How to Use ESM-IF1

ESM-IF1 is an inverse folding model that designs amino-acid sequences conditioned on a protein backbone. The original paper trained the model on millions of experimentally determined and AlphaFold2-predicted structures, showing that large-scale structural pretraining improves recovery of sequences compatible with a target fold.

On Neurosnap, researchers upload an Input Structure, specify Designed Chains, and sample a library of candidate sequences with Number Sequences. That makes the workflow useful for fold-constrained redesign, interface-chain replacement, and structure-aware exploration around a known backbone.

The results page emphasizes sequence ranking rather than generation alone. Mean log-likelihood, perplexity, sequence recovery, and position-wise amino-acid frequency views make it easier to separate conservative redesigns from broader explorations of sequence space.

The method learns residue probabilities directly from backbone atom geometry, so it can propose sequences that are structurally compatible with a given fold instead of merely extrapolating from sequence homology. In the paper, this geometry-aware setup was effective for both single chains and multichain complexes.

On Neurosnap, Designed Chains lets users redesign only the chains of interest while the rest of the structure remains context. Sampling Temperature controls the exploitation-versus-diversity trade-off, and Training Mode activates dropout-driven sampling when a broader design set is more valuable than the most conservative predictions.

Researchers usually treat ESM-IF1 output as a ranked candidate library for downstream folding, stability, binding, or wet-lab screening. The sequence statistics and amino-acid frequency map are especially useful when deciding whether the model is converging on one robust solution or several distinct sequence families.

• Backbone-native design: ESM-IF1 starts from a protein structure, which is exactly the right input for fold-constrained sequence redesign.
• Chain-selective control: Designed Chains makes it possible to redesign only the chains that matter while preserving structural context from the rest of the complex.
• Sampling controls: Sampling Temperature and Training Mode help move between conservative recovery and more diverse design libraries.
• Ranked sequence evidence: Log-likelihood, perplexity, sequence recovery, and amino-acid frequency views support much better candidate triage than raw sequences alone.