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• Review for "High‐accuracy protein structure prediction in CASP14"
  crossref 2021-07-05T09:40:55Z 置信度 0.70
• New statistical potentials for improved protein structure prediction
  This dissertation presents a new scheme to derive four-body contact potentials as a way to consider protein interactions in a more cooperative model. These new four-body contact potentials, noted as SET1 four-body contact potentials (sequential information inc…
  crossref Yaping Feng 2018-08-10T19:38:52Z 置信度 0.70
• Mapping the pathogenic nexus: Gene overlap and protein interaction networks in Alzheimer’s and breast cancer as a precursor to protein structure prediction and analysis
  This chapter focuses on how advanced computational techniques can reveal common pathways and interactions seemingly between Alzheimer's disease (AD) and breast cancer (BC). It also highlights their roles in bridging the gap between neurodegenerative and oncoge…
  europepmc 2025 置信度 0.80
• Hybrid Methods for Protein Structure Prediction
  This chapter contains sections titled: Introduction Sources of Limited Structural Data Translation into Structural Restraints Use of Limited Experimental Data to Elucidate Structure Conclusions and Future Outlook Acknowledgement References
  crossref Dmitri Mourado, Bostjan Kobe, Nicholas E. Dixon, Thomas Huber 2011-06-25T14:40:03Z 置信度 0.70
• Improving protein structure prediction with multicom
  Proteins play essential roles in biological processes and understanding their three-dimensional (3D) structures is vital for revealing their complex functions. This study addresses the challenge of accurately predicting protein tertiary and quaternary structur…
  crossref Jian Liu 2025-07-03T14:48:39Z 置信度 0.70
• Protein-DNA interaction prediction and protein structure modeling by machine learning
  [EMBARGOED UNTIL 8/1/2023] Proteins are large, complex molecules that perform most essential functions within organisms. In this work, we mainly focus on two important aspects that determine their functional properties: the tertiary structure of the proteins a…
  crossref Chen Chen 2023-02-07T18:43:35Z 置信度 0.70
• Foldify: Web Application for Protein Structure Prediction.
  Protein structure prediction models released in recent years have presented tectonic changes in the field of structural biology. However, their potential has not yet been harnessed to its fullest due to their demands on hardware and technical expertise require…
  europepmc 2026 置信度 0.80
• A Comprehensive Evaluation of Protein Structure Prediction Models for Short Peptides
  Short peptides pose distinct challenges for computational structural biology due to their lack of stable tertiary structures, high conformational flexibility, and limited evolutionary signals. To address how modern deep-learning architectures navigate these ch…
  europepmc 2026 置信度 0.80
• The accuracy of electrostatic interactions captured by AI protein structure prediction models.
  A variant of the U1A protein containing four substitutions to ionizable residues was generated serendipitously due to a miscommunication. Biophysical measurements reveal this variant has twice the helical structure of wild-type U1A and is trimeric, unlike the …
  europepmc 2026 置信度 0.80
• Protein Structure Prediction Methods.
  Protein structure prediction, a fundamental challenge emerging from the protein folding problem, forms the basis of modern computational biology. This field addresses the critical question of how the amino acids sequence determines its three-dimensional struct…
  europepmc 2026 置信度 0.80
• Comprehensive Tutorial for Computational Methods of Protein Structure Prediction Incorporating Mass Spectrometry Data.
  Here we present a series of tutorials demonstrating the use of various methods which integrate structural mass spectrometry (MS) data with computational protein structure prediction methods. We give usage examples of widely used modeling frameworks, including …
  europepmc 2026 置信度 0.80
• HighRes_Builder: improved access and modeling of noncanonical residues for protein structure prediction.
  The growing support for noncanonical amino acids in structure prediction tools such as AlphaFold3 has been largely facilitated by the Chemical Component Dictionary (CCD). However, the limited coverage of modified residues in CCD continues to restrict the appli…
  europepmc 2026 置信度 0.80
• Exploring quantum frontiers in protein structure prediction: techniques, challenges, and opportunities.
  Protein folding is governed by the principle of free energy minimization, where a protein's native tertiary structure corresponds to the global minimum on an energy landscape shaped by quantum mechanical interactions such as hydrogen bonding, van der Waals for…
  europepmc 2026 置信度 0.80
• Beyond AlphaFold: Charting the Future of AI in Protein Structure Prediction.
  europepmc 2026 置信度 0.80
• Do AI Models for Protein Structure Prediction Get Electrostatics Right?
  A variant of the U1A protein containing four substitutions to ionizable residues was generated serendipitously due to a miscommunication. Biophysical measurements show that this variant has at least twice as much helical structure as the wild-type U1A and is t…
  europepmc 2026 置信度 0.80
• Efficient protein structure prediction from compact computers to datacenters with OpenFold-TRT
  We introduce accelerations for deep learning inference with OpenFold and TensorRT that, combined with MMseqs2-GPU on an x86 system with one NVIDIA RTX PRO 6000 Blackwell Server Edition GPU, reach up to 131× faster inference compared to AlphaFold2. ARM-optimiza…
  europepmc 2026 置信度 0.80
• Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.
  Proteins play essential roles in cellular processes, and accurate three-dimensional structures are critical for understanding function and enabling drug discovery. High-resolution methods such as cryo-electron microscopy (cryo-EM) and X-ray crystallography can…
  europepmc 2026 置信度 0.80
• Protein structure prediction powered by artificial intelligence: from biochemical foundations to practical applications.
  The three-dimensional structure of a protein underpins its biological function, making structure determination and prediction central challenges in structural biology. Although experimental techniques such as X-ray crystallography, nuclear magnetic resonance (…
  europepmc 2026 置信度 0.80
• Incorporating Surfaced-Induced Dissociation Mass Spectrometry Data into an AlphaFold-derived deep learning network improves protein structure prediction
  Surface-Induced Dissociation native Mass Spectrometry (SID-nMS) is a tandem MS activation method that yields information on the connectivity and stoichiometry of protein complexes. While insufficient for direct structure elucidation, the data derived from SID-…
  europepmc 2026 置信度 0.80
• Co-folding of Membrane Proteins and Lipid Molecules Improves Membrane-Protein Structure Prediction Accuracy
  Recent advances in deep-learning-based structure prediction have greatly improved the accuracy of protein structure modeling and enabled prediction of biomolecular complexes. However, the surrounding molecular environments are typically not represented explici…
  europepmc 2026 置信度 0.80
• DeepStruct: Multi-modal deep learning integrating omics and energy scoring for protein structure prediction in plants.
  Reliable prediction of the structural and functional characteristics of plant proteins remains a formidable challenge, largely due to a lack of appropriate template structures and the complex effects of environmental stressors on protein stability and function…
  europepmc 2026 置信度 0.80
• AlphaInterp: Mechanistic Interpretability of AlphaFold 3 Reveals How Evolutionary Information Shapes Protein Structure Prediction
  AlphaFold 3 predicts biomolecular structures with unprecedented accuracy, yet the computations transforming sequence and evolutionary data into structural coordinates remain poorly understood. Here, we present a systematic mechanistic interpretability analysis…
  europepmc 2026 置信度 0.80
• SimpleFold-Turbo: Adaptive Inference Caching Yields 14-fold Acceleration of Flow-matching Protein Structure Prediction
  We apply TeaCache, an adaptive caching technique from video diffusion to SimpleFold’s flow-matching protein structure prediction and achieve (9 to 14)-fold inference speedups with negligible quality loss. We determine that flow matching’s near-linear generativ…
  europepmc 2026 置信度 0.80
• Context-Dependent Protein Structure Prediction Analysis and Stoichiometry Inference with MultimerMapper
  Advances in artificial intelligence (AI) have transformed the field of protein structure, notably with the accuracy level reached by AlphaFold in the prediction of monomeric and multimeric protein structures. However, while cloud implementations have broadened…
  europepmc 2025 置信度 0.80
• From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.
  Four years ago, at the 14th Critical Assessment of Structure Prediction (CASP14), John Moult made a historic announcement that the long-standing challenge of Protein Structure Prediction- a problem that had confounded scientists for over five decades-had been …
  europepmc 2026 置信度 0.80
• Protein structure prediction and design for high-throughput computing
  Recent advances in structural biology and machines learning have resulted in a revolution in molecular biology. This revolution is driven by protein structure prediction and design tools such as Alphafold3, Chai-1, and Boltz-2 which are now able to accurately …
  europepmc 2025 置信度 0.80
• Enhancing protein structure prediction: evaluating the role of amino acid physicochemical features in homology search.
  Computational models like AlphaFold2 have achieved high accuracy in protein structure prediction, but their homology search step-key to generating multiple sequence alignments (MSAs)-remains computationally expensive and prone to introducing alignment noise. W…
  europepmc 2026 置信度 0.80
• A comprehensive application of FiveFold for conformation ensemble-based protein structure prediction.
  The emergence of artificial intelligence in protein structure prediction has significantly advanced our understanding of protein folding. Yet, challenges remain in accurately modeling intrinsically disordered proteins (IDPs) and capturing conformational divers…
  europepmc 2025 置信度 0.80
• Active Learning for Protein Structure Prediction
  Accurate protein structure prediction is challenging especially for families and types that are not well-represented in current training data. Active learning selects candidates for labeling with the aim of most rapidly improving model performance. In a genera…
  europepmc 2025 置信度 0.80
• Conformational ensembles for protein structure prediction.
  Acquisition of conformational ensembles for a protein is a challenging task, which is actually involving to the solution for protein folding problem and the study of intrinsically disordered protein. Despite AlphaFold with artificial intelligence acquired unpr…
  europepmc 2025 置信度 0.80
• GhostFold: Accurate protein structure prediction using structure-constrained synthetic coevolutionary signals
  ABSTRACT The accuracy of protein structure prediction models such as AlphaFold2 is tightly coupled to the depth and quality of multiple sequence alignments ( MSAs ), posing a persistent challenge for proteins with few or no identifiable homologs. We present Gh…
  europepmc 2025 置信度 0.80
• Enhancing protein structure prediction accuracy by prioritizing important residues using protein language models
  Accurate prediction of protein tertiary structures from amino acid sequences remains a fundamental challenge in computational biology. Although AlphaFold2 represents a major advance, systematic discrepancies persist between its predictions and experimentally d…
  europepmc 2025 置信度 0.80
• Accurate Immune Protein Structure Prediction by Large Language Model and Transfer Learning
  Abstract Accurate prediction of immune protein structures is critical for advancing immunotherapy. However, deep learning-based methods like AlphaFold and RosettaFold struggle with immune proteins due to the limited number of solved immune protein structures a…
  europepmc 2025 置信度 0.80
• Emerging frontiers in protein structure prediction following the AlphaFold revolution.
  Models of protein structures enable molecular understanding of biological processes. Current protein structure prediction tools lie at the interface of biology, chemistry and computer science. Millions of protein structure models have been generated in a very …
  europepmc 2025 置信度 0.80
• DeepFold-PLM: accelerating protein structure prediction via efficient homology search using protein language models.
  Motivation Protein structure prediction has been revolutionized and generalized with the advent of cutting-edge AI methods such as AlphaFold, but reliance on computationally intensive multiple sequence alignments (MSA) remains a major limitation. Results We in…
  europepmc 2025 置信度 0.80
• De Novo Protein Structure Prediction by Model Quality Assessment Dynamic Feedback Mechanism Using Deep Learning.
  Accurate de novo protein structure prediction remains a fundamental challenge, particularly in cases where homologous templates are unavailable or evolutionary information is weak. While end-to-end methods such as AlphaFold2 have achieved unprecedented accurac…
  europepmc 2025 置信度 0.80
• Adaptive gradient scaling: integrating Adam and landscape modification for protein structure prediction.
  Background Protein structure prediction is one of the most important scientific problems, on the one hand, it is one of the NP-hard problems, and on the other hand, it has a wide range of applications including drug discovery and biotechnology development. Sin…
  europepmc 2025 置信度 0.80
• Advancing protein structure prediction beyond AlphaFold2.
  Accurate prediction of protein structures is essential for understanding their biological functions. The release of AlphaFold2 in 2021 marked a significant breakthrough, delivering unprecedented accuracy. However, challenges remain, particularly for proteins w…
  europepmc 2025 置信度 0.80
• Protein structure prediction via deep learning: an in-depth review.
  The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. Accurate protein structures are crucial for understanding biological processes and designing effective therapeutics. Traditionall…
  europepmc 2025 置信度 0.80
• Novel De Novo DLL4 Missense and Highly Accurate Protein Structure Prediction in Adams-Oliver Type 6 Syndrome.
  Adams-Oliver syndrome (AOS) is a rare disease classically described with scalp vertex aplasia cutis and terminal transverse limb defects. This syndrome is frequently misdiagnosed by taking each feature of the disease separately. A novel de novo missense varian…
  europepmc 2025 置信度 0.80
• Highly accurate protein structure prediction-based virtual docking pipeline accelerating the identification of anti-schistosomal compounds.
  Schistosomiasis is a major neglected tropical disease that lacks an effective vaccine and faces increasing challenges from praziquantel resistance, underscoring the urgent need for novel therapeutics. Target-based drug discovery (TBDD) is a powerful strategy f…
  europepmc 2025 置信度 0.80
• PySSA for Windows: End-User Protein Structure Prediction and Visual Analysis with ColabFold and PyMOL.
  PySSA (Python rich client for visual protein Sequence to Structure Analysis) for Windows is a comfortable open Graphical User Interface (GUI) application combining the protein sequence to structure prediction capabilities of ColabFold with the open-source vari…
  europepmc 2025 置信度 0.80
• Visual-like 2D Geometric Template Diffusion for Boosting Single-Sequence Protein Structure Prediction
  Single-sequence protein structure prediction has drawn increasing attention due to the high computational costs associated with obtaining homologous information. Here, we propose a visual-like 2D geometric template ∗ diffusion method, named TDFold, to generate…
  europepmc 2025 置信度 0.80
• Artificial Intelligence (AI)-Based Protein Structure Prediction and Analysis.
  One of the significant challenges in fields of drug discovery and bioinformatics is the prediction of protein's 3-D structure due to its computational complexity. The vast conformational space and intricate energy functions make it hard to accurately predict p…
  europepmc 2025 置信度 0.80
• Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives.
  This review provides a comprehensive analysis of AlphaFold (AF) and its derivatives (AF2 and AF3) in protein structure prediction. These tools have revolutionized structural biology with their highly accurate predictions, driving progress in protein modeling, …
  europepmc 2025 置信度 0.80
• HighPlay: Cyclic Peptide Sequence Design Based on Reinforcement Learning and Protein Structure Prediction.
  The structural diversity and good biocompatibility of cyclic peptides have led to their emergence as potential therapeutic agents. Existing cyclic peptide design methods, whether traditional or emerging AI-assisted, rely on a multitude of experiments and face …
  europepmc 2025 置信度 0.80
• Visual-like Template Diffusion: Boosting Single-Sequence Protein Structure Prediction by Adapting Image Diffusion Models
  Abstract Single-sequence protein structure prediction has drawn increasing attention due to the high computational costs associated with obtaining homologous information. Here, we propose a visual-like template diffusion method, named TDFold, to achieve accura…
  europepmc 2025 置信度 0.80
• Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.
  The dominant success of deep learning techniques on protein structure prediction has challenged the necessity and usefulness of traditional force field-based folding simulations. We proposed a hybrid approach, deep-learning-based iterative threading assembly r…
  europepmc 2026 置信度 0.80
• Advancements in one-dimensional protein structure prediction using machine learning and deep learning.
  The accurate prediction of protein structures remains a cornerstone challenge in structural bioinformatics, essential for understanding the intricate relationship between protein sequence, structure, and function. Recent advancements in Machine Learning (ML) a…
  europepmc 2025 置信度 0.80
• Retrieval Augmented Protein Language Models for Protein Structure Prediction
  Abstract The advent of advanced artificial intelligence technology has significantly accelerated progress in protein structure prediction, with AlphaFold2 setting a new benchmark for prediction accuracy by leveraging the Evoformer module to automatically extra…
  europepmc Pan Li, Xingyi Cheng, Le Song, Eric Xing 2024 置信度 0.80
• GeoFlow-V2: A Unified Atomic Diffusion Model for Protein Structure Prediction and De Novo Design
  We introduce GeoFlow-V2, a unified atomic diffusion model that seamlessly integrates structure prediction and de novo protein design across multiple biological modalities, including proteins, nucleic acids (DNA/RNA), and small molecules. The model’s core innov…
  europepmc 2025 置信度 0.80
• Deciphering Features of Metalloprotease Cleavage Targets Using Protein Structure Prediction
  Introduction ADAM10 (A Disintegrin and Metalloproteinase 10) cleaves specific substrates, influencing diverse physiological and pathological processes. However, its substrate specificity and cleavage sites remain insufficiently characterized. This study aimed …
  europepmc 2025 置信度 0.80
• LightRoseTTA: High-Efficient and Accurate Protein Structure Prediction Using a Light-Weight Deep Graph Model.
  Accurately predicting protein structure, from sequences to 3D structures, is of great significance in biological research. To tackle this issue, a representative deep big model, RoseTTAFold, is proposed with promising success. Here, "a light-weight deep graph …
  europepmc 2025 置信度 0.80
• OmegaFold: Deep Learning Paradigm for Universal Protein Structure Prediction via Attention-Based Geometric Transformers and Evolutionary Language Modeling
  Abstract This paper introduces a groundbreaking computational paradigm for protein structure prediction through novel OmegaFold architecture that fundamentally transforms traditional multiple sequence alignment dependent methodologies. Theresearch establishes …
  europepmc 2025 置信度 0.80
• Easy and accurate protein structure prediction using ColabFold.
  Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological questions, by using predicted protein structures of single monomers or full complexes, a common practice. ColabFold-AF2 is an open-source Jupyter Notebook inside Google Colabo…
  europepmc 2025 置信度 0.80
• Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.
  Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularly with the…
  europepmc Ahmet Gurkan Genc, Liam J. McGuffin 2024-11-22T10:43:18Z 置信度 0.80
• Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning
  We address protein structure prediction in the 3D Hydrophobic-Polar lattice model through two novel deep learning architectures. For proteins under 36 residues, our hybrid reservoir-based model combines fixed random projections with trainable deep layers, achi…
  europepmc 2025 置信度 0.80
• DCBLSTM-Deep Convolutional Bidirectional Long Short-Term Memory neural network for Q8 secondary protein structure prediction.
  Protein secondary structure prediction involves determining a protein's secondary structure from its primary amino acid sequence, serving as a critical step toward tertiary structure prediction. This, in turn, is essential for applications in drug design, prot…
  europepmc 2025 置信度 0.80
• Persistent diagrams for protein structure prediction
  Abstract Topological approaches for protein structure analysis often comes with an input matrix describing the suitable filtration of our data and helping to choose adequate statistical tests to come up with a final shape by using an algebraic invariant, which…
  europepmc ZAKARIA LAMINE 2024 置信度 0.80
• Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction.
  In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and con…
  europepmc 2025 置信度 0.80
• Extracting Residue Solvent Exposure from Covalent Labeling Data with Machine Learning: A Hybrid Approach for Protein Structure Prediction.
  Hydroxyl radical protein footprinting (HRPF) coupled with mass spectrometry yields information about residue solvent exposure and protein topology. However, data from these experiments are sparse and require computational interpretation to generate useful stru…
  europepmc 2025 置信度 0.80
• AI-Driven Deep Learning Techniques in Protein Structure Prediction.
  Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein mode…
  europepmc 2024 置信度 0.80
• Protein Structure Prediction with High Degrees of Freedom in a Gate-Based Quantum Computer.
  Protein folding, which traces the protein three-dimensional (3D) structure from its amino acid sequence, is a half-a-century-old problem in biology. The function of the protein correlates with its structure, emphasizing the need to study protein folding to und…
  europepmc 2024 置信度 0.80
• Predicting Epitopes of Synthetic Binding Proteins Using Protein Structure Prediction and Protein-Protein Docking.
  Synthetic binding proteins (SBPs) engineered from privileged protein scaffolds usually have high specificity to protein target. However, epitope information of currently known SBPs is still limited, which hinders the development of protein binders with desired…
  europepmc 2025 置信度 0.80
• [Advances in the application of AlphaFold2: a protein structure prediction model].
  Protein structure prediction is an important research field in life sciences and medicine, and it is also a key application scenario of artificial intelligence in scientific research. AlphaFold2 is a protein structure prediction system developed by DeepMind ba…
  europepmc 2024 置信度 0.80
• Integrating Large-Scale Protein Structure Prediction into Human Genetics Research.
  The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense prot…
  europepmc 2024 置信度 0.80
• A Perspective on Protein Structure Prediction Using Quantum Computers.
  Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum comput…
  europepmc 2024 置信度 0.80
• Conditioned Protein Structure Prediction
  Abstract Deep learning based protein structure prediction has facilitated major breakthroughs in biological sciences. However, current methods struggle with alternative conformation prediction and offer limited integration of expert knowledge on protein dynami…
  europepmc Tengyu Xie, Zilin Song, Jing Huang 2023 置信度 0.80
• Systematic analysis of the relationship between fold-dependent flexibility and artificial intelligence protein structure prediction.
  Artificial Intelligence (AI)-based deep learning methods for predicting protein structures are reshaping knowledge development and scientific discovery. Recent large-scale application of AI models for protein structure prediction has changed perceptions about …
  europepmc 2024 置信度 0.80
• Deep learning for protein structure prediction and design-progress and applications.
  Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underli…
  europepmc 2024 置信度 0.80
• Beyond current boundaries: Integrating deep learning and AlphaFold for enhanced protein structure prediction from low-resolution cryo-EM maps.
  Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the …
  europepmc 2025 置信度 0.80
• [AlphaFold: a revolutionary AI-based protein structure prediction system and its applications in drug discovery research].
  europepmc 2025 置信度 0.80
• An easy-to-use three-dimensional protein-structure-prediction online platform "DPL3D" based on deep learning algorithms.
  The change in the three-dimensional (3D) structure of a protein can affect its own function or interaction with other protein(s), which may lead to disease(s). Gene mutations, especially missense mutations, are the main cause of changes in protein structure. D…
  europepmc 2025 置信度 0.80
• Cerebra: A Computationally Efficient Framework for Accurate Protein Structure Prediction
  Abstract Remarkable progress has been made in the field of protein structure prediction in the past years. State-of-the-art methods like AlphaFold2 and RoseTTAFold2 achieve prediction accuracy close to experimental structural determination, but at the cost of …
  europepmc Haipeng Gong, Jian Hu, Weizhe Wang 2024 置信度 0.80
• Genetic and protein structure prediction analyses identify a rare pathogenic PKD1 variant causing autosomal dominant polycystic kidney disease.
  Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common monogenic kidney disorders. The diagnosis of ADPKD requires imaging findings showing multiple kidney cysts or genetic testing, in cases where a family history is unknown. We report …
  europepmc 2025 置信度 0.80
• Recent Advances and Challenges in Protein Structure Prediction.
  Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unkn…
  europepmc 2024 置信度 0.80
• Apprehensions and emerging solutions in ML-based protein structure prediction.
  The three-dimensional structure of proteins determines their function in vital biological processes. Thus, when the structure is known, the molecular mechanism of protein function can be understood in more detail and obtained information utilized in biotechnol…
  europepmc 2024 置信度 0.80
• Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites.
  In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. Traditionally, methods like X-ray crystallography and cr…
  europepmc 2024 置信度 0.80
• Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design
  Peptide design, with the goal of identifying peptides possessing unique biological properties, stands as a crucial challenge in peptide-based drug discovery. While traditional and computational methods have made significant strides, they often encounter hurdle…
  europepmc Negin Manshour, Fei He, Duolin Wang, Dong Xu 2024 置信度 0.80
• Protein Structure Prediction in Drug Discovery.
  When the results of DeepMind's AlphaFold2 at CASP were announced in 2020, the scientific world was so amazed by how effectively it performed that "it will change everything" became the motto for this revolution [...].
  europepmc 2023 置信度 0.80
• The end of protein structure prediction: Improving prediction accuracy in chimeric proteins by windowed multiple sequence alignment
  Abstract AlphaFold2 has predicted the structures of almost every known protein. A simple means to create proteins beyond those found in nature, is by unnaturally fusing together two known proteins. Here we demonstrate that dependence on multiple sequence align…
  europepmc Sanketh Vedula, Alex Bronstein, Ailie Marx 2024 置信度 0.80
• Single-sequence protein structure prediction by integrating protein language models.
  Protein structure prediction has been greatly improved by deep learning in the past few years. However, the most successful methods rely on multiple sequence alignment (MSA) of the sequence homologs of the protein under prediction. In nature, a protein folds i…
  europepmc 2024 置信度 0.80
• A Perspective on the Prospective Use of AI in Protein Structure Prediction.
  AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimenta…
  europepmc 2024 置信度 0.80
• Making Use of Averaging Methods in MODELLER for Protein Structure Prediction.
  Recent advances in protein structure prediction, driven by AlphaFold 2 and machine learning, demonstrate proficiency in static structures but encounter challenges in capturing essential dynamic features crucial for understanding biological function. In this co…
  europepmc 2024 置信度 0.80
• MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-Training
  Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the evolutionary trajectories of protein families. The accuracy of protein structure predictions is often compromised for protein sequences that lack sufficient homologous information to const…
  europepmc 2024 置信度 0.80
• AlphaFold2 for Protein Structure Prediction: Best Practices and Critical Analyses.
  AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design, and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basi…
  europepmc Ragousandirane Radjasandirane, Alexandre G. de Brevern 2024 置信度 0.80
• Receptor- ligand interactions in plant inmate immunity revealed by AlphaFold protein structure prediction
  One of the common mechanisms to trigger plant innate immunity is recognition of pathogen avirulence gene products directly by products of major resistance ( R ) genes in a gene for gene manner. In the USA, the R genes, Pik-s, PiKh/m , and Pi-ta, Pi-39(t) , and…
  europepmc 2024 置信度 0.80
• The transformative power of transformers in protein structure prediction.
  Transformer neural networks have revolutionized structural biology with the ability to predict protein structures at unprecedented high accuracy. Here, we report the predictive modeling performance of the state-of-the-art protein structure prediction methods b…
  europepmc 2023 置信度 0.80
• Critical assessment of methods of protein structure prediction (CASP)-Round XV.
  Computing protein structure from amino acid sequence information has been a long-standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments …
  europepmc 2023 置信度 0.80
• Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?
  Understanding the conformational dynamics of proteins, such as the inward-facing (IF) and outward-facing (OF) transition observed in transporters, is vital for elucidating their functional mechanisms. Despite significant advances in protein structure predictio…
  europepmc 2024 置信度 0.80
• Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development.
  Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified …
  europepmc Xinru Qiu, H. Li, Greg Ver Steeg, Adam Godzik 2024 置信度 0.80 Computer scienceDrug discoveryDrug developmentData scienceProcess (computing)
• Comparative Analysis of Active LTR Retrotransposons in Sunflower (<i>Helianthus annuus</i> L.): From Extrachromosomal Circular DNA Detection to Protein Structure Prediction.
  Plant genomes possess numerous transposable element (TE) insertions that have occurred during evolution. Most TEs are silenced or diverged; therefore, they lose their ability to encode proteins and are transposed in the genome. Knowledge of active plant TEs an…
  europepmc 2024 置信度 0.80
• A Novel Approach for Protein Structure Prediction
  The idea of this project is to study the protein structure and sequence relationship using the hidden markov model and artificial neural network. In this context we have assumed two hidden markov models. In first model we have taken protein secondary structure…
  arxiv Saurabh Sarkar, Prateek Malhotra, Virender Guman 2012-06-15T16:31:45Z 置信度 0.78 cs.LGq-bio.BM
• AlphaFold predicts the most complex protein knot and composite protein knots
  The computer artificial intelligence system AlphaFold has recently predicted previously unknown three-dimensional structures of thousands of proteins. Focusing on the subset with high-confidence scores, we algorithmically analyze these predictions for cases wh…
  arxiv Maarten A. Brems, Robert Runkel, Todd O. Yeates, Peter Virnau 2022-07-15T11:38:45Z 置信度 0.78 q-bio.BMcond-mat.softphysics.bio-ph
• Mass Balance Approximation of Unfolding Improves Potential-Like Methods for Protein Stability Predictions
  The prediction of protein stability changes following single-point mutations plays a pivotal role in computational biology, particularly in areas like drug discovery, enzyme reengineering, and genetic disease analysis. Although deep-learning strategies have pu…
  arxiv Ivan Rossi, Guido Barducci, Tiziana Sanavia, Paola Turina 等 2025-04-09T11:53:02Z 置信度 0.78 q-bio.QMcs.LGphysics.bio-ph
• Atomic-accuracy prediction of protein loop structures through an RNA-inspired ansatz
  Consistently predicting biopolymer structure at atomic resolution from sequence alone remains a difficult problem, even for small sub-segments of large proteins. Such loop prediction challenges, which arise frequently in comparative modeling and protein design…
  arxiv Rhiju Das 2012-08-02T19:06:33Z 置信度 0.78 q-bio.BM
• One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model
  Despite the significant increase in computational power, molecular modeling of protein structure using classical all-atom approaches remains inefficient, at least for most of the protein targets in the focus of biomedical research. Perhaps the most successful …
  arxiv Sebastian Kmiecik, Andrzej Kolinski 2015-11-25T15:46:58Z 置信度 0.78 q-bio.BM
• ISLAND: In-Silico Prediction of Proteins Binding Affinity Using Sequence Descriptors
  Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein …
  arxiv Wajid Arshad Abbasi, Fahad Ul Hassan, Adiba Yaseen, Fayyaz Ul Amir Afsar Minhas 2017-11-22T07:54:31Z 置信度 0.78 q-bio.QMcs.LG
• New Approach for Prediction Pre-cancer via Detecting Mutated in Tumor Protein P53
  Tumor protein P53 is believed to be involved in over half of human cancers cases, the prediction of malignancies plays essential roles not only in advance detection for cancer, but also in discovering effective prevention and treatment of cancer, till now ther…
  arxiv Ayad Ghany Ismaeel 2013-10-08T15:43:25Z 置信度 0.78 cs.CEq-bio.OT
• Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized
  Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the predi…
  arxiv Thomas Hamelryck, Mikael Borg, Martin Paluszewski, Jonas Paulsen 等 2010-08-24T10:49:18Z 置信度 0.78 q-bio.BMcond-mat.stat-mech