Biology subjects

Chatterjee, P.

Publications and source records attributed to Chatterjee, P..

At least 19 recordsLinked to original sources

bioRxiv2026

PepRePs: Peptide-Retargeted Phosphatases Designed with Generative Language Models

Phosphorylation is a fast, reversible layer of post-translational control that directly governs protein activity, signaling flux, and cell state. Kinases and phosphatases collectively establish these phosphorylation states, but their dysregulation drives cancer, neurodegenerative diseases, and developmental disorders. Selectively manipulating the phosphorylation of individual proteins in cells remains challenging, particularly for disordered substrates. Here, we introduce Peptide-Retargeted Phosphatases (PepRePs), a genetically encoded system that fuses a peptide binder to a serine/threonine phosphatase subunit to localize phosphatase activity to a target protein. We first establish the PepReP architecture using characterized tau-binding peptides, then apply protein language model-guided peptide design to target Rab8A, PTEN, and STAT3. Across endogenous and exogenous targets, PepRePs reduce phosphorylation, with downstream effects on tau aggregation, Rab8A-associated protein interactions, and cellular proliferation following PTEN and STAT3 targeting. Sequence-based peptide design enables targeting without structural inputs, supporting application to substrates with disordered regions that challenge conventional structure-based binder design. By coupling programmable peptide binders with modular phosphatase subunits, PepRePs enable targeted control of protein phosphorylation to probe cellular signaling and develop strategies for correcting disease-associated protein dysfunction.

synthetic biology↗

bioRxiv2026

Metabolically Activated Proteostasis Regulators Reduce Differentiation of CD4+ TH17 Cells

The differentiation of naive CD4+ T cells into effector T cell subsets such as TH1, TH2, TH17 and TREG cells is governed by tightly coordinated activation programmes dependent on T cell receptor (TCR) engagement, co-stimulation, and cytokine signalling. This differentiation process involves regulation of stress-responsive signalling pathways such as the unfolded protein response (UPR) and the oxidative stress response (OSR) to adapt to the physiologic demands specific to distinct T cell subset functions. This suggests that pharmacologically targeting stress-responsive pathways offers a unique opportunity to selectively remodel the differentiation of these different T cell subsets. We previously identified compound AA147 as a metabolically activated proteostasis regulator that can induce both the ATF6 signalling arm of the UPR and, in certain cell types, the NRF2-regulated arm of the OSR. (Paxman et al., 2018; Plate et al., 2016) Here, we show that treatment with AA147 selectively reduces differentiation of pro-inflammatory TH17 cells by promoting degradation of the lineage-specifying transcription factor RORgammat, without impacting the transcription factors of other CD4+ T cell subsets. AA147-dependent reduction in TH17 differentiation is independent of ATF6 activation and involves activation of NRF2, which reduces intracellular reactive oxygen species (ROS) to hinder TH17 cell differentiation. Apart from RORgammat, we found that AA147 decreases expression of the TCR-responsive factor IRF4, thus suppressing production of select effector cytokines across effector T cells, demonstrating additional ways in which this compound reshapes the activities of these essential T cell subsets. Our results demonstrate the potential for metabolically activated proteostasis regulators such as AA147 to selectively reshape TH17 cell identity while broadly dampening effector cytokine responses across effector T cell subsets, through both NRF2-dependent and independent mechanisms during T cell differentiation.

cell biology↗

bioRxiv2026

A Mammalian High-Throughput Screen for AI-Designed Peptide-Guided Protein Degraders

Targeted protein degradation (TPD) offers a route to eliminate disease-driving proteins that remain inaccessible to conventional inhibitors. However, degrader discovery remains low-throughput, labor-intensive, and dependent on randomized libraries or non-human display systems, limiting functional selection in mammalian cells. Here, we present a high-throughput, human cell-based platform for screening peptide-guided ubiquibodies (uAbs). These genetically encodable, doxycycline-inducible degraders fuse peptide guides generated by protein language models to the CHIP{Delta}TPR E3 ligase domain, creating a modular, CRISPR-like system for programmable TPD. For each target, we introduce a pooled uAb library into the corresponding fluorescent reporter cell line, isolate cells with reduced target abundance by FACS, and recover enriched peptide guides by sequencing. For {beta}-catenin, enriched uAbs reduced endogenous {beta}-catenin abundance and Wnt signaling in DLD1 cells. GFAP-directed uAbs reduced endogenous GFAP abundance and cell viability in U251 glioblastoma cells, while EWS::FLI1-directed uAbs reduced fusion oncoprotein abundance, suppressed EWSAT1 expression, and increased apoptosis in Ewing sarcoma models. Finally, a screen using endogenously tagged GATA2 further identified uAbs that reduced GATA2 under native genomic regulation. Overall, our platform connects generative peptide design to functional mammalian selection and establishes a scalable strategy for CRISPR-like proteome perturbation.

synthetic biology↗

bioRxiv2026

AI-Driven Discovery and BSL-4 Validation of Cross-Filovirus Ebola-Marburg Inhibitors and their Synergistic Combinations

Filovirus outbreaks caused by Ebola virus (EBOV) and Marburg virus (MARV), pose severe global health threats characterized by high rates of fatal hemorrhagic fever. While species-specific vaccines and therapeutic monoclonal antibodies are approved for Zaire ebolavirus, broadly-active therapeutics remain unavailable, leaving populations vulnerable to MARV and other pathogenic Ebola species, such as Bundibugyo (BDBV) and Sudan (SUDV) ebolaviruses. Here we report a computationally guided, infectious virus validated screening platform for the rapid discovery of broad-spectrum filovirus antivirals. By leveraging quantitative structure-activity relationship (QSAR) models, we screened 142,382 compounds in silico to prioritize 125 high-potential candidates. Subsequent dose-response and viability profiling identified 23 compounds exhibiting potent, low-micromolar pan-filovirus activity and favorable cytotoxicity profiles. Molecular docking indicates these compounds target conserved structural and functional domains--primarily the VP35 and L proteins--which may disrupt essential viral replication and immune antagonism. Furthermore, systematic combinatorial screening revealed three highly synergistic compound pairs, notably NCGC00113249-01 and NCGC00118008-01, demonstrating robust cross-species efficacy. By targeting conserved vulnerabilities across the filovirus family, this integrated in silico and in vitro pipeline provides a scalable framework to rapidly nominate and optimize synergistic therapeutic regimens against both endemic and emerging viral threats including BDBV. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/737586v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@531939org.highwire.dtl.DTLVardef@19d1454org.highwire.dtl.DTLVardef@1a1c9e3org.highwire.dtl.DTLVardef@cd861b_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

A cofactor-promiscuous HMGR from the Lyme disease pathogen illuminates diversity in bacterial isoprenoid biosynthesis

The Lyme disease pathogen Borrelia burgdorferi contains a highly reduced genome lacking many primary metabolic pathways. However, B. burgdorferi retains the mevalonate pathway that synthesizes isopentenyl pyrophosphate (IPP), the precursor to the peptidoglycan carrier lipid. While the mevalonate pathway and the enzyme that catalyzes its rate-limiting step (3-hydroxy-3-methyl glutaryl coenzyme A reductase, HMGR) are well studied in vertebrates, little is known about the pathway in B. burgdorferi and many pathogenic bacteria. In this work, we reveal that HMGR is a critical metabolic enzyme in B. burgdorferi. We demonstrate that loss of HMGR causes morphological defects and muted de novo synthesis of peptidoglycan; these defects are ameliorated by exogenous mevalonate and IPP. Biochemical characterization unveiled the HMGR as a highly unusual cofactor-promiscuous oxidoreductase that functions with both nicotinamide cofactors. Bioinformatics and biochemical characterization uncovered examples of similarly promiscuous HMGRs and revealed a previously unrecognized evolutionary link to cofactor choice. Moreover, structures of the enzyme reveal a highly divergent active site architecture. Together, these findings firmly establish HMGR as an opportunity target for the development of antibacterials for a diderm pathogen while highlighting cofactor promiscuity as an evolutionary acquired feature in HMGRs.

biochemistry↗

bioRxiv2026

Enhanced Prediction of Gut Microbiome-Related Diseases Using Hybrid Machine Learning Models

The human gut, containing 100 trillion microbes, is also considered the "second brain," having control over the different functions of the physiological system. With advancements in bioinformatics and the development of sequencing technologies, researchers are able to explore the diversity and functional implications of gut microbiota (GM), which have become strongly associated with a variety of diseases. Microbial imbalance, or dysbiosis, acts as a biomarker for early detection and prognosis of a disease. Artificial Intelligence and Machine Learning (AI/ML) methods, although extensively used in predicting GM associated diseases, are seldom translated to having practical real-world outcomes, necessitating the design of robust AI/ML models applicable in real-world scenario. We have therefore come up with designing stacking-based ensemble architectures (EM1 and EM2), developed by integrating multiple ML-based learning algorithms for improving disease prediction accuracy. The GM datasets, after split into training and test sets, were eventually fed into the proposed two-layer ensemble models, which combines the output from standardized base learners via a meta-classifier, strengthening classification robustness as well as ensuring consistency in optimized performance across diverse datasets. Both the proposed hybrid ensemble models have emerged to be superior performers over all baseline and deep learning models, with an average accuracy of 0.87 and 0.84 respectively. By combining multiple learners, the proposed ensemble models outperform traditional single-algorithm-based approaches to attain higher accuracy and robustness on complex GM datasets. Key messagesO_LIDevelopment of stacking-based hybrid ensemble models (EM), which can be employed to integrate different AI/ML algorithms with better prediction accuracy of gut microbiome (GM)-associated diseases. C_LIO_LIUse of independent GM datasets with preprocessing methods such as SMOTE and PCA to address class imbalance and high dimensionality. C_LIO_LIAll the proposed EM architectures are mostly superior to the existing state-of-the-art AI/ML methods (highest prediction accuracy: 0.87 and 0.84 with EM1 and EM2 models respectively) for GM diseases predictions. C_LIO_LIThe cross-cohort validation demonstrates high prediction accuracy and robustness, (AUC values close to 0.98 and 0.99, for EM1 and EM2). C_LIO_LIThese therefore demonstrate the effectiveness of EM frameworks for GM associated disease prediction, paving the way for corresponding applications in precision medicine. C_LI

microbiology↗

bioRxiv2026

A lipid compendium of a metabolically compromised bacterium provides insights into lipid acquisition, biosynthesis, and metabolism

The Lyme disease agent Borrelia burgdorferi belongs to a class of metabolically compromised bacteria that cannot survive without host-derived lipids. Survival of the agent in tick and vertebrate hosts requires substantial nutrient acquisition and potential cell envelope remodeling. While prior studies identified cholesterol, cholesterol glycolipids, and phosphatidylcholines as membrane lipids in B. burgdorferi, the identity of many other membrane lipids, their origin, and their physiological relevance remain unknown. Here, we used a suite of untargeted and targeted high-resolution mass spectrometry methods to reveal a complex lipid profile of the pathogen and to identify the origin of its lipids. The analysis detected more than 500 lipids in B. burgdorferi, the majority of which are sourced from the environment. However, the bacterium selectively accumulates certain lipids while excluding others, suggesting discriminatory uptake. These include cholesteryl esters and triglycerides that are organized in foci within the pathogen. Intriguingly, the pathogen also synthesizes predominantly eukaryotic lipids such as the lysosomal bis(monoacylglycerol)phosphate and the plant glycolipid sulfoquinovosyl diacylglycerol (SQDG). The biosynthesis of the latter is carried out by enzymes that exhibit structural homology to plant oxidoreductases and galactosyltransferases, yet their closest orthologs are found in bacteria. This hints that the capability of SQDG synthesis is more widespread in spirochaetes and other bacteria. Together, the comprehensive lipid profiling we report here uncovers novel aspects of the physiology of the metabolically challenged B. burgdorferi and highlights lipid acquisition and synthesis pathways as potentially critical for pathogen survival.

biochemistry↗

bioRxiv2026

Stochastic Gene Expression Model with State-Dependent Protein Activation Delay

Cells must maintain stable protein levels despite the inherently stochastic nature of gene expression, as excessive fluctuations can disrupt cellular function and impair the reliability of decision-making. Regulatory mechanisms, such as negative feedback, buffer protein fluctuations. Yet, it remains unclear how fluctuations are affected by delays that depend on a molecules specific state. Here, we develop a stochastic model in which proteins are produced in bursts as inactive molecules and pass through a series of intermediate steps before becoming active. The duration of such activation delays depends on the current level of active protein, creating a state-dependent feedback loop. Our model provides explicit analytical expressions relating the delay structure and feedback strength to the variability of active protein levels, quantified using the Fano factor, and shows that state-dependent delays can reduce fluctuations below the baseline expected from simple bursty production. Stochastic simulations confirm these predictions, and incorporating negative feedback in burst production further decreases variability while keeping system behavior predictable. These results reveal how temporal and state-dependent regulation stabilizes protein expression, offering guidance for understanding natural cellular control and designing robust synthetic gene circuits.

systems biology↗

bioRxiv2026

AptaBLE: A Deep Learning Platform for Aptamer Generation and Analysis

Aptamers are single-stranded oligonucleotides that bind molecular targets with high affinity and specificity. However, their discovery remains time-consuming, expensive, and susceptible to experimental biases. Here we present AptaBLE, a deep learning framework for predicting aptamer-protein binding. Additionally, we demonstrate two de novo generation methods that produce novel aptamers with desired specificity profiles and Kds as low as 31 nM to-date. AptaBLE represents a significant advance towards therapeutic and diagnostic aptamer development.

bioinformatics↗

bioRxiv2026

PeptiVerse: A Unified Platform for Therapeutic Peptide Property Prediction

Therapeutic peptides combine the advantages of small molecules and antibodies, offering target flexibility and low immunogenicity, yet their successful translation requires careful evaluation of multiple developability properties beyond binding alone. As chemically modified peptides become increasingly common in drug design, no unified platform currently supports systematic property assessment across both canonical sequences and SMILES-based representations. Leveraging the generalizability of large foundational models trained on protein and chemical data, we introduce PeptiVerse, a universal therapeutic peptide property prediction platform. PeptiVerse accepts either amino acid sequences or chemically modified peptide SMILES, delivers state-of-the-art performance across diverse property prediction tasks, and provides both a web interface and open-source implementation for rapid, accessible, and scalable pep-tide developability analysis. By unifying property prediction across representations, PeptiVerse directly supports early-stage peptide therapeutic development campaigns and property-aware generative design workflows.

bioinformatics↗

bioRxiv2025

A foundation model for microbial growth dynamics

Microbial growth dynamics contain rich information about microbial populations, which support applications from antibiotic testing to microbiome engineering. However, the high dimensionality of growth data and the scarcity of large, task-specific datasets have limited generalizable modeling analysis across systems. Here, we develop a foundation model for microbial growth dynamics. It is a large-scale, self-supervised representation model trained on [~]370,000 experimental and simulated growth curves spanning diverse microbial species, environmental conditions, and community contexts. The model learns lower-dimensional latent embeddings that capture essential dynamical features of raw growth data and enable accurate reconstruction of these data. The concise representations enhance predictive performance in diverse downstream applications. Using these embedding, we achieve few-shot learning for antibiotic classification and concentration prediction, accurate forecasting of simulated and experimental communities, and inference of total abundance from relative-abundance data. By extracting transferable representations from heterogeneous datasets, our model provides a general framework for analyzing and predicting microbial community dynamics from limited measurements.

synthetic biology↗

bioRxiv2025

A Two-Component Regulatory System Mediates Quorum Sensing-Dependent Morphology and Motility Transitions in the Archaeon Haloferax volcanii

Quorum sensing (QS) enables microorganisms--including bacteria, eukaryotes, and viruses--to coordinate collective behaviors in response to population density. Despite their ecological and evolutionary significance, QS mechanisms in Archaea remain poorly characterized. The halophilic archaeon Haloferax volcanii provides a model for archaeal QS, transitioning from motile rods to non-motile disks in a density-dependent response to a secreted disk-forming signal (DFS). To identify components of the DFS regulatory network, we screened for spontaneous mutants that retained motility in DFS-containing soft-agar medium. One candidate, HVO_1357, encodes a predicted response regulator located adjacent to a histidine kinase (HVO_1356) and a second response regulator (HVO_1358), consistent with an extended two-component regulatory system (TCS). Based on our results, these genes encode quorum-sensing associated regulators (Qar), therefore, we propose rename them qarA (HVO_1357), qarB (HVO_1356), and qarC (HVO_1358). Deletion of qarA enabled cells to swim on DFS-containing soft-agar plates and conferred hypermotility on standard soft-agar media; however, these phenotypes were not due to changes in motility-related parameters, but a reduced sensitivity to DFS for induction of the non-motile, disk-shaped state. In contrast, {Delta}qarB and {Delta}qarC strains were non-motile and exhibited premature disk formation during normal growth. Suppressor mutations that restored motility to {Delta}qarB and {Delta}qarC mapped exclusively to qarA, suggesting QarA is the central regulator of this system. Transcriptomic analyses revealed that qarA deletion leads to upregulation of genes involved in motility and rod-shape formation. Together, these findings reveal qarABC as a DFS-responsive regulatory module and represent the first TCS in archaea shown to control QS-dependent behavior. IMPORTANCEArchaea are ubiquitous and play key roles across diverse ecosystems--including human microbiomes--yet little is known about how they communicate with one another and with other organisms, or how these interactions shape their ecological impact. Such intercellular communication, including quorum sensing (QS), allows microorganisms to coordinate behaviors critical for survival, adaptation, and community organization. In this study, we identify the first archaeal two-component regulatory system that is involved in QS-dependent regulation, providing a foundation for understanding how organisms in this domain sense and respond to population cues. By revealing a previously unknown aspect of archaeal biology, this work represents an important step toward understanding how archaeal communication shapes both their physiology and their interactions within complex microbial communities.

microbiology↗

bioRxiv2025

Multi-omics integration at cell type resolution uncovers gene-metabolite mechanisms underlying osteoarthritis heterogeneity

Metabolic dysregulation is an important factor for osteoarthritis pathogenesis, but comprehensive studies of underlying mechanisms and pathways are rare. We analyzed newly generated metabolomics data on bone marrow from 119 osteoarthritis patients, along with single-cell transcriptomics data to reconstruct networks of gene-metabolite associations at cell type resolution. Hubs of these networks - cell type-specific as well as pan-cell type hubs - revealed key molecular factors of osteoarthritis heterogeneity. Systems-level analysis of hubs revealed major roles for glycerophospholipid, glycerolipid and sphingolipid metabolism pathways, along with lipid signaling. We used Machine Learning models of gene-metabolite relationships to discover cell types most relevant to each metabolite. Integrative analysis of disease severity scores along with multi-omics data revealed a shift in specific immune cell subtypes in low versus high grade disease. We conclude that leveraging gene-metabolite covariation in a patient cohort can uncover underlying molecular mechanisms, overcoming the challenges posed by high dimensionality of multi-omics data.

bioinformatics↗

bioRxiv2025

BenchIDPs: Evaluation of Conformational Preferences Molecular Mechanics and Solvent Interactions of IDPs across popular Force Fields and Water Models

Intrinsically disordered proteins (IDPs), unlike globular proteins, lack stable secondary structure and exist as dynamic ensembles of conformations in physiological conditions. These conformations allow them to adapt to many roles while interacting with other proteins, forming partially folded soluble oligomers or insoluble plaques rich in {beta}-sheet, and are responsible for different pathological diseases. The dynamic nature of IDPs and the lack of well-defined binding sites make drug discovery and development both challenging. Although computational protocols, predominantly involving molecular dynamics (MD) simulations studies, have been complementing in understanding the structure and dynamics of IDPs, the choice of force fields become important in reproducing experimental observations. We herewith provide a systematic study to investigate the structural propensity of four IDPs (A{beta}42, Tau43, amylin and S), using 13 combinations of force field-water models (FF-wm). In addition to validating with NMR observables, we also examine the water dynamics surrounding the chosen IDPs to underscore the generalizability of chosen FF-wms. C36-IDPSFF with amber99sb-dispersion water model (A99SB-disp) is found to perform optimal in simulating IDPs in comparison with other FF-wm combination based on structural propensity and comparison with experimental NMR 3JHN-H-Coupling data as well as dynamical observables.

biophysics↗

bioRxiv2025

Metalorian: De Novo Generation of Heavy Metal-Binding Peptides with Classifier-Guided Diffusion Sampling

Heavy metal contamination is a severe and ongoing global problem that demands selective and efficient chelation strategies for environmental remediation. As a solution, we introduce Metalorian, a conditional diffusion model that generates de novo heavy metal-binding peptides, guided by MetaLATTE, a multi-label classifier fine-tuned to include underrepresented metal classes. Leveraging both continuous protein embeddings and discrete metal-binding constraints, Metalorian produces peptides with controllable lengths and user-defined binding specificity while preserving essential physicochemical properties. Crucially, our data augmentation strategy enables robust classification and generation even with sparse training data for certain metals. We demonstrate the effectiveness of our approach through peptides designed for copper (Cu), cadmium (Cd), and zinc (Zn), which retain key features such as net charge and hydrophobicity, while significantly reducing sequence length and molecular weight compared to known metal-binding proteins. Metalorian-generated peptides exhibit stable conformations and favorable binding energetics, as confirmed through molecular dynamics simulations. To validate binding in vitro, we developed a streamlined SUMO-fusion expression and cleavage system coupled with ELISA-based quantification, confirming robust Cu and Zn binding by multiple Metalorian-generated peptides. Overall, our work establishes a foundational platform for engineering heavy metal-binding peptides tailored to diverse and underrepresented targets, and highlights the potential of well-trained continuous latent spaces for diffusion-based de novo peptide design.

synthetic biology↗

bioRxiv2025

Stress-Induced Alteration of Small Extracellular Vesicles Drives Amyloid-Beta Sequestration and Exacerbates Alzheimer's Disease Pathogenesis

While small extracellular vesicles (sEVs) are implicated in amyloid-beta (A{beta}) trafficking, the mechanisms governing their interaction with A{beta} aggregates and plaque formation remain unresolved. Here, we report a paradigm-shifting discovery: sEVs undergo dynamic structural remodelling in response to stress, enabling selective binding to A{beta} aggregates-a phenomenon absent under normal physiological conditions. Using multimodal stressors, including mechanical (ultrasonication/agitation), physical (hyperthermia), and biological (oxidative damage), we demonstrate that stress-modified sEVs exhibit high-affinity binding to small A{beta} aggregates (SA) through scaffold reorganization, as validated by super-resolution microscopy and quantitative colocalization assays. Crucially, these remodelled sEVs act as potent carriers, enhancing SA internalization by neuronal cells in vitro. Strikingly, in post-mortem Alzheimers disease (AD) brains and APP-PS1 transgenic mice, sEVs were spatially enriched at amyloid plaque margins, suggesting a direct role in A{beta} sequestration and plaque expansion. Consistent with clinical relevance, sEVs isolated from AD patients exhibited an intrinsic SA-binding capacity, recapitulating stress-induced interactions observed experimentally. Our findings reveal that stress-primed sEVs function as pathological chaperones, binding to and internalizing A{beta} aggregates, thereby accelerating plaque nucleation and disease progression. This study provides the first evidence of stress-mediated sEV plasticity as a critical driver of A{beta} pathology, redefining therapeutic strategies targeting extracellular vesicle biology in neurodegenerative disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/655679v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@107c76borg.highwire.dtl.DTLVardef@1b56848org.highwire.dtl.DTLVardef@d18636org.highwire.dtl.DTLVardef@1c26db4_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

DPAC: Prediction and Design of Protein-DNA Interactions via Sequence-Based Contrastive Learning

Interactions between DNA and proteins are pivotal in natural biological processes, and designing proteins that can bind to DNA with high specificity is crucial for advancing genomic technologies. Existing state-of-the-art models for both modeling and designing protein-DNA interactions primarily rely on structural information, facing limitations in scalability and efficiency for large-scale applications. Notable methods like AlphaFold 3 and RosettaTTAFold All-Atom exist, but they are inefficient and inherently struggle at modeling conformationally unstable proteins, such as transcription factors, which arguably represent the most important class of DNA-binding proteins. Here, we present DPAC1 (DNA-Protein binding Alignment via Contrastive learning), which leverages pre-trained protein and DNA language models via a contrastive loss to align the two modalities in a high-dimensional shared latent space. DPAC not only significantly accelerates the design process compared to current structure-based methods but also demonstrates a strong ability to differentiate real binders from non-binders. Our model achieves an AUC score of 0.591 on a low identity set, outperforming state-of-the-art structure-based methods. Additionally, DPAC integrates simulated annealing for the design of new protein sequences with optimized DNA binding affinity, successfully recovering binding affinity in engineered sequences by up to 20% in in silico tests. Our results highlight DPACs potential for facilitating the design and discovery of sequence-specific DNA-binding proteins, paving the way for advancements in genomic research and biotechnology applications.

bioengineering↗

bioRxiv2025

Aberrant Fibro-Adipogenic Progenitor Subpopulations Drive Volumetric Muscle Loss-Induced Fibrosis

Volumetric muscle loss (VML) injuries result in chronic fibrosis, inflammation, and persistent functional deficits. Fibro-adipogenic progenitor (FAP) cells are a heterogeneous, muscle-resident stromal cell population that play a crucial role in muscle regeneration, but also contribute to fibrosis in muscle disease. The role of FAPs in VML is not well established and may be critical target to ensure functional muscle regeneration after VML. We utilized a VML model in the mouse quadriceps to study the location, secretome, surface marker distribution, gene expression, and single-cell transcriptional profile of FAPs after VML. After VML, a subpopulation of FAPs highly expressed {beta}1-integrin and were elevated in the post-VML muscle tissue; these FAPs had increased fibrotic gene expression and increased myofibroblast differentiation potential. Transforming growth factor-{beta}1 (TGF-{beta}1) and tissue inhibitor of matrix metalloproteinase 1 (TIMP1) were identified as secreted proteins from VML derived FAPs that produced both pro-fibrotic and anti-myogenic signaling. These data establish an aberrant FAP sub-population that are elevated in VML injury and provides novel targets for future scarless muscle regeneration in VML.

pathology↗