Biology subjects

Schlessinger, A.

Publications and source records attributed to Schlessinger, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Targeting an allosteric binding site in the citrate transporter NaCT (SLC13A5)

The Na+-dependent citrate transporter NaCT (SLC13A5) is a key regulator of citrate homeostasis and has emerged as a therapeutic target for metabolic and neurological disease, including the SLC13A5 Epilepsy, a rare disease marked by severe sezures and neurodevelopmental delays. Current NaCT inhibitors are substrate-like molecules that competitively bind the substrate binding site. In this study, we identify previously unknown small molecule inhibitors of NaCT by targeting a putative allosteric site located at the dimer interface. We performed a virtual screen of 3.5 million compounds from the ZINC20 database against this site and selected 54 candidates for experimental testing using a cell-based citrate uptake assay. Through initial experiments, we identified three weak inhibitors, and subsequent evaluation of 26 structurally related analogs yielded six compounds with improved potency (IC50 = 12.78 M and 15.49 M). We then performed further analysis of the putative binding site by integrating structural data with deep mutational scanning evidence and comparisons with homolog structures. This analysis highlighted the importance of key residues (e.g., Phe362) in ligand modulation. These findings reveal a promising allosteric pocket and establish a chemically distinct series of NaCT inhibitors, providing a foundation for rational development of pharmacological modulators of NaCT function.

biophysics↗

bioRxiv2025

Key residue Unlocks Dual PIP2-Dependent and Independent Gating in G Protein-Gated Inwardly Rectifying Potassium Channels

G protein-gated inwardly rectifying potassium (GIRK) channels mediate membrane hyperpolarization in response to G protein-coupled receptor activation and are critical for regulating neuronal excitability. The membrane phospholipid phosphatidylinositol 4,5-bisphosphate (PIP2) is essential for regulating a large family of ion channels, and disruptions in PIP2 interactions contribute to some neurological diseases. Structural analyses have identified key residues in PIP2-mediated gating of the GIRK2 channel. Notably, Arginine-92 (R92), a highly conserved basic residue at the membrane interface in GIRK2, interacts with PIP2 as well as cholesteryl hemisuccinate (CHS), a potentiator of GIRK2. Here, we used a combination of electrophysiological assays, fluorescent K+ flux measurements, cryo-electron microscopy, and molecular dynamics simulations, and find that mutations at R92 (Y, F, and Q) not only alter PIP2 sensitivity but can also reveal a novel gating mechanism that is independent of PIP2. These findings indicate that R92 plays a crucial role in modulating GIRK2 channel gating, offering new insights into developing potential therapeutic targets for treating neurological disorders linked to GIRK channel dysfunction.

biophysics↗

bioRxiv2024

Mechanisms of Cholesterol Binding to LAT1

The human L-type amino acid transporter 1 (LAT1; SLC7A5), is an amino acid exchanger protein, primarily found in the blood-brain-barrier, placenta, and testis, where it plays a key role in amino acid homeostasis. Cholesterol is an essential lipid that has been highlighted to play a role in regulating the activity of membrane transporters such as LAT1, yet little is known about the molecular mechanisms driving this phenomenon. Here we perform a comprehensive computational analysis to investigate cholesterols role in LAT1 structure and function, focusing on four cholesterol binding sites (CHOL1-4) identified in a recent LAT1-apo inward-open conformation cryo-EM structure. We performed four independent molecular dynamics (MD) simulations of LAT1 bound to each cholesterol molecule, as well as molecular docking, free energy calculation by MM/GBSA, and other analysis tools, to investigate LAT1-cholesterol interactions. Our results indicate that CHOL3 provides the most stable binding interactions with LAT1, and CHOL3 and CHOL1 sites have the largest stabilizing effect on LAT1s primary functional motifs (hash and bundle) and substrate binding site. Our analysis also uncovers an alternative cholesterol binding site to the originally assigned CHOL1. Our study improves the understanding of cholesterols modulatory effect on LAT1 and proposes candidate sites for discovery of future allosteric ligands with rational design.

biophysics↗

bioRxiv2023

KinAce: a web portal for exploring kinase-substrate interactions

Interactions between protein kinases and their substrates are critical for the modulation of complex signaling pathways. Currently, there is a large amount of information available about kinases and their substrates in disparate public databases. However, these data are difficult to interpret in the context of cellular systems, which can be facilitated by examining interactions among multiple proteins at once, such as the network of interactions that constitute a signaling pathway. We present KinAce, a user-friendly web portal that integrates and shares information about kinase-substrate interactions from multiple databases of post-translational modifications. KinAce enables the visual exploration of these interactions in systems contexts, such as pathways, domain families, and custom protein set inputs, in an interactive fashion. We expect KinAce to be useful as a knowledge discovery tool for kinase-substrate interactions, and the aggregated KinAce dataset to be useful for protein kinase studies and systems-level analyses. The portal is available at https://kinace.kinametrix.com/.

systems biology↗

bioRxiv2023

Exploring the Druggable Conformational Space of Protein Kinases Using AI-Generated Structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and [a]C-Helix motifs, which enable kinases to adopt various conformational states. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the kinase conformation(s) they bind. However, the limited availability of experimentally determined structural data for kinases in inactive states restricts drug discovery efforts for this major protein family. Modern AI-based structural modeling methods hold potential for exploring the previously experimentally uncharted druggable conformational space for kinases. Here, we first evaluated the currently explored conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) (1) and ESMFold (2), two prominent AI-based structure prediction methods. We then investigated AF2s ability to predict kinase structures in different conformations at various multiple sequence alignment (MSA) depths, based on this parameters ability to explore conformational diversity. Our results showed a bias within the PDB and predicted structural models generated by AF2 and ESMFold toward structures of kinases in the active state over alternative conformations, particularly those conformations controlled by the DFG motif. Finally, we demonstrate that predicting kinase structures using AF2 at lower MSA depths allows the exploration of the space of these alternative conformations, including identifying previously unobserved conformations for 398 kinases. The results of our analysis of structural modeling by AF2 create a new avenue for the pursuit of new therapeutic agents against a notoriously difficult-to-target family of proteins. Significance StatementGreater abundance of kinase structural data in inactive conformations, currently lacking in structural databases, would improve our understanding of how protein kinases function and expand drug discovery and development for this family of therapeutic targets. Modern approaches utilizing artificial intelligence and machine learning have potential for efficiently capturing novel protein conformations. We provide evidence for a bias within AlphaFold2 and ESMFold to predict structures of kinases in their active states, similar to their overrepresentation in the PDB. We show that lowering the AlphaFold2 algorithms multiple sequence alignment depth can help explore kinase conformational space more broadly. It can also enable the prediction of hundreds of kinase structures in novel conformations, many of whose models are likely viable for drug discovery.

bioinformatics↗

bioRxiv2023

Structural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue

Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the COSMIC database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.

pharmacology and toxicology↗

bioRxiv2022

Conserved allosteric inhibition mechanism in SLC1 transporters

Excitatory Amino Acid Transporter 1 (EAAT1) is a plasma-membrane glutamate transporter belonging to the SLC1 family of solute carriers. It plays a key role in neurotransmitter transport and contributes to the regulation of the extracellular glutamate concentration in the mammalian brain. The structure of EAAT1 was determined using cryo-EM, in complex with UCPH-101, a highly potent and non-competitive inhibitor of EAAT1. Alanine Serine Cysteine Transporter 2 (ASCT2) is a neutral amino acid transporter, which regulates pools of amino acids such as glutamine, serine and alanine between intracellular and extracellular compartments in a Na+ dependent manner. ASCT2 also belongs to the SLC1 family and shares 58% sequence similarity with EAAT1. However, allosteric modulation of ASCT2 via non-competitive inhibitors is unknown. Here we explore the UCPH-101 inhibitory mechanisms of EAAT1 and ASCT2 by using rapid kinetic experiments. Our results show that UCPH-101 slows substrate translocation rather than substrate or Na+ binding, confirming a non-competitive inhibitory mechanism, but only partially inhibits wild-type ASCT2 with relatively low affinity. Guided by computational modeling using ligand docking and molecular dynamics (MD) simulations, we selected two residues involved in UCPH-101/EAAT1 interaction, which were mutated in ASCT2 (F136Y, I237M, F136Y/I237M) in the corresponding positions. We show that in the F136Y/I237M double mutant transporter, 100% of the inhibitory effect of UCPH-101 on anion current could be restored, and the apparent affinity was increased (Ki = 9.3 M), much closer to the EAAT1 value of 0.6 M. Finally, we identify a novel non-competitive ASCT2 inhibitor, identified through virtual screening and experimental testing against the allosteric site, further supporting its localization. Together, these data indicate that the mechanism of allosteric modulation is conserved between EAAT1 and ASCT2. Due to the difference in binding site residues between ASCT2 and EAAT1, these results raise the possibility that more potent, and potentially selective inhibitors can be designed that target the ASCT2 allosteric binding site.

biophysics↗

bioRxiv2022

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of diverse feature set

Gain-of-function (GOF) variants give rise to increased or novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. GOF and LOF variants can result in markedly varying phenotypes, even when occurring in the same gene. However, experimental approaches for identifying GOF and LOF are generally slow and costly, whilst currently available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, an ensemble machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants. LoGoFunc was trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics, as well as network features summarizing the protein-protein interactome and structural features calculated from AlphaFold2 protein models. We analyzed GOF, LOF, and neutral variants in terms of local protein structure and function, splicing disruption, and phenotypic associations, thereby revealing previously unreported relationships between various biological phenomena and variant functional outcomes. For example, GOF and LOF variants exhibit contrasting enrichments in protein structural and functional regions, whilst LOF variants are more likely to disrupt canonical splicing as indicated by splicing-related features employed by the model. Further, by performing phenome-wide association studies (PheWAS), we identified strong associations between relevant phenotypes and high-confidence predicted GOF and LOF variants. LoGoFunc outperforms other tools trained solely to predict pathogenicity or general variant impact for the identification of pathogenic GOF and LOF variants.

genetics↗

bioRxiv2022

Describing Inhibitor Specificity for the Amino Acid Transporter LAT1 from Metainference Simulations

The human L-type amino acid transporter 1 (LAT1; SLC7A5) is a membrane transporter of amino acids, thyroid hormones, and drugs such as the Parkinsons disease drug L-Dopa. LAT1 is found in the blood-brain-barrier (BBB), testis, bone marrow, and placenta, and its dysregulation has been associated with various neurological diseases such as autism and epilepsy as well as cancer. In this study, we combine metainference molecular dynamics (MD) simulations, molecular docking, and experimental testing, to characterize LAT1-inhibitor interactions. We first conducted a series of molecular docking experiments to identify the most relevant interactions between LAT1s substrate binding site and ligan ds, including both inhibitors and substrates. We then performed metainference MD simulations using cryo-EM structures in different conformations of LAT1 with the electron density map as a spatial restraint, to explore the inherent heterogeneity in the structures. We analyzed the LAT1 substrate binding site to map important LAT1-ligand interactions as well as newly described druggable pockets. Finally, this analysis guided the discovery of previously unknown LAT1 ligands using virtual screening and cellular uptake experiments. Our results improve our understanding of LAT1-inhibitor recognition, providing a framework for rational design of future lead compounds targeting this key drug target. Statement of SignificanceLAT1 is a membrane transporter of amino acids, thyroid hormones, and therapeutic drugs, that is primarily found in the BBB and placenta, as well as in tumor cells of several cancer types. We combine metainference MD simulations, molecular docking, and experimental testing, to characterize LAT1-inhibitor interactions. Our computational analysis predicts S66, G67, F252, G255, Y259, W405 are critical residues for inhibitor binding and druggable sub-pockets in the outward-occluded conformation that are ideal for LAT1 inhibitor discovery. Using virtual screening and functional testing, we discovered multiple LAT1 inhibitors with diverse scaffolds and binding modes. Our results improve our understanding of LAT1s structure and function, providing a framework for development of future therapeutics targeting LAT1 and other SLC transporters.

biophysics↗

bioRxiv2022

Substrate Binding and Inhibition of the Anion Exchanger 1 Transporter

Anion Exchanger 1 (AE1, SLC4A1) is the primary bicarbonate (HCO3-) transporter expressed in erythrocyte membranes where it mediates transport of CO2 between lungs and other tissues via import/export of bicarbonate. It is also a key regulator of erythrocyte structure and antigenic recognition. Previous biochemical studies, and a low-resolution crystal structure of the transmembrane domain have provided initial insight into AE1 structure and function. However, key questions remain regarding substrate binding and transport as well as the mechanism of inhibition. The orientation of the intracellular domain as well as the localization of lipid and sterol binding sites also remain enigmatic. We herein present seven novel high resolution cryo-EM structures of the full length human transporter in the apo, bicarbonate-bound, and several inhibitor-bound states combined with uptake- and computational studies. To our knowledge, these studies represent the first full length human, and substrate bound, SLC4 transporter structure. Our results reveal important molecular details about substrate binding and transport, as well as the diverse mechanisms of AE1 inhibition by both research chemicals and prescription drugs. We also provide novel insights into the full-length transporter architecture, identify the conformational space of the Diego blood antigen system and elucidate multiple lipid and sterol binding sites.

biochemistry↗

bioRxiv2021

Dual targeting of salt inducible kinases and CSF1R uncouples bone formation and bone resorption

Bone formation and resorption are typically coupled, such that the efficacy of anabolic osteoporosis treatments may be limited by bone destruction. The multi-kinase inhibitor YKL-05-099 potently inhibits salt inducible kinases (SIKs) and may represent a promising new class of bone anabolic agents. Here we report that YKL-05-099 increases bone formation in hypogonadal female mice without increasing bone resorption. Postnatal mice with inducible, global deletion of SIK2 and SIK3 show increased bone mass, increased bone formation, and, distinct from the effects of YKL-05-099, increased bone resorption. No cell-intrinsic role of SIKs in osteoclasts was noted. In addition to blocking SIKs, YKL-05-099 also binds and inhibits CSF1R, the receptor for the osteoclastogenic cytokine M-CSF. Modeling reveals that YKL-05-099 binds to SIK2 and CSF1R in a similar manner. Dual targeting of SIK2/3 and CSF1R induces bone formation without concomitantly increasing bone resorption and thereby may overcome limitations of most current anabolic osteoporosis therapies.

physiology↗

bioRxiv2021

PredictProtein - Predicting Protein Structure and Function for 29 Years

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProteins infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold; user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. AvailabilityFreely accessible webserver PredictProtein.org; Source and docker images: github.com/rostlab

bioinformatics↗

bioRxiv2021

Crowdsourced Identification of Multi-Target Kinase Inhibitors for RET- and TAU- Based Disease: the Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ( polypharmacology). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology. Author SummaryMany modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact, dirty; they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop cleaner therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease.

pharmacology and toxicology↗

bioRxiv2020

Protein structure-based gene expression signatures

Gene expression signatures (GES) connect phenotypes to mRNA expression patterns, providing a powerful approach to define cellular identity, function, and the effects of perturbations. However, the use of GES has suffered from vague assessment criteria and limited reproducibility. The structure of proteins defines the functional capability of genes, and hence, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from various levels of protein structure (e.g. domain, fold) encoded by the transcribed genes in GES, to describe cellular phenotypes. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), ARCHS4, and mRNA expression of drug effects on cardiomyocytes show that structural GES (sGES) are useful for identifying robust signatures of biological phenomena. sGES also enables the characterization of signatures across experimental platforms, facilitates the interoperability of expression datasets, and can describe drug action on cells.

bioinformatics↗

bioRxiv2020

Structural basis for stereospecific inhibition of ASCT2 from rational design

ASCT2 (SLC1A5) is a sodium-dependent neutral amino acid transporter that controls amino acid homeostasis in peripheral tissues. ASCT2 is upregulated in cancer, where it modulates intracellular glutamine levels, fueling cell proliferation. Nutrient deprivation via ASCT2 inhibition provides an emerging strategy for cancer therapy. Here, guided by a homology model of ASCT2 in an outward-facing conformation, we rationally designed novel inhibitors exploiting stereospecific pockets in the substrate binding site. A cryo-EM structure of ASCT2 in complex with inhibitor (Lc-BPE) validated our predictions and was subsequently refined based on computational analysis. The final structures, combined with MD simulations, show that the inhibitor samples multiple conformations in the ASCT2 binding site. Our results demonstrate the utility of combining computational modeling and cryo-EM for SLC ligand discovery, and a viable strategy for structure determination of druggable conformational states for challenging membrane protein targets.

biochemistry↗

bioRxiv2020

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular targets, there are currently small-molecules available only for a small proportion of the human proteome. Effective approaches are therefore required to map the massive space of unexplored compound-target interactions for novel and potent activities. Here, we carried out a crowdsourced benchmarking of predictive models for kinase inhibitor potencies across multiple kinase families using unpublished bioactivity data. The top-performing predictions were based on kernel learning, gradient boosting and deep learning, and their ensemble resulted in predictive accuracy exceeding that of kinase activity assays. We then made new experiments based on the model predictions, which further improved the accuracy of experimental mapping efforts and identified unexpected potencies even for under-studied kinases. The open-source algorithms together with the novel bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking new prediction algorithms and for extending the druggable kinome.

biochemistry↗

bioRxiv2019

PyVOL: a PyMOL plugin for visualization, comparison, and volume calculation of drug-binding sites

MotivationBinding pocket volumes are a simple yet important predictor of small molecule binding; however, generating visualizations of pocket topology and performing meaningful volume comparisons can be difficult with available tools. Current programs for accurate volume determination rely on extensive user input to define bulk solvent boundaries and to partition cavities into subpockets, increasing inter-user variability in measurements as well as time demands. ResultsWe developed PyVOL, a python package with a PyMOL interface and GUI, to visualize, to characterize, and to compare binding pockets. PyVOLs pocket identification algorithm is designed to maximize reproducibility through minimization of user-provided parameters, avoidance of grid-based methods, and automated subpocket identification. This approach permits efficient, scalable volume calculations. AvailabilityPyVOL is released under the MIT License. Source code and documentation are available through github (https://github.com/schlessingerlab/pyvol/) with distribution through PyPI (bio-pyvol). Contactavner.schlessinger@mssm.edu, arvin.dar@mssm.edu

pharmacology and toxicology↗

bioRxiv2018

Human neurons from Christianson syndrome iPSCs reveal allele-specific responses to rescue strategies

Human genetic disorders provide a powerful lens to understanding the human brain. Induced pluripotent stem cells (iPSC) represent an important, new resource for mechanistic studies and therapeutic development. Christianson syndrome (CS), an X-linked neurological disorder with attenuation of brain growth postnatally (postnatal microcephaly), is caused by mutations in SLC9A6, the gene encoding endosomal Na+/H+ exchanger 6 (NHE6). We developed CS iPSC lines from patients with a mutational spectrum, as well as robust biologically-related and isogenic controls. We demonstrate that mutations in CS lead to loss of protein function by a variety of mechanisms. Regardless of mutation, all patient-derived neurons demonstrate reduced neurite growth and arborization, likely underlying diminished postnatal brain growth in patients. Additionally, phenotype rescue strategies show allele-specific responses: a gene replacement strategy shows efficacy in nonsense mutations but not in a missense mutation, whereas application of exogenous trophic factors (BDNF or IGF-1) rescues arborization phenotypes across all mutations. Our data emphasize the important principle of personalized medicine whereby success of some therapeutic strategies may be more linked to patient genotype than others.

neuroscience↗