How to Use TEMPRO Nanobody Melting Temperature Prediction

TEMPRO is a nanobody-focused thermostability model that predicts melting temperature (Tm) directly from amino-acid sequence. The Scientific Reports paper frames the method around protein-language-model embeddings, which makes the workflow useful when researchers need thermal-stability estimates before structural characterization or experimental screening.

This is especially relevant for nanobody discovery and developability triage. Researchers often need to compare many binders, affinity-matured variants, or framework edits early in a campaign, and Tm is one of the clearest quick-readouts for whether a candidate is likely to behave robustly during purification, formulation, or downstream engineering.

On Neurosnap, users submit one or many Input Sequences and receive a CSV of predicted melting temperatures. That batch-oriented setup is best used comparatively across a nanobody panel rather than as a definitive substitute for thermal unfolding experiments.

The published TEMPRO workflow uses embeddings from the ESM-2 protein language model and feeds those sequence representations into a regression network trained to estimate nanobody melting temperature. The important methodological point is that the predictor does not rely on a manually engineered feature set or an explicit structure model; it leverages information already encoded in large-scale protein-language-model representations.

On Neurosnap, Model Size exposes the released TEMPRO workflows built on the 650M, and 3B ESM-2 backbones. Larger models are heavier to run, but they let researchers choose between faster screening and the highest-capacity embedding model described by the original tool. Batch Size controls how many sequences are embedded per forward pass, which mainly affects throughput and memory use rather than the biological interpretation of the results.

Researchers should interpret the output as a ranking signal for developability. Higher predicted Tm values can help prioritize which nanobodies deserve expression, thermal shift assays, framework optimization, or more resource-intensive downstream characterization.

• Nanobody-native stability screening: TEMPRO is designed specifically for nanobody melting-temperature estimation rather than generic protein-property scoring.
• No structure prerequisite: Candidates can be ranked from sequence alone, which is useful before folding, docking, or wet-lab stability assays.
• Multiple embedding scales: Model Size lets researchers choose among 650M, and 3B ESM-2 workflows depending on throughput needs and desired model capacity.
• Batch comparison workflow: Panels of nanobody variants can be screened in one run to identify the most promising candidates quickly.
• Simple downstream handoff: A CSV of sequence names, sequences, and predicted Tm values is easy to merge into broader binder-discovery or developability pipelines.