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• RaptorX-Single: single-sequence protein structure prediction by integrating protein language models
  Abstract 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…
  europepmc 2023 置信度 0.80
• A Comparative Study of Protein Structure Prediction Tools for Challenging Targets: Snake Venom Toxins
  Abstract Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several pro…
  europepmc 2023 置信度 0.80
• EvoBind: peptide binder design with inverse folding and protein structure prediction
  Abstract Protein design requires structural scaffolds generated with human guidance and the performance across known binding domains is not known. Here, we design peptide binders by combining known structural information searched with Foldseek, the protein des…
  europepmc 2023 置信度 0.80
• Protein Structure Prediction with Mass Spectrometry Data.
  Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are…
  europepmc 2022 置信度 0.80
• Tightening the (neural) net for protein structure prediction.
  europepmc 2022 置信度 0.80
• Single-sequence protein structure prediction using a language model and deep learning.
  AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) pr…
  europepmc 2022 置信度 0.80
• Ab initio protein structure prediction – the necessary presence of external force field as it is delivered by Hsp40 chaperone
  Abstract Background The aqueous environment directs the protein folding process towards the generation of micelle-type structures, which results in the exposure of hydrophilic residues on the surface (polarity) and the concentration of hydrophobic residues in …
  europepmc 2023 置信度 0.80
• Microproteins: a 3D protein structure prediction analysis.
  Microproteins are a novel and expanding group of small proteins encoded by less than 100-150 codons that are translated from small open reading frames (smORFs). It has been shown that smORFs and their corresponding microproteins make up a sizable fraction of t…
  europepmc 2022 置信度 0.80
• Constraint Guided Beta-Sheet Refinement for Protein Structure Prediction.
  Protein structure prediction (PSP) is a crucial issue in Bioinformatics. PSP has its important use in many vital research areas that include drug discovery. One of the important intermediate steps in PSP is predicting a protein's beta-sheet structures. Because…
  europepmc 2022 置信度 0.80
• Improving protein structure prediction with extended sequence similarity searches and deep-learning-based refinement in CASP15.
  The human predictor team PEZYFoldings got first place with the assessor's formulae (3rd place with Global Distance Test Total Score [GDT-TS]) in the single-domain category and 10th place in the multimer category in Critical Assessment of Structure Prediction 1…
  europepmc 2023 置信度 0.80
• The Protein Structure Prediction Revolution and Its Implications for Medicine: 2023 Albert Lasker Basic Medical Research Award.
  In this Viewpoint, 2023 Lasker award winners John Jumper and Demis Hassabis describe their invention, the artificial intelligence–based system AlphaFold, which is able to predict protein structure with great accuracy.
  europepmc 2023 置信度 0.80
• Structural and Functional Characterization of New Lipid Transfer Proteins with Chitin-Binding Properties: Insights from Protein Structure Prediction, Molecular Docking, and Antifungal Activity.
  Faced with the emergence of multiresistant microorganisms that affect human health, microbial agents have become a serious global threat, affecting human health and plant crops. Antimicrobial peptides have attracted significant attention in research for the de…
  europepmc 2024 置信度 0.80
• DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function.
  Motivation Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of…
  europepmc 2023 置信度 0.80
• Assessment of AI-Based Protein Structure Prediction for the NLRP3 Target.
  The recent successes of AlphaFold and RoseTTAFold have demonstrated the value of AI methods in highly accurate protein structure prediction. Despite these advances, the role of these methods in the context of small-molecule drug discovery still needs to be tho…
  europepmc 2022 置信度 0.80
• Method of the Year: protein structure prediction.
  europepmc 2022 置信度 0.80
• Peptide-binding specificity prediction using fine-tuned protein structure prediction networks.
  Peptide-binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in pa…
  europepmc 2023 置信度 0.80
• Protein Structure Prediction for Disease-Related Insertions/Deletions in Presenilin 1 Gene.
  More than 450 mutations, some of which have unknown toxicity, have been reported in the presenilin 1 gene, which is the most common cause of Alzheimer's disease (AD) with an early onset. PSEN1 mutations are thought to be responsible for approximately 80% of ca…
  europepmc 2023 置信度 0.80
• EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation
  Designing proteins to achieve specific functions often requires in silico modeling of their properties at high throughput scale and can significantly benefit from fast and accurate protein structure prediction. We introduce EquiFold, a new end-to-end different…
  europepmc 2022 置信度 0.80
• Solution of the protein structure prediction problem at last: crucial innovations and next frontiers.
  The protein structure prediction problem is solved, at last, thanks in large part to the use of artificial intelligence. The structures predicted by AlphaFold and RoseTTAFold are becoming the requisite starting point for many protein scientists. New frontiers,…
  europepmc 2022 置信度 0.80
• Sequence similarity network and protein structure prediction offer insights into the evolution of microbial pathways for ferrous iron oxidation.
  Importance Microbial Fe(II) oxidation is a crucial process that harnesses and converts the energy available in Fe, contributing significantly to global element cycling. However, there are still many aspects of this process that remain unexplored. In this study…
  europepmc 2023 置信度 0.80
• Protein-structure prediction revolutionized.
  europepmc 2021 置信度 0.80
• Correction to: DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function.
  europepmc 2023 置信度 0.80
• Outcome of Crash Course Training on Protein Structure Prediction with Artificial Intelligence
  Protein structure predictions have broad impact on several science disciplines such as biology, bioengineering, and medical science. AlphaFold2[1] and RoseTTAFold[2] are the current state-of-the-art AI methods to predict the structures of proteins with an accu…
  europepmc 2022 置信度 0.80
• Method of the Year 2021: Protein structure prediction.
  europepmc 2022 置信度 0.80
• Mapping synthetic binding proteins epitopes on diverse protein targets by protein structure prediction and protein-protein docking.
  Synthetic binding proteins (SBPs) are a class of artificial proteins engineered from privileged protein scaffolds, which can form highly specific molecular recognition interfaces with a variety of targets. Due to the characteristics of small size, high stabili…
  europepmc 2023 置信度 0.80
• Pareto Dominance Archive and Coordinated Selection Strategy-Based Many-Objective Optimizer for Protein Structure Prediction.
  Protein structure prediction (PSP) is predicting the three-dimensional of protein from its amino acid sequence only based on the information hidden in the protein sequence. One of the efficient tools to describe this information is protein energy functions. De…
  europepmc 2023 置信度 0.80
• Different methods, techniques and their limitations in protein structure prediction: A review.
  Because of the increase in different types of diseases in human habitats, demands for designing various types of drugs are also increasing. Protein and its structure play a very important role in drug design. Therefore researchers from different areas like mat…
  europepmc 2022 置信度 0.80
• The breakthrough in protein structure prediction.
  Proteins are the essential agents of all living systems. Even though they are synthesized as linear chains of amino acids, they must assume specific three-dimensional structures in order to manifest their biological activity. These structures are fully specifi…
  europepmc 2021 置信度 0.80
• Single-sequence protein structure prediction using supervised transformer protein language models.
  Significant progress has been made in protein structure prediction in recent years. However, it remains challenging for AlphaFold2 and other deep learning-based methods to predict protein structure with single-sequence input. Here we introduce trRosettaX-Singl…
  europepmc 2022 置信度 0.80
• Improving Protein Structure Prediction with Extended Sequence Similarity Searches and Deep-Learning-Based Refinement in CASP15
  The human predictor team PEZYFoldings got third place with GDT-TS (First place with the Assessor’s formulae) in the single-domain category and tenth place in the multimer category in CASP15. In this paper, I describe the exact method used by PEZYFoldings in co…
  europepmc 2023 置信度 0.80
• Research on protein structure prediction and folding based on novel remote homologs recognition
  ABSTRACT Recognition of remote homologous structures is a necessary module in AlphaFold2 and is also essential for the exploration of protein folding pathways. Here, we developed a new method, PAthreader, which identifies remote homologous structures based on …
  europepmc 2022 置信度 0.80
• Applications of AlphaFold beyond Protein Structure Prediction
  Predicting structures accurately for natural protein sequences by DeepMind’s AlphaFold is certainly one of the greatest breakthroughs in biology in the twenty-first century. For designed or engineered sequences, which can be unstable, predicting the stabilitie…
  europepmc 2021 置信度 0.80
• Protein structure prediction improves the quality of amino-acid sequence alignment.
  The basic operation in analysis of protein evolution is alignment: the specification of residue-residue correspondences. A structural alignment is a specification of residue-residue correspondences based on the atomic positions in the structures of two or more…
  europepmc 2022 置信度 0.80
• Protein structure prediction based on particle swarm optimization and tabu search strategy.
  Background The stability of protein sequence structure plays an important role in the prevention and treatment of diseases. Results In this paper, particle swarm optimization and tabu search are combined to propose a new method for protein structure prediction…
  europepmc 2022 置信度 0.80
• Target-template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations
  Improving protein thermostability has been a labor- and time-consuming process in industrial applications of protein engineering. Advances in computational approaches have facilitated the development of more efficient strategies to allow the prioritization of …
  europepmc 2022 置信度 0.80
• AI-Based Protein Structure Prediction in Drug Discovery: Impacts and Challenges.
  Proteins are the molecular machinery of the human body, and their malfunctioning is often responsible for diseases, making them crucial targets for drug discovery. The three-dimensional structure of a protein determines its biological function, its conformatio…
  europepmc 2022 置信度 0.80
• Ultra-fast protein structure prediction to capture effects of sequence variation in mutation movies
  Top protein three-dimensional (3D) structure predictions require evolutionary information from multiple-sequence alignments (MSAs) and deep, convolutional neural networks and appear insensitive to small sequence changes. Here, we describe EMBER3D using embeddi…
  europepmc 2022 置信度 0.80
• Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.
  In living organisms, proteins are considered as the executants of biological functions. Owing to its pivotal role played in protein folding patterns, comprehension of protein structure is a challenging issue. Moreover, owing to numerous protein sequence explor…
  europepmc 2023 置信度 0.80
• Quantum Speedup for Protein Structure Prediction.
  Protein structure prediction (PSP) predicts the native conformation for a given protein sequence. Classically, the problem has been shown to belong to the NP-complete complexity class. Its applications range from physics, through bioinformatics to medicine and…
  europepmc 2021 置信度 0.80
• So ManyFolds, So Little Time: Efficient Protein Structure Prediction With pLMs and MSAs
  In recent years, machine learning approaches for de novo protein structure prediction have made significant progress, culminating in AlphaFold which approaches experimental accuracies in certain settings and heralds the possibility of rapid in silico protein m…
  europepmc 2022 置信度 0.80
• Machine learning in protein structure prediction.
  Prediction of protein structure from sequence has been intensely studied for many decades, owing to the problem's importance and its uniquely well-defined physical and computational bases. While progress has historically ebbed and flowed, the past two years sa…
  europepmc 2021 置信度 0.80
• Protein structure prediction in the era of AI: Challenges and limitations when applying to <i>in silico</i> force spectroscopy.
  Mechanoactive proteins are essential for a myriad of physiological and pathological processes. Guided by the advances in single-molecule force spectroscopy (SMFS), we have reached a molecular-level understanding of how mechanoactive proteins sense and respond …
  europepmc 2022 置信度 0.80
• Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information.
  Protein structure prediction continues to pose multiple challenges despite outstanding progress that is largely attributable to the use of novel machine learning techniques. One of the widely used representations of local 3D structure-protein blocks (PBs)-can …
  europepmc 2023 置信度 0.80
• Highly accurate protein structure prediction and drug screen of monkeypox virus proteome.
  europepmc 2023 置信度 0.80
• AlphaFold2 transmembrane protein structure prediction shines
  Transmembrane (TM) proteins are major drug targets, indicated by the high percentage of prescription drugs acting on them. For a rational drug design and an understanding of mutational effects on protein function, structural data at atomic resolution are requi…
  europepmc 2021 置信度 0.80
• The trRosetta server for fast and accurate protein structure prediction.
  The trRosetta (transform-restrained Rosetta) server is a web-based platform for fast and accurate protein structure prediction, powered by deep learning and Rosetta. With the input of a protein's amino acid sequence, a deep neural network is first used to pred…
  europepmc 2021 置信度 0.80
• Fast and accurate Ab Initio Protein structure prediction using deep learning potentials.
  Despite the immense progress recently witnessed in protein structure prediction, the modeling accuracy for proteins that lack sequence and/or structure homologs remains to be improved. We developed an open-source program, DeepFold, which integrates spatial res…
  europepmc 2022 置信度 0.80
• Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction.
  While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock …
  europepmc 2023 置信度 0.80
• Annotating Metagenomically Assembled Bacteriophage from a Unique Ecological System using Protein Structure Prediction and Structure Homology Search
  ABSTRACT Emergent long read sequencing technologies such as Oxford’s Nanopore platform are invaluable in constructing high quality and complete genomes from a metagenome, and are needed investigate unique ecosystems on a genetic level. However, generating info…
  europepmc 2023 置信度 0.80
• Highly accurate protein structure prediction with AlphaFold.
  Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1-4 , the structures of around 100,000 unique proteins have been determined 5 , but this rep…
  europepmc 2021 置信度 0.80
• Using metagenomic data to boost protein structure prediction and discovery.
  Over the past decade, metagenomic sequencing approaches have been providing an ever-increasing amount of protein sequence data at an astonishing rate. These constitute an invaluable source of information which has been exploited in various research fields such…
  europepmc 2022 置信度 0.80
• How can the protein design community best support biologists who want to harness AI tools for protein structure prediction and design?
  europepmc 2023 置信度 0.80
• High-accuracy protein structure prediction in CASP14.
  The application of state-of-the-art deep-learning approaches to the protein modeling problem has expanded the "high-accuracy" category in CASP14 to encompass all targets. Building on the metrics used for high-accuracy assessment in previous CASPs, we evaluated…
  europepmc 2021 置信度 0.80
• Toward the solution of the protein structure prediction problem.
  Since Anfinsen demonstrated that the information encoded in a protein's amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure predicti…
  europepmc 2021 置信度 0.80
• Critical assessment of methods of protein structure prediction (CASP)-Round XIV.
  Critical assessment of structure prediction (CASP) is a community experiment to advance methods of computing three-dimensional protein structure from amino acid sequence. Core components are rigorous blind testing of methods and evaluation of the results by in…
  europepmc 2021 置信度 0.80
• Protein structure prediction now easier, faster.
  AI approach is accessible to all structural biology, drug discovery researchers.
  europepmc 2021 置信度 0.80
• Peptide binding specificity prediction using fine-tuned protein structure prediction networks
  Peptide binding proteins play key roles in biology, and predicting their binding specificity is a long-standing challenge. While considerable protein structural information is available, the most successful current methods use sequence information alone, in pa…
  europepmc 2022 置信度 0.80
• Construct a variable-length fragment library for de novo protein structure prediction.
  Although remarkable achievements, such as AlphaFold2, have been made in end-to-end structure prediction, fragment libraries remain essential for de novo protein structure prediction, which can help explore and understand the protein-folding mechanism. In this …
  europepmc 2022 置信度 0.80
• Publisher Correction: Single-sequence protein structure prediction using a language model and deep learning.
  europepmc 2022 置信度 0.80
• HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative
  Abstract AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) as inputs to learn the co-evolution information from the homolog…
  europepmc 2022 置信度 0.80
• Protein structure prediction by AlphaFold2: are attention and symmetries all you need?
  The functions of most proteins result from their 3D structures, but determining their structures experimentally remains a challenge, despite steady advances in crystallography, NMR and single-particle cryoEM. Computationally predicting the structure of a prote…
  europepmc 2021 置信度 0.80
• Deep Generative Models for Representing and Predicting Protein Structures
  Protein structure prediction remains a significant challenge in bioinformatics, directly impacting our understanding of biological processes and drug discovery. Traditional methods, such as homology modeling and fragment assembly, often struggle with novel pro…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Deep Generative Models for Representing and Predicting Protein Structures
  Protein structure prediction remains a significant challenge in bioinformatics, directly impacting our understanding of biological processes and drug discovery. Traditional methods, such as homology modeling and fragment assembly, often struggle with novel pro…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Bio-Information Dimension Prediction
  This paper introduces a novel dimension prediction model based on integrated biological genome and protein structure data. The model aims to predict key molecular interaction and biological process dimensions, offering a shift from primarily data analysis towa…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Bio-Information Dimension Prediction
  This paper introduces a novel dimension prediction model based on integrated biological genome and protein structure data. The model aims to predict key molecular interaction and biological process dimensions, offering a shift from primarily data analysis towa…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Cerebra: a computationally efficient framework for accurate protein structure prediction
  Update A newer Cerebra release with updated model checkpoints and the CAMEO 2025 test set is available at: Cerebra v2 model checkpoints and CAMEO 2025 test set | Zenodo Please use the new Zenodo record for the latest model parameters, test data, and release in…
  datacite Hu, Jian, Wang, Weizhe, Gong, Haipeng 2024 置信度 0.66
• RNAcon: Prediction and classification of ncRNAs using structural information
  RNAcon: Prediction and Classification of ncRNAs Using Structural Information RNAcon is a computational tool developed for the prediction and classification of non-coding RNAs, also known as ncRNAs. The tool first discriminates coding RNA sequences from non-cod…
  datacite Panwar, Bharat, Arora, Amit, Raghava, Gajendra 2014 置信度 0.66
• RNAcon: Prediction and classification of ncRNAs using structural information
  RNAcon: Prediction and Classification of ncRNAs Using Structural Information RNAcon is a computational tool developed for the prediction and classification of non-coding RNAs, also known as ncRNAs. The tool first discriminates coding RNA sequences from non-cod…
  datacite Panwar, Bharat, Arora, Amit, Raghava, Gajendra 2014 置信度 0.66
• Machine Learning Prediction of Red Fluorescent Proteins (RFPs) - Data and Code Repository
  This repository contains the datasets and computational notebooks supporting the manuscript "Machine Learning Models for Local Optimization of Red Fluorescent Protein Variants in a Low-Data Setting" for publication in the Journal of Chemical Information and Mo…
  datacite Ji, Ran, Jung, Jean, Cheng, Howard, Xu, Ella 等 2026 置信度 0.66
• Machine Learning Prediction of Red Fluorescent Proteins (RFPs) - Data and Code Repository
  This repository contains the datasets and computational notebooks supporting the manuscript "Machine Learning Models for Local Optimization of Red Fluorescent Protein Variants in a Low-Data Setting" for publication in the Journal of Chemical Information and Mo…
  datacite Ji, Ran, Jung, Jean, Cheng, Howard, Xu, Ella 等 2026 置信度 0.66
• The Seam in the Leaf: Why Photosynthesis Works at the Exact Boundary Between Order and Chaos
  A plain-language companion essay to the physics preprint "Coherence-Decoherence Rate Matching in Photosynthetic Quantum Transport" (doi:10.5281/zenodo.20246828). Written for readers in adjacent fields and for a general scientific audience. The essay explains w…
  datacite Shchevyev, Nikita Sergeyevich 2026 置信度 0.66 photosynthesisquantum biologyenvironment-assisted quantum transportENAQTFMO complex
• The Seam in the Leaf: Why Photosynthesis Works at the Exact Boundary Between Order and Chaos
  A plain-language companion essay to the physics preprint "Coherence-Decoherence Rate Matching in Photosynthetic Quantum Transport" (doi:10.5281/zenodo.20246828). Written for readers in adjacent fields and for a general scientific audience. The essay explains w…
  datacite Shchevyev, Nikita Sergeyevich 2026 置信度 0.66 photosynthesisquantum biologyenvironment-assisted quantum transportENAQTFMO complex
• ymaa19/ProRB: ProRB
  codes for "ProRB: A Structure-free Foundation Model for Joint Prediction and Design of Protein-RNA Interactions"
  datacite ymaa19 2026 置信度 0.66
• ymaa19/ProRB: v2.2
  codes for "ProRB: A Structure-free Unified Framework for Joint Prediction and Design of Protein-RNA Interactions"
  datacite ymaa19 2026 置信度 0.66
• Quantum Information in Protein Structure Prediction: A Geometric Modeling Framework
  Quantum Information is rapidly emerging as a transformative technology with the potential to revolutionize various fields, including chemistry and biology. Protein structure prediction, a cornerstone of biological research, has long been a significant challeng…
  datacite Zhang, Jincheng 2026 置信度 0.66
• Quantum Information in Protein Structure Prediction: A Geometric Modeling Framework
  Quantum Information is rapidly emerging as a transformative technology with the potential to revolutionize various fields, including chemistry and biology. Protein structure prediction, a cornerstone of biological research, has long been a significant challeng…
  datacite Zhang, Jincheng 2026 置信度 0.66
• AlphaFold3-predicted structures of the AAG/UV-DDB complex in the absence and presence of abasic site-containing DNA
  This dataset contains AlphaFold3-predicted structural models of the UV-DDB/AAG protein complex generated in support of the manuscript "UV-DDB as a Dynamic Regulator Linking Base Excision and Nucleotide Excision Repair via AAG Interaction." All predictions were…
  datacite Eom, Jiwon, Ko, Yubin, Choi, Jeongwoo, Yang, Soobin 等 2026 置信度 0.66 AAGUV-DDBbase excision repairnucleotide excision repairAlphaFold3
• Data-Centric Evaluation of Protein Function Prediction Pipelines
  Data-Centric Evaluation of Protein Function Prediction Pipelines presents a reproducible computational workflow designed to examine how methodological decisions shape the construction, evaluation, and interpretation of protein machine-learning workflows. Using…
  datacite Soto Garcia, Nicole, Murillo-Acevedo, Norma, García - Vinuesa, Julián Alfonso, Islas-Ávila, Ana Luisa 等 2026 置信度 0.66 Protein machine learning benchmarkingData-centric machine learningAntioxidant proteinsRedundancy reductionDataset splitting
• Data-Centric Evaluation of Protein Function Prediction Pipelines
  Data-Centric Evaluation of Protein Function Prediction Pipelines presents a reproducible computational workflow designed to examine how methodological decisions shape the construction, evaluation, and interpretation of protein machine-learning workflows. Using…
  datacite Soto Garcia, Nicole, Murillo-Acevedo, Norma, García - Vinuesa, Julián Alfonso, Islas-Ávila, Ana Luisa 等 2026 置信度 0.66 Protein machine learning benchmarkingData-centric machine learningAntioxidant proteinsRedundancy reductionDataset splitting
• Structural modelling and biophysical analyses reveal a dimeric coiled-coil architecture in the FAZ10 central region of Trypanosoma brucei
  This Zenodo record contains the datasets associated with the FAZ10 study. The deposit includes experimental data, modeling predictions, all-atom molecular dynamics (AA-MD) simulations, coarse-grained molecular dynamics (CG-MD) simulations with GōMartini 3, and…
  datacite Osorio Mogollon, Cleidy Mirela, Leonardo, Diego Antonio, Clarice, Izumi, Cioca Alves, Leticia 等 2026 置信度 0.66 Molecular Dynamics SimulationFAZ10
• Structural modelling and biophysical analyses reveal a dimeric coiled-coil architecture in the FAZ10 central region of Trypanosoma brucei
  This Zenodo record contains the datasets associated with the FAZ10 study. The deposit includes experimental data, modeling predictions, all-atom molecular dynamics (AA-MD) simulations, coarse-grained molecular dynamics (CG-MD) simulations with GōMartini 3, and…
  datacite Osorio Mogollon, Cleidy Mirela, Leonardo, Diego Antonio, Clarice, Izumi, Cioca Alves, Leticia 等 2026 置信度 0.66 Molecular Dynamics SimulationFAZ10
• SPDETune-TCR: Data, Predictions, and Baselines (Part 1 of 2)
  Val/test structural features, model predictions, baseline comparisons, raw experimental data, and dataset manifests for reproducing results in: More Generalizable Prediction of TCR-pMHC Binding with Protein Structure Models. See README-val.md for more details.
  datacite Portnoi, Tally 2026 置信度 0.66
• SPDETune-TCR: Data, Predictions, and Baselines (Part 1 of 2)
  Val/test structural features, model predictions, baseline comparisons, raw experimental data, and dataset manifests for reproducing results in: More Generalizable Prediction of TCR-pMHC Binding with Protein Structure Models. See README-val.md for more details.
  datacite Portnoi, Tally 2026 置信度 0.66
• MMGPE: Beyond Conventional AI Drug Discovery (Target-Centric AI)
  Artificial intelligence has transformed early-stage drug discovery by accelerating target identification, protein structure prediction, molecular design, and virtual screening. Representative platforms such as Isomorphic Labs, Insilico Medicine, Recursion, and…
  datacite Chu, Melinda 2026 置信度 0.66
• MMGPE: Beyond Conventional AI Drug Discovery (Target-Centric AI)
  Artificial intelligence has transformed early-stage drug discovery by accelerating target identification, protein structure prediction, molecular design, and virtual screening. Representative platforms such as Isomorphic Labs, Insilico Medicine, Recursion, and…
  datacite Chu, Melinda 2026 置信度 0.66
• Recursive Harmonic Architectures and the Mechanics of Fold Pressure: An Analysis of the Ontological Inversion and Stratifiead Stability Constants
  Recursive Harmonic Architectures and the Mechanics of Fold Pressure: An Analysis of the Ontological Inversion and Stratifiead Stability Constants The transition from verification-based systems to measurement-centric frameworks in computational physics represen…
  datacite Kulik, Dean 2026 置信度 0.66
• Recursive Harmonic Architectures and the Mechanics of Fold Pressure: An Analysis of the Ontological Inversion and Stratifiead Stability Constants
  Recursive Harmonic Architectures and the Mechanics of Fold Pressure: An Analysis of the Ontological Inversion and Stratifiead Stability Constants The transition from verification-based systems to measurement-centric frameworks in computational physics represen…
  datacite Kulik, Dean 2026 置信度 0.66
• Hierarchical Breakdown of RNA Structure Prediction in CASP16: From Reliable Local Features to Speculative Multimer Assembly
  Companion code archive for "Hierarchical Breakdown of RNA Structure Prediction in CASP16: From Reliable Local Features to Speculative Multimer Assembly" by Chandran Nithin, Smita P. Pilla, and Sebastian Kmiecik (University of Warsaw, Biological and Chemical Re…
  datacite Nithin, Chandran, PIlla, Smita Priyadarshini, Kmiecik, Sebastian 2026 置信度 0.66 RNA Structure predictionCASP16RNA modelingRNA multimersRNA-protein complexes
• Hierarchical Breakdown of RNA Structure Prediction in CASP16: From Reliable Local Features to Speculative Multimer Assembly
  Companion code archive for "Hierarchical Breakdown of RNA Structure Prediction in CASP16: From Reliable Local Features to Speculative Multimer Assembly" by Chandran Nithin, Smita P. Pilla, and Sebastian Kmiecik (University of Warsaw, Biological and Chemical Re…
  datacite Nithin, Chandran, PIlla, Smita Priyadarshini, Kmiecik, Sebastian 2026 置信度 0.66 RNA Structure predictionCASP16RNA modelingRNA multimersRNA-protein complexes
• Supplementary data for "One clip, many genomes: how genome state may shape the use of tandem HMG-box proteins in organellar nucleoids"
  This dataset contains the underlying data and analysis files associated with the review article “One clip, many genomes: how genome state may shape the use of tandem HMG-box proteins in organellar nucleoids” by Mari Takusagawa and Yoshiki Nishimura. The deposi…
  datacite Takusagawa, Mari, Nishimura, Yoshiki 2026 置信度 0.66 organellar nucleoidHMG-box proteingenome copy numberAlphaFold3Chlamydomonas reinhardtii
• Supplementary data for "One clip, many genomes: how genome state may shape the use of tandem HMG-box proteins in organellar nucleoids"
  This dataset contains the underlying data and analysis files associated with the review article “One clip, many genomes: how genome state may shape the use of tandem HMG-box proteins in organellar nucleoids” by Mari Takusagawa and Yoshiki Nishimura. The deposi…
  datacite Takusagawa, Mari, Nishimura, Yoshiki 2026 置信度 0.66 organellar nucleoidHMG-box proteingenome copy numberAlphaFold3Chlamydomonas reinhardtii
• ViruFunc Atlas v1.0: a leakage-aware benchmark and reusable evaluation resource for viral protein function annotation
  ViruFunc Atlas v1.0 is a leakage-aware benchmark and reusable evaluation resource for viral protein function annotation. The release provides frozen manifests for 713,487 viral proteins, 19,149 genomes, 1,283 viral families, and 17 primary function labels. It …
  datacite Wang, Kuanghao 2026 置信度 0.66 viral protein function annotationviral genomicsbenchmarkleakage-aware evaluationgenome context
• ViruFunc Atlas v1.0: a leakage-aware benchmark and reusable evaluation resource for viral protein function annotation
  ViruFunc Atlas v1.0 is a leakage-aware benchmark and reusable evaluation resource for viral protein function annotation. The release provides frozen manifests for 713,487 viral proteins, 19,149 genomes, 1,283 viral families, and 17 primary function labels. It …
  datacite Wang, Kuanghao 2026 置信度 0.66 viral protein function annotationviral genomicsbenchmarkleakage-aware evaluationgenome context
• ViruFunc Atlas v1.0: a leakage-aware benchmark and reusable evaluation resource for viral protein function annotation
  ViruFunc Atlas v1.0 is a leakage-aware benchmark and reusable evaluation resource for viral protein function annotation. The release provides frozen manifests for 713,487 viral proteins, 19,149 genomes, 1,283 viral families, and 17 primary function labels. It …
  datacite Wang, Kuanghao 2026 置信度 0.66 viral protein function annotationviral genomicsbenchmarkleakage-aware evaluationgenome context
• ViruFunc Atlas v1.0: a leakage-aware benchmark and reusable evaluation resource for viral protein function annotation
  ViruFunc Atlas v1.0 is a leakage-aware benchmark and reusable evaluation resource for viral protein function annotation. The release provides frozen manifests for 713,487 viral proteins, 19,149 genomes, 1,283 viral families, and 17 primary function labels. It …
  datacite Wang, Kuanghao 2026 置信度 0.66 viral protein function annotationviral genomicsbenchmarkleakage-aware evaluationgenome context
• PROTEIN UNFOLDING ON INTERFACES: A STRUCTURAL AND FUNCTIONAL STUDY
  The spontaneous adsorption of protein molecules on interfaces is an ubiquitous phenomenon in natural and man-made systems. The structural rearrangement caused by the direct contact with the sorbent phase may affect protein biological activity, including bioava…
  datacite M. Miriani 2012 置信度 0.66
• Blueprinting, implementation, and application of fully automatic protocols for the QM/MM modeling of photo-excited states of rhodopsin variants
  The semi-automatic building of QM/MM models of rhodopsins has been recently proposed, by means of a new technology called Automatic Rhodopsin Model protocol. In its original version, here called original ARM protocol, published in 2016, the constructed QM/MM m…
  datacite PEDRAZA GONZ�LEZ, LAURA MILENA 2021 置信度 0.66
• Quantum Theory in Drug Discovery: From Atomic Structure to Molecular Interactions
  This extensive guide provides a deep dive into the application of quantum theory within pharmaceutical research and development, bridging the gap between fundamental atomic physics and advanced drug design. The article first establishes the core principles of …
  datacite quantum chemical science 2026 置信度 0.66 Quantum MechanicsDrug DiscoveryQM/MMDensity Functional TheorySchrodinger Equation
• Quantum Theory in Drug Discovery: From Atomic Structure to Molecular Interactions
  This extensive guide provides a deep dive into the application of quantum theory within pharmaceutical research and development, bridging the gap between fundamental atomic physics and advanced drug design. The article first establishes the core principles of …
  datacite quantum chemical science 2026 置信度 0.66 Quantum MechanicsDrug DiscoveryQM/MMDensity Functional TheorySchrodinger Equation
• 25th Annual Computational Neuroscience Meeting: CNS-2016.
  A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking…
  datacite Sharpee, Tatyana O, Destexhe, Alain, Kawato, Mitsuo, Sekulić, Vladislav 等 2016 置信度 0.66 32 Biomedical and Clinical Sciences3209 NeurosciencesBasic Behavioral and Social ScienceNeurosciencesBehavioral and Social Science