High-resolutionde novostructure prediction from primary sequence

Abstract

Recent breakthroughs have used deep learning to exploit evolutionary information in multiple sequence alignments (MSAs) to accurately predict protein structures. However, MSAs of homologous proteins are not always available, such as with orphan proteins and fast-evolving proteins like antibodies, and a protein typically folds in a natural setting from its primary amino acid sequence into its three-dimensional structure, suggesting that evolutionary information and MSAs should not be necessary to predict a protein's folded form. Here, we introduce OmegaFold, the first computational method to successfully predict high-resolution protein structure from a single primary sequence alone. Using a new combination of a protein language model that allows us to make predictions from single sequences and a geometry-inspired transformer model trained on protein structures, OmegaFold outperforms RoseTTAFold and achieves similar prediction accuracy to AlphaFold2 on recently released structures. OmegaFold enables accurate predictions on orphan proteins that do not belong to any functionally characterized protein family and antibodies that tend to have noisy MSAs due to fast evolution. Our study fills a much-needed structure prediction gap and brings us a step closer to understanding protein folding in nature.

The Protein Common Assembly Database (ProtCAD) – A comprehensive structural resource of protein complexes

“…Recently, our ProtCID database has been widely used for benchmarking interface/assembly predictors in the 3D-BioInfo community of ELIXIR (https://elixir-europe.org/communities/3d-bioinfo). Similarly, we believe the common assembly clusters in ProtCAD can also be used for benchmark data, as well as training and testing data sets for structure prediction of protein complexes, especially in the rapidly developing field of deep learning structure predictors (42,44,45).…”

“…can also be used for benchmark data, as well as training and testing data sets for structure prediction of protein complexes, especially in the rapidly developing field of deep learning structure predictors (42,44,45).…”

LambdaPP: Fast and accessible protein-specific phenotype predictions

“…Further parameters are an early stop criterion of a prediction certainty (pLDDT) above 85 or below 40, a default recycle count of 3 and a compilation of only the best performing out of five AlphaFold2 models. As the goal of LambdaPP is to provide a single reference for pLM-based predictions, 3D structure will soon be predicted using tools just recently presented in the literature (35; 72; 74), which will allow structure prediction to happen in seconds rather than in minutes, at accuracy comparable with MSA-based methods.…”

“…While some pLM-based methods do not reach the performance of MSA-based methods (23; 38; 72), others exceed those (5; 10; 23; 39; 42; 64; 28; 29; 36). Prediction performance has risen so much that sequence-specific predictions based on pLMs can capture some aspects of structural and functional dynamics better than much more accurate family-averaged solutions even from AlphaFold2 (35; 72; 74).…”

“…All results are linked to 3D structure visualizations, currently retrieved from the AlphaFold Database (71) or if unavailable predicted using ColabFold , easing the access to AlphaFold2 at similar performance (45). As the only method of LambdaPP not based on pLM inputs, it will soon be complemented by pLM-based solutions (35; 72; 74). As novel AI tools leveraging embeddings emerge, e.g.…”