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• Kuhfeldrf/hpc.cqls: HPC Computational Biology Workflow v1.0.0 - Peptide Docking and Molecular Dynamics Pipeline
  HPC Computational Biology Workflow v1.0.0 Version 1.0.0 — Initial public release SLURM-based pipeline for high-throughput peptide-protein docking and molecular dynamics simulation on the ORCA HPC cluster at Oregon State University. Overview This repository con…
  datacite Rusty Kuhfeld 2026 置信度 0.66
• Kuhfeldrf/hpc.cqls: HPC Computational Biology Workflow v1.0.0 - Peptide Docking and Molecular Dynamics Pipeline
  HPC Computational Biology Workflow v1.0.0 Version 1.0.0 — Initial public release SLURM-based pipeline for high-throughput peptide-protein docking and molecular dynamics simulation on the ORCA HPC cluster at Oregon State University. Overview This repository con…
  datacite Rusty Kuhfeld 2026 置信度 0.66
• Data from Coral Dark Gene analysis
  Results files from manuscript "Cosmopolitan Gene Families With Known Functions Are Hotspots for the Evolution of Novel Genes in Stony Corals" (Genome Biology and Evolution, Volume 18, Issue 4, April 2026, evag072, https://doi.org/10.1093/gbe/evag072). Orthogro…
  datacite Stephens, Timothy 2026 置信度 0.66
• Data from Coral Dark Gene analysis
  Results files from manuscript "Cosmopolitan Gene Families With Known Functions Are Hotspots for the Evolution of Novel Genes in Stony Corals" (Genome Biology and Evolution, Volume 18, Issue 4, April 2026, evag072, https://doi.org/10.1093/gbe/evag072). Orthogro…
  datacite Stephens, Timothy 2026 置信度 0.66
• UniDTI: A Multi-modal and Multi-scale Aware Unified Framework for Drug-Target Interactions Prediction
  Abstract: Accurate identification of drug-target interactions (DTIs) is central to modern drug discovery, yet remains challenging due to the intrinsically multi-scale and heterogeneous nature of molecular recognition. Existing computational approaches typicall…
  datacite Huang, Shaoxin, Dong, Junlin, Wu, Chenyang, Lin, Han 等 2026 置信度 0.66
• UniDTI: A Multi-modal and Multi-scale Aware Unified Framework for Drug-Target Interactions Prediction
  Abstract: Accurate identification of drug-target interactions (DTIs) is central to modern drug discovery, yet remains challenging due to the intrinsically multi-scale and heterogeneous nature of molecular recognition. Existing computational approaches typicall…
  datacite Huang, Shaoxin, Dong, Junlin, Wu, Chenyang, Lin, Han 等 2026 置信度 0.66
• Attention Is All You Needed, and What It Needed: A Narrative Review of the Transformer Architecture from Seq2Seq to Foundation Models
  The transformer is the deep learning architecture of its era: the self-attention mechanism introduced in 2017 now underlies language models, vision systems, speech, and protein structure prediction. This article presents a narrative review of the architecture'…
  datacite Revista, Zen, IA, 10 2026 置信度 0.66 transformerself-attentionseq2seqLSTMBERT
• Attention Is All You Needed, and What It Needed: A Narrative Review of the Transformer Architecture from Seq2Seq to Foundation Models
  The transformer is the deep learning architecture of its era: the self-attention mechanism introduced in 2017 now underlies language models, vision systems, speech, and protein structure prediction. This article presents a narrative review of the architecture'…
  datacite Revista, Zen, IA, 10 2026 置信度 0.66 transformerself-attentionseq2seqLSTMBERT
• RiboGRAM Archives
  RiboGRAM Archives This release contains the source code, model checkpoints, benchmark datasets, and example outputs for the study RiboGRAM learns multi-scale non-coding RNA grammar for structural and regulatory inference. RiboGRAM is a self-supervised RNA repr…
  datacite Wang, Hui 2026 置信度 0.66
• RiboGRAM Archives
  RiboGRAM Archives This release contains the source code, model checkpoints, benchmark datasets, and example outputs for the study RiboGRAM learns multi-scale non-coding RNA grammar for structural and regulatory inference. RiboGRAM is a self-supervised RNA repr…
  datacite Wang, Hui 2026 置信度 0.66
• Pangenome-scale annotation of mycobacteriophages for dissecting phage–host interactions based on a Sequence Clustering and Structural Homology analysis strategy
  This record contains data related to the article "Pangenome-scale annotation of mycobacteriophages for dissecting phage–host interactions based on a Sequence Clustering and Structural Homology analysis strategy." Supplemental Table 1: Mycobacteriophages used i…
  datacite Guo, Xiao, He, Zheng-Guo 2026 置信度 0.66
• Pangenome-scale annotation of mycobacteriophages for dissecting phage–host interactions based on a Sequence Clustering and Structural Homology analysis strategy
  This record contains data related to the article "Pangenome-scale annotation of mycobacteriophages for dissecting phage–host interactions based on a Sequence Clustering and Structural Homology analysis strategy." Supplemental Table 1: Mycobacteriophages used i…
  datacite Guo, Xiao, He, Zheng-Guo 2026 置信度 0.66
• Molecular machine learning past the chemical Kármán line
  This repository contains all data and code required to replicate the figures from my PhD thesis. The original, raw data for each chapter can be found in their respective published paper.Thesis Summary:Molecular machine learning has the potential to revolutioni…
  datacite van Tilborg, Derek 2025 置信度 0.66
• Molecular machine learning past the chemical Kármán line
  This repository contains all data and code required to replicate the figures from my PhD thesis. The original, raw data for each chapter can be found in their respective published paper.Thesis Summary:Molecular machine learning has the potential to revolutioni…
  datacite van Tilborg, Derek 2025 置信度 0.66
• The Geometric Taxonomy of Computation: Enumeration, Recursive Folding, and the Boundary of the Mark 1 Attractor
  The Geometric Taxonomy of Computation: Enumeration, Recursive Folding, and the Boundary of the Mark 1 Attractor 1. Introduction: The Epistemological Fracture and the Ontological Inversion The contemporary landscape of theoretical physics, computational ontolog…
  datacite Kulik, Dean 2026 置信度 0.66
• The Geometric Taxonomy of Computation: Enumeration, Recursive Folding, and the Boundary of the Mark 1 Attractor
  The Geometric Taxonomy of Computation: Enumeration, Recursive Folding, and the Boundary of the Mark 1 Attractor 1. Introduction: The Epistemological Fracture and the Ontological Inversion The contemporary landscape of theoretical physics, computational ontolog…
  datacite Kulik, Dean 2026 置信度 0.66
• ProteinFP: A Multi-Module Pipeline for Protein Function Prediction and Drug Discovery
  ProteinFP is a comprehensive, modular Python pipeline for automated protein function prediction and computational drug candidate design. Given a UniProt accession, it integrates 21+ analysis modules spanning structural bioinformatics, protein language models, …
  datacite Naghi, Felix 2026 置信度 0.66 Protein Function PredictionBioinformaticsDrug discoveryStructural BioinformaticsGene Ontology
• ProteinFP: A Multi-Module Pipeline for Protein Function Prediction and Drug Discovery
  ProteinFP is a comprehensive, modular Python pipeline for automated protein function prediction and computational drug candidate design. Given a UniProt accession, it integrates 21+ analysis modules spanning structural bioinformatics, protein language models, …
  datacite Naghi, Felix 2026 置信度 0.66 Protein Function PredictionBioinformaticsDrug discoveryStructural BioinformaticsGene Ontology
• Ly Algebra III-B: Physical Realizations — A Comprehensive Summary
  Ly Algebra III-B: Physical Realizations — A Comprehensive Summary Overview This document is Volume III-B of the Ly Algebra corpus, serving as the physical realizations companion to the cryptographic Volume III-A. Authored by Chloe Jane Tully (August 2026), it …
  datacite Tully, Chloe 2026 置信度 0.66
• Ly Algebra III-B: Physical Realizations — A Comprehensive Summary
  Ly Algebra III-B: Physical Realizations — A Comprehensive Summary Overview This document is Volume III-B of the Ly Algebra corpus, serving as the physical realizations companion to the cryptographic Volume III-A. Authored by Chloe Jane Tully (August 2026), it …
  datacite Tully, Chloe 2026 置信度 0.66
• BAN-SDBPred: Improving Single-stranded and Double-stranded DNA-binding Protein Prediction using Attention Network with Bilinear Convolution and Adaptive Sampling Strategy
  --- # BAN-SDBPred: Improving Single-stranded and Double-stranded DNA-binding Protein Prediction using Attention Network with Bilinear Convolution and Adaptive Sampling Strategy This repository contains the **PyTorch implementation of BAN-SDBPred**, a novel mul…
  datacite Arshad, Kamran 2026 置信度 0.66
• BAN-SDBPred: Improving Single-stranded and Double-stranded DNA-binding Protein Prediction using Attention Network with Bilinear Convolution and Adaptive Sampling Strategy
  --- # BAN-SDBPred: Improving Single-stranded and Double-stranded DNA-binding Protein Prediction using Attention Network with Bilinear Convolution and Adaptive Sampling Strategy This repository contains the **PyTorch implementation of BAN-SDBPred**, a novel mul…
  datacite Arshad, Kamran 2026 置信度 0.66
• The Folded Question: A Narrative Review of Protein Folding, from Levinthal's Paradox to AlphaFold
  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 hyp…
  datacite Revista, Zen, CHEMISTRY, 10 2026 置信度 0.66 protein foldingLevinthal paradoxAnfinsen hypothesismolecular chaperonesintrinsically disordered proteins
• The Folded Question: A Narrative Review of Protein Folding, from Levinthal's Paradox to AlphaFold
  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 hyp…
  datacite Revista, Zen, CHEMISTRY, 10 2026 置信度 0.66 protein foldingLevinthal paradoxAnfinsen hypothesismolecular chaperonesintrinsically disordered proteins
• GateSolv: homology-controlled multimodal stacking for protein solubility prediction under cross-source evaluation
  GateSolv is a homology-controlled multimodal stacking toolkit for protein-solubility prediction. The frozen deployable model combines 66 sequence/structure descriptors, a full-sequence pooled ESM-2 representation and a residue-summary ESM-2 representation thro…
  datacite Liu, Yugeng 2026 置信度 0.66 protein solubilityprotein language modelESM-2protein structuremultimodal learning
• GateSolv: homology-controlled multimodal stacking for protein solubility prediction under cross-source evaluation
  GateSolv is a homology-controlled multimodal stacking toolkit for protein-solubility prediction. The frozen deployable model combines 66 sequence/structure descriptors, a full-sequence pooled ESM-2 representation and a residue-summary ESM-2 representation thro…
  datacite Liu, Yugeng 2026 置信度 0.66 protein solubilityprotein language modelESM-2protein structuremultimodal learning
• NitroXAI: An Interpretable Framework for Residue-Level S-Nitrosylation Site Prediction (v0.1.0)
  NitroXAI is an interpretable framework for residue-level S-nitrosylation (SNO) site prediction that integrates lightweight deep learning, explainable artificial intelligence (XAI), and structure-aware visualization to identify candidate S-nitrosylated cysteine…
  datacite Soumyadeep Ray 2026 置信度 0.66
• NitroXAI: An Interpretable Framework for Residue-Level S-Nitrosylation Site Prediction (v0.1.0)
  NitroXAI is an interpretable framework for residue-level S-nitrosylation (SNO) site prediction that integrates lightweight deep learning, explainable artificial intelligence (XAI), and structure-aware visualization to identify candidate S-nitrosylated cysteine…
  datacite Soumyadeep Ray 2026 置信度 0.66
• ProtTransVAE: Machine Learning Assisted Prediction of Protein Transition Pathways
  This archive contains the source code, trained models, input structures, and processed data for ProtTransVAE, a machine-learning method for predicting the conformational transition pathways of proteins. It accompanies the manuscript "ProtTransVAE: Machine Lear…
  datacite Tajkhorshid, Emad, Park, Hyun, Li, Yifei, Kelich, Payam 2026 置信度 0.66
• ProtTransVAE: Machine Learning Assisted Prediction of Protein Transition Pathways
  This archive contains the source code, trained models, input structures, and processed data for ProtTransVAE, a machine-learning method for predicting the conformational transition pathways of proteins. It accompanies the manuscript "ProtTransVAE: Machine Lear…
  datacite Tajkhorshid, Emad, Park, Hyun, Li, Yifei, Kelich, Payam 2026 置信度 0.66
• Systematic Prioritization of Candidate Genes in Camptothecin Biosynthesis Using Multi-omics and Deep Learning
  Camptothecin (CPT), a plant-derived monoterpene indole alkaloid first identified in Camptotheca acuminata, is a drug precursor widely used for cancer chemotherapeutics. However, the full set of genes responsible for CPT biosynthesis remains unclear, hindering …
  datacite Wang, Shenqi, Wu, Xing 2026 置信度 0.66
• Systematic Prioritization of Candidate Genes in Camptothecin Biosynthesis Using Multi-omics and Deep Learning
  Camptothecin (CPT), a plant-derived monoterpene indole alkaloid first identified in Camptotheca acuminata, is a drug precursor widely used for cancer chemotherapeutics. However, the full set of genes responsible for CPT biosynthesis remains unclear, hindering …
  datacite Wang, Shenqi, Wu, Xing 2026 置信度 0.66
• A Transceiver Interpretation of Post-Mortem Cortical Plasticity: Information Dynamics Explains Ex-Vivo Associative Learning in Human Brain Explants
  In the framework of Information Dynamics, the brain is modeled not as a classical information processor, but as a transceiver coupled to a non-temporal virtual information space. The virtual space is assumed to have a geometric structure (Gaussian curvature \(…
  datacite Huang, Kai, Liu, Hongkui, Huang, Ziwei 2026 置信度 0.66 information dynamicspost-mortem plasticityconsciousness continuum
• A Transceiver Interpretation of Post-Mortem Cortical Plasticity: Information Dynamics Explains Ex-Vivo Associative Learning in Human Brain Explants
  In the framework of Information Dynamics, the brain is modeled not as a classical information processor, but as a transceiver coupled to a non-temporal virtual information space. The virtual space is assumed to have a geometric structure (Gaussian curvature \(…
  datacite Huang, Kai, Liu, Hongkui, Huang, Ziwei 2026 置信度 0.66 information dynamicspost-mortem plasticityconsciousness continuum
• Plyknot v1.0 — scientific measurement convergence infrastructure
  Plyknot is an infrastructure for scientific measurement convergence - tracking where independent measurements agree, where they disagree, and the shared assumptions behind apparent consensus. This v1.0 release comprises 29 Python modules with 216 automated tes…
  datacite Ehnemark, Klas 2026 置信度 0.66 measurement convergencescientific metrologyAI reliabilityAI evaluationecho chamber detection
• Plyknot v1.0 — scientific measurement convergence infrastructure
  Plyknot is an infrastructure for scientific measurement convergence - tracking where independent measurements agree, where they disagree, and the shared assumptions behind apparent consensus. This v1.0 release comprises 29 Python modules with 216 automated tes…
  datacite Ehnemark, Klas 2026 置信度 0.66 measurement convergencescientific metrologyAI reliabilityAI evaluationecho chamber detection
• Progressive feature enrichment for ligand binding-site prediction: from tabular chemistry to protein language models
  An early step in structure-based drug discovery is the identification of ligand-binding-sites on protein structures. Existing approaches range from geometric and energetic heuristics to supervised learning pipelines on handcrafted structural descriptors and le…
  datacite Hageneder, Maximilian 2026 置信度 0.66
• Progressive feature enrichment for ligand binding-site prediction: from tabular chemistry to protein language models
  An early step in structure-based drug discovery is the identification of ligand-binding-sites on protein structures. Existing approaches range from geometric and energetic heuristics to supervised learning pipelines on handcrafted structural descriptors and le…
  datacite Hageneder, Maximilian 2026 置信度 0.66
• gKO-BCE / KO-BCE: structure-aware conformational B-cell epitope prediction (code + weights)
  Reproducible code and trained weights for the paper "gKO-BCE: A Novel Deep Learning Approach to Predict B-Cell Epitopes, Even Within Glycosylated Antigens." Includes the full preprocessing, feature-extraction, training and inference pipeline, the fitted ESM PC…
  datacite Odeyemi, Jethro, Kashyap, Monika, Wilson, Heather L., Khatooni, Zahed 2026 置信度 0.66 B-cell epitopeepitope predictionglycosylationtransformerprotein structure
• OpenBind Enteroviral 2A Protease Benchmark Data
  This record contains the prepared datasets and computational benchmarking outputs associated with the OpenBind enteroviral 2A protease structure–affinity dataset. The accompanying preprint describing the dataset, benchmark construction, and results is availabl…
  datacite Khan, Omeir, Nelen, Jochem, Adams, Etowah, AlQuraishi, Mohammed 等 2026 置信度 0.66 OpenBindProtein-Ligand ComplexesPDBDockingCofolding
• OpenBind Enteroviral 2A Protease Benchmark Data
  This record contains the prepared datasets and computational benchmarking outputs associated with the OpenBind enteroviral 2A protease structure–affinity dataset. The accompanying preprint describing the dataset, benchmark construction, and results is availabl…
  datacite Khan, Omeir, Nelen, Jochem, Adams, Etowah, AlQuraishi, Mohammed 等 2026 置信度 0.66 OpenBindProtein-Ligand ComplexesPDBDockingCofolding
• Graph Neural Networks for Protein Structure Prediction
  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 app…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Graph Neural Networks for Protein Structure Prediction
  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 app…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Title: Bio-Information Quantum Algorithm
  This paper explores the potential of quantum algorithms to revolutionize bioinformatics, particularly in genomic analysis, protein structure prediction, and drug discovery. The core concept centers on leveraging the inherent information richness within biologi…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Title: Bio-Information Quantum Algorithm
  This paper explores the potential of quantum algorithms to revolutionize bioinformatics, particularly in genomic analysis, protein structure prediction, and drug discovery. The core concept centers on leveraging the inherent information richness within biologi…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Topology-Driven Computational Framework
  This paper introduces a novel computational framework, the Topology-Driven Computational Framework (TDCF), designed to efficiently model and simulate complex biological systems, particularly focusing on gene expression and protein structure prediction. The cor…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Topology-Driven Computational Framework
  This paper introduces a novel computational framework, the Topology-Driven Computational Framework (TDCF), designed to efficiently model and simulate complex biological systems, particularly focusing on gene expression and protein structure prediction. The cor…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Quantum Bioinformatics: Protein Structure Prediction Based on Quantum Entanglement
  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.…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Quantum Bioinformatics: Protein Structure Prediction Based on Quantum Entanglement
  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.…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Quantum Biology-Inspired Bioinformatics Algorithms
  This paper explores the application of principles from quantum biology to design novel bioinformatics algorithms. Traditional bioinformatics relies heavily on classical computational models, often neglecting the potential influence of quantum effects observed …
  datacite Zhang, Jincheng 2026 置信度 0.66
• Quantum Biology-Inspired Bioinformatics Algorithms
  This paper explores the application of principles from quantum biology to design novel bioinformatics algorithms. Traditional bioinformatics relies heavily on classical computational models, often neglecting the potential influence of quantum effects observed …
  datacite Zhang, Jincheng 2026 置信度 0.66
• Dynamic residue graphs with hybrid learning and self-distillation for structure-free protein druggability prediction
  datacite Ding, Wenze, Chen, Hao 2026 置信度 0.66
• Dynamic residue graphs with hybrid learning and self-distillation for structure-free protein druggability prediction
  datacite Ding, Wenze, Chen, Hao 2026 置信度 0.66
• Data and analysis scripts for: Domain misannotation in BAG3: AlphaFold3 and molecular dynamics show the second WW domain is disordered
  This dataset contains the AlphaFold3 structural models, molecular dynamics analysis outputs, and analysis scripts supporting the manuscript "Domain misannotation in BAG3: AlphaFold3 and molecular dynamics show the second WW domain is disordered," submitted to …
  datacite Ika, Mbateudi Danjuma, Akberova, Natalia Ivanovna 2026 置信度 0.66 BAG3Molecular DynamicsProtein Structure PredictionStructural BiologyProtein Folding
• Data and analysis scripts for: Domain misannotation in BAG3: AlphaFold3 and molecular dynamics show the second WW domain is disordered
  This dataset contains the AlphaFold3 structural models, molecular dynamics analysis outputs, and analysis scripts supporting the manuscript "Domain misannotation in BAG3: AlphaFold3 and molecular dynamics show the second WW domain is disordered," submitted to …
  datacite Ika, Mbateudi Danjuma, Akberova, Natalia Ivanovna 2026 置信度 0.66 BAG3Molecular DynamicsProtein Structure PredictionStructural BiologyProtein Folding
• Computational insights into mercuric reductase from <i>Pseudomonas fluorescens</i>: a bioinformatic and molecular dynamics approach for mercury detoxification
  Mercury (Hg) pollution poses significant threats to human health and ecosystems worldwide due to its persistence, bioaccumulation, and toxic effects. This study focuses on mercuric reductase from Pseudomonas fluorescens (UniProt ID Q51772), a key enzyme involv…
  datacite Anila Hoda, Valbona Kolaneci, Mirela Lika (Çekani), Xhelil Koleci 2024 置信度 0.66 BiophysicsBiochemistryGeneticsBiotechnologyChemical Sciences not elsewhere classified
• Computational insights into mercuric reductase from <i>Pseudomonas fluorescens</i>: a bioinformatic and molecular dynamics approach for mercury detoxification
  Mercury (Hg) pollution poses significant threats to human health and ecosystems worldwide due to its persistence, bioaccumulation, and toxic effects. This study focuses on mercuric reductase from Pseudomonas fluorescens (UniProt ID Q51772), a key enzyme involv…
  datacite Anila Hoda, Valbona Kolaneci, Mirela Lika (Çekani, Xhelil Koleci 2025 置信度 0.66 BiophysicsBiochemistryGeneticsBiotechnologyChemical Sciences not elsewhere classified
• Assessment of AI-based Protein Structure Prediction for the NLRP3 Target
  The recent success of AlphaFold and RoseTTAFold has demonstrated the values of AI methods in predicting highly accurate protein structures. Despite the advances, their roles in the context of small-molecule drug discovery need to be thoroughly explored. In thi…
  preprints 2021 置信度 0.74
• Protein structure prediction using AI and quantum computers
  This work seeks to combine the combined advantage of leveraging these emerging areas of Artificial Intelligence and quantum computing in applying it to solve the specific biological problem of protein structure prediction using Quantum Machine Learning algorit…
  preprints 2021 置信度 0.74
• Automated protein-protein structure prediction of the T cell receptor-peptide major histocompatibility complex
  ABSTRACT T Cell Receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is a crucial component of the adaptive immune response. The identification of therapeutically relevant TCR-pMHC pairs is a significant bottleneck in the implementat…
  preprints 2022 置信度 0.74
• Protein Structure Prediction Using Threading
  This chapter discusses the protocol for computational protein structure prediction by protein threading. First, we present a general procedure and summarize some typical ideas for each step of protein threading. Then, we describe the design and implementation …
  crossref Jinbo Xu, Feng Jiao, Libo Yu 2008-01-10T11:13:01Z 置信度 0.70
• Review for "Protein structure search to support the development of protein structure prediction methods"
  crossref 2021-01-18T20:18:22Z 置信度 0.70
• Review for "Protein structure search to support the development of protein structure prediction methods"
  crossref 2021-01-18T20:18:22Z 置信度 0.70
• Review for "Protein structure search to support the development of protein structure prediction methods"
  crossref 2021-01-18T20:18:22Z 置信度 0.70
• Review for "Protein structure search to support the development of protein structure prediction methods"
  crossref 2021-01-18T20:18:22Z 置信度 0.70
• The Assessment of Methods for Protein Structure Prediction
  Methods for protein structure prediction are flourishing and becoming widely available to both experimentalists and computational biologists. But, how good are they? What is their range of applicability and how can we know which method is better suited for the…
  crossref Anna Tramontano, Domenico Cozzetto, Alejandro Giorgetti, Domenico Raimondo 2008-01-10T06:13:01Z 置信度 0.70
• Protein Prediction - Protein Prediction - Introduction to Proteins
  crossref Marco Punta, Burkhard Rost 2010-04-28T09:08:22Z 置信度 0.70
• Protein structure prediction
  crossref 2018-09-10T13:07:31Z 置信度 0.70
• Scoring Functions for De Novo Protein Structure Prediction Revisited
  De novo protein structure prediction methods attempt to predict tertiary structures from sequences based on general principles that govern protein folding energetics and/or statistical tendencies of conformational features that native structures acquire, witho…
  crossref Shing-Chung Ngan, Ling-Hong Hung, Tianyun Liu, Ram Samudrala 2008-01-10T06:13:01Z 置信度 0.70
• Decision letter for "Protein structure search to support the development of protein structure prediction methods"
  crossref 2021-01-18T20:18:22Z 置信度 0.70
• Decision letter for "Protein structure search to support the development of protein structure prediction methods"
  crossref 2021-01-18T20:18:22Z 置信度 0.70
• Protein Structure Prediction
  This chapter concentrates on protein structure prediction programs. It provides strong foundational knowledge of protein structures and the Protein Data Bank. The chapter reviews how secondary and tertiary structures of proteins are experimentally determined a…
  crossref Jamil Momand, Alison Mccurdy, Silvia Heubach, Nancy Warter-Perez 2023-09-15T07:36:48Z 置信度 0.70
• Hidden Markov Models for Prediction of Protein Features
  Hidden Markov Models (HMMs) are an extremely versatile statistical representation that can be used to model any set of one-dimensional discrete symbol data. HMMs can model protein sequences in many ways, depending on what features of the protein are represente…
  crossref Christopher Bystroff, Anders Krogh 2008-01-10T11:13:01Z 置信度 0.70
• Author response for "Protein structure search to support the development of protein structure prediction methods"
  crossref Ronald Ayoub, Yugyung Lee 2021-01-18T20:18:22Z 置信度 0.70
• Improved cryoEM-Guided Iterative Molecular DynamicsRosetta Protein Structure Refinement Protocol for High Precision Protein Structure Prediction
  Many excellent methods exist that\nincorporate cryo-electron microscopy\n(cryoEM) data to constrain computational protein structure prediction\nand refinement. Previously, it was shown that iteration of two such\northogonal sampling and scoring methods – Roset…
  crossref 2026-08-23T14:06:24Z 置信度 0.70
• Predictive Models of Protein-Active Sites
  For many proteins, we do not have enough information to even attempt to predict the three-dimensional (3D) structure. Many drug receptors, e.g., are membrane-bound proteins for which there is not a crystal structure of any homologous protein available. But dru…
  crossref D. Eric Walters 2003-11-15T01:38:03Z 置信度 0.70
• Protein structure prediction using bidirectional neural networks
  This thesis is focused on protein secondary structure (PSS) prediction which is one of the most important problems in bioinformatics. Most of existing prediction methods either use a sliding fixed-sized window centered on the residue of interest or train on si…
  crossref Jin Miao Chen 2019-10-03T05:55:02Z 置信度 0.70
• Third Generation Prediction of Secondary Structures
  The sequence-structure gap is rapidly increasing. Currently, databases for protein sequences (e.g., SWISS-PROT []) are expanding rapidly, largely due to large-scale genome sequencing projects: at the beginning of 1998, we know already all sequences for a dozen…
  crossref Burkhard Rost, Chris Sander 2003-11-15T01:38:03Z 置信度 0.70
• Review for "Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14"
  crossref 2021-09-10T07:23:05Z 置信度 0.70
• Review for "Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14"
  crossref 2021-09-10T07:23:05Z 置信度 0.70
• Protein structure prediction with AlphaFold-Multimer v1
  This protocol describes the protein structure prediction with AlphaFold-Multimer.
  crossref Elias Adriaenssens 2024-08-29T06:40:17Z 置信度 0.70
• Scatter search for protein structure prediction. (c2008)
  crossref Christine Houry Kehyayan 2017-09-19T09:16:58Z 置信度 0.70
• Structure prediction of alternative protein conformations
  Abstract Proteins are dynamic molecules whose movements result in different conformations with different functions. Neural networks such as AlphaFold2 can predict the structure of single-chain proteins in the conformations most likely to exist in the PDB. Howe…
  crossref Patrick Bryant 2023-09-25T12:10:19Z 置信度 0.70
• Figure 4: Decision-making chart for protein structure prediction method.
  crossref 2023-12-08T04:14:22Z 置信度 0.70
• Review for "Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14"
  crossref 2021-09-10T07:23:05Z 置信度 0.70
• Review for "Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14"
  crossref 2021-09-10T07:23:05Z 置信度 0.70
• Review for "High‐accuracy protein structure prediction in CASP14"
  crossref 2021-07-05T09:40:55Z 置信度 0.70
• Review for "Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14"
  crossref 2021-09-10T07:23:05Z 置信度 0.70
• Prediction of Protein Structural Classes from Amino Acid Compositions
  crossref Peter Y. Chou 2011-10-01T04:40:23Z 置信度 0.70
• Computational studies for protein structure and function prediction
  Υπολογιστικές μελέτες πρόγνωσης δομής και λειτουργίας πρωτεϊνών. Οι μεμβρανικές πρωτεΐνες συναντώνται ευρέως σε όλα τα ζωντανά κύτταρα όπου διαδραματίζουν καθοριστικό ρόλο στα μεταβολικά μονοπάτια (Cymer et al., 2015). Με βάση την φύση των αλληλεπιδράσεων μεμβ…
  crossref Άχμεντ Ρούμια 2021-03-29T13:55:27Z 置信度 0.70
• Review for "High‐accuracy protein structure prediction in CASP14"
  crossref 2021-07-05T09:40:55Z 置信度 0.70
• Review for "Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14"
  crossref 2021-09-10T07:23:05Z 置信度 0.70
• Ab Initio Protein Structure Prediction
  crossref Jooyoung Lee, Sitao Wu, Yang Zhang 2008-12-10T00:12:02Z 置信度 0.70
• DOCK/PIERR: Web Server for Structure Prediction of Protein–Protein Complexes
  In protein docking we aim to find the structure of the complex formed when two proteins interact. Protein-protein interactions are crucial for cell function. Here we discuss the usage of DOCK/PIERR. In DOCK/PIERR, a uniformly discrete sampling of orientations …
  crossref Shruthi Viswanath, D. V. S. Ravikant, Ron Elber 2014-02-26T12:52:04Z 置信度 0.70
• The MULTICOM Protein Structure Prediction Server Empowered by Deep Learning and Contact Distance Prediction
  Prediction of the three-dimensional (3D) structure of a protein from its sequence is important for studying its biological function. With the advancement in deep learning contact distance prediction and residue-residue coevolutionary analysis, significant prog…
  crossref Jie Hou, Tianqi Wu, Zhiye Guo, Farhan Quadir 等 2020-07-03T15:06:28Z 置信度 0.70
• Protein Structure Prediction as a Systems Problem
  crossref Dong Xu, Ying Xu 2010-05-01T07:58:37Z 置信度 0.70
• Utilization of Protein Tertiary Contacts to Improve Protein Structure Prediction Using Sequence Homology
  The structure of a protein ultimately determines its function; therefore, knowledge of three-dimensional structure is essential for understanding its function and mechanism of action. The two most common methods for determining protein structure are x-ray crys…
  crossref Trung Thanh Nguyen 2019-05-03T17:20:21Z 置信度 0.70
• Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction
  Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have resulted in considerable efforts in the d…
  crossref Elijah MacCarthy, Derrick Perry, Dukka B. KC 2019-04-04T01:37:05Z 置信度 0.70
• Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X
  Accurate prediction of protein secondary structure and other one-dimensional structure features is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. SPINE-X is a software package to predict secondary stru…
  crossref Eshel Faraggi, Andrzej Kloczkowski 2016-10-27T08:21:44Z 置信度 0.70
• Protein Contact Map Prediction
  crossref Xin Yuan, Christopher Bystroff 2007-08-24T08:51:05Z 置信度 0.70