{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-highly-accurate-protein-structure-prediction-with-alphafold-alphafold","slug":"highly-accurate-protein-structure-prediction-with-alphafold-alphafold","name":"Highly accurate protein structure prediction with AlphaFold (Alphafold)","type":"product","url":"https://www.nature.com/articles/s41586-021-03819-2","page_url":"https://unfragile.ai/highly-accurate-protein-structure-prediction-with-alphafold-alphafold","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-highly-accurate-protein-structure-prediction-with-alphafold-alphafold__cap_0","uri":"capability://data.processing.analysis.end.to.end.differentiable.protein.structure.prediction.from.sequence","name":"end-to-end differentiable protein structure prediction from sequence","description":"Predicts 3D protein structures from amino acid sequences using a deep learning architecture that combines MSA (multiple sequence alignment) embeddings with pairwise distance predictions and angle regression. The model uses attention mechanisms to learn evolutionary and structural patterns from homologous sequences, then outputs atomic coordinates with confidence scores (pLDDT) for each residue. Works by processing raw protein sequences through transformer-based encoders that learn both sequence context and structural constraints in a single forward pass.","intents":["predict the 3D structure of a protein given only its amino acid sequence","obtain high-confidence structural predictions without experimental validation","generate atomic coordinates for downstream molecular modeling and drug design","assess structural confidence per residue to identify reliable vs uncertain regions"],"best_for":["structural biologists and computational chemists validating experimental hypotheses","drug discovery teams screening protein targets for binding pockets","researchers studying protein function without access to cryo-EM or X-ray crystallography","teams building structure-based ML models that require ground-truth 3D coordinates"],"limitations":["Prediction quality degrades for proteins with few homologous sequences in databases (rare proteins)","Cannot predict dynamic conformational changes or intrinsically disordered regions reliably","Requires significant computational resources (GPU/TPU) for inference on large proteins (>1500 residues)","Confidence scores (pLDDT) may be overconfident in some cases; experimental validation still recommended","Does not model post-translational modifications, ligand binding, or protein-protein interactions directly"],"requires":["Protein sequence in FASTA format","Access to sequence databases (UniRef90, BFD) for MSA generation or pre-computed MSA","GPU with 8GB+ VRAM for inference (16GB+ for large proteins)","Python 3.8+","JAX or PyTorch runtime"],"input_types":["amino acid sequence (FASTA format)","multiple sequence alignment (optional, pre-computed)","protein ID (for database lookup)"],"output_types":["PDB file with atomic coordinates","per-residue confidence scores (pLDDT)","PAE (predicted aligned error) matrix","JSON with structure metadata"],"categories":["data-processing-analysis","scientific-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-highly-accurate-protein-structure-prediction-with-alphafold-alphafold__cap_1","uri":"capability://data.processing.analysis.multi.chain.protein.complex.structure.assembly","name":"multi-chain protein complex structure assembly","description":"Extends single-chain prediction to model quaternary structures by predicting inter-chain interfaces and relative orientations between protein subunits. The architecture processes multiple sequences jointly through shared attention layers that learn cross-chain spatial relationships, then outputs coordinates for all chains with interface confidence metrics. Handles homo-oligomers and hetero-complexes by treating them as a single prediction problem with chain-aware masking.","intents":["predict how multiple protein chains dock together in a complex","model homo-oligomeric assemblies (e.g., dimers, trimers) from individual sequences","identify interface residues and binding modes between subunits","generate full quaternary structures for structural validation of multi-subunit proteins"],"best_for":["structural biologists studying protein complexes and signaling pathways","drug designers targeting protein-protein interaction interfaces","teams modeling viral capsids or enzyme complexes","researchers validating biochemical interaction data with structural models"],"limitations":["Prediction quality decreases with increasing number of chains (>5 chains may have lower confidence)","Cannot model transient or dynamic complexes; assumes stable quaternary structure","Requires all component sequences as input; cannot predict unknown binding partners","Interface confidence may be lower than single-chain predictions, especially for weak interactions","Does not account for post-translational modifications that affect complex assembly"],"requires":["Sequences of all protein chains in FASTA format","MSA for each chain (or access to sequence databases)","GPU with 16GB+ VRAM for large complexes","Python 3.8+","JAX or PyTorch runtime"],"input_types":["multiple amino acid sequences (FASTA format)","chain identifiers and stoichiometry","pre-computed MSAs (optional)"],"output_types":["PDB file with all chains and coordinates","per-residue pLDDT scores","interface PAE matrices (chain-to-chain)","assembly confidence metrics"],"categories":["data-processing-analysis","scientific-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-highly-accurate-protein-structure-prediction-with-alphafold-alphafold__cap_2","uri":"capability://data.processing.analysis.per.residue.confidence.scoring.and.uncertainty.quantification","name":"per-residue confidence scoring and uncertainty quantification","description":"Assigns pLDDT (predicted local distance difference test) scores to each residue, quantifying the model's confidence in predicted coordinates. Computed from the model's internal logits during inference, reflecting how well the model learned to predict that residue's position from training data. Also generates PAE (predicted aligned error) matrices showing expected positional errors between residue pairs, enabling identification of unreliable regions and inter-chain interfaces.","intents":["identify which regions of a predicted structure are reliable vs uncertain","filter out low-confidence predictions before using structures in downstream analysis","assess whether a prediction is suitable for drug design or functional studies","visualize structural uncertainty to guide experimental validation efforts"],"best_for":["researchers deciding whether to trust a prediction for critical applications","teams building automated pipelines that need to filter low-quality predictions","structural biologists planning experimental validation (NMR, cryo-EM) based on model confidence","developers creating visualization tools that highlight uncertain regions"],"limitations":["pLDDT scores are calibrated on training data; may be overconfident for out-of-distribution proteins","Confidence does not directly correlate with biological relevance (high-confidence wrong folds are possible)","PAE matrices are computationally expensive for very large proteins; may require downsampling","Confidence scores do not account for missing regions (e.g., signal peptides, intrinsically disordered domains)","No built-in mechanism to distinguish between confident predictions of rare folds vs confident predictions of common folds"],"requires":["Completed structure prediction (pLDDT and PAE generated during inference)","No additional input beyond standard prediction pipeline","Minimal computational overhead (scores computed during forward pass)"],"input_types":["model logits from structure prediction","predicted distance and angle distributions"],"output_types":["pLDDT scores (0-100 per residue)","PAE matrix (NxN, where N = number of residues)","confidence-filtered PDB files","JSON with per-residue metrics"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-highly-accurate-protein-structure-prediction-with-alphafold-alphafold__cap_3","uri":"capability://data.processing.analysis.homology.aware.structure.prediction.via.msa.embeddings","name":"homology-aware structure prediction via msa embeddings","description":"Leverages multiple sequence alignments (MSAs) to encode evolutionary information, using aligned homologous sequences to inform structure prediction. The model processes MSA rows through transformer encoders to extract covariation patterns (residue pairs that co-evolve), which are strong indicators of structural contacts. This evolutionary signal is combined with the query sequence to predict structures more accurately than sequence alone, especially for proteins with rich homologous data.","intents":["improve structure prediction accuracy by incorporating evolutionary information from homologous proteins","predict structures for proteins with abundant homologous sequences in databases","leverage covariation patterns to identify likely contact residues","reduce reliance on template-based modeling by using evolutionary signals"],"best_for":["researchers studying conserved protein families with many sequenced homologs","teams working with well-characterized protein domains (e.g., kinases, GPCRs)","structural biologists analyzing evolutionary relationships through structure","developers building structure-based phylogenetic tools"],"limitations":["Prediction quality degrades significantly for proteins with few homologs (orphan proteins, novel folds)","MSA generation is computationally expensive (can take hours for large databases)","MSA quality depends on database completeness; rare organisms may have sparse alignments","Covariation signals can be noisy if MSA contains functionally divergent sequences","Does not improve predictions for intrinsically disordered regions even with good MSAs"],"requires":["Protein sequence in FASTA format","Access to sequence databases (UniRef90, BFD) or pre-computed MSA","MSA generation tool (e.g., HHblits, MMseqs2) or pre-computed alignment","Python 3.8+","4GB+ RAM for MSA processing"],"input_types":["amino acid sequence (FASTA)","multiple sequence alignment (A3M or Stockholm format)","database of homologous sequences"],"output_types":["MSA embeddings (intermediate representation)","covariation matrices","PDB file with structure","per-residue confidence scores"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-highly-accurate-protein-structure-prediction-with-alphafold-alphafold__cap_4","uri":"capability://automation.workflow.batch.structure.prediction.with.resource.optimization","name":"batch structure prediction with resource optimization","description":"Processes multiple protein sequences in parallel or sequential batches with automatic resource management, including GPU memory optimization and inference scheduling. The system can handle variable-length sequences by padding and masking, and includes checkpointing strategies to reduce peak memory usage during inference. Supports both single-GPU and multi-GPU inference with automatic load balancing.","intents":["predict structures for hundreds or thousands of proteins efficiently","run large-scale structural genomics projects on limited hardware","integrate structure prediction into high-throughput screening pipelines","minimize inference time and cost for batch predictions"],"best_for":["structural genomics consortia processing proteomes","drug discovery teams screening large target libraries","researchers building structure databases for entire organisms","cloud-based services offering structure prediction as a service"],"limitations":["Memory optimization adds ~5-10% latency overhead per prediction","Multi-GPU scaling efficiency decreases with very small proteins (overhead dominates)","No built-in fault tolerance; failed predictions require manual retry","Batch processing requires all sequences upfront; cannot stream predictions","Resource optimization is hardware-specific; may not generalize across different GPU architectures"],"requires":["Python 3.8+","GPU with 8GB+ VRAM (16GB+ for large batches)","JAX or PyTorch with CUDA support","Batch input file (FASTA with multiple sequences)","Optional: multi-GPU setup (NVIDIA NCCL for distributed inference)"],"input_types":["FASTA file with multiple sequences","batch configuration (sequence count, memory limits)","resource constraints (max GPU memory, timeout)"],"output_types":["directory of PDB files (one per sequence)","batch results JSON with metadata","per-sequence timing and resource usage logs","failure report for unsuccessful predictions"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-highly-accurate-protein-structure-prediction-with-alphafold-alphafold__cap_5","uri":"capability://data.processing.analysis.structure.based.functional.annotation.and.motif.detection","name":"structure-based functional annotation and motif detection","description":"Analyzes predicted 3D structures to identify functional sites, binding pockets, and conserved structural motifs by comparing predicted coordinates against known structural databases (SCOP, Pfam). Uses geometric hashing and spatial clustering to detect recurring structural patterns (e.g., zinc fingers, kinase domains) without requiring sequence homology. Outputs annotated PDB files with predicted functional regions highlighted.","intents":["annotate functional domains and binding sites in predicted structures","identify structural homologs even when sequence identity is low","predict protein function from structure alone","detect novel structural motifs in proteins with unknown function"],"best_for":["structural biologists annotating proteins with unknown function","drug designers identifying druggable pockets in target proteins","researchers studying structural evolution and domain shuffling","teams building structure-based functional annotation pipelines"],"limitations":["Motif detection relies on structural database completeness; rare folds may not be recognized","Geometric hashing is sensitive to small coordinate errors; low-confidence predictions may yield false positives","Cannot distinguish between functional and non-functional structural similarities","Binding pocket predictions are geometric only; do not account for chemical properties or ligand specificity","Requires pre-computed structural databases; updates lag behind new PDB entries"],"requires":["Predicted protein structure (PDB file)","Access to structural databases (SCOP, Pfam, or custom database)","Python 3.8+","Geometric hashing library (e.g., scikit-learn, custom implementation)"],"input_types":["PDB file with predicted structure","structural database (SCOP, Pfam)","optional: sequence annotations"],"output_types":["annotated PDB file with functional regions","motif match report (JSON or text)","binding pocket predictions (coordinates and volume)","functional annotation summary"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-highly-accurate-protein-structure-prediction-with-alphafold-alphafold__cap_6","uri":"capability://data.processing.analysis.ligand.binding.site.prediction.and.pocket.characterization","name":"ligand binding site prediction and pocket characterization","description":"Predicts likely small-molecule binding pockets in predicted protein structures by analyzing surface geometry, hydrophobicity, and spatial clustering of residues. Uses a combination of geometric analysis (concavity detection, pocket volume calculation) and machine learning to score pocket druggability. Outputs pocket coordinates, residue lists, and predicted binding affinity ranges based on pocket properties.","intents":["identify potential drug binding sites in target proteins","assess druggability of predicted structures before experimental validation","guide structure-based drug design by highlighting promising binding pockets","prioritize targets based on predicted binding site quality"],"best_for":["drug discovery teams screening targets for ligandability","structural biologists predicting allosteric sites and regulatory pockets","computational chemists planning virtual screening campaigns","teams building target prioritization pipelines"],"limitations":["Predictions are geometric only; do not account for protein dynamics or conformational changes","Cannot predict binding specificity; high-scoring pockets may bind non-specific ligands","Accuracy depends on prediction quality; errors in structure propagate to pocket predictions","Does not model water-mediated binding or metal coordination","Pocket scoring is calibrated on known drug targets; may overestimate druggability of novel proteins"],"requires":["Predicted protein structure (PDB file)","Python 3.8+","Geometric analysis library (e.g., CGAL, scikit-learn)","Optional: machine learning model for druggability scoring"],"input_types":["PDB file with predicted structure","pocket detection parameters (minimum volume, depth thresholds)","optional: known ligand coordinates for validation"],"output_types":["pocket coordinates (center, radius, volume)","residue lists for each pocket","druggability scores (0-1)","predicted binding affinity ranges","annotated PDB file with pockets highlighted"],"categories":["data-processing-analysis","scientific-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-highly-accurate-protein-structure-prediction-with-alphafold-alphafold__cap_7","uri":"capability://data.processing.analysis.structure.validation.and.quality.assessment","name":"structure validation and quality assessment","description":"Validates predicted structures against known quality metrics including Ramachandran plot analysis (phi/psi angle distributions), clash detection (steric overlaps), and comparison against experimental structures when available. Computes RMSD, TM-score, and GDT_TS metrics to quantify structural accuracy. Generates detailed quality reports identifying problematic regions (clashes, unusual angles, outliers).","intents":["assess whether a predicted structure is physically plausible","identify regions with potential structural errors or artifacts","compare predicted structures against experimental references","validate structures before using them in downstream applications"],"best_for":["researchers validating predictions before publication or use","teams building quality control pipelines for high-throughput predictions","structural biologists comparing predicted vs experimental structures","developers creating structure visualization and analysis tools"],"limitations":["Validation metrics are calibrated on experimental structures; predicted structures may have different error distributions","Clash detection is geometry-only; does not account for dynamic behavior or transient interactions","Ramachandran analysis assumes standard amino acids; modified residues may be flagged incorrectly","RMSD and TM-score require experimental reference; not applicable for novel proteins","Quality assessment does not predict functional correctness; high-quality structures may still be functionally wrong"],"requires":["Predicted protein structure (PDB file)","Optional: experimental reference structure (PDB file) for comparison","Python 3.8+","Geometry analysis library (e.g., BioPython, MDAnalysis)"],"input_types":["predicted PDB file","optional: experimental PDB file","validation parameters (clash threshold, angle tolerances)"],"output_types":["quality report (JSON or HTML)","per-residue quality scores","Ramachandran plot data","clash list with coordinates","RMSD, TM-score, GDT_TS (if reference available)","annotated PDB with quality flags"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-highly-accurate-protein-structure-prediction-with-alphafold-alphafold__cap_8","uri":"capability://search.retrieval.alphafold.database.integration.and.structure.retrieval","name":"alphafold database integration and structure retrieval","description":"Provides access to pre-computed structure predictions for millions of proteins across major organisms (human, model organisms, pathogens) via the AlphaFold Database. Enables rapid retrieval of structures without running inference, with metadata including pLDDT scores, prediction date, and source organism. Supports bulk downloads and API-based queries for integration into bioinformatics pipelines.","intents":["quickly retrieve pre-computed structures for well-characterized proteins","access structures for entire proteomes without computational overhead","integrate AlphaFold predictions into existing bioinformatics workflows","compare structures across organisms to study evolutionary conservation"],"best_for":["researchers studying well-characterized proteins (human, model organisms)","teams building structure-based analysis pipelines","structural biologists comparing orthologs across species","developers integrating structure data into web applications"],"limitations":["Database coverage is limited to pre-selected organisms; rare species not included","Predictions are static; cannot update structures if new sequences become available","Database does not include all isoforms or splice variants","Bulk downloads require significant storage (terabytes for complete proteomes)","API rate limits may restrict high-volume queries"],"requires":["Internet connection for database access","Protein ID or sequence for lookup","Optional: API key for bulk queries","Storage for downloaded structures (varies by organism)"],"input_types":["protein ID (UniProt, Ensembl)","organism name or taxonomy ID","optional: sequence for similarity search"],"output_types":["PDB file (downloaded or streamed)","metadata JSON (pLDDT, prediction date, organism)","bulk download manifest","API response with structure URLs"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Protein sequence in FASTA format","Access to sequence databases (UniRef90, BFD) for MSA generation or pre-computed MSA","GPU with 8GB+ VRAM for inference (16GB+ for large proteins)","Python 3.8+","JAX or PyTorch runtime","Sequences of all protein chains in FASTA format","MSA for each chain (or access to sequence databases)","GPU with 16GB+ VRAM for large complexes","Completed structure prediction (pLDDT and PAE generated during inference)","No additional input beyond standard prediction pipeline"],"failure_modes":["Prediction quality degrades for proteins with few homologous sequences in databases (rare proteins)","Cannot predict dynamic conformational changes or intrinsically disordered regions reliably","Requires significant computational resources (GPU/TPU) for inference on large proteins (>1500 residues)","Confidence scores (pLDDT) may be overconfident in some cases; experimental validation still recommended","Does not model post-translational modifications, ligand binding, or protein-protein interactions directly","Prediction quality decreases with increasing number of chains (>5 chains may have lower confidence)","Cannot model transient or dynamic complexes; assumes stable quaternary structure","Requires all component sequences as input; cannot predict unknown binding partners","Interface confidence may be lower than single-chain predictions, especially for weak interactions","Does not account for post-translational modifications that affect complex assembly","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.33,"ecosystem":0.25,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-06-17T09:51:03.041Z","last_scraped_at":"2026-05-03T14:00:27.894Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=highly-accurate-protein-structure-prediction-with-alphafold-alphafold","compare_url":"https://unfragile.ai/compare?artifact=highly-accurate-protein-structure-prediction-with-alphafold-alphafold"}},"signature":"d8nvCka01I5eSWnhec86Jx0vPZjMMxMkPp0JOXr9EwgpGZtzOa9xcX+1Ur1/128AeGf/rHnPjDs2ZVUgS7upDw==","signedAt":"2026-06-21T20:15:48.196Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/highly-accurate-protein-structure-prediction-with-alphafold-alphafold","artifact":"https://unfragile.ai/highly-accurate-protein-structure-prediction-with-alphafold-alphafold","verify":"https://unfragile.ai/api/v1/verify?slug=highly-accurate-protein-structure-prediction-with-alphafold-alphafold","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}