'binding' 검색 결과

Use this skill when editing CRM Analytics dashboard JSON directly to implement advanced bindings, custom SAQL/SOQL step queries, layout changes, step parameters, or cross-widget interactions. Trigger keywords: dashboard JSON, SAQL step, binding syntax, mustache binding, dashboard REST API, widget layout, step limit, datasetId, datasetVersionId. NOT for building the dashboard in the builder UI or picking chart types — use admin/analytics-dashboard-design. NOT for writing or tuning the SAQL itself — use admin/saql-query-development.

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Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

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Predict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes. Covers the binding-affinity (BA) vs eluted-ligand (EL/presentation) distinction, why %Rank beats raw nM for cross-allele work, the MS abundance bias that misranks low-expression neoantigens, allele-coverage inequity, and length bias. Use when scanning a protein or peptide set for class I epitopes, scoring neoantigen candidates, or choosing a binding predictor. For CD4/HLA class II see mhc-class-ii-prediction.

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Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

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Predict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes. Covers the binding-affinity (BA) vs eluted-ligand (EL/presentation) distinction, why %Rank beats raw nM for cross-allele work, the MS abundance bias that misranks low-expression neoantigens, allele-coverage inequity, and length bias. Use when scanning a protein or peptide set for class I epitopes, scoring neoantigen candidates, or choosing a binding predictor. For CD4/HLA class II see mhc-class-ii-prediction.

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Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.

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Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.

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• Plan binding screens for candidate protein sequences
• Estimate experiment costs before submission
• Track laboratory progress and retrieve results

Audit Kubernetes access rules to find excessive permissions and service account token abuse paths. Trace which users or workloads could gain cluster administrator access, then produce prioritized findings and recommendations for reducing permissions.

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• Reviewing Kubernetes privilege escalation paths
• Checking permissions after an RBAC change
• Tracing workloads with overprivileged service accounts

Manage KubeSphere’s network extension, from installation and upgrades to Calico IP address pools and traffic restrictions. Inspect live cluster resources and bundled references to configure namespace bindings, plan pool migrations, and verify changes.

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• Installing or upgrading the network extension
• Managing Calico IP pools and namespace bindings
• Checking allocations before an IP pool migration

Plan or run quality checks and peak calling, which identifies enriched genomic regions, for ChIP-seq, CUT&RUN, and CUT&Tag. The workflow accounts for controls and replicates and prepares signal tracks, quality reports, and differential binding designs.

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• Check data quality and enrichment signals
• Choose narrow or broad peaks for the biological target
• Create bigWig tracks and quality dashboards

Screen supplied proteins, peptides, antibodies, or nanobodies against a target with Boltz. Prepares inputs, obtains a cost estimate for approval, downloads results, and ranks candidates by binding confidence, a score for predicted binding reliability.

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• Rank existing protein binders against a target
• Compare antibody or nanobody candidates
• Screen candidates using target sequences alone

Generate new small molecules intended to bind a target protein with Boltz. The workflow covers cost estimation, job submission, result downloads, and ranking candidates by binding confidence or a score for improving promising molecules.

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• Generate binding candidates without an existing compound library
• Rank generated molecules by binding confidence
• Review solubility and permeability of promising candidates

Generate new protein, peptide, antibody, or nanobody candidates that bind a chosen target using Boltz. The workflow covers target exploration, design setup, cost estimates, job submission, result downloads, and ranking by predicted binding confidence.

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• Design new peptide binders for a target
• Generate antibody or nanobody candidates
• Explore binding sites and target input configurations

Configure dependency injection in .NET MAUI, a way to supply objects with the services they need. Covers service registration, instance lifetimes, page and ViewModel constructors, Shell navigation, platform implementations, and test substitutions.

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• Register services, Pages, and ViewModels
• Choose shared or freshly created service instances
• Connect Shell navigation with constructor injection

Use Boltz-2 to predict structures of complexes containing proteins, DNA, RNA, and small molecules. Obtain structure files and confidence scores to assess designed binders, with optional predictions of binding strength between a protein and a small molecule.

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• Assessing a designed binder against its target
• Predicting a protein and small-molecule complex
• Predicting complexes containing DNA or RNA

Design protein sequences while accounting for bound small molecules, nucleic acids, or metals. Use LigandMPNN to redesign binding pockets or metal-coordinating sites, and export sequence files alongside PDB structures with the designed sequences applied.

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• Redesign a binding pocket around a small molecule or cofactor
• Design residues around a metal-coordinating site
• Export designed sequences as FASTA and PDB files

Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.

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Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.

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Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.

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Implement and verify new React-library ports or copied/rewritten React surfaces in Octane bindings from npm names or npm/GitHub links/lists. Enforces immutable provenance, license boundaries, dependency ordering, full-surface parity, and machine evidence. Existing-binding audits and maintenance start with update-bindings.

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Audit one, several, or all existing Octane bindings; implement selected maintenance findings or remove redundant copied files with evidence matched to source ownership. Use for binding updates, lifecycle fixes, dependency or metadata maintenance, and convenience-import requests.

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Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.

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Identify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers. Encodes the field's hard truth that binding prediction is the easy, near-solved part and single-digit-percent PPV lives downstream — so it centers clonality/CCF, HLA LOH (the silent invalidator), expression, proximal-variant phasing, agretopicity/foreignness quality, and the predicted->presented->immunogenic validation tiers. Use when nominating vaccine targets, ranking neoantigens, or building a tumor-to-candidate pipeline. Binding details in mhc-binding-prediction; ranking in immunogenicity-scoring.

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Predict peptide-MHC class II (HLA-DR/DQ/DP) binding and presentation for CD4 T-cell epitopes with NetMHCIIpan-4.3 and MixMHC2pred-2.0. Covers why class II is far less reliable than class I (open binding groove, 9-mer register ambiguity, sparse noisy training data, DR>DP>DQ accuracy asymmetry), the DQ/DP heterodimer alpha/beta pairing trap, and the looser 1%/5% %Rank thresholds. Use when predicting CD4 epitopes for vaccine help, mapping class II neoantigens, or scoring long peptides against DR/DQ/DP. For CD8/class I see mhc-binding-prediction.

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