An Efficient Greedy Incremental Sequence Clustering Algorithm

Ju, Zhen; Zhang, Huiling; Meng, Jingtao; Zhang, Jingjing; Li, Xuelei; Fan, Jianping; Pan, Yi; Liu, Weiguo; Wei, Yanjie

doi:10.1007/978-3-030-91415-8_50

Cited by 2 publications

(1 citation statement)

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“…The search will stop if the target MSA has N seq > 25*L (L is the sequence length) and N eff > 8*L, where N seq is the number of sequences (with sequence coverage >50%) and N eff [defined in ( Zhang et al, 2021 )] is the number of effective sequences in the MSA. After the search, the final MSA is obtained through sequence clustering (with sequence identity of 95%) using our in-house software nGIA ( Ju et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Inter-Residue Distance Prediction From Duet Deep Learning Models

et al. 2022

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Residue distance prediction from the sequence is critical for many biological applications such as protein structure reconstruction, protein–protein interaction prediction, and protein design. However, prediction of fine-grained distances between residues with long sequence separations still remains challenging. In this study, we propose DuetDis, a method based on duet feature sets and deep residual network with squeeze-and-excitation (SE), for protein inter-residue distance prediction. DuetDis embraces the ability to learn and fuse features directly or indirectly extracted from the whole-genome/metagenomic databases and, therefore, minimize the information loss through ensembling models trained on different feature sets. We evaluate DuetDis and 11 widely used peer methods on a large-scale test set (610 proteins chains). The experimental results suggest that 1) prediction results from different feature sets show obvious differences; 2) ensembling different feature sets can improve the prediction performance; 3) high-quality multiple sequence alignment (MSA) used for both training and testing can greatly improve the prediction performance; and 4) DuetDis is more accurate than peer methods for the overall prediction, more reliable in terms of model prediction score, and more robust against shallow multiple sequence alignment (MSA).

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Section: Methodsmentioning

confidence: 99%