Biology subjects

Dent, J.

Publications and source records attributed to Dent, J..

4 recordsLinked to original sources

bioRxiv2025

Drug-like antibody design against challenging targets with atomic precision

Computational antibody design has seen rapid progress, with high success rates enabling direct translation to characterization without any high-throughput screening required. In this work, we markedly expand the scope of de novo antibody design by applying our state-of-the-art Chai-2 platform to design drug-like antibodies in full-length monoclonal format. We find that >86% of these full-length mAbs have strong developability profiles on par with therapeutic antibodies. We further show that experimentally determined structures of Chai-2 designs closely match their in silico predictions, demonstrating that Chai-2 produces atomically accurate models of designed antibodies. Building on these foundational capabilities, we showcase two potential applications of Chai-2 against different targets: designing functional antibodies mediating GPCR agonism, and highly specific antibodies selectively binding tumor-specific neoepitopes. Taken together, this work brings new flexibility to modern discovery pipelines, accelerating the path from in silico design to functional validation across both conventional and challenging targets. Beyond reducing the cost and timelines associated with large screening campaigns, in silico design can now open new frontiers for creative, targeted therapeutics that address unmet clinical needs.

molecular biology↗

bioRxiv2025

Molecular mechanism of naturally-encoded signaling-bias at the complement anaphylatoxin receptors

The conceptual framework of biased signaling has revolutionized our understanding of GPCR signaling and regulatory paradigms, and greatly impacted the efforts focused on the discovery of GPCR-targeted therapeutics. However, the mechanistic basis of biased signaling remains primarily defined based on synthetic ligands and receptor mutants with relatively limited progress in understanding naturally-encoded signaling-bias. Here, we present fundamental molecular and structural insights into naturally-encoded signaling-bias at the complement anaphylatoxin C5a receptors namely, C5aR1 and C5aR2. We first discover that C5a-d-Arg, the naturally-occurring version of C5a lacking the terminal arginine, exhibits robust G-protein signaling-bias at C5aR1, characterised by attenuated {beta}arr recruitment. This signaling-bias manifests in both cytokine release from primary human immune cells, and in vivo, during neutrophil mobilization. We combine the cryo-EM structures of C5a/C5a-d-Arg-C5aR1 complexes with MD simulation, site-directed mutagenesis, and cellular experiments to elucidate that the G-protein-bias exhibited by C5a-d-Arg results from a distinct orientation of TM7 and helix 8 in C5aR1 leading to inefficient GRK recruitment and receptor phosphorylation. Next, we determine the first cryo-EM structures of C5aR2, a naturally-encoded {beta}-arrestin-biased receptor, in an apo state, complexed with the natural agonists C5a and C5a-d-Arg, and three peptide agonists including a first-in-class, newly discovered C5aR2-selective agonist, R8Y. These structural snapshots reveal key differences between the binding of C5a and C5a- d-Arg to C5aR1 and C5aR2, and provide a molecular basis of functional specialization at these two receptors. Moreover, the structural insights also allow us to decipher the molecular basis of naturally-encoded signaling-bias at C5aR2 originating from a shallower cytoplasmic interface with hydrophobic interior pocket that is not permissive to efficient G-protein-coupling and activation. Finally, we also engineer and characterize loss-of-function and gain-of-function variants of C5aR1 and C5aR2, which in turn corroborate and validate the structural observations presented here. Collectively, our findings offer crucial insights into previously lacking molecular mechanisms of the naturally-encoded signaling-bias at GPCRs, which have broad implications not only for the general framework of biased-signaling, but also for novel therapeutic design.

biochemistry↗

bioRxiv2025

Zero-shot antibody design in a 24-well plate

Despite breakthroughs in protein design enabled by artificial intelligence, reliably designing functional antibodies from scratch has remained an elusive challenge. Recent works show promise but still require large-scale experimental screening of thousands to millions of designs to reliably identify hits. In this work, we introduce Chai-2, a multimodal generative model that achieves a 16% hit rate in fully de novo antibody design, representing an over 100-fold improvement compared to previous computational methods. We prompt Chai-2 to design[≤] 20 antibodies or nanobodies to 52 diverse targets, completing the workflow from AI design to wet-lab validation in under two weeks. Crucially, none of these targets have a preexisting antibody or nanobody binder in the Protein Data Bank. Remarkably, in just a single round of experimental testing, we find at least one successful hit for 50% of targets, often with strong affinities and favorable drug-like profiles. Beyond antibody design, Chai-2 achieves a 68% wet-lab success rate in miniprotein design - routinely yielding picomolar binders. The high success rate of Chai-2 enables rapid experimental validation and characterization of novel antibodies in under two weeks, paving the way toward a new era of rapid and precise atomic-level molecular engineering. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=56 SRC="FIGDIR/small/663018v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@69655borg.highwire.dtl.DTLVardef@17d44dborg.highwire.dtl.DTLVardef@133b7ddorg.highwire.dtl.DTLVardef@6c4319_HPS_FORMAT_FIGEXP M_FIG 52 antigens targeted by Chai-2. Blue boxes indicate targets with at least one successful binder out of [≤]20 assayed designs, representing 50% of the tested targets. Date: June 30, 2025 C_FIG

synthetic biology↗

bioRxiv2024

Chai-1: Decoding the molecular interactions of life

We introduce Chai-1, a multi-modal foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of tasks relevant to drug discovery. Chai-1 can optionally be prompted with experimental restraints (e.g. derived from wet-lab data) which boosts performance by double-digit percentage points. Chai-1 can also be run in single-sequence mode with-out MSAs while preserving most of its performance. We release Chai-1 model weights and inference code as a Python package for non-commercial use and via a web interface where it can be used for free including for commercial drug discovery purposes.

synthetic biology↗