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• Bayesian-Steered Structure Prediction of Mechanical Biomolecules Using Twisted Diffusion
  Deep learning approaches have revolutionized protein structure prediction. These tools are trained using experimental data and recapitulate reported conformations, but there is great interest in predicting conformations that may be functionally relevant althou…
  preprints 2026 置信度 0.86 cs.lgq-bio.qmq-bio.bmphysical sciencesphysics.bio-ph
• Cancer Cell Line Heterogeneity Imposes a Primary Bottleneck for Virtual Perturbation Screening at Scale
  UniPert-G2CP (Li et al., Cell, 2026) bridges genetic and chemical screens from molecular representation to phenotype modeling across five cancer cell lines [1]. Here we extend this architecture to 162 cell lines, 32,039 compounds, and genome-wide output (12,32…
  preprints 2026 置信度 0.86 cs.lgbiologymedicinecs.aibiological sciences
• Handshake: Partner-Specific Protein-Protein Binding Site Prediction at Scale Using ProstT5 and Cross-Chain Attention
  Partner-specific protein-protein binding site prediction, identifying which residues of a protein form the interface when bound to a specific partner, remains a challenging task with significant implications for drug discovery and understanding of protein stru…
  preprints 2026 置信度 0.86 cs.lgcs.aicomputer and information sciencescomputer scienceartificial intelligence
• Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?
  Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a re…
  preprints 2018 置信度 0.82 biologycomputational biologyq-bio.bmphysics.bio-phq-bio.qm
• Structure-free, site-resolved contrastive learning extends small-molecule discovery beyond the reach of structure-based modeling
  Virtual screening asks which molecules, among an enormous space of drug-like chemistry, are worth synthesizing and testing against a protein target. Most modern methods answer this question by building and scoring an explicit three-dimensional pose through mol…
  preprints 2026 置信度 0.86 drug discoverycs.lgcs.aibiologyq-bio.qm
• GeoEPred: A Multimodal Structure-Aware Geometric Deep Learning Framework for Gram-Negative Bacterial Secreted Effector Prediction with Sequence Semantics
  Accurate prediction of effector proteins secreted by Gram-negative bacteria is important for elucidating bacterial pathogenic mechanisms and developing precise anti-infective strategies. Although existing methods have benefited from the strong sequence feature…
  preprints 2026 置信度 0.86 cs.lgcs.aicomputer and information sciencesartificial intelligencecomputer science
• Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins
  Protein structure predictors achieve high single-state accuracy, but it remains unclear whether they can recover functionally relevant conformational ensembles or account for the presence of ligands and/or binding partners. Here, we benchmark AlphaFold3, Boltz…
  preprints 2026 置信度 0.86 q-bio.bmbiologycs.lgdrug discoverycs.ai
• A geometric atlas of how ESM3 organizes modalities across depth
  Protein language models learn general-purpose representations from large collections of protein sequences and structures, and have advanced the prediction of protein structure and function. ESM3 is a multimodal protein language model that ingests a protein thr…
  preprints 2026 置信度 0.86 biologycomputer and information sciencescs.aics.lgai safety
• Structure-Guided Prioritization of Amphipathic Antimicrobial Peptide Candidates against ESKAPE Pathogens via Generative Design and Physicochemical Filtering
  Abstract Antimicrobial resistance (AMR) among ESKAPE pathogens poses an escalating threat to global health as conventional antibiotic pipelines fail to keep pace with emerging resistance. Antimicrobial peptides (AMPs) have re-emerged as a promising alternative…
  preprints 2026 置信度 0.86 cs.aibiologycomputer sciencedrug discoveryartificial intelligence
• FuncSeek: Multi-PLM contrastive learning for protein functional similarity search
  Below 30% pairwise sequence identity, alignment-based methods struggle to reliably distinguish true homologs from chance, and enzyme function prediction degrades accordingly: on proteins in this regime, even advanced methods (CLEAN) achieves only 55.1% accurac…
  preprints 2026 置信度 0.86 cs.lgcomputer scienceartificial intelligencecs.aibiology
• ProMiSE: Protein Multi-State Evaluation Benchmark in Biological Contexts
  Proteins are inherently dynamic, with biological functions often emerging from transitions between multiple conformational states. While recent breakthroughs have largely addressed the static structure prediction problem, no systematic benchmark exists to demo…
  preprints 2026 置信度 0.86 cs.lgcs.aicomputer and information sciencesq-bio.bmq-bio.qm
• Comparison of AI protein structure ensemble prediction tools
  Multiple AI prediction tools for protein structural ensembles have recently been released, building on the much heralded advances from AlphaFold, large language models, and other machine-learning approaches. Here we report on a comparison of several tools (Bio…
  preprints 2026 置信度 0.86 biologycs.aicomputational biologycs.lgcomputer science
• Decoding antibiotic modes of action from multimodal cellular responses
  Abstract Antibiotic resistance continues to rise, yet most new drug candidates act through long-established targets. Faster mode of action (MoA) assessment would enable more effective prioritization of screening hits and help identify compounds with novel mech…
  preprints 2026 置信度 0.86 biologycomputer sciencecomputer and information sciencesdrug discoverycs.ai
• A Chromatin Biology Assessment of AlphaFold3
  Biomolecular structure prediction tools such as AlphaFold have achieved remarkable success in predicting structures of single proteins and multiprotein complexes. AlphaFold3 now incorporates the capability to model complexes containing nucleic acids and chemic…
  preprints 2026 置信度 0.86 biologyartificial intelligenceq-bio.bmcomputational biologycs.ai
• ThermoFusion: A Multimodal Deep Learning Framework for Generalizable Prediction of Enzyme Thermostability
  Protein thermostability is a critical property for both industrial and biomedical enzyme applications, yet experimental evaluation of mutation-induced stability changes remains laborious and costly. Here, we present ThermoFusion, a hybrid deep learning framewo…
  preprints 2026 置信度 0.86 cs.lgcomputer and information sciencescs.aimachine learningq-bio.bm
• Application of vision transformers to protein-ligand affinity prediction
  Abstract Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D…
  preprints 2026 置信度 0.86 cs.lgcomputer sciencecomputer and information sciencescs.aiartificial intelligence
• Ancient Reconstructed Proteins: A Framework for Resurrecting Protein Structures from Million-Year-Old Metagenomes
  Abstract DNA sequences derived from ancient samples provide insights into human history, paleoenvironments, and evolutionary biology. Advances in laboratory techniques and computational tools have established ancient DNA research as a distinct field. However, …
  preprints 2026 置信度 0.86 biologyq-bio.bmcomputational biologycs.lgq-bio.qm
• Benchmarking MSA pairing for protein-protein complex structure prediction reveals a depth-over-pairing principle
  ABSTRACT AlphaFold-Multimer (AFM) and AlphaFold3 (AF3) have revolutionized protein-protein complex structure prediction. However, it remains unclear whether elaborate MSA pairing is a strict prerequisite for achieving high accuracy. Here, we systematically eva…
  preprints 2026 置信度 0.86 cs.lgcs.aibiologyartificial intelligencecomputational biology
• Just Add Structure: Protein Language Models Combined with Structural Equivariance Excel at Protein Tasks
  Accurate in silico prediction of protein properties, functional fitness, and mutational effects remains a central challenge in protein engineering and therapeutic design. While Protein Language Models (PLMs) successfully capture rich evolutionary and functiona…
  preprints 2026 置信度 0.86 cs.lgcs.aicomputer and information sciencescs.cvcs.cl
• Drug Design Studio (DDS) 2.0: A Unified Platform for Network Pharmacology Integrated with Docking and Virtual Screening Workflow for Covalent/Non-Covalent Binders
  Network pharmacology has become a central paradigm in modern drug discovery, replacing the reductionist "one drug, one target" view with a systems-level understanding of how compounds engage networks of proteins that are linked to disease. Despite its impact, …
  preprints 2026 置信度 0.86 computational biologybiologydrug discoverycomputer sciencecs.ai
• Zero-Shot Generation of Protein Conformational Ensembles Through AlphaFold Latent Flooding
  Abstract Despite transformative advances in protein structure prediction, generating conformational ensembles directly from sequence in an efficient and accessible manner remains a central challenge. We introduce a heuristic importance sampling framework for t…
  preprints 2026 置信度 0.86 cs.lgcomputer and information sciencescs.aiq-bio.bmq-bio.qm
• Promera: a unified model for biomolecular structure prediction, filtering, and design
  Generative models have become staple tools for modeling and designing biomolecular structures. However, although these tools have improved in structural prediction accuracy, their ability to filter designed binders—an essential use case—remains insufficient; w…
  preprints 2026 置信度 0.86 cs.aics.lgcomputer and information sciencescomputer sciencecomputational biology
• EditorForge: An Active-Site-Aware Framework for Inverse-Folding-Based Protein Redesign
  Inverse-folding models can rapidly generate protein sequences compatible with a supplied backbone, but unconstrained redesign is poorly suited to enzyme and genome-editor-associated domains, where catalytic, substrate-proximal, and conserved structural regions…
  preprints 2026 置信度 0.86 computer and information sciencescs.aimulti-agent systemsai governancebiological sciences
• Assessing phylogenetic inference in the era of AI-driven protein structures
  Phylogenetic inference is one of the most common downstream applications of multiple sequence alignment. Quantitative assessment of inferred trees is essential for evaluating the reliability of both alignments and phylogenetic inferences. Establishing a robust…
  preprints 2026 置信度 0.86 biologycs.lgq-bio.qmcognitive sciencebiotechnology
• Mapping pathogenic patterns in membrane transporters from the GLUT transporter family
  Non-synonymous amino acid substitutions (missense mutations) are common in the general population; some are causative of serious disease. Depending on their structural context, they can disrupt protein function, folding, or dynamics. Computational predictive m…
  preprints 2026 置信度 0.86 biologyneurosciencemedicinegeneticsstat.ap
• Scaling and democratising structure-based protein function prediction with metagenomic-deepFRI
  High-quality protein structure models have become widely available, offering insights into protein function, yet they remain underutilized. Here, we introduce metagenomic-deepFRI, a framework incorporating structural templates into functional annotation pipeli…
  preprints 2026 置信度 0.86 cs.lgcs.aiq-bio.bmcomputational biologyq-bio.qm
• ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures
  Deep learning-based protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet reliably assessing the quality of computationally predicted multimeric structures remains challenging. Recent methods have demonstrat…
  preprints 2026 置信度 0.86 cs.lgcs.aiartificial intelligencecomputer sciencecomputer and information sciences
• makeshift: a lightweight software for accessing and analyzing NMR data and protein dynamics
  Nuclear magnetic resonance (NMR) spectroscopy yields rich residue-level information on biomolecular dynamics and chemical environments, two frontiers for quantitative predictive methods in biochemistry. Decades of data are publicly archived in the Biological M…
  preprints 2026 置信度 0.86 q-bio.qmcs.lgcomputer sciencecomputational biologyworkflow
• Artificial intelligence aided design of peptides with custom secondary structure motifs and reduced amino acid alphabets
  Proteins are highly diverse functional polymers where the specific sequence of amino acids, selected from a standard genetically-encoded alphabet of twenty (C20), determines the structure and ultimately the function of the resulting folded protein. This standa…
  preprints 2026 置信度 0.86 cs.lgq-bio.bmq-bio.qmphysics.bio-phcs.ai
• Earthquake aftershock forecasting with conditional generative models
  Abstract Forecasting how aftershocks evolve in space and time after a large earthquake is a central problem in statistical seismology and underpins operational earthquake forecasting. Existing forecasting methods rest on statistical point-process models such a…
  preprints 2026 置信度 0.86 cs.lgcs.aiartificial intelligenceq-bio.qmcomputer and information sciences
• Improving Biological Sequence Prediction with AlphaFold2 Representation
  Motivation Accurate prediction of functional sites from primary sequences is essential for elucidating biological mechanisms and advancing rational drug design. However, traditional sequence-based features are inherently unable to capture complex structural pr…
  preprints 2026 置信度 0.86 cs.lgcs.aiartificial intelligencecomputer sciencecomputer and information sciences
• What makes a banana false? How the genome of Ethiopian orphan staple Ensete ventricosum differs from the banana A and B sub-genomes
  Abstract Background Ensete ventricosum , also known as the “tree against hunger” plays a key role in Ethiopian food security and farming systems, feeding more than 20 million people. Since domestication via clonal selection in the south-west Ethiopian highland…
  preprints 2026 置信度 0.86 biologycomputational biologygeneticsq-bio.gnsynthetic biology
• Overestimating zero-shot fitness prediction: Broad benchmarks mask local failures and practical limitations
  Deep learning models have emerged as promising tools in protein engineering. In particular, they can be used to predict mutation fitness without the need for task-specific training, a process known as zero-shot prediction. However, the respective strengths and…
  preprints 2026 置信度 0.86 cs.lgcomputer scienceq-bio.qmartificial intelligencecs.ai
• Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites
  Allosteric regulation represents a fundamental mechanism of protein function, yet distinguishing allosteric from orthosteric protein binding sites remains a persistent computational challenge. While multimodal protein foundation models offer the potential to i…
  preprints 2026 置信度 0.86 cs.aics.lgcomputer and information sciencesartificial intelligencecomputer science
• Stoic: Fast and accurate protein stoichiometry prediction
  Motivation Protein complexes are central to cellular function, but experimental determination of their structures remains challenging. Structure prediction methods require prior knowledge of stoichiometry - the number of copies of each protein entity within a …
  preprints 2026 置信度 0.86 cs.lgcs.aicomputer and information sciencescomputational biologyq-bio.qm
• A Linguistic Model for Protein Structure Generation
  Computational generation of protein backbone geometry is a critical upstream step in de novo protein design, but the existing state of the art approaches typically requires large pretrained structure prediction networks, significant computing in frastructure, …
  preprints 2026 置信度 0.86 cs.aics.lgcomputer and information sciencesartificial intelligencecs.cv
• Improving All-Atom Molecular Dynamics Models for Quantitative Prediction of Nanopore Blockade Current
  All-atom molecular dynamics has become an indispensable tool in development of nanopore sensors of biological information. In a typical nanopore experiment, measurements of ionic current flowing through a nanopore report on the chemical structure of biomolecul…
  preprints 2026 置信度 0.86 materials sciencephysics.comp-phq-bio.bmcond-mat.mtrl-sciphysics
• Systematic Evaluation of AlphaFold2 and OpenFold3 on Protein–Peptide Complexes
  Protein–peptide interactions are important mediators of diverse biological processes. While deep learning has revolutionized protein structure prediction, comparative evaluation of these methods, specifically for protein–peptide complexes, remains an area of a…
  preprints 2026 置信度 0.86 cs.lgcs.aibiologycomputer sciencemedicine
• Scalable Extraction of Information on Protein-Protein Interactions using Topological Data Analysis
  Protein-protein interactions (PPIs) govern a wide range of cellular functions. The ability to predict PPI interfaces from protein molecular surfaces is important for understanding protein function and enabling therapeutic discovery. While recent advances in st…
  preprints 2026 置信度 0.86 cs.lgcomputer and information sciencescomputer sciencecs.aimachine learning
• SPINDLE: Unlocking protein dynamics from single-field NMR relaxation data using a deep learning ensemble
  A protein’s function is derived from its three-dimensional structure and the motions of the atoms about that structure. The detailed characterization of both macromolecular structure and dynamics provides an opportunity for understanding enzyme catalysis, liga…
  preprints 2026 置信度 0.86 cs.lgq-bio.qmartificial neural networkstat.mlphysics
• ProtJEPA: A Multimodal Joint-Embedding Predictive Architecture for Protein Biological World Modeling with Multi-Teacher Modality-Attentive Fusion
  Over 99.9% of known protein sequences lack experimentally validated functional annotations. We present ProtJEPA, a multimodal Joint-Embedding Predictive Architecture that trains a sequence-only student encoder to predict joint embeddings spanning ten biologica…
  preprints 2026 置信度 0.86 cs.aiartificial intelligencecomputer and information sciencesbiologycomputational biology
• Biological Network Organization, Not Generic Graph Topology, Drives Graph-Based Gene Essentiality Prediction
  Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulnerabilities. Graph neural networks (GNNs) integrate molecular interaction networks with gene-level features, but…
  preprints 2026 置信度 0.86 computer and information sciencescs.lgartificial intelligencecomputer sciencecs.ai
• A New Enzyme Family Catalyzing Methyllanthionine Sulfoxide Formation in Anti-phage Lanthipeptides
  Lanthipeptides are one of the largest classes of ribosomally synthesized and post-translationally modified peptides (RiPPs). The coi biosynthetic gene cluster (BGC) from Streptomyces coelicolor A3(2) encodes a canonical class I lanthipeptide dehydratase (CoiB)…
  preprints 2026 置信度 0.86 biologygeologychemistrycell biologygenetics
• MAXWELL: Calibrating the probabilistic outputs of protein language models to the mutation-induced stability change landscape
  Designing mutations that enhance protein stability is a central goal in protein engineering. However, experimentally screening large numbers of candidate mutations is costly and time-consuming, creating a strong need for computational methods that can identify…
  preprints 2026 置信度 0.86 cs.lgcs.aicomputational biologyq-bio.bmcomputer and information sciences
• CryoARC: Atomic-resolution conformational landscapes of protein assemblies from cryo-EM single particles with evolutionary priors
  Single-particle cryo-electron microscopy (cryo-EM) reveals structural heterogeneity in macromolecular complexes, but recovering continuous conformational landscapes at high resolution remains challenging. Here, we introduce CryoARC, a deep learning framework t…
  preprints 2026 置信度 0.86 cs.lgq-bio.bmartificial intelligencecomputer and information sciencescomputer vision and pattern recognition
• BoltzProt-1: Towards Efficient De Novo Binder Design with Good Developability
  Designing binders against novel protein targets remains a central challenge in computational drug discovery. Here we introduce BoltzProt-1 , a pipeline for generating protein binders, including nanobodies, with improved hit rates and favorable developability p…
  preprints 2026 置信度 0.86 drug discoverymedicinecomputational biologypharmacologymaterials science
• Computational prediction resolves thousands of homooligomeric phage protein structures
  Bacteriophages (phages) play essential roles in microbial systems, yet most phage proteins remain poorly characterised. Protein tertiary and quaternary structure information contributes valuable information about protein function. As many phage proteins functi…
  preprints 2026 置信度 0.86 biologycs.lgcomputational biologyq-bio.bmcs.ai
• G-SPRI: A Structure-Centric Graph Model for Comprehensive Prediction of Cancer Driver Events from Missense Mutations
  ABSTRACT In silico approaches for predicting the functional impact of missense mutations are critical for interpreting personal genomes and identifying disease-related biomarkers. Existing methods largely rely on sequence-based information or intuitive structu…
  preprints 2026 置信度 0.86 biologycs.lgcomputational biologycomputer and information sciencescs.ai
• Graph Machine Learning for Physiological Role Prediction in Protein Contact Networks: A Large-Scale Comparative Study on the Human Proteome
  Proteins are fundamental macromolecules involved in virtually all biological processes. Their physiological roles are tightly linked to their three-dimensional structure, which can be naturally abstracted as Protein Contact Networks (PCNs), i.e., graphs where …
  preprints 2026 置信度 0.86 cs.lgcomputer scienceartificial intelligencecomputer and information sciencescs.ai
• A structure-derived contact-network responsiveness atlas of human proteins
  Protein structures encode non-local contact organization, but static coordinates do not directly quantify how a contact network responds when effective stabilizing interactions are strengthened or weakened. Here we introduce Contact-Network Responsiveness (CNR…
  preprints 2026 置信度 0.86 cs.lgai safetybiologypharmacologyai governance
• Integrating AI and molecular modeling for structural prediction of a closed state of the hERG channel
  The voltage-gated potassium channel hERG (Kv11.1) plays a central role in cardiac repolarisation by mediating the rapid delayed rectifier K⁺ current (IKr). Blockage of hERG by small molecules can lead to delayed repolarisation, QT interval prolongation, and po…
  preprints 2026 置信度 0.86 computer and information sciencesq-bio.bmcs.aics.lgbiology
• Antibody–Antigen Affinity Prediction with Chain-Aware Protein Language Modeling
  Motivation Antibody-antigen affinity determines which antibodies advance in therapeutic discovery, repertoire analysis and affinity maturation, but experimental measurements are sparse relative to the scale of sequence libraries. Structure-based predictors can…
  preprints 2026 置信度 0.86 computer sciencecomputer and information sciencescs.aics.lgartificial intelligence
• Drug Design Studio (DDS) 2.0: A Unified Platform for Network Pharmacology Integrated with Docking and Virtual Screening Workflow for Covalent/Non-Covalent Binders
  Network pharmacology has become a central paradigm in modern drug discovery, replacing the reductionist "one drug, one target" view with a systems-level understanding of how compounds engage networks of proteins that are linked to disease. Despite its impact, …
  preprints 2026 置信度 0.86 computer sciencecs.aidrug discoverycomputational biologybiology
• Rv0810c: a genus-conserved, structurally ordered small protein of unknown function carrying DUF3073 in Mycobacterium tuberculosis
  Small annotated open reading frames are the most neglected part of the functionally un-characterised Mycobacterium tuberculosis genome. We revisit Rv0810c, a 60-residue protein carrying the unknown-function domain DUF3073 (Pfam PF11273), flagged as an Actinoba…
  preprints 2026 置信度 0.86 biologymedicineartificial intelligenceai governancephysics
• Phyre2.2: A Community Resource for Template-based Protein Structure Prediction.
  Template-based modelling, also known as homology modelling, is a powerful approach to predict the structure of a protein from its amino acid sequence. The approach requires one to identify a sequence similarity between the query sequence and that of a known st…
  europepmc 2025 置信度 0.94 biologycomputer sciencecomputational biologyq-bio.bmq-bio.gn
• HighPlay: Cyclic Peptide Sequence Design Based on Reinforcement Learning and Protein Structure Prediction
  The structural diversity and good biocompatibility of cyclic peptides has led to their emergence as potential therapeutic agents. Traditional cyclic peptide design relies on natural template modification and combinatorial chemical library screening, but suffer…
  europepmc 2025 置信度 0.94 computational biologybiotechnologydrug discoveryq-bio.bmgenetics
• 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 0.94 medicinedrug discoverybiologypharmacologycomputational biology
• Advances in Protein Structure Prediction Highlight Unexpected Commonalities Between Gram-positive and Gram-negative Conjugative T4SSs.
  Despite recent advances in our understanding of the structure and function of conjugative Type 4 Secretion Systems (T4SSs), there is still only very scarce data available for the ones from Gram-positive (G + ) bacteria. This is a problem, as conjugative T4SSs …
  europepmc 2025 置信度 0.94 biologycomputer sciencecs.lgcomputational biologycs.ai
• Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design
  Abstract 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 encount…
  europepmc Dong Xu 2024 置信度 0.94 computer sciencecs.lgdrug discoveryartificial intelligencecomputational biology
• Cerebra: a computationally efficient framework for accurate protein structure prediction
  A bstract 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 Jian Hu, Weizhe Wang, Haipeng Gong 2024 置信度 0.94 cs.lgq-bio.bmcomputational biologycs.aiconvolutional neural network
• Easy and accurate protein structure prediction using ColabFold
  Abstract Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological questions, using predicted protein structures of single monomers or full complexes, a common practice. ColabFold-AF2 is an open-source Jupyter Notebook inside Google …
  europepmc 2023 置信度 0.91 ai governanceq-bio.qmcs.lgcs.aicomputer and information sciences
• Protein structure prediction from the complementary science perspective.
  A comparative analysis between two problems-apparently unrelated-which are solved in a period of ~400 years, viz., the accurate prediction of both the planetary orbits and the protein structures, leads to inferred conjectures that go far beyond the existence o…
  europepmc 2023 置信度 0.91 q-bio.bmq-bio.gnq-bio.qmcs.lgcs.hc
• Bilinear optimization of protein structure prediction: An exact approach via AB off-lattice model.
  Protein structure prediction (PSP) remains a central challenge in computational biology due to its inherent complexity and high dimensionality. While numerous heuristic approaches have appeared in the literature, their success varies. The AB off-lattice model,…
  europepmc 2024 置信度 0.94 cs.lgq-bio.bmelectrical engineering, electronic engineering, information engineeringsignal processingcomputer and information sciences
• Peptide binder design with inverse folding and protein structure prediction.
  The computational design of peptide binders towards a specific protein interface can aid diagnostic and therapeutic efforts. Here, we design peptide binders by combining the known structural space searched with Foldseek, the protein design method ESM-IF1, and …
  europepmc 2023 置信度 0.91 computational biologybiologyphysics.bio-phgeneticsbiotechnology
• Starting at Go: Protein structure prediction succumbs to machine learning.
  This year's Lasker Basic Science Award recognizes the invention of AlphaFold, a revolutionary advance in the history of protein research which for the first time offers the practical ability to accurately predict the three-dimensional arrangement of amino acid…
  europepmc 2023 置信度 0.91 q-bio.bmdrug discoveryq-bio.qmbiologycomputational biology
• Challenges in bridging the gap between protein structure prediction and functional interpretation.
  The rapid evolution of protein structure prediction tools has significantly broadened access to protein structural data. Although predicted structure models have the potential to accelerate and impact fundamental and translational research significantly, it is…
  europepmc 2025 置信度 0.94 drug discoveryq-bio.bmbiologycs.lgcomputational biology
• Progress at protein structure prediction, as seen in CASP15.
  In Dec 2020, the results of AlphaFold version 2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational method could challenge experimental accuracy for structure prediction of s…
  europepmc 2023 置信度 0.91 q-bio.bmphysics.bio-phq-bio.qmdrug discoverycomputational biology
• Unsupervisedly Prompting AlphaFold2 for Accurate Few-Shot Protein Structure Prediction.
  Data-driven predictive methods that can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining an accurate folding landscape using coevolutiona…
  europepmc 2023 置信度 0.91 cs.lgcs.aicomputer scienceq-bio.bmcomputational biology
• An automated pipeline integrating AlphaFold 2 and MODELLER for protein structure prediction.
  The ability to predict a protein's three-dimensional conformation represents a crucial starting point for investigating evolutionary connections with other members of the corresponding protein family, examining interactions with other proteins, and potentially…
  europepmc 2023 置信度 0.91 computer sciencecs.lgcomputational biologycs.aiq-bio.qm
• AlphaFold2 protein structure prediction: Implications for drug discovery.
  The drug discovery process involves designing compounds to selectively interact with their targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificial intellig…
  europepmc 2023 置信度 0.91 drug discoveryq-bio.bmcomputational biologybiologypharmacology
• 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. CASP (Critical Assessment of Structure Prediction) conducts community experiments aimed at advancing solutions to this and related problems. Experiments …
  europepmc 2023 置信度 0.91 computer scienceartificial intelligencecs.lgdeep learningbiology
• EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.
  Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule producing dist…
  europepmc 2024 置信度 0.94 cs.aics.lgq-bio.bmphysics.bio-phq-bio.qm
• Recent Advances in NMR Protein Structure Prediction with ROSETTA.
  Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be …
  europepmc 2023 置信度 0.91 q-bio.bmq-bio.qmcomputer sciencecomputational biologycs.lg
• A comparative study of protein structure prediction tools for challenging targets: Snake venom toxins.
  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 protein stru…
  europepmc 2024 置信度 0.94 computer sciencecs.lgbiologyq-bio.qmpsychology
• Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15.
  We present the monomer and multimer structure prediction results of our methods in CASP15. We first designed an elaborate pipeline that leverages complementary sequence databases and advanced database searching algorithms to generate high-quality multiple sequ…
  europepmc 2023 置信度 0.91 q-bio.bmcs.lgcs.ceq-bio.gnphysics.bio-ph
• Protein structure prediction using the evolutionary algorithm USPEX.
  Protein structure prediction is one of major problems of modern biophysics: current attempts to predict the tertiary protein structure from amino acid sequence are successful mostly when the use of big data and machine learning allows one to reduce the "predic…
  europepmc 2023 置信度 0.91 q-bio.bmcs.lgq-bio.qmphysics.bio-phcomputational biology
• Before and after AlphaFold2: An overview of protein structure prediction.
  Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimen…
  europepmc 2023 置信度 0.91 biologycs.lgq-bio.bmcomputational biologyq-bio.qm
• Protein structure prediction with energy minimization and deep learning approaches.
  In this paper we discuss the advantages and problems of two alternatives for ab initio protein structure prediction. On one hand, recent approaches based on deep learning, which have significantly improved prediction results for a wide variety of proteins, are…
  europepmc 2023 置信度 0.91 cs.lgq-bio.bmq-bio.qmcomputational biologycs.ce
• Deep Learning for Protein Structure Prediction: Advancements in Structural Bioinformatics
  Motivation Accurate prediction of protein structures is crucial for understanding protein function, stability, and interactions, with far-reaching implications in drug discovery and protein engineering. As the fields of structural bioinformatics and artificial…
  europepmc 2023 置信度 0.91 cs.lgq-bio.bmcomputational biologycs.aiq-bio.qm
• Challenges in Bridging the Gap Between Protein Structure Prediction and Functional Interpretation
  The rapid evolution of protein structure prediction tools has significantly broadened access to protein structural data. Although predicted structure models have the potential to accelerate and impact fundamental and translational research significantly, it is…
  europepmc 2023 置信度 0.91 drug discoveryq-bio.bmbiologycs.lgcomputational biology
• Salp-J Colony Optimization-based advanced hybrid ensemble deep predictor with LSTM for protein structure prediction.
  Protein structure prediction (PSP) is a key concern in computational biology, which is considered a challenging task that is vital to determine the structure and the protein function since each protein possesses a definite shape, whereas the protein secondary …
  europepmc 2025 置信度 0.94 q-bio.bmcs.lgq-bio.qmphysics.bio-phalgorithm
• 3 = 1 + 2: how the divide conquered de novo protein structure prediction and what is next?
  Protein structure prediction has been a challenging problem for >60 years. Its progress has been considered incremental for the past 20 years until leaps were made by AlphaFold that seemed to suddenly reach experimental accuracy. We argue that this unexpected …
  europepmc 2023 置信度 0.84
• Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.
  Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack …
  europepmc 2023 置信度 0.91 cs.lgq-bio.bmq-bio.qmphysics.bio-phartificial intelligence
• Crosslinking mass spectrometry data bolster protein structure prediction.
  europepmc 2023 置信度 0.73
• Protein structure prediction in the deep learning era.
  Significant advances have been achieved in protein structure prediction, especially with the recent development of the AlphaFold2 and the RoseTTAFold systems. This article reviews the progress in deep learning-based protein structure prediction methods in the …
  europepmc 2022 置信度 0.91 q-bio.bmcs.lgq-bio.gnphysics.bio-phcs.ce
• Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Proteins?
  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 2023 置信度 0.91 cs.lgcomputer sciencecs.aibiologyq-bio.qm
• Protein Structure Prediction with Expectation Reflection
  Sequence covariation in multiple sequence alignments of homologous proteins has been used extensively to obtain insights into protein structure. However, global statistical inference is required in order to ascertain direct relationships between amino acid pos…
  europepmc 2022 置信度 0.91 cs.lgq-bio.bmcomputer and information sciencesq-bio.qmcs.ai
• Protein Structure Prediction with Expectation Reflection
  Sequence covariation in multiple sequence alignments of homologous proteins has been used extensively to obtain insights into protein structure. However, global statistical inference is required in order to ascertain direct relationships between amino acid pos…
  europepmc 2022 置信度 0.91 cs.lgq-bio.bmcomputer and information sciencesq-bio.qmcs.ai
• Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.
  Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein …
  europepmc 2023 置信度 0.91 cs.lgcomputational biologyq-bio.qmcs.aiq-bio.bm
• Evaluating the performance of protein structure prediction in detecting structural changes of pathogenic nonsynonymous single nucleotide variants
  A bstract Protein structure prediction serves as an efficient tool, saving time and circumventing the need for laborious experimental endeavors. Distinguished methodologies, including AlphaFold, RoseTTAFold, and ESMFold, have proven their precision through rig…
  europepmc 2023 置信度 0.91 q-bio.bmbiologycomputational biologycs.cecs.lg
• Protein structure prediction has reached the single-structure frontier.
  europepmc 2023 置信度 0.73
• LightRoseTTA: High-efficient and Accurate Protein Structure Prediction Using an Ultra-Lightweight Deep Graph Model
  Accurately predicting protein structure, from amino acid sequences to three-dimensional structures, is of great significance in biological research. To tackle this issue, a representative deep big model, RoseTTAFold, has been proposed with promising success. H…
  europepmc 2023 置信度 0.91 cs.lgcs.aicomputer and information sciencesq-bio.qmcomputer science
• 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 2023 置信度 0.91 computer sciencebiologycomputational biologycs.lggenetics
• Improving protein structure prediction using templates and sequence embedding.
  Motivation Protein structure prediction has been greatly improved by deep learning, but the contribution of different information is yet to be fully understood. This article studies the impacts of two kinds of information for structure prediction: template and…
  europepmc 2023 置信度 0.91 cs.lgq-bio.bmcomputer sciencecs.aistat.ml
• TemPred: A Novel Protein Template Search Engine to Improve Protein Structure Prediction.
  Among new protein structure predictors, the recently developed AlphaFold predictor relies on contact map in line with contact map potential based threading model that basically relies on fold recognition. In parallel, sequence similarity based homology model r…
  europepmc 2023 置信度 0.91 q-bio.bmcs.lgq-bio.qmcs.cecomputational biology
• Emerging insight of whole genome sequencing coupled with protein structure prediction into the pyrazinamide-resistance signature of Mycobacterium tuberculosis.
  Pyrazinamide (PZA) is considered to be a pivotal drug to shorten the treatment of both drug-susceptible and drug-resistant tuberculosis, but its use is challenged by the reliability of drug-susceptibility testing (DST). PZA resistance in Mycobacterium tubercul…
  europepmc 2024 置信度 0.94 biologygeneticsbiochemistryphysics.bio-phpharmacology
• Proteomic analysis and protein structure prediction of Shigella phage Sfk20 based on a comparative study using structure prediction approaches.
  Bacteriophages are the natural predators of bacteria and are available abundantly everywhere in nature. Lytic phages can specifically infect their bacterial host (through attachment to the receptor) and use their host replication machinery to replicate rapidly…
  europepmc 2024 置信度 0.94 biologycomputational biologymedical physicsinternal medicinechemistry
• Ab initio protein structure prediction: the necessary presence of external force field as it is delivered by Hsp40 chaperone.
  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 the cente…
  europepmc 2023 置信度 0.91 q-bio.bmadsorptioncs.cephysics.space-phquantum dot
• The prospects and opportunities of protein structure prediction with AI.
  europepmc 2022 置信度 0.73
• Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning.
  While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or for which few homologous sequences are known. Here we introduce AlphaLink, a modified version of the Alpha…
  europepmc 2023 置信度 0.91 q-bio.bmq-bio.qmphysics.bio-phcs.lgcomputational biology