Novel Protein–Protein Interaction Prediction with Updated Deep Radial Graph Basis Prism Refraction Search Convolutional Networks Model DOI

S. Nivedha,

Bhavani Sridharan

Journal of Biomedical Nanotechnology, Год журнала: 2024, Номер 20(12), С. 1804 - 1823

Опубликована: Дек. 1, 2024

Predicting Protein–Protein Interactions (PPIs) is essential to comprehending biological functions and pivotal for drug discovery disease understanding.However, accurately predicting these interactions remains a difficult issue because of the intricate multifaceted nature protein networks. Traditional models often fail fully capture relationships between proteins their interactions, especially when diverse datasets are involved. To address challenges, novel approach, named Deep Radial Graph Basis Prism Refraction Search Convolutional Networks(DRGB-PRSCN) model, proposed PPI prediction using three distinct datasets: Human PPI, STRING, DIP.The method employs Gradient Domain Guided Filtering effective data preprocessing, ensuring noise reduction while preserving features. Feature extraction carried out an Elastic Decision Transformer, which effectively captures key Networks (DGCNs) leveraged model complex dependencies among proteins. The DRGB-PRSCN with its advanced architecture, employed predict high precision. achieves performance evaluation score 99.9%, demonstrating efficacy in PPI. This approach outperforms traditional methods by providing superior accuracy robustness, making it highly beneficial network analysis discovery. model’s primary benefit capacity efficiently handle PPIs exceptional

Язык: Английский

Structure-Based Approaches for Protein–Protein Interaction Prediction Using Machine Learning and Deep Learning DOI Creative Commons
Despoina P. Kiouri, Georgios Batsis, Christos T. Chasapis

и другие.

Biomolecules, Год журнала: 2025, Номер 15(1), С. 141 - 141

Опубликована: Янв. 17, 2025

Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health disease. Structure-based PPI has emerged as robust alternative to sequence-based methods, offering greater biological accuracy by integrating three-dimensional spatial biochemical features. This work summarizes the recent advances computational approaches leveraging protein structure information for prediction, focusing on machine learning (ML) deep (DL) techniques. These methods not only improve predictive but also provide insights into functional sites, such binding catalytic residues. However, challenges limited high-resolution structural data need effective negative sampling persist. Through integration of experimental tools, structure-based paves way comprehensive proteomic network analysis, holding promise advancements drug discovery, biomarker identification, personalized medicine. Future directions include enhancing scalability dataset reliability expand these across diverse proteomes.

Язык: Английский

Процитировано

5
Emerging regulatory mechanisms and functions of biomolecular condensates: implications for therapeutic targets DOI Creative Commons
Soyoung Jeon, Yong‐Duck Chung, Jae‐Sung Lim

и другие.

Signal Transduction and Targeted Therapy, Год журнала: 2025, Номер 10(1)

Опубликована: Янв. 6, 2025

Cells orchestrate their processes through complex interactions, precisely organizing biomolecules in space and time. Recent discoveries have highlighted the crucial role of biomolecular condensates-membrane-less assemblies formed condensation proteins, nucleic acids, other molecules-in driving efficient dynamic cellular processes. These condensates are integral to various physiological functions, such as gene expression intracellular signal transduction, enabling rapid finely tuned responses. Their ability regulate signaling pathways is particularly significant, it requires a careful balance between flexibility precision. Disruption this can lead pathological conditions, including neurodegenerative diseases, cancer, viral infections. Consequently, emerged promising therapeutic targets, with potential offer novel approaches disease treatment. In review, we present recent insights into regulatory mechanisms by which influence pathways, roles health disease, strategies for modulating condensate dynamics approach. Understanding these emerging principles may provide valuable directions developing effective treatments targeting aberrant behavior diseases.

Язык: Английский

Процитировано

3
SpatialPPIv2: Enhancing protein–protein interaction prediction through graph neural networks with protein language models DOI Creative Commons
Wenxing Hu, Masahito Ohue

Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
LPBERT: A Protein–Protein Interaction Prediction Method Based on a Pre-Trained Language Model DOI Creative Commons
An Hu, Linai Kuang, Dinghai Yang

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(6), С. 3283 - 3283

Опубликована: Март 17, 2025

The prediction of protein–protein interactions is a key task in proteomics. Since protein sequences are easily available and understandable, they have become the primary data source for predicting interactions. With development natural language processing technology, models research hotspot recent years, also been developed accordingly. Compared with single-encoding methods, such as Word2Vec one-hot, specifically designed proteins expected to extract more comprehensive information from sequences, thereby enhancing performance interaction methods. Inspired by model ProteinBERT, this study LPBERT deep learning framework, which novel end-to-end architecture. LPBERT, based on combines Convolutional Neural Networks, Transformer encoders, Bidirectional Long Short-Term Memory networks achieve efficient prediction. Upon evaluation using BioGRID H. sapiens S. cerevisiae datasets, outperformed other comparison where it achieved accuracies 98.93% 97.94%, respectively. Moreover, demonstrated good performances multiple datasets. These experimental results indicate that performed excellently tasks, substantiating effectiveness introducing field.

Язык: Английский

Процитировано

0
ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks DOI Creative Commons

Lin Zhu,

Yi Fang,

Shuting Liu

и другие.

Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Assessing the potential toxicity of proteins is crucial for both therapeutic and agricultural applications. Traditional experimental methods protein evaluation are time-consuming, expensive, labor-intensive, highlighting requirement efficient computational approaches. Recent advancements in language models deep learning have significantly improved prediction, yet current often lack ability to integrate evolutionary structural information, which accurate assessment proteins. In this study, we present ToxDL 2.0, a novel multimodal model prediction that integrates information derived from pretrained AlphaFold2. 2.0 consists three key modules: (1) Graph Convolutional Network (GCN) module generating graph embeddings based on AlphaFold2-predicted structures, (2) domain embedding capturing representations, (3) dense combines these predict toxicity. After constructing comprehensive benchmark dataset, obtained results an original non-redundant test set (comprising pre-2022 sequences) independent (a holdout post-2022 sequences), demonstrating outperforms existing state-of-the-art methods. Additionally, utilized Integrated Gradients discover known toxic motifs associated with A web server publicly available at www.csbio.sjtu.edu.cn/bioinf/ToxDL2/.

Язык: Английский

Процитировано

0
Multimeric protein interaction and complex prediction: structure, dynamics and function DOI Creative Commons
Da Lu, Shu‐Hong Yu,

Yihong Huang

и другие.

Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Unveiling the influence of fastest nobel prize winner discovery: alphafold’s algorithmic intelligence in medical sciences DOI
Niki Najafi,

Reyhaneh Karbassian,

Helia Hajihassani

и другие.

Journal of Molecular Modeling, Год журнала: 2025, Номер 31(6)

Опубликована: Май 19, 2025

Язык: Английский

Процитировано

0
The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials DOI Creative Commons

Vera Malheiro,

Beatriz Santos, Ana Figueiras

и другие.

Pharmaceuticals, Год журнала: 2025, Номер 18(6), С. 788 - 788

Опубликована: Май 25, 2025

Artificial intelligence (AI) is a subfield of computer science focused on developing systems that can execute tasks traditionally associated with human intelligence. AI work through algorithms based rules or instructions enable the machine to make decisions. With advancement science, more sophisticated techniques, such as learning and deep learning, have been developed, allowing machines learn from large amounts data improve their performance over time. The pharmaceutical industry has greatly benefited development this technology. revolutionized drug discovery by enabling rapid effective analysis vast volumes biological chemical during identification new therapeutic compounds. developed predict efficacy, toxicity, possible adverse effects drugs, optimize steps involved in clinical trials, reduce time costs, facilitate implementation innovative drugs market, making it easier develop precise therapies tailored individual genetic profile patients. Despite significant advancements, there are still gaps application AI, particularly due lack comprehensive regulation. constant evolution technology requires ongoing in-depth legislative oversight ensure its use remains safe, ethical, free bias. This review explores role development, assessing potential enhance formulation, accelerate discovery, repurpose existing medications. It highlights AI’s impact across all stages, initial research emphasizing ability processes, drive innovation, outcomes.

Язык: Английский

Процитировано

0
In Silico Analysis of Protein–Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe DOI Creative Commons
Dalia González-Esparragoza, Alan Carrasco‐Carballo, Nora Hilda Rosas-Murrieta

и другие.

Current Issues in Molecular Biology, Год журнала: 2024, Номер 46(5), С. 4609 - 4629

Опубликована: Май 12, 2024

Ermp1 is a putative metalloprotease from

Язык: Английский

Процитировано

1
Protein Structure Inspired Drug Discovery DOI Creative Commons
Fangfang Qiao,

T. Andrew Binknowski,

Irene Broughan

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Май 20, 2024

Drug discovery starts with known function, either of a compound or protein, in-turn prompting investigations to probe 3D structure the compound-protein interface. As protein determines we hypothesized that unique structural motifs represent primary information denoting function can drive novel agents. Using physics-based analysis platform developed by us, designed conduct computationally intensive at supercomputing speeds, probed high-resolution x-ray crystallographic library us. We selected whose was not otherwise established, offered environments supporting binding drug-like chemicals and were present on proteins established therapeutic targets. For each eight potential pockets six different accessed 60 million used our evaluate binding. eight-day colony formation assays acquired compounds screened for efficacy against human breast, prostate, colon lung cancer cells toxicity bone marrow stem cells. Compounds selectively inhibiting growth segregated two separate proteins. The compound, Dxr2-017, exhibited selective activity melanoma in NCI-60 cell line screen, had an IC50 19 nM M14 assay, while over 2100-fold higher concentrations inhibited less than 30%. show Dxr2-017 induces anoikis, form programmed death need targeted therapeutics. predicted target is expressed bacteria, humans. This supports strategy focusing motifs. It functionally important structures are evolutionarily conserved. Here demonstrate proof-of-concept represents high value data support approach widely applicable.

Язык: Английский

Процитировано

1