ed.)

   Central among the tools and approaches used for ligand discovery and design are Molecular Dynamics (MD) simulations, which follow the dynamic changes in molecular structure in response to the environmental condition, interactions with other proteins, and the effects of ligand binding. The need for, and successes of, MD simulations in providing this type of essential information are well documented, but so are the challenges presented by the size of the resulting datasets encoding the desired information. The difficulty of extracting information on mechanistically important state-to-state transitions in response to ligand binding and other interactions is compounded by these being rare events in the MD trajectories of complex molecular machines, such as G-protein-coupled receptors (GPCRs). To address this problem, we have developed a protocol for the efficient detection of such events. We show that the novel Rare Event Detection (RED) protocol reveals functionally relevant and pharmacologically discriminating responses to the binding of different ligands to the 5-HT2AR orthosteric site in terms of clearly defined, structurally coherent, and temporally ordered conformational transitions. This information from the RED protocol offers new insights into specific ligand-determined functional mechanisms encoded in the MD trajectories, which opens a new and rigorously reproducible path to understanding drug activity with application in drug discovery. 

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