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

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

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

Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD

Neuropsychiatric disorders classically lack defining brain pathologies, but recent work has demonstrated dysregulation at the molecular level, characterized by transcriptomic and epigenetic alterations1-3. In autism spectrum disorder (ASD), this molecular pathology involves the upregulation of microglial, astrocyte and neural-immune genes, the downregulation of synaptic genes, and attenuation of gene-expression gradients in cortex1,2,4-6. However, whether these changes are limited to cortical association regions or are more widespread remains unknown. To address this issue, we performed RNA-sequencing analysis of 725 brain samples spanning 11 cortical areas from 112 post-mortem samples from individuals with ASD and neurotypical controls. We find widespread transcriptomic changes across the cortex in ASD, exhibiting an anterior-to-posterior gradient, with the greatest differences in primary visual cortex, coincident with an attenuation of the typical transcriptomic differences between cortical regions. Single-nucleus RNA-sequencing and methylation profiling demonstrate that this robust molecular signature reflects changes in cell-type-specific gene expression, particularly affecting excitatory neurons and glia. Both rare and common ASD-associated genetic variation converge within a downregulated co-expression module involving synaptic signalling, and common variation alone is enriched within a module of upregulated protein chaperone genes. These results highlight widespread molecular changes across the cerebral cortex in ASD, extending beyond association cortex to broadly involve primary sensory regions