Biology subjects

Koehl, A.

Publications and source records attributed to Koehl, A..

7 recordsLinked to original sources

bioRxiv2026

Deep models of protein evolution in time generate realistic evolutionary trajectories and functional proteins

Models of protein evolution are foundational to biology, underpinning essential techniques such as phylogenetic tree inference, ancestral sequence reconstruction, multiple sequence alignment, variant effect prediction, and protein design. Historically, for computational tractability, these models have relied on the simplifying - but biologically unrealistic - assumption that sites in a given protein evolve independently of each other. A crucial test of any evolutionary model is its ability to simulate realistic evolutionary trajectories, but the independent-sites assumption leads to simulations that poorly reflect the complexity of natural protein evolution. Here we introduce PEINT (Protein Evolution IN Time), a flexible and generalizable deep learning framework for modeling how the entire protein sequence evolves over time while incorporating complex interactions between sites. This framework enables learning realistic patterns of constrained evolutionary transitions directly from millions of protein sequences spanning diverse fold families. Furthermore, unlike classical models that require pre-aligned sequences, PEINT learns indel dynamics directly from raw, unaligned sequences, thereby eliminating potential biases from alignment errors that can lead to incorrect inference of evolutionary patterns. By capturing higher-order epistatic interactions and modeling insertion-deletion processes that classical models typically ignore, PEINT accurately reproduces key signatures of natural evolution, including conservation patterns and family-specific dynamics. When simulating evolution along phylogenetic trees, PEINT generates highly novel sequences that preserve protein function, which we validate through experimental characterization of simulated carbonic anhydrase variants that retain enzymatic activity. PEINT thus enables realistic simulation of protein evolution that explores new sequence space while respecting structural and functional constraints. This evolution-informed generative modeling framework offers a powerful new tool for advancing both phylogenetic inference and protein engineering.

evolutionary biology↗

bioRxiv2026

An engineered nanobody inhibitor for molecular-to-circuit control of opioid receptor function

Opioid receptors (ORs) orchestrate pain relief, reward, and dependence, yet their signaling arises from diverse cell types and subcellular compartments that cannot be selectively interrogated with existing pharmacological or genetic approaches. Single-domain antibodies, or nanobodies (Nbs), can probe receptor states, but their potential as tools for controlling native receptor signaling at the system level has remained unexplored. Here, we engineer a suite of high-affinity intracellular Nbs that bind active ORs through structure-guided evolution and in silico design. Iterative optimization yields Nb64, a potent inhibitor that rapidly suppresses transducer engagement, receptor internalization, and downstream signaling, including endogenous pathways in neuronal cells. Organelle targeting highlights Nb64s capacity to control OR activity with subcellular precision, while bio-reversible cell-penetrating peptide (CPP) conjugation enables non-genetic cytosolic delivery. Cell-type-specific expression of Nb64 in VTA interneurons attenuates fentanyl-evoked dopamine release and behavioral responses in mice, demonstrating targeted control of opioid actions in vivo. Nb64 provides a versatile strategy for dissecting OR biology and establishes a generalizable framework for precision inhibition of native GPCR signaling in vivo.

neuroscience↗

bioRxiv2023

Step-wise activation of a Family C GPCR

Metabotropic glutamate receptors belong to a family of G protein-coupled receptors that are obligate dimers and possess a large extracellular ligand-binding domain (ECD) that is linked via a cysteine-rich domain (CRDs) to their 7-transmembrane (TM) domain. Upon activation, these receptors undergo a large conformational change to transmit the ligand binding signal from the ECD to the G protein-coupling TM. In this manuscript, we propose a model for a sequential, multistep activation mechanism of metabotropic glutamate receptor subtype 5. We present a series of structures in lipid nanodiscs, from inactive to fully active, including agonist-bound intermediate states. Further, using bulk and single-molecule fluorescence imaging we reveal distinct receptor conformations upon allosteric modulator and G protein binding.

biophysics↗

bioRxiv2022

Cross-protein transfer learning substantially improves zero-shot prediction of disease variant effects

Genetic variation in the human genome is a major determinant of individual disease risk, but the vast majority of missense variants have unknown etiological effects. Here, we present a robust learning framework for leveraging saturation mutagenesis experiments to construct accurate computational predictors of proteome-wide missense variant pathogenicity. We train cross-protein transfer (CPT) models using deep mutational scanning data from only five proteins and achieve state-of-the-art performance on clinical variant interpretation for unseen proteins across the human proteome. High sensitivity is crucial for clinical applications and our model CPT-1 particularly excels in this regime. For instance, at 95% sensitivity of detecting human disease variants annotated in ClinVar, CPT-1 improves specificity to 68%, from 27% for ESM-1v and 55% for EVE. Furthermore, for genes not used to train REVEL, a supervised method widely used by clinicians, we show that CPT-1 compares favorably with REVEL. Our framework combines predictive features derived from general protein sequence models, vertebrate sequence alignments, and AlphaFold2 structures, and it is adaptable to the future inclusion of other sources of information. We find that vertebrate alignments, albeit rather shallow with only 100 genomes, provide a strong signal for variant pathogenicity prediction that is complementary to recent deep learning-based models trained on massive amounts of protein sequence data. We release predictions for all possible missense variants in 90% of human genes. Our results demonstrate the utility of mutational scanning data for learning properties of variants that transfer to unseen proteins.

genetics↗

bioRxiv2022

The Impact of Protein Dynamics on Residue-Residue Coevolution and Contact Prediction

The need to maintain protein structure constrains evolution at the sequence level, and patterns of coevolution in homologous protein sequences can be used to predict their 3D structures with high accuracy. Our understanding of the relationship between protein structure and evolution has traditionally been benchmarked by computational models ability to predict contacts from a single representative, experimentally determined structure per protein family. However, proteins in vivo are highly dynamic and can adopt multiple functionally relevant conformations. Here we demonstrate that interactions that stabilize alternate conformations, as well those that mediate conformational changes, impose an underappreciated but significant set of evolutionary constraints. We analyze the extent of these constraints over 56 paralogous G protein coupled receptors (GPCRs), {beta}-arrestin and the human SARS-CoV2 receptor ACE2. Specifically, we observe that contacts uniquely found in molecular dynamics (MD) simulation data and alternate-conformation crystal structures are successfully predicted by unsupervised language models. In GPCRs, adding these contacts as positives increases the percentage of top contacts classified as true positives, as predicted by a state-of-the-art language model, from 69% to 87%. Our results show that protein dynamics impose constraints on molecular evolution and demonstrate the ability of unsupervised language models to measure these constraints.

bioinformatics↗

bioRxiv2021

Transferability of Geometric Patterns from Protein Self-Interactions to Protein-Ligand Interactions

There is significant interest in developing machine learning methods to model protein-ligand interactions but a scarcity of experimentally resolved protein-ligand structures to learn from. Protein self-contacts are a much larger source of structural data that could be leveraged, but currently it is not well understood how this data source differs from the target domain. Here, we characterize the 3D geometric patterns of protein self-contacts as probability distributions. We then present a flexible statistical framework to assess the transferability of these patterns to protein-ligand contacts. We observe that the level of transferability from protein self-contacts to protein-ligand contacts depends on contact type, with many contact types exhibiting high transferability. We then demonstrate the potential of leveraging information from these geometric patterns to aid in ligand pose-selection problems in protein-ligand docking. We publicly release our extracted data on geometric interaction patterns to enable further exploration of this problem.

biophysics↗

bioRxiv2019

Structural characterization of an intermediate reveals a unified mechanism for the CLC Cl-/H+ transport cycle

Among coupled exchangers, CLCs uniquely catalyze the exchange of oppositely charged ions (Cl- for H+). Transport-cycle models to describe and explain this unusual mechanism have been proposed based on known CLC structures. While the proposed models harmonize many experimental findings, there have remained gaps and inconsistencies in our understanding. One limitation has been that global conformational change - which occurs in all conventional transporter mechanisms - has not been observed in any high-resolution structure. Here, we describe the 2.6 [A] structure of a CLC mutant designed to mimic the fully H+-loaded transporter. This structure reveals a global conformational change to a state that has improved accessibility for the Cl- substrate from the extracellular side and new conformations for two key glutamate residues. Based on this new structure, together with DEER measurements, MD simulations, and functional studies, we propose a unified model of the CLC transport mechanism that reconciles existing data on all CLC-type proteins.

biophysics↗