Harness the Power of Evolutionary Scale Modeling (ESM) on Vecura

What is ESM?

Evolutionary Scale Modeling (ESM) is a powerful family of transformer-based protein language models developed by Meta FAIR. These models are trained on hundreds of millions of protein sequences, allowing them to learn deep biological insights directly from sequence data without the need for computationally expensive multiple sequence alignments (MSAs).

It helps users rapidly predict 3D structures, score the fitness of protein variants, design new protein sequences, and extract rich numerical embeddings for downstream machine learning tasks. It is especially useful for researchers in biotechnology and drug discovery who need to perform structural and functional protein analysis quickly and at scale.

What can users do with ESM on Vecura?

With ESM on Vecura, users can:

• Generate 3D Protein Structures: Predict full-atom atomic-resolution structures from single sequences in seconds using ESMFold.
• Predict Variant Effects: Prioritize candidate mutations by scoring their predicted evolutionary fitness impacts.
• Design Sequences via Inverse Folding: Generate new sequences that are structurally compatible with a given backbone using ESM-IF1.
• Extract Protein Embeddings: Derive high-quality residue and sequence-level numerical representations for use in clustering, classification, and other predictive modeling workflows.

What the output means

The output provides a comprehensive suite of data, including PDB-formatted structural files with pLDDT confidence scores, variant fitness scores, FASTA sequences for novel protein designs, and multidimensional embedding vectors.

This output should be used to support scientific decision making. It does not replace experimental validation.

Why this matters

The traditional methods for predicting protein structure and function, such as those relying on MSAs, can be time-consuming and sometimes unfeasible for novel or obscure protein sequences. By bypassing these requirements, ESM provides a significantly faster, more scalable alternative that maintains competitive accuracy.

This advancement democratizes access to sophisticated structural biology tools, enabling a broader range of researchers to accelerate protein engineering, disease mechanism research, and therapeutic design without needing extensive local computational infrastructure.

• Developed by: Meta Fundamental AI Research (FAIR)
• Source: Official GitHub Repository
• Reference: Lin et al. 2023, Science