Biology subjects

Hall, B. W.

Publications and source records attributed to Hall, B. W..

5 recordsLinked to original sources

bioRxiv2026

The selectivity implications of docking libraries with greater and lesser similarities to bio-like molecules

As virtual libraries have expanded into the tens of billions via make-on-demand chemistry, their similarity to metabolites, natural products, and drugs ("bio-like" molecules) has rapidly diminished. Despite this divergence, molecular docking of these ultra-large libraries has yielded molecules at higher experimental hit-rates and with improved affinities. The structural divergence from bio-like space raises the possibility that molecules from these ultra-large libraries have improved selectivity. Just as plausibly, if hit-rates on-target are divorced from similarity to bio-like molecules, so too may be selectivity against off-targets. Here, we test whether docking hits for the 5-HT2A serotonin receptor from ultra-large libraries are more selective than those from smaller and more bio-like "in-stock" libraries. Chemoinformatic similarity predicts that docking actives from the in-stock library have more off-targets than the more chemically novel hits emerging from docking the ultra-large library. This may reflect the bias of the known, however, as when tested experimentally at scale against 318 GPCRs, both 16 agonists from the ultra-large library and 20 actives from the in-stock library had similar numbers of off-targets. While the ultra-large library hits are more sub-type selective for the 5-HT2A over the 5-HT2B and 5-HT2C receptors, overall these results may suggest that selectivity against off-targets, like affinity and hit-rates for on-targets, is divorced from library similarity to bio-like molecules.

biophysics↗

bioRxiv2025

Large scale prospective evaluation of co-folding across 557 Mac1-ligand complexes and three virtual screens

Accurate prediction of ligand-bound protein complexes and ranking them by affinity are central problems in drug discovery. While deep learning co-folding methods can help address these challenges, their evaluation has been hampered by the difficulties in assessing independence from training data and insufficiently large test sets. Here we test the ability of co-folding methods to predict the structures of 551 ligands, 489 of which are of sufficient quality for evaluation, bound to the SARS-CoV-2 NSP3 macrodomain (Mac1) that were determined after the training cut-off dates. AlphaFold3 (AF3), Boltz-2, and Chai-1 each reproduced >50% of the Mac1 ligand poses to better than 2 [A] RMSD of experiment. Despite the potential for co-folding to describe protein conformational changes that stabilize ligand binding, we did not find that common conformational rearrangements, including peptide flip and a large loop opening, were recapitulated by the co-folding prediction. For AF3 and Chai-1, ligand pose prediction confidence weakly, but significantly, tracked experimental potency, while DOCK3.7 energies were only weakly correlated. Boltz-2 affinity predictions showed the strongest correlation with measured potency and, after calibration, achieved lower mean absolute error than a baseline predictor. We next assessed whether co-folding scores could rescore docking hit-lists to distinguish true ligands from non-binders among hundreds of molecules prospectively experimentally tested against AmpC {beta}-lactamase, the dopamine D4 and the {sigma}2 receptors. AF3 ligand pose confidence values did not separate true ligands from high-scoring false-positives as effectively as docking scores or Boltz-2 affinity predictions did. Taken together, the modest, but independent correlations of docking score and co-folding confidence or affinity suggests that integrating physics-based and deep-learning approaches may help with hit prioritization and subsequent optimization in structure-based ligand discovery.

biophysics↗

bioRxiv2025

Structure based discovery of antipsychotic-like TAAR1 agonists

Schizophrenia is a severe mental illness whose current treatments primarily target dopamine and serotonin receptors. These drugs often cause side effects and vary in effectiveness across patients. The trace amine-associated receptor 1 (TAAR1), which modulates monoamine signaling, has emerged as a promising alternative target. To discover new TAAR1 ligands, we computationally docked 65 million molecules against the active state of TAAR1 and experimentally tested 55 of those highly ranked. Fourteen molecules active against TAAR1 with potencies ranging from mid-nanomolar to micromolar, all as agonists. This high functional selectivity may reflect the compact conformation adopted by the activated TAAR1 orthosteric site. While this was favorable for agonist prioritization, simulations suggest that it can be over-optimized for initial hit rates at the expense of subsequent affinity maturation. Here, hit optimization yielded nanomolar agonists whose docking-predicted poses were confirmed by cryo-EM. Three agonists had high brain exposure and potencies apparently better than the investigational drug ulotaront and sufficient for behavioral studies. All three potently normalized amphetamine-induced pre-pulse inhibition in mice, a model for schizophrenia, without catalepsy, a common side effect of traditional antipsychotics.

biochemistry↗

bioRxiv2025

A database for large-scale docking and experimental results

The rapid expansion of readily accessible compounds over the past six years has transformed molecular docking, improving hit rates and affinities. While many millions of molecules may score well in a docking campaign, the results are rarely fully shared, hindering the benchmarking of machine learning and chemical space exploration methods that seek to explore the expanding chemical spaces. To address this gap, we develop a website providing access to recent large library campaigns, including poses, scores, and in vitro results for campaigns against 11 targets, with 6.3 billion molecules docked and 3729 compounds experimentally tested. In a simple proof-of-concept study that speaks to the new librarys utility, we use the new database to train machine learning models to predict docking scores and to find the top 0.01% scoring molecules while evaluating only 1% of the library. Even in these proof-of-concept studies, some interesting trends emerge: unsurprisingly, as models train on larger sets, they perform better; less expected, models could achieve high correlations with docking scores and yet still fail to enrich the new docking-discovered ligands, or even the top 0.01% of docking-ranked molecules. It will be interesting to see how these trends develop for methods more sophisticated than the simple proof-of-concept studies undertaken here; the database is openly available at lsd.docking.org.

biophysics↗

bioRxiv2023

Tools for Genetic Engineering and Gene Expression Control in Novosphingobium aromaticivorans and Rhodobacter sphaeroides

Alphaproteobacteria have a variety of cellular and metabolic features that provide important insights into biological systems and enable biotechnologies. For example, some species are capable of converting plant biomass into valuable biofuels and bioproducts have the potential to form the backbone of the sustainable bioeconomy. Among the Alphaproteobacteria, Novosphingobium aromaticivorans, Rhodobacter sphaeroides, and Zymomonas mobilis, show particular promise as organisms that can be engineered to convert extracted plant lignin or sugars into bioproducts and biofuels. Genetic manipulation of these bacteria is needed to introduce engineered pathways and modulate expression of native genes with the goal of enhancing bioproduct output. Although recent work has expanded the genetic toolkit for Z. mobilis, N. aromaticivorans and R. sphaeroides still need facile, reliable approaches to deliver genetic payloads to the genome and to control gene expression. Here, we expand the platform of genetic tools for N. aromaticivorans and R. sphaeroides to address these issues. We demonstrate that Tn7 transposition is an effective approach for introducing engineered DNA into the chromosome of N. aromaticivorans and R. sphaeroides. We screen a synthetic promoter library to identify inducible promoters with strong, regulated activity in both organisms. Combining Tn7 integration with promoters from our library, we establish CRISPR interference systems for N. aromaticivorans and R. sphaeroides that can target essential genes and modulate engineered pathways. We anticipate that these systems will greatly facilitate both genetic engineering and gene function discovery efforts in these industrially important species and other Alphaproteobacteria. IMPORTANCEIt is important to increase our understanding of the microbial world to improve health, agriculture, the environment and biotechnology. For example, building a sustainable bioeconomy depends on the efficient conversion of plant material to valuable biofuels and bioproducts by microbes. One limitation in this conversion process is that microbes with otherwise excellent properties for conversion are challenging to genetically engineer. Here, we report systems to overcome that barrier in the Alphaproteobacteria, Novosphingobium aromaticivorans and Rhodobacter sphaeroides, by producing genetic tools that allow easy insertion of engineered pathways into their genomes and to precisely control gene expression by inducing genes with synthetic promoters or repressing genes using CRISPR interference. These tools can be used in future work to gain additional insight into these and other Alphaproteobacteria and to optimize yield of biofuels and bioproducts.

microbiology↗