RoseTTAFold — deep learning-based protein structure prediction using a three-track neural network architecture for simultaneous processing of 1D sequence, 2D distance maps, and 3D coordinates. Predict
Use with AI
Install the MCP server or CLI to instantly fetch RoseTTAFold documentation:
Install command
claude mcp add biocontext7 -- npx @biocontext7/mcpOr share this page: biocontext7.com/tools/rosettafold
AlphaFold2 — deep learning system for predicting 3D protein structures from amino acid sequences with atomic-level accuracy. Uses multiple sequence alignments (MSAs) and an attention-based Evoformer a
3 shared topics • 2 shared operations
Infernal — RNA covariance model (CM) toolkit for homology search and structural alignment of RNA sequences. Builds profile CMs from Stockholm alignments (cmbuild), calibrates E-values (cmcalibrate), s
3 shared topics • 2 shared operations
MDAnalysis — Python library for analyzing molecular dynamics trajectories and atomic coordinate data. Supports 40+ file formats (DCD, XTC, TRR, PDB, GRO, AMBER, LAMMPS). Provides RMSD, RMSF, hydrogen
3 shared topics • 2 shared operations
MDTraj — Python library for reading, writing, and analyzing molecular dynamics trajectories. Supports 20+ formats (DCD, XTC, TRR, PDB, HDF5, NetCDF, etc.) with NumPy-native arrays. Provides RMSD, RMSF
3 shared topics • 2 shared operations
Boltz-1 — open-source deep learning model for predicting biomolecular 3D structures and interactions, approaching AlphaFold3-level accuracy. Supports protein, DNA, RNA, and small-molecule ligand struc
2 shared topics • 3 shared operations