Abstract: Embodiments of the present disclosure provides a method, an electronic device and a storage medium. In the method, a plurality of first residue features for a plurality of residues in the first conformation and a plurality of second residue features for a plurality of residues in a second conformation are determined based on a protein sequence and a first conformation, the protein sequence comprising a plurality of residues; the plurality of second residue features are updated based on temporal information and spatial information of the plurality of first residue features; and the second conformation is generated based on the updated plurality of second residue features.

Abstract: Embodiments of the present disclosure provides a method, an electronic device and a storage medium. In the method, a number of residues of a protein structure is obtained, a protein backbone of the protein structure in a first scale is generated, and the protein structure in a second scale is generated based on the number and the protein backbone, where the second scale is larger than the first scale.

Abstract: Embodiments of the disclosure provide a solution for a protein language model. A method includes: obtaining a sequence representation of a protein comprising a plurality of amino acid residues, the sequence representation characterizing an amino acid sequence of the protein; determining, by a language model, a predicted discrete structure representation based on the sequence representation, wherein the predicted discrete structure representation comprises a plurality of bit sequences corresponding to the plurality of amino acid residues respectively, a bit sequence of the plurality of bit sequences represents a predicted local structure of a corresponding amino acid residue; and generating a target structure of the protein based on the predicted discrete structure representation.

Abstract: Embodiments of the present disclosure provide a solution for conformation generation optimization. A method includes: determining a sequence feature representation and a conformation feature representation of a protein, the sequence feature representation characterizing an amino acid sequence of the protein and the conformation feature representation characterizing a conformation of the protein; fusing the sequence feature representation and the conformation feature representation based on a plurality of reference conformations in a conformation sequence of the protein, to obtain a fused feature representation; and generating a target conformation at a target temporal position in the conformation sequence at least based on the fused feature representation.

Abstract: The present disclosure describes techniques for generating protein sequences using machine learning models. A machine learning model is configured by implanting a structural adapter into a sequence decoder. The machine learning model is configured to generate a protein sequence from a specified structure. The machine learning model is endowed with protein structural awareness by the structural adapter. The machine learning model is equipped with protein sequential evolutionary knowledge by the sequence decoder. The machine learning model comprises the structural adapter, the sequence decoder, and a structure encoder. An initial sequence is generated based on the specified structure by the structure encoder. The protein sequence is optimized through an iterative process. The iterative process comprises progressively refining the protein sequence by iterative decoding. The structural adapter non-linearly imposes representations of the specified structure on a sequence predicted in the iterative process.

Abstract: The present disclosure provides a cryo-electron microscopy image processing method and apparatus, a terminal, and a storage medium. The cryo-electron microscopy image processing method includes: obtaining a cryo-electron microscopy image; encoding the cryo-electron microscopy image into latent variables through an encoder; converting the latent variables into an atomic model structure through a decoder; correcting the atomic model structure using a loss function to obtain a corrected atomic model structure, where the loss function includes a bond length constraint loss function, a clash constraint loss function, and a spring constraint loss function; and converting, through a projector module, the corrected atomic model structure into a density map represented by Gaussian spheres, and projecting the density map to obtain a projection image.