arXiv subjects

Teresa Head-Gordon

Publications and source records attributed to Teresa Head-Gordon.

At least 55 records · Page 3Linked to original sources

arXiv2022

Learning Correlations between Internal Coordinates to improve 3D Cartesian Coordinates for Proteins

We consider a generic representation problem of internal coordinates (bond lengths, valence angles, and dihedral angles) and their transformation to 3-dimensional Cartesian coordinates of a biomolecule. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. This general problem has been solved with machine learning for proteins, but with appropriately formulated data is extensible to any type of chain biomolecule including RNA, DNA, and lipids. We show that the internal-to-Cartesian process relies on correctly predicting chemically subtle correlations among the internal coordinates themselves, and learning these correlations increases the fidelity of the Cartesian representation. We developed a machine learning algorithm, Int2Cart, to predict bond lengths and bond angles from backbone torsion angles and residue types of a protein, and allows reconstruction of protein structures better than using fixed bond lengths and bond angles, or a static library method that relies on backbone torsion angles and residue types on a single residue. The Int2Cart algorithm has been implemented as an individual python package at https://github.com/THGLab/int2cart.

physics.bio-ph↗

arXiv2021

NewtonNet: A Newtonian message passing network for deep learning of interatomic potentials and forces

We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of directional information from trainable latent force vectors, and physics-infused operators that are inspired by the Newtonian physics, the entire model remains rotationally equivariant, and many-body interactions are inferred by more interpretable physical features. We test NewtonNet on the prediction of several reactive and non-reactive high quality ab initio data sets including single small molecule dynamics, a large set of chemically diverse molecules, and methane and hydrogen combustion reactions, achieving state-of-the-art test performance on energies and forces with far greater data and computational efficiency than other deep learning models.

physics.chem-ph↗

arXiv2020

The Critical Role of Thermal Fluctuations for Electrocatalytic Metal Surface Properties and CO Binding Trends

This work addresses a longstanding theoretical discrepancy using Density Functional Theory (DFT) with experimental observations of CO binding trends on electrocatalytically relevant metals for the CO2 reduction reaction (CO2RR). By introducing thermal fluctuations using appropriate statistical mechanical NVT and NPT ensembles, we show that DFT with universal dispersion interactions yields qualitatively better metal surface strain trends and CO binding energetics, consistently predicts the correct site preference for all metals due to thermally induced surface distortions that preferentially exposes the undercoordinated atop site for Cu(111) and Pt(111), and for the weak binding Ag(111) and Au(111) surfaces at finite temperatures shows CO-metal interactions that are a mixture of chemisorbed and physisorbed species. This study better places theory as an equal partner to experimental heterogeneous catalysis by demonstrating the need to fully account for finite temperature fluctuations to make contact with surface science experiments.

physics.chem-ph↗

arXiv2020

Configurational Entropy of Folded Proteins and its Importance for Intrinsically Disordered Proteins

Many pairwise additive force fields are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), some of which modify energetic terms to improve description of IDPs/IDRs, but are largely in disagreement with solution experiments for the disordered states. We have evaluated representative pairwise and many-body protein and water force fields against experimental data on representative IDPs and IDRs, a peptide that undergoes a disorder-to-order transition, and for seven globular proteins ranging in size from 130-266 amino acids. We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states simultaneously better describe IDPs and IDRs and disorder to order transitions. Hence the crux of what a force field should exhibit to well describe IDRs/IDPs is not just the balance between protein and water energetics, but the balance between energetic effects and configurational entropy of folded states of globular proteins.

q-bio.BM↗

arXiv2020

An Isolated Water Droplet in the Aqueous Solution of a Supramolecular Tetrahedral Cage

Water under nanoconfinement at ambient conditions has exhibited low-dimensional ice formation and liquid-solid phase transitions, but with structural and dynamical signatures which map onto known regions of waters phase diagram. Using THz absorption spectroscopy and ab initio molecular dynamics, we have investigated the ambient water confined in a supramolecular tetrahedral assembly, and determined that a distinct network of 9-10 water molecules is present within the nanocavity of the host. The low-frequency absorption spectrum and theoretical analysis of the water in the $Ga_4$$L_6$$^{-12}$ host demonstrate that the structure and dynamics of the encapsulated droplet is distinct from any known phase of water. A further inference is that the release of the highly unusual encapsulated water droplet creates a strong thermodynamic driver for the high affinity binding of guests in aqueous solution for the $Ga_4$$L_6$$^{-12}$ supramolecular construct.

physics.chem-ph↗

arXiv2020

Strong Anisotropy in Liquid Water upon Librational Excitation using Terahertz Laser Fields

Tracking the excitation of water molecules in the homogeneous liquid is challenging due to the ultrafast dissipation of rotational excitation energy through the hydrogen-bonded network. Here we demonstrate strong transient anisotropy of liquid water through librational excitation using single-color pump-probe experiments at 12.3 THz. We deduce a third order response of chi^3 exceeding previously reported values in the optical range by three orders of magnitude. Using a theory that replaces the nonlinear response with a material response property amenable to molecular dynamics simulation, we show that the rotationally damped motion of water molecules in the librational band is resonantly driven at this frequency, which could explain the enhancement of the anisotropy in the liquid by the external Terahertz field. By addition of salt (MgSO4), the hydration water is instead dominated by the local electric field of the ions, resulting in reduction of water molecules that can be dynamically perturbed by THz pulses.

physics.chem-ph↗

arXiv2020

Stochastic Constrained Extended System Dynamics for Solving Charge Equilibration Models

We present a new stochastic extended Lagrangian solution to charge equilibration that eliminates self-consistent field (SCF) calculations, eliminating the computational bottleneck in solving the many-body solution with standard SCF solvers. By formulating both charges and chemical potential as latent variables, and introducing a holonomic constraint that satisfies charge conservation, the SC-XLMD method accurately reproduces structural, thermodynamic, and dynamics properties using ReaxFF, and shows excellent weak- and strong-scaling performance in the LAMMPS molecular simulation package.

physics.comp-ph↗

arXiv2020

Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Algorithms

Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we focus on the interplay of machine learning algorithm with the chemically motivated descriptors and the size and type of data sets needed for molecular property prediction. Using Nuclear Magnetic Resonance chemical shift prediction as an example, we demonstrate that success is predicated on the choice of feature extracted or real-space representations of chemical structures, whether the molecular property data is abundant and/or experimentally or computationally derived, and how these together will influence the correct choice of popular machine learning algorithms drawn from deep learning, random forests, or kernel methods.

physics.chem-ph↗

arXiv2020

Convergence of Stochastic-extended Lagrangian molecular dynamics method for polarizable force field simulation

Extended Lagrangian molecular dynamics (XLMD) is a general method for performing molecular dynamics simulations using quantum and classical many-body potentials. Recently several new XLMD schemes have been proposed and tested on several classes of many-body polarization models such as induced dipoles or Drude charges, by creating an auxiliary set of these same degrees of freedom that are reversibly integrated through time. This gives rise to a singularly perturbed Hamiltonian system that provides a good approximation to the time evolution of the real mutual polarization field. To further improve upon the accuracy of the XLMD dynamics, and to potentially extend it to other many-body potentials, we introduce a stochastic modification which leads to a set of singularly perturbed Langevin equations with degenerate noise. We prove that the resulting Stochastic-XLMD converges to the accurate dynamics, and the convergence rate is both optimal and is independent of the accuracy of the initial polarization field. We carefully study the scaling of the damping factor and numerical noise for efficient numerical simulation for Stochastic-XLMD, and we demonstrate the effectiveness of the method for model polarizable force field systems.

math.NA↗

arXiv2019

Extended Experimental Inferential Structure Determination Method for Evaluating the Structural Ensembles of Disordered Protein States

Characterization of proteins with intrinsic or unfolded state disorder comprises a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. We introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, that calculates the maximum log-likelihood of a protein structural ensemble by accounting for the uncertainties of a wide range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects, paramagnetic relaxation enhancements, residual dipolar couplings, and hydrodynamic radii, single molecule fluorescence Förster resonance energy transfer efficiencies and small angle X-ray scattering intensity curves. We apply X-EISD to the drkN SH3 unfolded state domain and show that certain experimental data types are more influential than others for both eliminating structural ensemble models, while also finding equally probable disordered ensembles that have alternative structural properties that will stimulate further experiments to discriminate between them.

physics.bio-ph↗

arXiv2019

Interplay of Water and a Supramolecular Capsule for Catalysis of Reductive Elimination Reaction from Gold

Supramolecular assemblies have gained tremendous attention due to their apparent ability to catalyze reactions with the efficiencies of natural enzymes. Using Born-Oppenheimer molecular dynamics and density functional theory, we identify the origin of the catalytic power of the supramolecular assembly Ga$_4$L$_{612-}$ on the reductive elimination reaction from gold complexes and their similarity to enzymes. By comparing the catalyzed and uncatalyzed reaction in explicit solvent to identify the reaction free energies of the reactants, transition states, and products, we determine that a catalytic moiety -- an encapsulated water molecule -- generates electric fields that contribute significant reduction in the activation free energy. Although this is unlike the biomimetic scenario of catalysis through direct host-guest interactions, the nanocage host preconditions the transition state for greater sensitivity to electric field projections onto the breaking carbon bonds to complete the reductive elimination reaction with greater catalytic efficiency. However it is also shown that the nanocage poorly organizes the interfacial water, which in turn creates electric fields that misalign with the breaking bonds of the substrate, thus identifying new opportunities for catalytic design improvements in nanocage assemblies.

physics.chem-ph↗

arXiv2019

Diels-Alder Reactions in Water are Determined by Microsolvation

Nanoconfined aqueous environments and the recent advent of accelerated chemistry in microdroplets are increasingly being investigated for catalysis. The mechanisms underlying the enhanced reactivity in alternate solvent environments, and whether the enhanced reactivity due to nanoconfinement is a universal phenomenon, are not fully understood. Here, we use ab initio molecular dynamics simulations to characterize the free energy of a retro-Diels-Alder reaction in bulk water at very different densities and in water nanoconfined by parallel graphene sheets. We find that the broadly different global solvation environments accelerate the reactions to a similar degree with respect to the gas phase reaction, with activation free energies that do not differ by more than kbT from each other. The reason for the same acceleration factor in the extremely different solvation environments is that it is the microsolvation of the dienophile's carbonyl group that governs the transition state stabilization and mechanism, which is not significantly disrupted by either the lower density in bulk water or the strong nanoconfinement conditions used here. Our results also suggest that significant acceleration of Diels Alder reactions in microdroplets or on-water conditions can't arise from local microsolvation when water is present, but instead must come from highly altered reaction environments that drastically change the reaction mechanisms.

physics.chem-ph↗

arXiv2019

Accurate Prediction of Chemical Shifts for Aqueous Protein Structure for "Real World" Cases using Machine Learning

Accurate prediction of NMR chemical shifts can in principle help refine aqueous solution structure of proteins to the quality of X-ray structures. We report a new machine learning algorithm for protein chemical shift prediction that outperforms existing chemical shift calculators on realistic NMR solution data. Our UCBShift predictor implements two modules: a transfer prediction module that employs both sequence and structural alignment to select reference candidates for experimental chemical shift replication, and a redesigned machine learning module based on random forest regression which utilizes more, and more carefully curated, feature extracted data. When combined together, this new predictor achieves state of the art accuracy for predicting chemical shifts on a "real-world" dataset, with root-mean-square errors of 0.31 ppm for amide hydrogens, 0.19 ppm for Halpha, 0.87 ppm for C, 0.81 ppm for Calpha, 1.01 ppm for Cbeta, and 1.83 ppm for N, exceeding current prediction accuracy of popular chemical shift predictors such as SPARTA+ and SHIFTX2.

physics.chem-ph↗

arXiv2019

A Multi-Resolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography

We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multi-resolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training dataset by an order of magnitude without additional cost. We obtain very good agreement for 13C, 15N, and 17O chemical shifts, with the highest accuracy found for 1H chemical shifts that is equivalent to the best predictions using ab initio quantum chemistry methods.

physics.chem-ph↗

arXiv2019

Inertial Extended-Lagrangian Scheme for Solving Charge Equilibration Models

The inertial extended Lagrangian/self-consistent field scheme (iEL-SCF) has been adopted for solving charge equilibration in LAMMPS as part of the reactive force field ReaxFF, which due to the charge conservation constraint requires solving two sets of linear system of equations for the new charges at each molecular dynamics time-step. Therefore, the extended Lagrangian for charge equilibration is comprised of two auxiliary variables for the intermediate charges which serve as an initial guess for the real charges. We show that the iEL-SCF is able to reduce the number of SCF cycles by 50-80% of the original conjugate gradient self-consistent field solver as tested across diverse systems including water, ferric hydroxide, nitramine RDX, and hexanitrostilbene.

physics.chem-ph↗

arXiv2019

Water is not a Dynamic Polydisperse Branched Polymer

The contributed paper by Naserifar and Goddard reports that their RexPoN water model under ambient conditions simulates liquid water as a dynamic polydisperse branched polymer, which they speculate explains the existence of the liquid-liquid critical point (LLCP) in the supercooled region. Our work addresses several serious factual errors and needless speculation in their paper about their interpretation of their model and its implication for the LLCP in supercooled water.

physics.chem-ph↗

arXiv2019

Fluctuations of Electric Fields in the Active Site of the Enzyme Ketosteroid Isomerase

We report the effect of conformational dynamics on the fluctuations of electric fields in the active site of the enzyme Ketosteroid Isomerase (KSI). While KSI is considered rigid with little conformational variation to support different stages of the catalytic cycle, we show that KSI utilizes cooperative side chain motions of the entire protein scaffold outside the active site, which contribute negligibly to the electric fields on the substrate, by progressively stabilizing electric field contributions by particular active site residues at different timescales. The design of synthetic enzymes could benefit from strategies that can take advantage of the dynamics by using electric fields fluctuations as a guide.

physics.chem-ph↗

arXiv2017

Improvements to the APBS biomolecular solvation software suite

The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve the equations of continuum electrostatics for large biomolecular assemblages that has provided impact in the study of a broad range of chemical, biological, and biomedical applications. APBS addresses three key technology challenges for understanding solvation and electrostatics in biomedical applications: accurate and efficient models for biomolecular solvation and electrostatics, robust and scalable software for applying those theories to biomolecular systems, and mechanisms for sharing and analyzing biomolecular electrostatics data in the scientific community. To address new research applications and advancing computational capabilities, we have continually updated APBS and its suite of accompanying software since its release in 2001. In this manuscript, we discuss the models and capabilities that have recently been implemented within the APBS software package including: a Poisson-Boltzmann analytical and a semi-analytical solver, an optimized boundary element solver, a geometry-based geometric flow solvation model, a graph theory based algorithm for determining p$K_a$ values, and an improved web-based visualization tool for viewing electrostatics.

q-bio.BM↗