AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding

Zheng, Lingyan; Shi, Shuiyang; Lu, Mingkun; Fang, Pan; Pan, Ziqi; Zhang, Hongning; Zhou, Zhimeng; Zhang, Hanyu; Mou, Minjie; Huang, Shijie; Tao, Lin; Xia, Weiqi; Li, Honglin; Zeng, Zhenyu; Zhang, Shun; Chen, Yuzong; Li, Zhaorong; Zhu, Feng

doi:10.1186/s13059-024-03166-1

Cited by 31 publications

(2 citation statements)

References 69 publications

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“…Perhaps surprisingly, a comparison with recent deep learning models [ 11–13 , 15 , 35 , 36 ] for GO prediction shows that BLASTp with S 2 is better many deep learning models, including DeepGO-SE [ 35 ], DeepGOplus [ 11 ], ProteInfer [ 15 ], AnnoPro [ 36 ], and TALE [ 12 ] ( Fig. S5 ).…”

Section: Resultsmentioning

confidence: 99%

A large-scale assessment of sequence database search tools for homology-based protein function prediction

Zhang,

Freddolino

2024

Briefings in Bioinformatics

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Sequence database searches followed by homology-based function transfer form one of the oldest and most popular approaches for predicting protein functions, such as Gene Ontology (GO) terms. These searches are also a critical component in most state-of-the-art machine learning and deep learning-based protein function predictors. Although sequence search tools are the basis of homology-based protein function prediction, previous studies have scarcely explored how to select the optimal sequence search tools and configure their parameters to achieve the best function prediction. In this paper, we evaluate the effect of using different options from among popular search tools, as well as the impacts of search parameters, on protein function prediction. When predicting GO terms on a large benchmark dataset, we found that BLASTp and MMseqs2 consistently exceed the performance of other tools, including DIAMOND—one of the most popular tools for function prediction—under default search parameters. However, with the correct parameter settings, DIAMOND can perform comparably to BLASTp and MMseqs2 in function prediction. Additionally, we developed a new scoring function to derive GO prediction from homologous hits that consistently outperform previously proposed scoring functions. These findings enable the improvement of almost all protein function prediction algorithms with a few easily implementable changes in their sequence homolog-based component. This study emphasizes the critical role of search parameter settings in homology-based function transfer and should have an important contribution to the development of future protein function prediction algorithms.

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Section: Resultsmentioning

confidence: 99%

A large-scale assessment of sequence database search tools for homology-based protein function prediction

Zhang,

Freddolino

2024

Briefings in Bioinformatics

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“…We will include the MSA-free HelixFold-Single method to predict 3D peptide structures as another alternative to ESMFold . Additionally, it will be added new strategies to build the graph-based representations, such as the use of contact maps predicted by the ESM-2 models, the use of distance functions other than the Euclidean distance, the use of the criteria implemented in the Graphein library, among others . In this way, it can be analyzed if topologically different graphs could be built to improve the performance of GDL-based models.…”

Section: Future Outlookmentioning

confidence: 99%

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

Cordoves-Delgado,

García-Jacas

2024

J. Chem. Inf. Model.

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Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.

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Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling

Karampuri,

Kundur,

Perugu

2024

Computers in Biology and Medicine

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AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding

Cited by 31 publications

References 69 publications

A large-scale assessment of sequence database search tools for homology-based protein function prediction

A large-scale assessment of sequence database search tools for homology-based protein function prediction

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling

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