Biology subjects

Jaladanki, C. K.

Publications and source records attributed to Jaladanki, C. K..

7 recordsLinked to original sources

bioRxiv2025

Mitoxantrone Hydrochloride Targets APP and LRRK2 to Improve Neurodegeneration in Parkinson's Models

BackgroundParkinsons disease (PD) is the second common neurodegenerative disorder, driven by the loss of dopaminergic neurons and pathological -synuclein protein accumulation. Currently, there is no disease-modifying therapy that can halt PD progression. Our previous study uncovered a critical pathogenic feed-forward loop between amyloid precursor protein (APP) and leucine-rich repeat kinase 2 (LRRK2), in which the two proteins mutually enhance each others expression, ultimately leading to mitochondrial dysfunction and neurotoxicity. Targeting this vicious cycle represents a promising therapeutic strategy for PD. MethodsTo discover novel inhibitors targeting this axis, we performed high-throughput screening of an FDA-approved drug library using a fluorescence-based biosensor system. We identified Mitoxantrone hydrochloride (MH), an antineoplastic agent, as a lead compound that inhibits both APP and LRRK2 expression. Its efficacy was validated in cellular models, including patient induced pluripotent stem cell (iPSC)-derived dopaminergic neurons and human peripheral blood mononuclear cells (PBMCs). Motor behavioural and safety assessments were subsequently conducted in PD mouse models. ResultsWe demonstrated that MH suppresses both APP and LRRK2 expression in various cell types in a dosage-dependent manner. In addition, MH also inhibits phosphorylation of LRRK2 and its downstream substrate Rab10. We further showed MH inhibits LRRK2 activity through direct binding to its kinase domain. Critically, MH treatment rescued dopaminergic neuron loss and reversed motor deficits in both 6-Hydroxydopamine (6-OHDA)-induced and LRRK2G2019S genetic PD mouse models. Moreover, we found that oral administration of MH is therapeutically effective, providing superior neuroprotection and behavioral recovery without detectable cardiotoxicity or gastrointestinal damage. ConclusionsOur findings demonstrate MH as a compelling, repurposable therapeutic candidate capable of disrupting a core pathogenic mechanism in PD.

neuroscience↗

bioRxiv2025

Integrating Multi-Structure Covalent Docking with Machine Learning Consensus Scoring Enhances Virtual Screening of Human Acetylcholinesterase Inhibitors

Acetylcholinesterase (AChE) inhibition is a key mechanism in the treatment of neurodegenerative diseases and in counteracting toxic exposures to pesticides and nerve agents. However, virtual screening of AChE remains challenging due to the enzymes structural flexibility and the chemical diversity of its covalently binding inhibitors. In this study, we developed an in silico protocol that integrates multi-structure covalent docking and machine learning (ML) consensus scoring to improve the prediction of AChE inhibitors. We analyzed 65 ligand-bound (holo) human AChE crystal structures using hierarchical clustering to identify four representative conformations, along with one high-resolution apo structure, for multi-structure docking. A curated library of 412 organophosphate and carbamate inhibitors was then docked covalently and non-covalently into each receptor conformation. The resulting docking scores were evaluated against inhibitors experimental logIC50 values using Spearmans rank correlation coefficient (r). Covalent docking outperformed non-covalent docking (r values up to 0.54 vs 0.18), and our ML consensus model trained on the five structures covalent docking scores achieved the highest predictive accuracy (r = 0.70), surpassing all single-structure and conventional consensus baselines. Chemical cluster analysis revealed structure-activity trends based on ligand flexibility, polarity, and aromaticity. SHapley Additive exPlanations analysis highlighted the ML consensus models ability to flexibly distribute the influence each structures scores played on its predictions. It identified and exploited relationships based on its training dataset that would be difficult to anticipate through a manual analysis of individual structures docking performance metrics. This framework is broadly applicable to other covalently targeted proteins, offering a generalizable and interpretable strategy for data-driven covalent inhibitor discovery.

bioinformatics↗

bioRxiv2025

Discovery of A Small Molecule non-IMiD Degrader of ZBTB7A for the Treatment of β-hemoglobinopathies

Sickle cell disease and {beta}-thalassemia, two major {beta}-hemoglobinopathies, pose significant clinical challenges globally. Current treatments often face limitations in efficacy and tolerability. The transcription factor ZBTB7A has emerged as a promising therapeutic target for reactivating fetal hemoglobin expression. Here, we report the discovery and characterization of SH6, a small molecule non-IMiD degrader of ZBTB7A. SH6 induces fetal hemoglobin in erythroid cell lines in a CRBN and ZBTB7A-dependent manner, and it is capable of inducing fetal hemoglobin expression in healthy donor, SCD and {beta}-thalassemia patient CD34+ cell derived erythroid cells. The efficacy of SH6 is confirmed in a xenotransplantation humanized mouse model. SH6 outperforms currently available therapeutic agents in vitro, and shows synergy with hypomethylating agents. SH6 exhibits a favorable in vivo toxicity profile. Our findings establish SH6 as a promising therapeutic lead candidate for further optimization towards clinical development for treatment of sickle cell disease and {beta}-thalassemia.

cell biology↗

bioRxiv2025

Evaluating Molecular Docking Programs for RNA-Targeted Ligand Screening: Influence of Binding Modes and Ligand Types

Structured RNAs are critical regulators of gene expression and represent promising targets for drug discovery. Despite this potential, structure-based virtual screening approaches--in particular, molecular docking--have been underutilized for RNA targets, largely due to the intrinsic conformational flexibility of RNA and the limited docking methods specifically optimized for RNA. In our study, we evaluated the efficacy of three widely used docking programs for RNA-ligand docking screens. The performance of these programs -- Glide, Gold, and rDock, were evaluated with the dataset of 25 diverse RNA targets with 432 small molecule RNA binders. We found that the performance was similar across docking methods, but consistent differences emerged among RNA classes. We further explored consensus scoring across both receptor conformations and docking methods, finding that selecting the average scores across docking programs and RNA structures yielded stable performance when compared to individual methods. Finally, clustering analysis of RNA binders revealed distinct scoring function preferences for different chemotypes of RNA binders, underscoring the potential for tailored scoring strategies in RNA-targeted drug discovery. Together, this work provides a comprehensive evaluation of the performance of available docking methods in different cases of RNA-ligand docking and offers practical guidance for improving virtual screening against structured RNAs.

bioinformatics↗

bioRxiv2025

Deep contrastive feature compression with classical machine learning enables ligand discovery through efficient triage of large chemical libraries

Improving in silico compound-protein interaction (CPI) predictability is critical for productive drug discovery. Current deep learning approaches largely rely on end-to-end models trained on limited labeled CPI datasets, overlooking the representational power of large-scale biochemical foundation models. We present COMRADE (Contrastive Multirepresentation Accelerated Docking Engine), a hybrid virtual screening framework that accelerates docking by triaging compounds using CE-Screen (Contrastive Embedding-Screen). CE-Screen leverages seven high-dimensional pretrained representations - including those from protein language models and molecular transformers, along with an original physics-based interaction potential encoding - for rapid first-pass screening [~]100x faster than docking. Its contrastive compression neural network maps these inputs onto a single compact, discriminative representation optimized for CPI prediction via a lightweight ensemble classifier. CE-Screen outperforms state-of-the-art end-to-end models by up to 111.11% on retrospective benchmarks and is successfully used to triage [~]10.8 million compounds against five targets, yielding novel hits for each one - including a new scaffold for the branched-chain ketoacid dehydrogenase kinase (BCKDK), an understudied yet high-value target in metabolic disease and oncology.

bioinformatics↗

bioRxiv2024

Targeting transcription factors through an IMiD independent zinc finger domain

Immunomodulatory imide drugs (IMiDs) degrade specific C2H2 zinc finger degrons in transcription factors, making them effective against certain cancers. SALL4, a cancer driver, contains seven C2H2 zinc fingers in four clusters, including an IMiD degron in zinc finger cluster two (ZFC2). Surprisingly, IMiDs do not inhibit growth of SALL4 expressing cancer cells. To overcome this limit, we focused on a non-IMiD degron, SALL4 zinc finger cluster four (ZFC4). By combining AlphaFold and the ZFC4-DNA crystal structure, we identified a potential ZFC4 drug pocket. Utilizing an in silico docking algorithm and cell viability assays, we screened chemical libraries and discovered SH6, which selectively targets SALL4-expressing cancer cells. Mechanistic studies revealed that SH6 degrades SALL4 protein through the CUL4A/CRBN pathway, while deletion of ZFC4 abolished this activity. Moreover, SH6 led to significant 62% tumor growth inhibition of SALL4+ xenografts in vivo and demonstrated good bioavailability in pharmacokinetic studies. In summary, these studies represent a new approach for IMiD independent drug discovery targeting C2H2 transcription factors in cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/574032v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@b8e4e8org.highwire.dtl.DTLVardef@bc6f01org.highwire.dtl.DTLVardef@11bbe85org.highwire.dtl.DTLVardef@791cb1_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Angiomotin Counteracts the Negative Regulatory Effect of Host WWOX on Viral PPxY-Mediated Egress

Filoviridae family members Ebola (EBOV) and Marburg (MARV) viruses and Arenaviridae family member Lassa virus (LASV) are emerging pathogens that can cause hemorrhagic fever and high rates of mortality in humans. A better understanding of the interplay between these viruses and the host will inform about the biology of these pathogens, and may lead to the identification of new targets for therapeutic development. Notably, expression of the filovirus VP40 and LASV Z matrix proteins alone drives assembly and egress of virus-like particles (VLPs). The conserved PPxY Late (L) domain motifs in the filovirus VP40 and LASV Z proteins play a key role in the budding process by mediating interactions with select host WW-domain containing proteins that then regulate virus egress and spread. To identify the full complement of host WW-domain interactors, we utilized WT and PPxY mutant peptides from EBOV and MARV VP40 and LASV Z proteins to screen an array of GST-WW-domain fusion proteins. We identified WW domain-containing oxidoreductase (WWOX) as a novel PPxY-dependent interactor, and we went on to show that full-length WWOX physically interacts with eVP40, mVP40 and LASV Z to negatively regulate egress of VLPs and of a live VSV/Ebola recombinant virus (M40). Interestingly, WWOX is a versatile host protein that regulates multiple signaling pathways and cellular processes via modular interactions between its WW-domains and PPxY motifs of select interacting partners, including host angiomotin (AMOT). Notably, we demonstrated recently that expression of endogenous AMOT not only positively regulates egress of VLPs, but also promotes egress and spread of live EBOV and MARV. Toward the mechanism of action, we show that the competitive and modular interplay among WWOX-AMOT-VP40/Z regulates VLP and M40 virus egress. Thus, WWOX is the newest member of an emerging group of host WW-domain interactors (e.g. BAG3; YAP/TAZ) that negatively regulate viral egress. These findings further highlight the complex interplay of virus-host PPxY/WW-domain interactions and their potential impact on the biology of both the virus and the host during infection. Author SummaryFiloviruses (Ebola [EBOV] and Marburg [MARV]) and arenavirus (Lassa virus; LASV) are zoonotic, emerging pathogens that cause outbreaks of severe hemorrhagic fever in humans. A fundamental understanding of the virus-host interface is critical for understanding the biology of these viruses and for developing future strategies for therapeutic intervention. Here, we identified host WW-domain containing protein WWOX as a novel interactor with VP40 and Z, and showed that WWOX inhibited budding of VP40/Z virus-like particles (VLPs) and live virus in a PPxY/WW-domain dependent manner. Our findings are important to the field as they expand the repertoire of host interactors found to regulate PPxY-mediated budding of RNA viruses, and further highlight the competitive interplay and modular virus-host interactions that impact both the virus lifecycle and the host cell.

microbiology↗