Boltz is an artificial-intelligence method for predicting biomolecular structures consisting of proteins, RNA, DNA, and other molecules such as ligands, cofactors, and drugs. Inspired by AlphaFold 3, Boltz is fully open-source and freely available for both academic and commercial use under the MIT license. See:
Boltz-1 Democratizing Biomolecular Interaction Modeling. Wohlwend J, Corso G, Passaro S, Reveiz M, Leidal K, Swiderski W, Portnoi T, Chinn I, Silterra J, Jaakkola T, Barzilay R. bioRxiv [Preprint]. 2024 Dec 27:2024.11.19.624167.
The ChimeraX Boltz tool installs and runs Boltz-1 on the local machine. The prediction runs on Mac, Linux, and Windows without requiring an Nvidia GPU, typically taking on the order of minutes, and is run in background so that ChimeraX can be used for other tasks. The ChimeraX Boltz tool can be opened from the Structure Prediction section of the Tools menu and manipulated like other panels (more...). It is also implemented as the boltz command.
Boltz-predicted structures vary in confidence levels (see coloring) and should be interpreted with caution. Residue-residue alignment errors for the modeled structures are shown in the Error Plot. See the ChimeraX Boltz example and video Boltz structure prediction in ChimeraX. See also: AlphaFold, ESMFold, Modeller Comparative, Model Loops
Boltz Installation
Defining and Running a Prediction
Error Plot
Limitations
Boltz-1 installation only needs to be done once per computer, as long as the ChimeraX installation is not moved or deleted. Clicking the Install Boltz button on the tool dialog creates a Python virtual environment to install Boltz from PyPi. Boltz uses Torch and other packages, and the total installation including Boltz-1, the trained neural network weights, and the PDB Chemical Component Dictionary for defining residue types is about 4 GB and may take 10 minutes or more to download and install, depending on network speed. Installation can also be done with the command boltz install.
The Options button shows/hides additional options:
The model for a sequence that was specified by structure chain will be superimposed on that chain and assigned structure-comparison attributes for further analysis (details...).
Error plot shows a plot of the predicted aligned error (PAE), in which color gradations show (for each pairwise combination of residues) the expected error in position of one residue when the true and predicted structures are aligned based on the other residue.
When first opened, the predicted structures are automatically colored by the pLDDT confidence measure (same as for AlphaFold models) in the B-factor field:
...in other words, using
color bfactor palette esmfoldThe Color Key graphical interface or a command can be used to draw a corresponding color key, for example:
key red:low orange: yellow: cornflowerblue: blue:high [other-key-options]
Besides the per-residue pLDDT confidence measure, Boltz gives for each pair of residues (X,Y) the expected position error at residue X if the predicted and true structures were aligned on residue Y. These residue-residue “predicted aligned error” (PAE) values can be shown in a plot by clicking the Error plot button on the Boltz dialog.
When the mouse cursor is over the plot, the residue pair and PAE value at its current position are reported in the bottom right corner of the window.
Clicking Color PAE Domains clusters the residues into coherent domains (sets of residues with relatively low PAE values) and uses randomly chosen colors to distinguish these domains in the structure (details...). Clicking Color pLDDT returns the structure to the default confidence coloring.
The plot's context menu includes:
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The Color Key graphical interface or a command can be used to draw (in the main graphics window) a color key for the PAE plot. For example, to make a color key that matches the pae or paegreen scheme, respectively:
key pae :0 : : :15 : : :30 showTool true
key paegreen :0 : : :15 : : :30 showTool true
A title for the color key (e.g., “Predicted Aligned Error (Å)”) would need to be created separately with 2dlabels.
Structure size. Boltz uses a lot of memory, and the amount of available memory limits the size of structures that can be predicted. For a computer with 32 Gbytes, the size limit is roughly 1000 residues plus ligand atoms (called “tokens”). Consumer Nvidia GPUs with 8 or 12 GB of memory (e.g. RTX 3070) may only be capable of much smaller predictions. Consumer Nvidia GPUs with 24 GB (RTX 3090 and RTX 4090) appear to be able to predict 1000 tokens. This is perhaps the most important shortcoming of Boltz compared to AlphaFold 3, which handles memory more efficiently and is able to predict 5000 tokens with 80GB of GPU memory, about twice the size that Boltz can predict. A drawback of AlphaFold 3 is that it requires Linux and an Nvidia GPU in addition to various licensing restrictions. We hope that in the future Boltz will optimize its memory use to allow predicting larger structures.
Run time. The computation time increases quadratically with the number of tokens, so a prediction with 3 times the number of residue and ligand atoms will take approximately 9 times longer to run.
Nvidia GPU support on Windows. Currently the torch 2.6.0 from PyPi used with the standard Boltz installation does not include Nvidia GPU support on Windows. If you wish to use the Nvidia GPU with CUDA version 12.6 on a Windows machine, run the following commands in a Windows Command Prompt. (For other CUDA versions refer to the Torch installation page for the correct install command.)
> cd C:\Users\username\boltz\Scripts > pip.exe uninstall torch > pip.exe install torch --index-url https://download.pytorch.org/whl/cu126
No covalently linked ligands. Although Boltz can predict covalently linked ligands, that capability is not yet available in the ChimeraX interface or command.
No chain identifiers assigned. It can be helpful to assign chain identifiers (A,B,C...) to the different molecular components to match existing structures. Boltz is capable of this, but the ChimeraX user interface does not currently allow it.
MSA sequence alignments. Boltz uses the Colabfold MSA server (https://api.colabfold.com) for computing deep sequence alignments. This requires internet connectivity and is subject to outages if that server (currently in Korea) is down.