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Open Access Publications from the University of California

Scholarly Works (6 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2022)

Molecules at temperatures above 0K are always in motion, translating, rotating, and undergoing conformational changes. In systems that are out of equilibrium, these motions often become more intense and complex, leading to interesting phenomena, including the existence of life. This dissertation presents theoretical and computational modeling for some of these phenomena. First, many enzymes appear to diffuse faster in the presence of their substrates and to drift along concentration gradients of their substrate, phenomena known respectively as enhanced enzyme diffusion and enzyme chemotaxis. Here, experimental findings and proposed mechanisms for these observations are critically reviewed, then we propose a kinematic and thermodynamic analysis to serve as a validity check for any mechanism that attributes enhanced enzyme diffusion to self-propulsion. Second, overcrowded alkene-based molecular motors, a class of synthetic small molecules designed for light-driven rotation of its rotor part relative to its stator part, exhibit fast rotation in the microsecond timescale. Here, the full rotation process is modeled by quantum surface-hopping molecular dynamics simulations coupled with classical molecular dynamics simulations. This study proposes a novel rotation pathway, as well as providing computational predictions for rotation rate and maximal power output. Encouraging agreement with experiments are found, after fitting critical forcefield parameters to reference quantum mechanical energy surfaces. In conclusion, these efforts contribute to better understanding of molecular motions out of equilibrium and how to conceptualize and model them.

    Cover page: Theory and Modeling of Molecular Motion out of Equilibrium
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2019)
    A number of enzymes reportedly exhibit enhanced diffusion in the presence of their substrates, with a Michaelis-Menten-like concentration dependence. Although no definite explanation of this phenomenon has emerged, a physical picture of enzyme self-propulsion using energy from the catalyzed reaction has been widely considered. Here, we present a kinematic and thermodynamic analysis of enzyme self-propulsion that is independent of any specific propulsion mechanism. Using this theory, along with biophysical data compiled for all enzymes so far shown to undergo enhanced diffusion, we show that the propulsion speed required to generate experimental levels of enhanced diffusion exceeds the speeds of well-known active biomolecules, such as myosin, by several orders of magnitude. Furthermore, the minimal power dissipation required to account for enzyme enhanced diffusion by self-propulsion markedly exceeds the chemical power available from enzyme-catalyzed reactions. Alternative explanations for the observation of enhanced enzyme diffusion therefore merit stronger consideration.
      Cover page: A Thermodynamic Limit on the Role of Self-Propulsion in Enhanced Enzyme Diffusion
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2022)
      We determined the effectiveness of absolute binding free energy (ABFE) calculations to refine the selection of active compounds in virtual compound screening, a setting where the more commonly used relative binding free energy approach is not readily applicable. To do this, we conducted baseline docking calculations of structurally diverse compounds in the DUD-E database for three targets, BACE1, CDK2 and thrombin, followed by ABFE calculations for compounds with high docking scores. The docking calculations alone achieved solid enrichment of active compounds over decoys. Encouragingly, the ABFE calculations then improved on this baseline. Analysis of the results emphasizes the importance of establishing high quality ligand poses as starting points for ABFE calculations, a nontrivial goal when processing a library of diverse compounds without informative co-crystal structures. Overall, our results suggest that ABFE calculations can play a valuable role in the drug discovery process.
        Cover page: Absolute binding free energy calculations improve enrichment of actives in virtual compound screening
        • Article
        • Peer Reviewed
        UC Davis Previously Published Works (2020)
        The parameterization of torsional/dihedral angle potential energy terms is a crucial part of developing molecular mechanics force fields. Quantum mechanical (QM) methods are often used to provide samples of the potential energy surface (PES) for fitting the empirical parameters in these force field terms. To ensure that the sampled molecular configurations are thermodynamically feasible, constrained QM geometry optimizations are typically carried out, which relax the orthogonal degrees of freedom while fixing the target torsion angle(s) on a grid of values. However, the quality of results and computational cost are affected by various factors on a non-trivial PES, such as dependence on the chosen scan direction and the lack of efficient approaches to integrate results started from multiple initial guesses. In this paper, we propose a systematic and versatile workflow called TorsionDrive to generate energy-minimized structures on a grid of torsion constraints by means of a recursive wavefront propagation algorithm, which resolves the deficiencies of conventional scanning approaches and generates higher quality QM data for force field development. The capabilities of our method are presented for multi-dimensional scans and multiple initial guess structures, and an integration with the MolSSI QCArchive distributed computing ecosystem is described. The method is implemented in an open-source software package that is compatible with many QM software packages and energy minimization codes.
          Cover page: Driving torsion scans with wavefront propagation
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2022)
          Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physics-based methods. We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties. Comprehensive experiments show that LIMO performs competitively on benchmark tasks and markedly outperforms state-of-the-art techniques on the novel task of generating drug-like compounds with high binding affinity, reaching nanomolar range against two protein targets. We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted K D (a measure of binding affinity) of 6 · 10-14 M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets. Code is available at https://github.com/Rose-STL-Lab/LIMO.
            Cover page: LIMO: Latent Inceptionism for Targeted Molecule Generation.
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2023)
            Here, we present remarkable epoxyketone-based proteasome inhibitors with low nanomolar in vitro potency for blood-stage Plasmodium falciparum and low cytotoxicity for human cells. Our best compound has more than 2,000-fold greater selectivity for erythrocytic-stage P. falciparum over HepG2 and H460 cells, which is largely driven by the accommodation of the parasite proteasome for a D-amino acid in the P3 position and the preference for a difluorobenzyl group in the P1 position. We isolated the proteasome from P. falciparum cell extracts and determined that the best compound is 171-fold more potent at inhibiting the β5 subunit of P. falciparum proteasome when compared to the same subunit of the human constitutive proteasome. These compounds also significantly reduce parasitemia in a P. berghei mouse infection model and prolong survival of animals by an average of 6 days. The current epoxyketone inhibitors are ideal starting compounds for orally bioavailable anti-malarial drugs.
              Cover page: Development of Potent and Highly Selective Epoxyketone‐Based Plasmodium Proteasome Inhibitors
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