Improved prediction of ligand-protein binding affinities by meta-modeling

The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts, as filtering potential candidates would save time and expenses for finding drugs. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Given many computational models for binding affinity prediction with varying results across targets, we herein develop a meta-modeling framework by integrating published empirical structure-based docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual models, training databases, and linear and nonlinear meta-modeling approaches. We show that many of our meta-models significantly improve affinity predictions over individual base models. Our best meta-models achieve comparable performance to state-of-the-art exclusively structure-based deep learning tools. Overall, we demonstrate that diverse modeling approaches can be ensembled together to gain substantial improvement in binding affinity prediction while allowing control over input features such as physicochemical properties or molecular descriptors.Comment: 61 pages, 3 main tables, 6 main figures, 6 supplementary figures, and supporting information. For 8 supplementary tables and code, see https://github.com/Lee1701/Lee2023

Enhancement of the optical gain in GaAs nanocylinders for nanophotonic applications

Semiconductor nanolasers based on micro disks, photonic crystal cavities, and metallo-dielectric nanocavities have been studied during the last decade for on-chip light source applications. However, practical realization of low threshold, room temperature operation of semiconductor nanolasers is still a challenge due to the large surface-to-volume ratio of the nanostructures, which results in low optical gain and hence higher lasing threshold. Also, the gain in nanostructures is an important parameter for designing all-dielectric metamaterial-based active applications. Here, we investigate the impact of p-type doping, compressive strain, and surface recombination on the gain spectrum and the spatial distribution of carriers in GaAs nanocylinders. Our analysis reveals that the lasing threshold can be lowered by choosing the right doping concentration in the active III-V material combined with compressive strain. This combination of strain and p-type doping shows 100x improvement in gain and ~5 times increase in modulation bandwidth for high-speed operation.Comment: 19 pages, 6 figure

Quantum computing at the frontiers of biological sciences

The search for meaningful structure in biological data has relied on cutting-edge advances in computational technology and data science methods. However, challenges arise as we push the limits of scale and complexity in biological problems. Innovation in massively parallel, classical computing hardware and algorithms continues to address many of these challenges, but there is a need to simultaneously consider new paradigms to circumvent current barriers to processing speed. Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences

Theoretical and Numerical Studies of Dynamics in Nucleic Acids based on Experimental NMR Data

Thesis (Ph.D.)--University of Washington, 2012The collection of work presented in this thesis is directed towards building an understanding of the dynamics of nucleic acid molecules and their components using data obtained from solid state and solution NMR experiments. The focus of these studies is to develop analytical and numerical methods of elucidating motional trajectories of residues in example molecules, by simulating the impact of specific choices of models on NMR observables. Specifically, the target molecules studied were the unbound HIV-1 TAR RNA, a 29 nucleotide RNA segment, and the unbound dodecamer HhaI methyltransferase-recognition DNA. The data available for various residues in these systems include solid state line shapes, longitudinal (T1Z) and quadrupolar (T1Q) relaxation times, as well as solution longitudinal (T1) and rotating frame (T1&rho) relaxation times and Nuclear Overhauser Effects (NOEs). The four projects discussed in this thesis form a cohesive whole, with each succeeding method either building upon previous work or adding a new means of analysis: firstly, a slow exchange theory is presented where discrete-jump motional models derived using solid state NMR data can be tested against solution relaxation times, by the inclusion of both overall molecular tumbling and exchange between conformers occurring at a time scale much slower than the tumbling time scale. The time scale separation allows for a particularly simple weighted summation over the spectral density contributions from the various conformers. The second project, discussed subsequently, removes this assumption of time scale separation, and allows for any rate of exchange between conformers. Both simulation protocols use the TAR RNA molecule as the test system. Parallel work on the HhaI-recognition DNA builds a framework for testing a discrete-jump trajectory constructed using pre-existing rotamers of the molecule against solid state relaxation times. This visualization of the dynamics of a residue is then carried over to the solution domain, where the properties of computationally energy-minimized structures of TAR RNA are used to define a solution trajectory. In this last case, data available for multiple sites on the molecule are used to test the model for the trajectory, as well as to fit the rates of motion

A REDOR ssNMR Investigation of the Role of an N‑Terminus Lysine in R5 Silica Recognition

Diatoms are unicellular algae that construct cell walls called frustules by the precipitation of silica, using special proteins that order the silica into a wide variety of nanostructures. The diatom species <i>Cylindrotheca fusiformis</i> contains proteins called silaffins within its frustules, which are believed to assemble into supramolecular matrices that serve as both accelerators and templates for silica deposition. Studying the properties of these biosilicification proteins has allowed the design of new protein and peptide systems that generate customizable silica nanostructures, with potential generalization to other mineral systems. It is essential to understand the mechanisms of aggregation of the protein and its coprecipitation with silica. We continue previous investigations into the peptide R5, derived from silaffin protein sil1p, shown to independently catalyze the precipitation of silica nanospheres in vitro. We used the solid-state NMR technique ¹³C­{²⁹Si} and ¹⁵N­{²⁹Si} REDOR to investigate the structure and interactions of R5 in complex with coprecipitated silica. These experiments are sensitive to the strength of magnetic dipole–dipole interactions between the ¹³C nuclei in R5 and the ²⁹Si nuclei in the silica and thus yield distance between parts of R5 and ²⁹Si in silica. Our data show strong interactions and short internuclear distances of 3.74 ± 0.20 Å between ¹³CO Lys3 and silica. On the other hand, the C_α and C_β nuclei show little or no interaction with ²⁹Si. This selective proximity between the K3 CO and the silica supports a previously proposed mechanism of rapid silicification of the antimicrobial peptide KSL (KKVVFKVKFK) through an imidate intermediate. This study reports for the first time a direct interaction between the N-terminus of R5 and silica, leading us to believe that the N-terminus of R5 is a key component in the molecular recognition process and a major factor in silica morphogenesis

A Study of Phenylalanine Side-Chain Dynamics in Surface-Adsorbed Peptides Using Solid-State Deuterium NMR and Rotamer Library Statistics

Extracellular matrix proteins adsorbed onto mineral surfaces exist in a unique environment where the structure and dynamics of the protein can be altered profoundly. To further elucidate how the mineral surface impacts molecular properties, we perform a comparative study of the dynamics of nonpolar side chains within the mineral-recognition domain of the biomineralization protein salivary statherin adsorbed onto its native hydroxyapatite (HAP) mineral surface versus the dynamics displayed by the native protein in the hydrated solid state. Specifically, the dynamics of phenylalanine side chains (viz., F7 and F14) located in the surface-adsorbed 15-amino acid HAP-recognition fragment (SN15: DpSpSEEKFLRRIGRFG) are studied using deuterium magic angle spinning (²H MAS) line shape and spin–lattice relaxation measurements. ²H NMR MAS spectra and <i>T</i>₁ relaxation times obtained from the deuterated phenylalanine side chains in free and HAP-adsorbed SN15 are fitted to models where the side chains are assumed to exchange between rotameric states and where the exchange rates and a priori rotameric state populations are varied iteratively. In condensed proteins, phenylalanine side-chain dynamics are dominated by 180° flips of the phenyl ring, i.e., the “π flip”. However, for both F7 and F14, the number of exchanging side-chain rotameric states increases in the HAP-bound complex relative to the unbound solid sample, indicating that increased dynamic freedom accompanies introduction of the protein into the biofilm state. The observed rotameric exchange dynamics in the HAP-bound complex are on the order of 5–6 × 10⁶ s^–1, as determined from the deuterium MAS line shapes. The dynamics in the HAP-bound complex are also shown to have some solution-like behavioral characteristics, with some interesting deviations from rotameric library statistics

Combining molecular and spin dynamics simulations with solid-state NMR: a case study of amphiphilic lysine-leucine repeat peptide aggregates

Interpreting dynamics in solid-state molecular systems requires characterization of the potentially heterogeneous environmental contexts of molecules. In particular, the analysis of solid-state NMR (ssNMR) data to elucidate molecular dynamics involves modeling the restriction to overall tumbling by neighbors, as well as the concentrations of water and buffer. In this exploration of the factors that influence motion, we utilize atomistic molecular dynamics (MD) trajectories of peptide aggregates with varying hydration to mimic an amorphous solid-state environment, and predict ssNMR relaxation rates. We also account for spin diffusion in multiply spin-labeled (up to 19 nuclei) residues, with several models of dipolar-coupling networks. The framework serves as a general approach to determine essential spin couplings affecting relaxation, benchmark MD force fields, and reveal the hydration-dependence of dynamics in a crowded environment. We demonstrate the methodology on a previously characterized amphiphilic 14-residue lysine-leucine repeat peptide, LKα14 (Ac-LKKLLKLLKKLLKL-c), which has an α-helical secondary structure and putatively forms leucine-burying tetramers in the solid state. We measure R1 relaxation rates of uniformly 13C-labeled, and site-specific 2H-labeled leucines in the hydrophobic core of LKα14 at multiple hydration levels. Studies of 9 and 18 tetramer bundles reveal that: (a) for the incoherent component of 13C relaxation, nearest-neighbor spin interactions dominate, while 1H-1H interactions have minimal impact; (b) AMBER ff14SB dihedral barriers for the leucine Cγ - Cδ bond (“methyl rotation barriers”) must be lowered by a factor of 0.7 to better match the 2H data; (c) proton-driven spin diffusion (PDSD) explains some of the discrepancy between experimental and simulated rates for the Cβ and Cα nuclei; and (d) 13C relaxation rates are mostly underestimated in the MD simulations at all hydrations, and the discrepancies identify likely motions missing in the 50 ns MD trajectories.<br/