ESMFold

ESMFold is a revolutionary protein structure prediction model developed by Meta AI (FAIR) that leverages Large Language Models (LLMs) to fold proteins directly from primary sequences. Unlike AlphaFold2, which relies on computationally expensive Multiple Sequence Alignments (MSAs), ESMFold utilizes the ESM-2 protein language model to infer structural information from evolutionary patterns captured during pre-training on billions of protein sequences. This architecture allows ESMFold to be up to 60 times faster than AlphaFold2 for sequences of average length while maintaining near-atomic resolution. By 2026, ESMFold has become the industry standard for high-throughput metagenomic analysis and initial structural screening in drug discovery pipelines. Its ability to predict structures for orphan proteins and dark matter in the protein universe—where no MSAs are available—makes it an indispensable tool for synthetic biology. The model's efficiency enables the folding of entire metagenomic databases, such as the ESM Metagenomic Atlas, which contains over 600 million predicted structures. While slightly less accurate than MSA-based methods on complex multi-domain proteins, its speed-to-accuracy trade-off is unmatched for large-scale genomic characterization.