Biology subjects

Mitra, R.

Publications and source records attributed to Mitra, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Reliable and Efficient Agentic Protein Design with Toolset Harnessing

Computational protein design workflows require the orchestration of multiple tools, data formats, and analysis steps that are often fragmented. Here, we introduce Chaperone, a lightweight and customizable framework that enables seamless integration of RFdiffusion3 (RFD3) with downstream sequence design, structure prediction, and evaluation tools, including ProteinMPNN, AlphaFold3 (AF3), and more than 50 other protein design tools. By combining a programmable agent-friendly interface with parallelized execution on high-performance computing systems, Chaperone as a harness enables rapid iteration and scalable exploration of complex protein design workflows. Across an evaluation of nine biomolecular design tasks, we find using Chaperone reduces total token usage relative to baseline agents by 86% and GPU costs by 64%, while increasing the overall computational design pass rate. We demonstrate Chaperones utility for large-scale design-space exploration through a two-week end-to-end small-molecule binder design campaign, yielding low nanomolar binders to cortisol zero shot. We further demonstrate its versatility across distinct design challenges, including the design of a symmetric 12-subunit self-assembly nanoparticle and a linker that mediates interactions between C2- and D3-symmetric protein complexes to form crystalline assemblies. Together, these results demonstrate how agent harnesses built around standardized tool interfaces can substantially improve the efficiency and success of complex, long-horizon agentic design workflows in biology. We make Chaperone freely available to the community on GitHub: https://github.com/RosettaCommons/chaperone.

biochemistry↗

bioRxiv2026

p38α MAPK integrates opposing oncogenic APC/β-catenin/TCF and GUCY2C tumor-suppressor axes in colorectal cancer

Introduction: Sporadic colorectal cancers (CRCs) form following initiating APC mutations, inducing constitutive {beta}-catenin/TCF signaling. Similarly, the guanylyl cyclase C (GUCY2C) tumor suppressor axis is universally silenced in CRC. While the GUCY2C receptor is retained, loss of its paracrine hormones guanylin (GUCA2A) and uroguanylin (GUCA2B) is among the earliest events in transformation. Mutant APC/{beta}-catenin/TCF signaling driving CRC transcriptionally silences GUCY2C hormone expression while suppressing that oncogenic pathway reconstitutes hormone production opposing tumorigenesis. p38 opposes tumor initiation, and its loss promotes transformation in chemical and genetic mouse models of CRC. In healthy intestine, p38 supports a gradient of epithelial differentiation along the crypt-surface axis. Here, we reveal that p38 links regulation of oncogenic APC/{beta}-catenin/TCF signaling and GUCY2C hormone expression. Methods: Regulation of p38 by APC/{beta}-catenin/TCF signaling was defined in four genetically engineered human CRC cell lines with conditional oncogenic APC/{beta}-catenin/TCF signaling and healthy human colon organoids. Expression of p38 was eliminated from conditional human CRC cell lines by pharmacological inhibition, RNA knockdown, and CRISPR/Cas9 gene editing. Transcriptomic analysis of these cells by RNA sequencing defined the set of genes regulated by APC/{beta}-catenin/TCF signaling controlled through p38. Results: In CRC cell lines, APC/{beta}-catenin/TCF signaling suppressed p38 phosphorylation and activity, whereas inhibition of APC/{beta}-catenin/TCF restored p38 signaling. In the absence of APC/{beta}-catenin/TCF signaling, inhibiting p38 repressed GUCY2C hormone expression. Inhibiting APC/{beta}-catenin/TCF signaling enriched phosphorylated p38 at the GUCA2A promoter. In that context, analysis of the CPTAC proteomic database revealed that phosphorylation of p38 and its upstream kinases MKK3 and MKK4 was reduced in human CRC. Finally, transcriptomic analysis revealed that p38 is a critical signaling node controlling the expression of a substantial subset of APC/{beta}-catenin/TCF-regulated genes in human CRC cells and patient tumors. Conclusions: APC/{beta}-catenin/TCF signaling inhibits p38 phosphorylation and transcriptional activity, suppressing GUCA2A and GUCA2B expression and functionally silencing the retained GUCY2C receptor. These findings identify p38 as a signaling intermediary integrating oncogenic APC/{beta}-catenin/TCF activity to extinction of the GUCY2C tumor-suppressor axis and reveal a broader p38-dependent component of the APC/{beta}-catenin/TCF-regulated transcriptome.

cancer biology↗

bioRxiv2026

Conformational Barrier-Driven Flux Redistribution across Competing Catalytic Pathways Unifies Diverse Enzyme Kinetic Regimes

Enzymes are central to biological function, catalyzing the chemical transformations that sustain metabolism, signalling, molecular transport, and cellular regulation. Understanding their catalytic activity requires connecting molecular structure and conformational dynamics to measurable reaction kinetics. Classical Michaelis--Menten kinetics describes turnover through a dominant catalytic pathway approaching a single saturation limit, whereas conformational-selection, induced-fit, allosteric, and dynamic-disorder models account for additional complexity arising from conformational exchange and heterogeneous catalytic states. Experimental studies further reveal concentration-dependent changes in pathway usage, cooperative or sigmoidal responses, transitions between distinct turnover regimes, and intermittent single-molecule activity. These behaviours are typically treated within separate kinetic descriptions, leaving their mechanistic relationship within a common physical picture incompletely resolved. Here, we formulate a minimal conformational free-energy landscape with interconverting enzyme states and competing catalytic routes to examine whether these apparently distinct kinetic signatures can arise from a shared physical mechanism. In this framework, substrate availability and conformational barriers redistribute catalytic flux between slower and faster pathways, thereby altering both pathway occupancy and turnover times. Stochastic Gillespie simulations together with deterministic mean-first-passage-time analysis show that different parameter regimes generate low- and high-turnover states, concentration-dependent kinetic crossovers, burst--halt intermittency consistent with single-molecule observations, and effectively Michaelis--Menten-like or sigmoidal allosteric-like responses. The same underlying landscape can therefore produce qualitatively different kinetic behaviours depending on how conformational exchange and substrate capture partition catalytic flux. It also explains how finite substrate windows can mask underlying bimodal behaviour. These results provide a physically interpretable framework linking conformational dynamics, pathway selection, and experimentally observed enzyme kinetics.

biophysics↗

bioRxiv2026

Modulating hydrogel architecture via cross-linker length for high-resolution tissue imaging and photochemical sectioning

The recent development of Volumetric Imaging via Photochemical Sectioning (VIPS) has enabled nanoscale imaging of whole-mount tissue samples of virtually any size by embedding intact tissue in a photocleavable, superabsorbent hydrogel. However, the efficacy of sample embedding, imaging, and photochemical sectioning is fundamentally governed by the mechanical stiffness, structural stability, and photodegradation kinetics of the photocleavable hydrogel (PC-gel) polymer network. To elucidate the effect of the photosensitive crosslinker design on these critical properties, we synthesized a set of photocleavable crosslinkers (PCs) with varying polyethylene glycol (PEG) backbone lengths and prepared the corresponding PC-gels under a fixed monomer formulation and polymerization condition. We quantified and compared the viscoelastic properties of the formed PC-gels at their swollen states, and found that the crosslinker length markedly reshaped the PC-gel mechanics. In addition, we evaluated the light-triggered degradation of the PC-gels using both wide-field and spatially-controlled illumination. We found that PC-1000, PC-1500, and PC-2000 gels remained comparably photodegradable, all enabling on-demand, spatially confined decrosslinking under such illuminations. These results provide practical guidelines for modulating the crosslinker architecture of PC-gel polymer networks to achieve optimal physicochemical properties for whole-mount tissue imaging using VIPS.

biophysics↗

bioRxiv2026

Methanogenic Ethanol Production from Acetate

Biological ethanol production is important for the circular carbon economy and makes up 73% of the U.S. biological fuels market. Previously, we produced ethanol by reversing methanogenesis and capturing methane by cloning methyl-coenzyme M reductase (Mcr) from an unculturable population of anaerobic methanotrophic archaea; this process was predicated on the generation of the intermediate acetate and its conversion by the methanogenic host to ethanol. Moreover, methanogens are generally thought to be detrimental for converting acetate to ethanol and are usually intentionally inhibited. Here, we demonstrate that direct growth on acetate as the sole carbon and energy source by the methanogen Methanosarcina acetivorans C2A results in 40% of the metabolized acetate becoming ethanol and that there is 430% more ethanol produced, compared to growth on methane via Mcr. In addition, we found growth on methanol results primarily in methane generation and low levels of ethanol. Therefore, acetate may be readily converted by the methanogen M. acetivorans to ethanol at high yields.

microbiology↗

bioRxiv2026

Local cooperative interactions reshape the folding transition in a one-dimensional spin-glass model

Protein folding is the process by which a polypeptide chain organizes into its three-dimensional structure through a balance of stabilizing and destabilizing interactions encoded by the sequence. A central question in protein biophysics is how thermodynamic factors guide a polypeptide toward its native folded state despite the rugged energy landscape and the competing influence of nonnative interactions. In many biomolecular processes, cooperativity provides a mechanism by which multiple weak interactions act collectively to generate a robust response. In the context of protein folding, such cooperative effects may arise when the formation of one native contact enhances the stability or likelihood of nearby native contacts, thereby promoting collective organization toward the folded state. At the same time, folding is opposed by the much larger number of non-native interactions, whose heterogeneity can introduce frustration and destabilize folding even when the average native bias favors the folded phase. The interplay of these competing effects in determining foldability remains unclear in statistical-mechanical models. Here, we address this problem using a one-dimensional spin-glass model of protein folding with explicit shared-residue cooperative interactions encoded through wedge-based motifs. We show that modest cooperative bias can stabilize folding even where the noncooperative system remains unfolded, whereas non-native energetic fluctuation suppresses folding and shifts the transition to higher cooperative strengths. We further find that partial cooperative coverage is sufficient to lower the folding threshold. Therefore, the model provides a mean-field framework for incorporating cooperative interaction strength into the native one-dimensional model of protein folding and for describing how local cooperativity reshapes the folding transition.

biophysics↗

bioRxiv2026

Engineering Methyl-Coenzyme M Reductase for Enhanced Methane and Carbon Dioxide Capture through Biofilm Growth

Methane is a potent greenhouse gas, nearly half of which is consumed anaerobically by anaerobic methanotrophic archaea (ANME) through methyl-coenzyme M reductase (Mcr). However, ANME cannot be grown as pure cultures, and obtaining active ANME Mcr in vitro remains extremely challenging, preventing previous efforts to engineer this key enzyme. Here, we used directed evolution in the methanogen Methanosarcina acetivorans to enhance ANME-1 Mcr (McrANME-1) activity for methane and carbon dioxide capture by selecting McrANME-1 variants with improved growth during methane-dependent cultivation. As a result, we discovered two beneficial substitutions in the catalytic -subunit of McrANME-1, S60P and I154V, that increased biofilm growth as well as acetate production and methane capture. AlphaFold structural predictions suggest possible mechanistic explanations for these beneficial substitutions. These findings demonstrate that Mcr can be engineered to enhance methane and carbon dioxide capture, establishing a foundation for biological greenhouse gas mitigation and carbon utilization technologies.

Synthetic Biology↗

bioRxiv2026

Evolution of a core ribosomal innovation in octopus

Much of biology focuses on how genetic changes mediate new functions, but less attention is given to adaptations in other steps of the central dogma. Octopuses exhibit complex nervous systems and sophisticated behaviors that rival vertebrates, but via an entirely divergent evolutionary history. Here, we serendipitously discovered that octopus ribosomes contain a structural break in the core ribosomal RNA that is unique among all animals. This break site enhances translation fidelity to reduce miscoding and subsequent protein aggregation, even when engineered into evolutionarily distant bacterial ribosomes. Furthermore, high fidelity translation by octopus ribosomes supports proteomic stability during extensive RNA editing observed in cephalopods, suggesting synergy between distinct non-canonical modes of gene regulation. This adaptation emerged in recently derived octopuses with expanded nervous systems, thereby revealing a mechanism that could broadly support the evolution of novel organismal traits.

biochemistry↗

bioRxiv2026

Enhancing Reverse Methanogenesis Using RNA-Guided Silencing

Utilizing methane and carbon dioxide before it can enter the upper atmosphere is beneficial for mitigating climate change as well as for producing valuable chemicals. Because anaerobic methanotrophic archaea (ANME) have not yet been cultured in isolation, we previously reversed methanogenesis by cloning the genes encoding methyl-coenzyme M reductase (Mcr) derived from Black Sea ANME-1 into the methanogen Methanosarcina acetivorans. The resulting engineered archaeal strain captures, rather than produces, methane and may be used to convert methane and carbon dioxide into electricity, acetate, L-lactate, and ethanol. However, the engineered M. acetivorans strain also contains a chromosomal locus encoding its native Mcr (McrM.a.), which produces methane from substrates such as methanol, whereas the heterologously expressed ANME-1 Mcr (McrANME-1) promotes methane oxidation. Therefore, we reasoned that McrM.a. may compete with McrANME-1-mediated reversal of methanogenesis. To enhance the reversal of methanogenesis, here we implemented an antisense RNA (asRNA) silencing approach to suppress McrM.a. during growth on methane while still allowing its expression during routine growth on methanol. We found that silencing McrM.a. during McrANME-1-mediated growth on methane increased ethanol and acetate production by more than an order of magnitude. These results were corroborated by both a more than 10-fold increase in methane utilization by McrANME-1 and a greater than 1,000-fold reduction in the McrM.a. mcrBGA transcript levels under methane-grown conditions. Therefore, asRNA-mediated silencing may be used to enhance methane capture by suppressing production of the host McrM.a. for biotechnological applications.

synthetic biology↗

bioRxiv2026

Generative design of sequence specific DNA binding proteins

De novo protein design has advanced rapidly in recent years, yet the programmable recognition of specific DNA sequences remains a longstanding challenge. Here we describe a deep learning based approach for designing sequence selective DNA binding proteins. Our method combines structure generation using RFdiffusion3 with explicit screening against off-target interactions using AlphaFold3. We test this approach by generating 96 designs for each of 15 diverse DNA targets and identify specific binders for 7 targets, representing a ~100-fold improvement in success rates over previous approaches. We further characterize the binding landscape using variant competition assays and randomized library screening, revealing robust sequence discrimination across diverse targets. Together, these results represent a significant step forward in de novo sequence specific DNA binder design.

synthetic biology↗

bioRxiv2026

Epigenome editing of human hematopoietic stem cells enables sustained and reversible thrombosis prevention

Thrombosis remains a major cause of cardiovascular and cerebrovascular diseases, driven in large part by platelet activation and aggregation. Because platelets are continuously produced from hematopoietic stem cells (HSCs), durable reprogramming of HSC output offers a unique opportunity for a one-time antithrombotic intervention. Here, we show that DNA methylation-based epigenome editors delivered transiently as RNA result in stable, heritable gene silencing in primary human HSCs that persists through long-term self-renewal and megakaryocytic differentiation, while remaining reversible through targeted demethylation. Targeting the platelet integrin {beta}3 (ITGB3), this approach achieves robust, sustained repression and yields platelets with impaired aggregation. Extending this framework to additional genetically-nominated platelet targets establishes HSC epigenome editing as a durable and reversible strategy to modulate thrombotic risk and highlights broader opportunities to engineer hematopoiesis.

genomics↗

bioRxiv2025

RNA sequence design and protein-DNA specificity prediction with NA-MPNN

RNA sequence design and protein-DNA binding specificity prediction can both be framed as nucleic acid inverse-folding problems: finding the most likely nucleic acid sequences given a fixed three-dimensional structure of a nucleic acid or nucleic acid-protein complex. While task-specific tools have been developed, no unified deep learning model for nucleic acid inverse folding has been described; a single model would have larger and more diverse datasets available for training and a considerably greater range of applicability. Here we introduce Nucleic Acid MPNN (NA-MPNN), a message-passing neural network that treats proteins, DNA, and RNA within a unified biopolymer graph representation. NA-MPNN outperforms previous methods on RNA sequence design and fixed-dock protein-DNA specificity prediction, and should be broadly useful for de novo RNA structure design and prediction of DNA-binding specificity.

biochemistry↗

bioRxiv2025

De novo Design of All-atom Biomolecular Interactions with RFdiffusion3

Deep learning has accelerated protein design, but most existing methods are restricted to generating protein backbone coordinates and often neglect interactions with other biomolecules. We present RFdiffusion3 (RFD3), a diffusion model that generates protein structures in the context of ligands, nucleic acids and other non-protein constellations of atoms. Because all polymer atoms are modeled explicitly, conditioning the model on complex sets of atom-level constraints for enzyme design and other challenges is both simpler and more effective than previous approaches. RFD3 achieves improved performance compared to prior approaches on a range of in silico benchmarks with one tenth the computational cost. Finally, we demonstrate the broad applicability of RFD3 by designing and experimentally characterizing DNA binding proteins and cysteine hydrolases. The ability to rapidly generate protein structures guided by complex sets of atom-level constraints in the context of arbitrary non-protein atoms should further expand the range of functions attainable through protein design.

biochemistry↗

bioRxiv2025

Accelerating Biomolecular Modeling with AtomWorks andRF3

Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilitate the development of new models, we present AtomWorks: a broadly applicable data framework for developing state-of-the-art biomolecular foundation models spanning diverse tasks, including structure prediction, generative protein design, and fixed backbone sequence design. We use AtomWorks to train RosettaFold-3 (RF3), a structure prediction network capable of predicting arbitrary biomolecular complexes with an improved treatment of chirality that narrows the performance gap between closed-source AlphaFold3 (AF3) and existing open-source implementations. We expect that AtomWorks will accelerate the next generation of open-source biomolecular machine learning models and that RF3 will be broadly useful as a structure prediction tool. To this end, we release the AtomWorks framework (https://github.com/RosettaCommons/atomworks), together with curated training data, code and model weights for RF3 (https://github.com/RosettaCommons/modelforge) under a permissive BSD license.

biochemistry↗

bioRxiv2025

The Art of Not Knowing: Accommodating Structured Missingness in Biomedical Research

Missing data remain a ubiquitous and critical challenge in large-scale clinical studies. Despite advances in imputation, most existing methods fail to address structured missingness, where data are missing according a deterministic pattern and which arise due to systematic patterns introduced by experimental design, site protocols, or cohort differences. These patterns violate key assumptions of most imputation algorithms, yet their impact is rarely evaluated. We demonstrate that structured missingness is a fundamental challenge to drawing valid inferences from standard imputation techniques. First, we present a comprehensive framework for understanding and accommodating the effects of structured missingness. Next, we show through simulations and real-world psychometric data with structured missingness, that widely used algorithms optimised for numerical precision (e.g., Extra Trees, AutoComplete) underperform due to site effects, while donor-based methods (e.g., MICE, hierarchical MICE) better preserve multivariate structure. We propose a novel hierarchical approach that provides optimal performance in simulated and experimental data. Finally, we show that commonly used accuracy metrics, such as mean squared error can obscure these failures, and are therefore inadequate for the evaluation of structured missingness. In contrast, other divergence-based metrics offer a more sensitive and interpretable alternative. We apply this approach to harmonising psychometric data across cohorts, which provides excellent item-level alignment across different instruments. Our study highlights the need for a paradigm shift in handling missing data within biomedical research, moving beyond conventional imputation frameworks to develop tools that can account for structured missingness. This shift is essential for ensuring reliable inference in multi-site clinical studies, precision medicine, and large-scale population analyses.

neuroscience↗

bioRxiv2025

A model of protein folding with multiple native states:Metamorphicity, Intrinsic disorderness and folding uponbinding of proteins

The sequence-structure-function paradigm in biology states that proteins amino acid sequence determines its unique folded state structure which in turn dictates its unique biological function. This classic concept has been severely challenged by the discovery of metamorphic and intrinsically disorder proteins (IDPs). Metamorphic proteins can fold into multiple native structures and perform multiple functions. IDPs, on the contrary, remain unstructured at physiological conditions, but can fold to unique structure upon binding to a target protein and show functionality. Here, we present a statistical mechanics model of protein folding with multiple native states and analyze their folding phase diagrams. While recovering classic sequence-structure-function paradigm for single native state, our model shows metamorphicity at lower number of native states. An expansion of unfolded region in the phase diagram at higher number of native states, making unfolded state as the stable state at physiological conditions, indicates the emergence of IDP-like scenario. Folding upon binding scenario of IDPs has also been demonstrated when an energetic bias is introduced for a specific native state. A regaining of folded region upon biasing, increasing the folding propensity of the system, with folding towards a specific native state is shown. Therefore, our model is general enough to reproduce the classic sequence-structure-function, metamorphicity, intrinsic disorderness and folding upon binding scenarios of proteins. Significance StatementProteins exhibit remarkable structural diversity, from having unique single native structure to having no particular structure but an inherently disordered ensemble that usually attains a single structure upon binding to another target. Here, we present a general statistical mechanics model of protein folding that reproduces single native state folding, metamorphicity, intrinsic disorder, and bindinginduced folding by incorporating multiple native states. A notable increase in propensity of being in unfolded state, resembling intrinsic disorder at physiological conditions, for system with larger number of native state was shown. Introduction of a bias was shown to increase the folding propensity of such proteins directed toward a specific native state. The models ability to capture a wide range of structural diversity in proteins makes it highly versatile.

biophysics↗

bioRxiv2024

Co-Therapy with S1P and Heparan Sulfate Derivatives to Restore Endothelial Glycocalyx and Combat Pro-Atherosclerotic Endothelial Dysfunction

Endothelial cell (EC) glycocalyx (GCX) shedding due to disturbed blood flow and chemical factors leads to low-density lipoprotein infiltration and reduced nitric oxide synthesis, causing vascular dysfunction and atherosclerosis. This study evaluates a novel therapy combining sphingosine-1-phosphate (S1P) and heparin (heparan sulfate derivative). We hypothesized that heparin/S1P would repair mechanically damaged EC GCX in disturbed flow (DF) regions and restore anti-atherosclerotic mechanotransduction function, addressing cardiovascular disease. We used a parallel-plate flow chamber to simulate flow conditions in vitro and a partial carotid ligation mouse model to mimic DF in vivo. Heparin and albumin-bound S1P were administered to assess their reparative effects on the endothelial GCX. Immunocytochemistry, fluorescent staining, confocal microscopy, cellular alignment studies, and ultrasound were performed to evaluate EC function and endothelial-dependent vascular function. Barrier functionality was assessed via macrophage uptake. Heparin/S1P mechanism-of-action insights were gained through fluid dynamics simulations and staining of GCX synthesis enzyme as well as S1P receptor. Statistical analyses validated results. In vitro data showed that heparin/S1P therapy improves the function of DF-conditioned ECs by restoring EC GCX and promoting EC alignment and elevated vasodilator eNOS (endothelial-type nitric oxide synthase) expression. The in vivo studies confirmed GCX degradation, increased vessel inflammation and hyperpermeability, and vessel wall thickening in the partially ligated left carotid artery. Heparin/S1P treatment restored GCX in the left carotid artery, enhancing GCX thickness and coverage of the blood vessel wall. This work advances a new approach to regenerating the EC GCX and restoring its function in ECs under DF conditions.

bioengineering↗

bioRxiv2024

CYP3A5 inhibition causes G1/S blockade and synergizes with CDK4/6 inhibitor to suppress prostate cancer cell growth: Implications in reducing health disparity

Prostate cancer (PC) is a leading cause of death in men because of the high incidence and long-term inefficacy of the existing treatment options. Furthermore, it exhibits significant health disparities that affect African-American (AA) men more adversely than others do. Previously, we established CYP3A5, a highly expressed protein in AAs PC, as a positive regulator of androgen receptor (AR) signaling. We examined the impact of CYP3A5 depletion on genome-wide transcriptional output using RNA sequencing to gain deeper mechanistic insights. The data revealed that 561 genes were downregulated and 263 were upregulated upon silencing of CYP3A5 in PC cells. Furthermore, in silico pathway analyses of differentially expressed genes suggested that the cell cycle regulation pathway was most significantly affected by CYP3A5 inhibition. Cell cycle analysis of CYP3A5-silenced cells and those treated with clobetasol, a specific CYP3A5 pharmacological inhibitor, showed G1/S phase blockade. Both CYP3A5-depletion and pharmacological inhibition resulted in the downregulation of cyclin D, cyclin B, and CDK2, along with the upregulation of p27kip1 but had minimal effects on CDK4/6 levels. Combination treatment with clobetasol and the CDK4/6 inhibitor palbociclib exhibited synergy with combination index (CI) values ranging from 0.28-0.78. Our findings support the utility of CYP3A5 as a druggable therapeutic target that works more effectively in combination with CDK4/6 inhibition to limit the progression of PC, especially for AA patients with AA. This combination addresses CDK4/6 inhibitor resistance, which is often linked to CDK2 overexpression, and can potentially be useful in reducing disparities in the clinical outcomes of PC. SignificanceOur study highlights CYP3A5 as a key regulator of the cell cycle in prostate cancer (PC). Its overexpression in African American (AA) patients may be a key molecular driver of disparities in outcomes. The combination of CYP3A5 and CDK4/6 inhibitors shows a synergistic effect on therapeutic outcomes and addresses CDK2-mediated resistance. Thus, targeting both CYP3A5 and CDK4/6 could improve treatment outcomes, especially in AA PC patients.

cancer biology↗