Propedia v2.3: A novel representation approach for the peptide-protein interaction database using graph-based structural signatures

Martins, Pedro; Mariano, Diego; Carvalho, Frederico Chaves; Bastos, Luana Luiza; Moraes, Lucas Brunelli de; Paixão, Vivian; Melo‐Minardi, Raquel Cardoso de

doi:10.3389/fbinf.2023.1103103

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…The number of final screened complex instances of peptide-protein pairs is 9070. Further, we screen the existing datasets of PropediaV2.3 (Martins et al, 2023) and PepBDB (Wen et al, 2018) with the same criterion, leading to additional 6523 instances to expand it. Appendix.…”

Section: Datasetmentioning

confidence: 99%

“…For training the deep learning model, we collect a benchmark dataset called PPBench2024 consisting of 15593 protein-peptide pairs with high quality. Compared with previous protein-peptide datasets (Agrawal et al, 2019;Martins et al, 2023;Wen et al, 2018), PPBench2024 augments and expands available data, and filters large amounts of low-quality data according to strict criteria, thus allowing deep-learning-based models to be trained on the datasets and to fulfill the peptide drug discovery tasks.…”

Section: Introductionmentioning

confidence: 99%

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PPFlow: Target-Aware Peptide Design with Torsional Flow Matching

Lin,

Zhang,

Zhao

et al. 2024

Preprint

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Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades. However, methods of AI-assisted peptide drug discovery are not fully explored. To fill the gap, we propose a target-aware peptide design method called PPFlow, based on conditional flow matching on torus manifolds, to model the internal geometries of torsion angles for the peptide structure design. Besides, we establish a protein-peptide binding dataset namedPPBench2024to fill the void of massive data for the task of structure-based peptide drug design and to allow the training of deep learning methods. Extensive experiments show that PPFlowreaches state-of-the-art performance in tasks of peptide drug generation and optimization in comparison with baseline models, and can be generalized to other tasks including docking and side-chain packing.

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Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PPFlow: Target-Aware Peptide Design with Torsional Flow Matching

Lin,

Zhang,

Zhao

et al. 2024

Preprint

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Peptide-Protein Interface Classification Using Convolutional Neural Networks

Santos,

Mariano,

Luiza Bastos

et al. 2023

Advances in Bioinformatics and Computational Biology

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The Role of Structural Bioinformatics in Understanding Tumor Necrosis Factor α-Interacting Protein Mechanisms in Chronic Inflammatory Diseases: A Review

Bastos,

Mariano,

Lemos

et al. 2024

Immuno

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Tumor necrosis factor α (TNF-α) is a multifunctional cytokine protein acknowledged as a vital mediator in cell differentiation, proliferation, and survival. Additionally, TNF-α is a crucial component of the host’s defense by mediating inflammatory and immune responses against various aggressive agents, including viruses, bacteria parasites, and tumors. However, excessive production can be detrimental to the body and is also implicated in developing several inflammatory and immune-mediated disorders. Therefore, there is great interest in studying its role and its modulation, in various diseases, both in in vitro, in vivo, and in silico experiments. In this review, we evaluated the structures of proteins related to TNF-α available in public databases. In addition, we described the main antibodies blocking this cytokine and its applications and commented on the potential of naturally produced binding molecules, such as TNF-α-binding proteins produced by ticks. We also discuss the role of structural bioinformatics techniques in understanding the mechanisms of chronic inflammatory diseases related to TNF-α. We hope that the data presented in this review will be useful for studies that aim to better understand the mechanisms of the interactions of TNF-α with other proteins and will lead to new drugs or treatments.

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Propedia v2.3: A novel representation approach for the peptide-protein interaction database using graph-based structural signatures

Cited by 4 publications

References 16 publications

PPFlow: Target-Aware Peptide Design with Torsional Flow Matching

PPFlow: Target-Aware Peptide Design with Torsional Flow Matching

Peptide-Protein Interface Classification Using Convolutional Neural Networks

The Role of Structural Bioinformatics in Understanding Tumor Necrosis Factor α-Interacting Protein Mechanisms in Chronic Inflammatory Diseases: A Review

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