Targeting the intrinsically disordered N-terminal domain of the androgen receptor (AR-NTD) represents a promising strategy to overcome resistance in prostate cancer. However, its inherent lack of a stable tertiary structure and highly dynamic conformational ensemble pose formidable challenges for rational drug design. This study introduces an integrated computational workflow that combines enhanced sampling techniques and machine learning collective variables to identify druggable conformations of the AR-NTD and elucidate the binding mechanism of its modulator, EPI-002. We characterize nine metastable states of the Tau-5 region and reveal that ligand recognition is driven by π–π stacking and structured water-mediated hydrogen bonds. Leveraging these insights, we perform structure-based virtual screening based on the identified druggable conformations and identify K53, a rationally designed AR-NTD antagonist, which exhibits potent anti-proliferative activity in enzalutamide-resistant prostate cancer cells. K53 directly binds the AR-NTD, suppresses AR transcriptional activity, and demonstrates high selectivity for cancer cells. This work provides a rational design paradigm for targeting intrinsically disordered proteins and offers a therapeutic candidate for resistant prostate cancer. In this work, the authors develop a machine learning–based enhanced sampling workflow to target the intrinsically disordered AR-NTD, identifying druggable conformations and enabling transferable modeling of ligand binding for rational drug discovery.
Kai Zhu, Huating Wang, Jintu Zhang et al.· Nature Communications· 0 citations
This paper proposes a novel approach to protein structure prediction leveraging principles from quantum mechanics, specifically entanglement and superposition. Traditional methods for protein folding, reliant on classical computational techniques, frequently struggle with accuracy and efficiency, particularly when dealing with complex protein structures. We posit that protein folding can be modeled as solving the Schrödinger equation, a problem ideally suited for quantum computation. This work outlines a framework where quantum algorithms are utilized to accelerate the solution of the Schrödinger equation for a given protein, significantly reducing the computational burden. Furthermore, we incorporate biological information, such as sequence data and known structural constraints, to refine the quantum solution and enhance predictive accuracy. The core claim is that utilizing quantum mechanical phenomena can lead to a more accurate protein structure prediction method. The mechanism involves transforming the protein folding problem into a quantum mechanical equation solving task, accelerating the process with quantum computation, and optimizing the solution with biological data. We present a conceptual model and discuss the potential benefits and challenges of this approach, highlighting its potential to surpass the limitations of current classical methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Frontotemporal lobar degeneration (FTLD) is a common cause of early-onset dementias marked by progressive declines in behavior, cognition, and/or movement. FTLD neuropathologies, including TDP-43 proteinopathies and primary tauopathies, do not have reliable fluid biomarkers for in-vivo diagnosis nor biomarkers that directly correspond to FTLD clinical features. Fluid biomarkers that forecast and track FTLD clinical progression, irrespective of pathology or clinical syndrome, are urgently needed to improve clinical trial designs. We previously identified the ratio between two cerebrospinal fluid (CSF) synaptic proteins, YWHAG and NPTX2, as a prognostic biomarker of cognitive decline in Alzheimers disease (AD), independent of core AD pathologies, amyloid and tau. Here, we evaluate its utility in sporadic and familial FTLD compared to other neurodegenerative diseases. Using CSF assays from four independent cohorts (UCSF-MAC, ALLFTD, GENFI, PDBP), we find CSF YWHAG:NPTX2 is substantially elevated across all sporadic and familial FTLD syndromes, AD, and dementia with Lewy bodies. CSF YWHAG:NPTX2 robustly correlates with clinical severity across sporadic and familial FTLD (C9orf72, GRN, or MAPT mutations), independent of current gold-standard neurodegeneration biomarker neurofilament light (NfL). In presymptomatic familial FTLD, CSF YWHAG:NPTX2 is estimated to rise roughly a decade before symptom onset and improves prediction of imminent symptomatic conversion by 1.7-fold compared to plasma NfL alone, more than halving the estimated sample size required for an FTLD prevention clinical trial. These findings underscore CSF YWHAG:NPTX2 as a cross-dementia synaptic biomarker of cognitive decline and a promising biomarker for disease staging and prognosis across the clinico-pathological continuum of FTLD.
Hamilton Oh, Joshua D. Downer, Connor D. Dietz et al.· bioRxiv (Cold Spring Harbor...· 0 citations
The simulation of complex biological systems, such as protein folding and gene regulation, presents significant challenges due to the inherent complexity and often intractable nature of these systems. Traditional computational methods struggle to capture the nuanced dynamics of biological processes, limiting our ability to understand and potentially manipulate them. This research proposes an innovative approach – adaptive quantum simulation – that leverages the principles of quantum mechanics to create dynamic, self-adjusting simulations of biological systems. We aim to develop an algorithm that continuously refines simulation parameters, automatically mimicking biological behavior to achieve unprecedented accuracy and fidelity. This work explores the potential of quantum computation to overcome limitations inherent in classical simulation techniques, offering a fundamentally new pathway for biological system modeling and analysis. This includes a detailed explanation of the algorithm's core mechanisms, potential applications, and preliminary results demonstrating its adaptability. The core claim is that this adaptive quantum simulation method will allow for a level of detail and accuracy previously unattainable through conventional computational methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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The field of topology has witnessed significant advancements in understanding and manipulating complex network structures, from protein folding to genome sequencing. This research introduces a novel approach to topology optimization – the "Adaptive Topology Optimization Algorithm" – that leverages a self-adaptive topology structure to efficiently explore and optimize intricate network configurations. Traditional methods often rely on manually crafted topologies, presenting significant limitations in scalability and adaptability. This algorithm employs a dynamic adjustment of topology parameters to guide the search towards optimal configurations, offering a powerful and potentially transformative method for tackling challenging topology problems. This paper details the core mechanisms and key advantages of this new algorithm, providing a comprehensive analysis of its capabilities and potential impact across diverse application domains.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Large Language Models (LLMs) have transformed protein engineering by capturing complex sequence patterns from large datasets, enabling applications such as structure prediction and functional annotation. Finenzyme applies conditional transfer learning to generate biologically plausible enzyme sequences conditioned on Enzyme Commission (EC) numbers.
In this work, we extended Finenzyme with an in silico selection pipeline that first identifies generated sequences most likely to preserve or enhance the functional characteristics of specific EC categories and then evaluates them through molecular dynamics (MD) simulations to assess their structural stability and conformational dynamics.
MD simulations of 236 Finenzyme-generated enzymes across four EC classes (59
µ
s total simulation time) confirmed high structural stability. Across all enzyme classes, 74-95% of the models maintained stable tertiary structures and correct folding throughout the trajectories, with 195 out of 236 structures (82.6%) exhibiting sustained stability.
By combining conditional pre-trained language model fine-tuning with dynamic structural evaluation, our framework advances beyond static sequence-based predictions to address the structural and functional dimensions of enzyme behavior, key aspects for both biomedical and industrial applications.
E. M. Fassi, M. Nicolini, Emanuele Saitto et al.· Frontiers in Artificial Inte...· 0 citations
This manuscript is the corrected version of a previously published paper. Glucose uptake by mammalian cells is a key mechanism to maintain cell and tissue homeostasis and relies mostly on plasma membrane-localized glucose transporter proteins (GLUTs). Two main cellular mechanisms regulate GLUT proteins in the cell: first, expression of GLUT genes is under dynamic transcriptional control and is used by cancer cells to increase glucose availability. Second, GLUT proteins are regulated by membrane traffic from storage vesicles to the plasma membrane (PM). This latter process is triggered by signaling mechanisms and is well studied in the case of insulin-responsive cells, which activate protein kinase AKT to phosphorylate TBC1D4, a RAB-GTPase–activating protein involved in membrane traffic regulation. Previously, we identified protein kinase WNK1 as another kinase able to phosphorylate TBC1D4 and regulate the surface abundance of the constitutive glucose transporter GLUT1. Here we describe that downregulation of WNK1 through RNA interference in HEK293 cells led to a two-fold decrease in cell-surface GLUT1 abundance, concomitant with a 40% decrease in glucose uptake. By mass spectrometry, we identified serine (S) 704 in TBC1D4 and also S565 in its paralogue TBC1D1 as candidate WNK1 phosphorylation sites. Transfection of the respective phosphomimetic or unphosphorylatable TBC1D mutants into cells revealed that both affected the cell-surface abundance of GLUT1. The results reinforce a regulatory role for WNK1 in GLUT1 trafficking and glucose uptake and may have potential impact for the understanding of metabolic dysregulation, as observed in many cancer cells or insulin-responsive cell types.
Andreia F.A. Henriques, Paulo Matos, Ana Sofía Carvalho et al.· Cells· 0 citations
This release contains the version of the FCKcat code associated with the manuscript: Overcoming systematic data biases enables accurate prediction of enzyme kcat fold-changes for computational protein design Authors: Yvan Rousset, Alexander Kroll, and Martin J. Lercher The data and trained model files required to reproduce the analyses are available separately on Zenodo: https://doi.org/10.5281/zenodo.20325541
Yvan Rousset· Zenodo (CERN European Organi...· 0 citations
Bayesian Geometric Chaos with Adaptive Constraint Propagation represents a novel approach to modeling complex systems, particularly those exhibiting intricate dynamics and high-dimensional parameter spaces. This paper explores the integration of a variational Bayesian framework, incorporating adaptive constraint propagation, to dynamically adjust model parameters and enhance prediction accuracy. Traditional Bayesian methods often fall short in these scenarios, struggling to effectively handle non-linearity and uncertainty. Our work proposes a fundamentally adaptive self-optimizing method, moving beyond static inference to a process where the model parameters are continually refined through a learned "chaos" function, guided by observed data. This leads to improved prediction capabilities across a range of applications, including fluid dynamics and protein folding simulations. The core mechanism leverages a variational Bayesian approach, utilizing observed data to update the model's parameters, and adaptive constraint propagation, which adjusts constraint parameters to guide the learning process. We demonstrate the efficacy of this framework through a series of simulations and analysis, highlighting its potential for addressing limitations of existing Bayesian methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The field of topology has witnessed significant advancements in understanding and manipulating complex network structures, from protein folding to genome sequencing. This research introduces a novel approach to topology optimization – the "Adaptive Topology Optimization Algorithm" – that leverages a self-adaptive topology structure to efficiently explore and optimize intricate network configurations. Traditional methods often rely on manually crafted topologies, presenting significant limitations in scalability and adaptability. This algorithm employs a dynamic adjustment of topology parameters to guide the search towards optimal configurations, offering a powerful and potentially transformative method for tackling challenging topology problems. This paper details the core mechanisms and key advantages of this new algorithm, providing a comprehensive analysis of its capabilities and potential impact across diverse application domains.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the potential of quantum computing to revolutionize bioinformatics, specifically focusing on enhanced gene sequence analysis and protein structure prediction. The core concept centers around utilizing quantum error correction and quantum computation to accelerate these computationally intensive tasks. We propose a novel algorithm architecture leveraging quantum entanglement and superposition to overcome limitations inherent in classical approaches. The paper details the design of a quantum algorithm for sequence alignment and protein folding, emphasizing the key quantum mechanisms that underpin its enhanced performance. We present a preliminary analysis of the algorithm's potential advantages, focusing on reduced computational complexity and improved accuracy compared to existing classical methods. The research highlights the significance of quantum algorithms in addressing critical challenges within the field of bioinformatics.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to protein structure prediction leveraging principles from quantum mechanics, specifically entanglement and superposition. Traditional methods for protein folding, reliant on classical computational techniques, frequently struggle with accuracy and efficiency, particularly when dealing with complex protein structures. We posit that protein folding can be modeled as solving the Schrödinger equation, a problem ideally suited for quantum computation. This work outlines a framework where quantum algorithms are utilized to accelerate the solution of the Schrödinger equation for a given protein, significantly reducing the computational burden. Furthermore, we incorporate biological information, such as sequence data and known structural constraints, to refine the quantum solution and enhance predictive accuracy. The core claim is that utilizing quantum mechanical phenomena can lead to a more accurate protein structure prediction method. The mechanism involves transforming the protein folding problem into a quantum mechanical equation solving task, accelerating the process with quantum computation, and optimizing the solution with biological data. We present a conceptual model and discuss the potential benefits and challenges of this approach, highlighting its potential to surpass the limitations of current classical methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
