Fig. 1: Design of CoCoFold.

a CoCoFold consists of three parts: a fixed part (frozen AF-m weights), a fine-tuning part (trainable weights initialized from AF-m), and a training part. The gradient descent employed for fine-tuning the fine-tuning part is backpropagated from the training part, which constitutes a Gaussian mixture Molmap module. This module generates a density map of the predicted structure using a Gaussian Mixture Model (GMM), e.g., MolMap. Projections are then computed under specified poses and CTFs and subsequently compared with raw particle images. The resulting FRC loss is backpropagated through the Gaussian mixture Molmap module, while the weights within this module undergo training simultaneously. b The fine-tuning part operates as follows: The Evoformer generates fixed pair representations and MSA representations, adhering to the original information flow of AF-m. A parallel refinement branch incorporates a lightweight attention mechanism and linear transformation to adapt MSA representations based on image-derived constraints. These updated features are integrated with the IPA module and backbone frames module to produce refined structures. Here, “f” and “f_image” denote features derived from the original information flow and the image-constrained information flow, respectively. c The training component operates as follows: First, the predicted atomic model is aligned to the experimental coordinate system using a fixed affine transformation. This transformation is derived by aligning the model to the initial map reconstructed from particle images. Notably, the map serves solely to provide the affine transformation matrix and does not participate in gradient updates. Subsequently, a simulated density based on GMM is generated, which is then utilized to produce 2D projections for iterative fine-tuning.