Journal of Chemical Theory and Computation最新文献

We have developed and tested an absolute free energy perturbation (FEP) protocol, which combines all-atom molecular dynamics, replica exchange with solute tempering (REST) enhanced sampling, and a spherical harmonic restraint applied to a ligand. Our objective was to compute the binding free energy together with the underlying binding mechanism for a ligand, which binds diffusively to a protein. Such ligands represent nearly impossible targets for traditional FEP simulations. To test our FEP/REST protocol, we selected a conserved motif peptide KKPK termed minNLS from the nuclear localization signal sequence of the Venezuelan equine encephalitis virus capsid protein. This peptide fragment binds diffusively to importin-α transport protein without forming well-defined poses. Our FEP/REST simulations with a spherical restraint provided a converged estimate of minNLS binding free energy. We found that minNLS binds with moderate affinity to importin-α utilizing an unusual, purely entropic mechanism in which binding free energy is determined by favorable entropic gain. For this cationic minNLS peptide, a favorable binding entropic gain is primarily associated with the release of water from the solvation shells of charged amino acids. We demonstrated that FEP/REST simulations sample the KKPK bound ensemble well, allowing us to characterize the distribution of bound structures, binding interactions, and locations on the importin-α surface. Analysis of experimental studies offered support to our rationale behind the KKPK entropic binding mechanism.

Directionality in molecular and biomolecular networks plays an important role in the accurate representation of the complex, dynamic, and asymmetrical nature of interactions present in protein–ligand binding, signal transduction, and biological pathways. Most traditional techniques of topological data analysis (TDA), such as persistent homology (PH) and persistent Laplacian (PL), overlook this aspect in their standard form. To address this, we present the persistent directed flag Laplacian (PDFL), which incorporates directed flag complexes to account for edges with directionality originated from polarization, gene regulation, heterogeneous interactions, etc. This study marks the first application of PDFL, providing an in-depth analysis of spectral graph theory combined with machine learning. In addition to its superior accuracy and reliability, the PDFL model offers simplicity by requiring only raw inputs without complex data processing. We validated our multikernel PDFL model for its scoring power against other state-of-the-art methods on three popular benchmarks, namely PDBbind v2007, v2013, and v2016. The computational results indicate that the proposed PDFL model outperforms competitors in protein–ligand binding affinity predictions, suggesting that PDFL is a promising tool for protein engineering, drug discovery, and general applications in science and engineering.

Directionality in molecular and biomolecular networks plays an important role in the accurate representation of the complex, dynamic, and asymmetrical nature of interactions present in protein-ligand binding, signal transduction, and biological pathways. Most traditional techniques of topological data analysis (TDA), such as persistent homology (PH) and persistent Laplacian (PL), overlook this aspect in their standard form. To address this, we present the persistent directed flag Laplacian (PDFL), which incorporates directed flag complexes to account for edges with directionality originated from polarization, gene regulation, heterogeneous interactions, etc. This study marks the first application of PDFL, providing an in-depth analysis of spectral graph theory combined with machine learning. In addition to its superior accuracy and reliability, the PDFL model offers simplicity by requiring only raw inputs without complex data processing. We validated our multikernel PDFL model for its scoring power against other state-of-the-art methods on three popular benchmarks, namely PDBbind v2007, v2013, and v2016. The computational results indicate that the proposed PDFL model outperforms competitors in protein-ligand binding affinity predictions, suggesting that PDFL is a promising tool for protein engineering, drug discovery, and general applications in science and engineering.

In sequential multiscale molecular dynamics simulations, which advantageously combine the increased sampling and dynamics at coarse-grained resolution with the higher accuracy of atomistic simulations, the resolution is altered over time. While coarse-graining is straightforward once the mapping between atomistic and coarse-grained resolution is defined, reintroducing the atomistic details is still a nontrivial process called backmapping. Here, we present ART-SM, a fragment-based backmapping framework that learns from atomistic simulation data to seamlessly switch from coarse-grained to atomistic resolution. ART-SM requires minimal user input and goes beyond state-of-the-art fragment-based approaches by selecting from multiple conformations per fragment via machine learning to simultaneously reflect the coarse-grained structure and the Boltzmann distribution. Additionally, we introduce a novel refinement step to connect individual fragments by optimizing specific bonds, angles, and dihedral angles in the backmapping process. We demonstrate that our algorithm accurately restores the atomistic bond length, angle, and dihedral angle distributions for various small and linear molecules from Martini coarse-grained beads and that the resulting high-resolution structures are representative of the input coarse-grained conformations. Moreover, the reconstruction of the TIP3P water model is fast and robust, and we demonstrate that ART-SM can be applied to larger linear molecules as well. To illustrate the efficiency of the local and autoregressive approach of ART-SM, we simulated a large realistic system containing the surfactants TAPB and SDS in solution using the Martini3 force field. The self-assembled micelles of various shapes were backmapped with ART-SM after training on only short atomistic simulations of a single water-solvated SDS or TAPB molecule. Together, these results indicate the potential for the method to be extended to more complex molecules such as lipids, proteins, macromolecules, and materials in the future.

In sequential multiscale molecular dynamics simulations, which advantageously combine the increased sampling and dynamics at coarse-grained resolution with the higher accuracy of atomistic simulations, the resolution is altered over time. While coarse-graining is straightforward once the mapping between atomistic and coarse-grained resolution is defined, reintroducing the atomistic details is still a nontrivial process called backmapping. Here, we present ART-SM, a fragment-based backmapping framework that learns from atomistic simulation data to seamlessly switch from coarse-grained to atomistic resolution. ART-SM requires minimal user input and goes beyond state-of-the-art fragment-based approaches by selecting from multiple conformations per fragment via machine learning to simultaneously reflect the coarse-grained structure and the Boltzmann distribution. Additionally, we introduce a novel refinement step to connect individual fragments by optimizing specific bonds, angles, and dihedral angles in the backmapping process. We demonstrate that our algorithm accurately restores the atomistic bond length, angle, and dihedral angle distributions for various small and linear molecules from Martini coarse-grained beads and that the resulting high-resolution structures are representative of the input coarse-grained conformations. Moreover, the reconstruction of the TIP3P water model is fast and robust, and we demonstrate that ART-SM can be applied to larger linear molecules as well. To illustrate the efficiency of the local and autoregressive approach of ART-SM, we simulated a large realistic system containing the surfactants TAPB and SDS in solution using the Martini3 force field. The self-assembled micelles of various shapes were backmapped with ART-SM after training on only short atomistic simulations of a single water-solvated SDS or TAPB molecule. Together, these results indicate the potential for the method to be extended to more complex molecules such as lipids, proteins, macromolecules, and materials in the future.

In this work, the implementation of a partial fourth order N-electron-valence perturbation theory (NEVPT) is reported and numerically evaluated. The method, termed NEVPT4(SD), includes the internally contracted functions that span the first-order-interacting space (FOIS) and evaluates their contribution to second-order in the wave function and fourth order in the energy. The triple- and quadruple excitations that would additionally enter the second-order-interacting space (SOIS) are not included. As discussed by Grimme [Chem. Phys. Lett. 2001, 334, 99-106] in order to obtain a size-consistent method, it is necessary to also drop the fourth-order renormalization term if the quadruple excitations are dropped. The NEVPT4(SD) method is demonstrated to be perfectly size consistent. Computationally, the method is still fairly affordable and requires about the same time as a single iteration of the fully internally contracted (FIC) MRCI or MRCEPA(0) and significantly cheaper than the FIC MRCC that serves as the reference for our calculations. The accuracy tests show that NEVPT4(SD) offers significant accuracy improvements over NEVPT2 for transition metal atom/ion multiplets as well as diatomic bond breaking potential energy surfaces. We find that going to fourth order in perturbation theory essentially eliminates the need for a second d-shell, thus showing that the latter primarily serves to capture higher-order dynamic correlation effects that are not present in a second-order treatment. Although it captures fourth-order correlation effects, NEVPT4(SD) is numerically not a large improvement over NEVPT2 for the calculation of Heisenberg exchange couplings as illustrated by test calculations on Cu(II) dimers.

In this work, the implementation of a partial fourth order N-electron-valence perturbation theory (NEVPT) is reported and numerically evaluated. The method, termed NEVPT4(SD), includes the internally contracted functions that span the first-order-interacting space (FOIS) and evaluates their contribution to second-order in the wave function and fourth order in the energy. The triple- and quadruple excitations that would additionally enter the second-order-interacting space (SOIS) are not included. As discussed by Grimme [Chem. Phys. Lett. 2001, 334, 99–106] in order to obtain a size-consistent method, it is necessary to also drop the fourth-order renormalization term if the quadruple excitations are dropped. The NEVPT4(SD) method is demonstrated to be perfectly size consistent. Computationally, the method is still fairly affordable and requires about the same time as a single iteration of the fully internally contracted (FIC) MRCI or MRCEPA(0) and significantly cheaper than the FIC MRCC that serves as the reference for our calculations. The accuracy tests show that NEVPT4(SD) offers significant accuracy improvements over NEVPT2 for transition metal atom/ion multiplets as well as diatomic bond breaking potential energy surfaces. We find that going to fourth order in perturbation theory essentially eliminates the need for a second d-shell, thus showing that the latter primarily serves to capture higher-order dynamic correlation effects that are not present in a second-order treatment. Although it captures fourth-order correlation effects, NEVPT4(SD) is numerically not a large improvement over NEVPT2 for the calculation of Heisenberg exchange couplings as illustrated by test calculations on Cu(II) dimers.

Metal transporters play crucial roles in the homeostasis and detoxification of beneficial and toxic metals in the human body. Due to experimental limitations in studying some metal transporters, numerous simulation studies have been conducted to understand the mechanisms of metal transport. However, studying the transport of divalent metal ions across the plasma membrane by metal transporters has been challenging with traditional molecular dynamics (MD) simulations. The metal ions often become trapped inside the transporter due to encountering high energy barriers during the transport process. In this study, we combined a recently developed metadynamics setup, known as well-tempered (WT) volume-based MTD, with the 12-6-4 Lennard-Jones (LJ) model representing transition metal-His/Asp/Glu side chain interactions. We used this approach to investigate the mechanism of action of a Zrt-/Irt-like protein (ZIP) transporter and compared the results with simulations using standard 12-6 LJ parameters for the transition metal-His/Asp/Glu side chain interactions. Our results show that the 12-6-4 LJ model for transition metal-His/Asp/Glu side chain interactions samples conformational space more broadly than the standard 12-6 LJ model for the same interactions in MTD simulations, facilitating the sampling of states that are hard to reach with the standard 12-6 model within the same time scale. This is even more remarkable given the fact that the model is dominated by 12-6 LJ interactions for the majority of the system, while the transition metal-His/Asp/Glu side chain interactions are the only interactions using the 12-6-4 LJ model. Hence, a small subset of interactions significantly modifies the states sampled by the entire protein leading to a more frequent observation of the transport of the transition metal ion. Overall, using 12-6-4 LJ to model the transition metal-His/Asp/Glu side chain interactions increases the potential for discovering additional metastable states by enabling metal ions to traverse more freely along the defined transport pathways.

The pseudomode model effectively captures the nonperturbative dynamics of open quantum systems with significantly reduced degrees of freedom. However, it is limited by the exponential growth of the Hilbert space dimension. To overcome the computational challenges, we propose a novel method that combines the multiple Davydov Ansatz with the Choi-Jamiolkowski isomorphism. Within this framework, the Lindblad equation is transformed into the non-Hermitian Schrödinger equation in a double Hilbert space, with its dynamics determined using the time-dependent variational principle. Three cases are calculated to demonstrate the effectiveness of the proposed method. We first discuss how the Davydov Ansatz works for the model with a single pseudomode. Extending the method to multiple pseudomodes, we show that the Ansatz effectively circumvents the exponential growth of the Hilbert space. Additionally, the method is also capable of addressing potential intersections that emerge in multibath scenarios. This approach offers potential applicability to various types of pseudomode models and other dissipative systems, providing a promising tool for the studies of open quantum dynamics.

The pseudomode model effectively captures the nonperturbative dynamics of open quantum systems with significantly reduced degrees of freedom. However, it is limited by the exponential growth of the Hilbert space dimension. To overcome the computational challenges, we propose a novel method that combines the multiple Davydov Ansatz with the Choi-Jamiolkowski isomorphism. Within this framework, the Lindblad equation is transformed into the non-Hermitian Schrödinger equation in a double Hilbert space, with its dynamics determined using the time-dependent variational principle. Three cases are calculated to demonstrate the effectiveness of the proposed method. We first discuss how the Davydov Ansatz works for the model with a single pseudomode. Extending the method to multiple pseudomodes, we show that the Ansatz effectively circumvents the exponential growth of the Hilbert space. Additionally, the method is also capable of addressing potential intersections that emerge in multibath scenarios. This approach offers potential applicability to various types of pseudomode models and other dissipative systems, providing a promising tool for the studies of open quantum dynamics.