Protein structure prediction remains a grand challenge in computational biology. Traditional methods often struggle to accurately capture the intricate relationships within a protein sequence, leading to suboptimal structural models. This work explores the application of Graph Neural Networks (GNNs) to address this challenge. We hypothesize that by representing protein sequences as graphs, where nodes represent amino acids and edges represent interactions, GNNs can effectively learn and model these complex relationships, ultimately improving the accuracy and efficiency of protein structure prediction. This paper details the framework for utilizing GNNs, focusing on the construction of protein graphs, the design of suitable GNN architectures, and the training process. We demonstrate the potential of this approach and discuss future research directions. The core claim of this work is the utilization of GNNs to enhance protein structure prediction. The core mechanism involves transforming protein sequences into graph structures, leveraging GNNs to learn structural information. This approach represents a novel way to tackle the protein folding problem. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of non-standard geometry to computational geometry, focusing on the development of a novel method for defining and computing geometric properties for complex systems, particularly fluid dynamics and protein folding. Traditional geometric computation often struggles with the inherent complexity and self-organization of these systems, necessitating the creation of intricate geometric constructs. We propose a 'geometric language' – a system of rules and symbolic representations – that enables efficient manipulation and analysis of these complex shapes. The core mechanism involves establishing a hierarchical structure within this language, allowing for the generation of novel geometric configurations through a combination of geometric transformations and parametric modeling. This approach aims to overcome limitations in current computational geometry by offering a framework for tackling problems that are currently computationally intractable. The paper will detail the conceptualization of this language, its implementation through a set of rules and algorithms, and initial explorations into its potential for solving specific problems within fluid dynamics and protein folding. Finally, we present preliminary results demonstrating the feasibility of this approach, highlighting its potential for advancing the field of computational geometry.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Predicting the folding pathway of a protein – the process by which a linear chain of amino acids adopts its functional three-dimensional structure – is a central challenge in computational biology. Existing methods often struggle to accurately represent the intricate and dynamic interactions between amino acids that govern this process. This paper proposes a novel approach leveraging Graph Neural Networks (GNNs) to address this limitation. We represent proteins as graphs, where nodes correspond to individual amino acids and edges encode the physical and chemical interactions between them. The GNN learns to predict the folding pathway by propagating information through this graph structure, effectively capturing the sequential and interconnected nature of the folding process. We demonstrate that this approach offers a significant improvement over traditional methods in capturing the complex relationships within protein sequences and predicting the pathways of protein folding. The core of our method lies in the ability of GNNs to learn representations that are robust to noise and variations in protein sequences, ultimately leading to more accurate predictions. This work highlights the potential of graph-based neural networks in tackling complex biological problems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Protein folding is a fundamental process in biology, crucial for protein function and stability. Traditional methods often rely on rigid, predefined folding rules, limiting flexibility and efficiency. This paper introduces a novel computational approach – a dynamic modeling algorithm – that adapts protein structure during folding, significantly enhancing stability. We propose a model leveraging self-adaptive mechanisms to dynamically adjust the protein's conformation, achieving a more robust and versatile folding process. This research addresses the limitations of existing methods by offering a flexible framework for predicting and controlling protein folding.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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This paper presents a novel approach to protein structure prediction leveraging the principles of quantum entanglement. Traditional protein structure prediction methods are often limited by the computational complexity of simulating large biomolecular systems. We propose a framework that utilizes quantum entanglement to model the complex correlations inherent in protein folding, potentially overcoming these limitations. The core idea involves translating the amino acid sequence of a protein into a quantum state and employing quantum computation, specifically entanglement-based algorithms, to predict the protein's three-dimensional structure. The theoretical framework outlines the transformation process, the quantum algorithm design, and the methods for interpreting the results. We explore the potential advantages of this approach, focusing on its ability to capture long-range interactions and conformational flexibility that are difficult to model accurately with classical methods. The ultimate goal is to establish a new paradigm for protein structure prediction, offering improved accuracy and efficiency.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Deterministic Topological Neutralization of the Aβ42 N-Terminal Stacking Interface by a D-Enantiomer RDK Cap This repository contains a theoretical computational proof of topological interface neutralization on a custom-physics protein-fold ledger. By utilizing a deterministic, non-Cartesian discrete topology (Axiom I-X), the engine successfully isolated the Aβ42 N-terminal stacking basin and deterministically generated a D-enantiomer RDK cap that occupies the previously unfulfilled negative space, neutralizing the interface. The package includes the full Markdown manuscript, validation scripts, and thermodynamic logs from the 100,000-tick terminal co-fold. Author's Note: I am Robert J. Weber (RJW). The insights driving this computational proof stem directly from my independent interpretation of physics and the mechanics of the universe. I never dreamed of building a system that resolves protein folding, but the custom physics framework I developed has achieved exactly that. It works, and it works well. Am I absolutely sure this translates perfectly to the wet lab? Not yet—but the math is solid, the physics are grounded, and it represents the universe exactly as I see it. Some will scoff. Others will wonder: What if he is right? This work is deeply personal. My Uncle Pete died trapped in his own mind and body. I am publishing this for him. To anyone reading this—especially those in pharmaceutical research—who can take this theoretical blueprint and develop it into a working clinical cure to spare others that same fate: I am here, and I am willing to help. My compensation to see this realized will be minimal indeed. Mankind needs this to reduce the suffering, to stop the tears, and to keep the Uncle Petes everywhere smiling alongside the people who love them. God be with you today and all the tomorrows you are granted — RJW
Robert Weber· Zenodo (CERN European Organi...· 0 citations
Resonance-Based Probability Distribution Modeling presents a novel probabilistic modeling framework predicated on the principles of resonant frequencies and vibrational modes within complex systems. This approach aims to enhance predictive accuracy across diverse domains, including protein folding, fluid dynamics, and other systems exhibiting dynamic behavior. The core mechanism involves constructing a complex, multi-dimensional resonance function to represent system stability and predict outcomes, offering a departure from conventional statistical approaches. This research investigates the potential of this framework to achieve unprecedented levels of predictive capability by leveraging the inherent sensitivity of systems to resonant frequencies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Abstract Background During host colonization, fungal plant pathogens secrete effector-like proteins that alter host cell physiology and target plant-associated microbes. However, rapid evolution and low sequence conservation hinder the study and characterization of these proteins. The fungus Zymoseptoria passerinii infects Hordeum spp. and includes lineages adapted to wild and domesticated barley. To date, the evolution of effector-like proteins in this species has not been addressed. Results We combined multiple structure-based and network analyses to unravel the secretome of Z. passerinii . We first compared AlphaFold2 and ESMFold predictions to establish the baseline for structural analyses. We identified 72 structural clusters in the secretome, revealing fold-level relationships across divergent sequences. We showed that effector-like proteins with predicted host immune-interfering functions evolved from a limited group of protein folds, whereas proteins with predicted antimicrobial properties were distributed across fold groups. Physicochemical comparisons indicate that putative antimicrobial effectors predominantly emerged through amino acid replacements on common effector-enriched scaffolds in Z. passerinii , reconfiguring surface charge and electrostatics. We analyzed intra- and interspecific variation in selected effector-enriched families by comparing Z. passerinii proteins and homologs across the genus Zymoseptoria . We describe constrained core folds, with local variation in loop and surface-exposed regions, consistent with fold stability while still enabling protein diversification. We further report that putative antimicrobial effector homologs are broadly distributed across the genus despite sequence divergence. Conclusions The secretome of Z. passerinii is organized around common structural folds that support diverse biological roles, including host manipulation and host-associated microbial interactions. Conserved scaffolds combined with surface and physicochemical variation likely contribute to rapid adaptive evolution of effector-like proteins in Z. passerinii.
Thaís C. S. Dal'Sasso, Eva Stukenbrock· BMC Microbiology· 0 citations
Protein folding is the chemistry of the sequence's decision: how a chain of amino acids—with astronomically many possible conformations—finds its native state in milliseconds, the paradox Cyrus Levinthal posed in 1968 and Christian Anfinsen's thermodynamic hypothesis answered: the sequence itself encodes the fold. This article presents a narrative review of the primary literature that built the field, from Sela, White, and Anfinsen's 1957 ribonuclease refolding and Anfinsen's 1973 principles, through Levinthal's 1968 paradox, Karplus and Weaver's 1976 diffusion-collision, Dill's 1985 hydrophobic collapse, Hemmingsen and colleagues' 1988 chaperonins, Ellis and van der Vies's 1991 chaperone synthesis, Wolynes, Onuchic, and Thirumalai's 1995 folding funnels, Wright and Dyson's 1999 intrinsically disordered proteins, Dobson's 2003 misfolding and disease, Dill and MacCallum's 2012 fifty-year assessment, and Jumper and colleagues' 2021 AlphaFold, whose neural prediction made the sequence's structure computable. The synthesis is organized around three themes: the thermodynamic settlement, in which the native state's stability and the paradox's resolution were established; the assisted and disordered revisions, in which chaperones and intrinsically disordered proteins extended the folding paradigm; and the computational settlement, in which funnels, misfolding, and AlphaFold closed the fifty-year question. It is concluded that protein folding's history is the conversion of a paradox into a science—and its latest chapter, the prediction of structure from sequence, into chemistry's most consequential computation.
Zen Revista, 10 CHEMISTRY· Zenodo (CERN European Organi...· 0 citations
Protein folding is the chemistry of the sequence's decision: how a chain of amino acids—with astronomically many possible conformations—finds its native state in milliseconds, the paradox Cyrus Levinthal posed in 1968 and Christian Anfinsen's thermodynamic hypothesis answered: the sequence itself encodes the fold. This article presents a narrative review of the primary literature that built the field, from Sela, White, and Anfinsen's 1957 ribonuclease refolding and Anfinsen's 1973 principles, through Levinthal's 1968 paradox, Karplus and Weaver's 1976 diffusion-collision, Dill's 1985 hydrophobic collapse, Hemmingsen and colleagues' 1988 chaperonins, Ellis and van der Vies's 1991 chaperone synthesis, Wolynes, Onuchic, and Thirumalai's 1995 folding funnels, Wright and Dyson's 1999 intrinsically disordered proteins, Dobson's 2003 misfolding and disease, Dill and MacCallum's 2012 fifty-year assessment, and Jumper and colleagues' 2021 AlphaFold, whose neural prediction made the sequence's structure computable. The synthesis is organized around three themes: the thermodynamic settlement, in which the native state's stability and the paradox's resolution were established; the assisted and disordered revisions, in which chaperones and intrinsically disordered proteins extended the folding paradigm; and the computational settlement, in which funnels, misfolding, and AlphaFold closed the fifty-year question. It is concluded that protein folding's history is the conversion of a paradox into a science—and its latest chapter, the prediction of structure from sequence, into chemistry's most consequential computation.
Zen Revista, 10 CHEMISTRY· 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.