Biology subjects

Kortzak, D.

Publications and source records attributed to Kortzak, D..

6 recordsLinked to original sources

bioRxiv2026

Activation pathway of the human voltage-gated proton channel

Many protein functions are coupled to proton transfer, yet the underlying mechanisms remain extremely difficult to resolve by experiment or conventional molecular dynamics (MD) simulations. The human voltage-gated proton channel HV1 (hHV1) plays key roles in human physiology, but how voltage and pH together drive its activation remains unresolved. Experimental structures of hHV1 are limited to the X-ray structure of a mouse chimera and NMR models of a truncated hHV1, both representing an intermediate resting state. Here we determine the activation pathway of hHV1, together with structural models of its resting and activated states, using an approach that integrates all-atom continuous constant-pH MD (CpHMD), weighted-ensemble (WE) path sampling, and AlphaFold modeling. Starting from an AlphaFold model selected for consistency with resting-state experimental data, over 100 s of simulations reveal a mechanism in which transmembrane voltage, assisted by proton titration, drives an outward displacement of the transmembrane segment S4 accompanied by a one-click reorganization of countercharge pairings. These events are enabled by a delicately balanced electrostatic landscape and pH-modulated breathing motions in the resting state that prime the channel for protonation-state switches upon voltage sensing, a principle that may underlie other proton-coupled voltage-gating processes. The simulations also uncover lipid-binding sites at which POPG selectively stabilizes the S4-up conformation, whereas POPE can stabilize either state. These predictions are validated against extensive electrophysiology, mutagenesis data, and new smFRET experiments, which support the one-click S4 displacement and the lipid effects. WE-CpHMD, implemented in the AMBER package, is applicable to other proton-coupled processes that remain elusive.

biophysics↗

bioRxiv2025

Illuminating the Druggable Proteome with an AI Protein Profiling Platform

Most human proteins lack chemical probes or pharmaceutical modulators, leaving much of the proteome unexplored. 1,2 Activity-based protein profiling has enabled proteome-scale discovery of protein-ligand interactions and covalent inhibitors, 3-5 but remains limited by probe chemistry, protein abundance, and discordant ligandability assignments across studies. 6-9 Machine-learning (ML) models can in principle generate proteomewide ligandability maps, but the predictive utility of current models is limited by the requirement for structures and the use of incomplete and weak training labels. 9-12 Here we developed an artificial intelligence protein profiling (AiPP), a sequence-based multitask platform built on the ESMC protein language model, 13,14 to accelerate proteomewide therapeutic discovery and target identification. While its primary task is identification of covalently ligandable cysteines, seven additional task heads and two external modules provide broader context by annotating reversible ligand-binding residues, disordered molecular recognition features, cysteine functional context, and reactivities. Central to the development is the LatentLift clustering approach, which leverages latent space similarities to reconcile conflicting experimental labels and facilitate model training. Applied to the human proteome, AiPP generated a cysteine-directed ligandability atlas that overcomes the limitations of current chemo-proteomic maps. As a proof of concept, we demonstrate that AiPP can guide the discovery of covalent allosteric inhibitors targeting the previously undruggable protein tyrosine phosphatase PTPN6. By linking sequence, ligandability and biological context, AiPP provides an open resource for therapeutic discovery, while LatentLift offers a general strategy for harmonizing proteomics data for ML applications.

biophysics↗

bioRxiv2024

KaMLs for Predicting Protein pKa Values andIonization States: Are Trees All You Need?

Despite its importance in understanding biology and computer-aided drug discovery, the accurate prediction of protein ionization states remains a formidable challenge. Physics-based approaches struggle to capture the small, competing contributions in the complex protein environment, while machine learning (ML) is hampered by scarcity of experimental data. Here we report the development of pKa ML (KaML) models based on decision trees and graph attention networks (GAT), exploiting physicochemical understanding and a new experiment pKa database (PKAD-3) enriched with highly shifted pKas. KaML-CBtree significantly outperforms the current state of the art in predicting pKa values and ionization states across all six titratable amino acids, notably achieving accurate predictions for deprotonated cysteines and lysines - a blind spot in previous models. The superior performance of KaMLs is achieved in part through several innovations, including separate treatment of acid and base, data augmentation using AlphaFold structures, and model pretraining on a theoretical pKa database. We also introduce the classification of protonation states as a metric for evaluating pKa prediction models. A meta-feature analysis suggests a possible reason for the lightweight tree model to outperform the more complex deep learning GAT. We release an end-to-end pKa predictor based on KaML-CBtree and the new PKAD-3 database, which facilitates a variety of applications and provides the foundation for further advances in protein electrostatics research. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/622800v3_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@15d2256org.highwire.dtl.DTLVardef@1795151org.highwire.dtl.DTLVardef@1c9ae02org.highwire.dtl.DTLVardef@1bf17a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

Allosteric modulation of proton binding confers Cl- activation and glutamate selectivity to vesicular glutamate transporters

Vesicular glutamate transporters (VGLUTs) fill synaptic vesicles with glutamate and remove luminal Cl- via an additional anion channel mode. Both of these transport functions are stimulated by luminal acidification, luminal-positive membrane potential, and luminal Cl-. We studied VGLUT1 transporter/channel activation using a combination of heterologous expression, cellular electrophysiology, fast solution exchange, and mathematical modeling. Cl- channel gating can be described with a kinetic scheme that includes two protonation sites and distinct opening, closing, and Cl--binding rates for each protonation state. Cl- binding promotes channel opening by modifying the pKa values of the protonation sites and rates of pore opening and closure. VGLUT1 transports glutamate and aspartate at distinct stoichiometries: H+-glutamate exchange at 1:1 stoichiometry and aspartate uniport. Neurotransmitter transport with variable stoichiometry can be described with an alternating access model that assumes that transporters without substrate translocate in the doubly protonated state to the inward-facing conformation and return with the bound amino acid substrate as either singly or doubly protonated. Glutamate, but not aspartate, promotes the release of one proton from inward-facing VGLUT1, resulting in preferential H+-coupled glutamate exchange. Cl- stimulates glutamate transport by making the glutamate-binding site accessible to cytoplasmic glutamate and by facilitating transitions to the inward-facing conformation after outward substrate release. We conclude that allosteric modification of transporter protonation by Cl- is crucial for both VGLUT1 transport functions.

biophysics↗

bioRxiv2021

Determination of protein stoichiometries via dual-color colocalization with single molecule localization microscopy

The oligomeric state of plasma membrane proteins is the result of the interactions between individual proteins and an important determinant of their function. Most approaches used to address this question rely on extracting these complexes from their native environment, which may disrupt weaker interactions. Therefore, microscopy techniques have been increasingly used in recent years to determine oligomeric states in situ. Classical light microscopy suffers from insufficient resolution, but super-resolution methods such as single molecule localization microscopy (SMLM) can circumvent this problem. When using SMLM to determine oligomeric states of proteins, subunits are labeled with fluorescent proteins that only emit light following activation or conversion at different wavelengths. Typically, individual molecules are counted based on a binomial distribution analysis of emission events detected within the same diffraction-limited volume. This strategy requires low background noise, a high recall rate for the fluorescent tag and intensive post-imaging data processing. To overcome these limitations, we developed a new method based on SMLM to determine the oligomeric state of plasma membrane proteins. Our dual-color colocalization (DCC) approach allows for accurate in situ counting even with low efficiencies of fluorescent protein detection. In addition, it is robust in the presence of background signals and does not require temporal clustering of localizations from individual proteins within the same diffraction limited volume, which greatly simplifies data acquisition and processing. We used DCC-SMLM to resolve the controversy surrounding the oligomeric state of two SLC26 multifunctional anion exchangers and to determine the oligomeric state of four members of the SLC17 family of organic anion transporters.

cell biology↗

bioRxiv2020

An amino-terminal point mutation increases EAAT2 anion currents without affecting glutamate transport rates

Excitatory amino acid transporters (EAATs) are prototypic dual function proteins that function as coupled glutamate/Na+/H+/K+ transporters and as anion-selective channels. Both transport functions are intimately intertwined at the structural level: secondary active glutamate transport is based on elevator-like movements of the mobile transport domain across the membrane, and the lateral movement of this domain results in anion channel opening. This particular anion channel gating mechanism predicts the existence of mutant transporters with changed anion channel properties, but without alteration in glutamate transport. We here report that the L46P mutation in the human EAAT2 transporter fulfils this prediction. L46 is a pore-forming residue of the EAAT2 anion channels at the cytoplasmic entrance into the ion conduction pathway. In whole-cell patch clamp recordings, we observed larger macroscopic anion current amplitudes for L46P than for WT EAAT2. Moreover, changes in selectivity made gluconate permeant in L46P EAAT2. Non-stationary noise analysis revealed unchanged unitary current amplitudes in mutant EAAT2 anion channels. Rapid L-glutamate application under forward transport conditions demonstrated that L46P does not reduce the transport rate of individual transporters. Since individual transport rates and unitary anion current amplitudes are unaffected, absolute open probabilities of EAAT2 anion channels were quantified as ratio of anion currents by glutamate uptake currents. We found up to sevenfold increased absolute open probability of L46P EAAT2 anion channels. Our results reveal an important determinant of the diameter of EAAT2 anion pore and demonstrate the existence of anion channel gating processes outside the EAAT uptake cycle.

physiology↗