Predict protein structures from single-sequence or MSA inputs.
npx skills add https://github.com/guanxiaol/open-science --skill esmfold2
Please help me install this Agent Skill. Skill: esmfold2 Source: https://github.com/guanxiaol/open-science/tree/main/runtime/skills/esmfold2 Command: npx skills add https://github.com/guanxiaol/open-science --skill esmfold2
ESMFold2 predicts protein structures from single-sequence inputs or Multiple Sequence Alignments (MSA) inputs and offers additional features such as embedding generation, mutation scoring, contact prediction, and SAE features for interpretability.
To run an ESMFold2 structure prediction with MSA input for your protein sequences, you can use the following command: python run_structure_prediction('my_seq.msa', output_format='cif')
High-intent search queries and answers about installing and using this skill.
Predict complex protein structures from single-sequence inputs by running a structure prediction script that processes raw amino acid sequences and outputs accurate 3D structural models for bioinformatics research.
Yes, you can run predictions using MSA inputs to validate binding and interface accuracy, ensuring high-precision structural analysis for designed protein binders in computational biology.
Validate designed binders rapidly using ESMFold2-Fast's rapid prediction speed to accurately assess complex protein structures before moving to slower MSA-based validation methods.
Yes, the protein structure prediction approach supports embedding generation, mutation scoring, contact prediction, and SAE features to provide interpretability for deep learning molecular biology analysis.
Output predicted protein structures in CIF format by specifying the output format parameter in the structure prediction command, generating structural files for bioinformatics and drug discovery workflows.
PKU-YuanGroupConverts PDF documents to clean, structured Markdown.
Runchuan-BUQuery the AlphaFold EBI API for UniProt protein structures and retrieve PDB/CIF files and confidence metrics.
adaptyvbioPredict protein structures from FASTA sequences using Chai-1.
PKU-YuanGroupPredict protein, nucleic-acid, and small-molecule complex structures with Boltz-2.
