Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function

Madani, Mohammad; Behzadi, Mohammad Mahdi; Song, Dongjin; Ilieş, Horea T.; Tarakanova, Anna

doi:10.1016/j.csbj.2022.11.020

Cited by 7 publications

(1 citation statement)

References 49 publications

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“…ResNets to predict distance maps; Si and Yan [28] hybridize 1D and 2D convolutions to increase the effective receptive field of the residual network. Madani et al [29] develop an accurate protein predictor via hybrid generative adversarial neural networks. Rahman et al [30] use three ResNets to predict the residue-residue distances within three ranges, and use the fourth ResNet to integrate their prediction results.…”

Section: Introductionmentioning

confidence: 99%

Freeprotmap: waiting-free prediction method for protein distance map

Huang,

Li,

Chen

et al. 2024

BMC Bioinformatics

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Background Protein residue–residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue–residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed. Result In this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue–residue distances in tens of milliseconds and has higher precision than the best structure prediction method. Conclusion Several groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue–residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.

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Section: Introductionmentioning

confidence: 99%