ReviewComputational and structural biotechnology journal2023
Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Integrative structural interactomics reveals protein organization and structure in a giant virus.Nature communications · 2026Article
- Membrane Protein Insertion in Cells: Principles, Pathways, and Quality Control.Chemical reviews · 2026Review
- Living at the border: biophysical gateways into membrane protein insertion and folding.Biophysical reviews · 2026Review
- MetaMP Ecosystem for Unified, Auditable, and Benchmark-Ready Data for Reliable Membrane Protein Annotation.Computational and structural biotechnology journal · 2026Article
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
- Unraveling ShuA detergent-induced colloidal behavior in solution: A comprehensive SEC-MALS, SAXS, and SANS study.Protein science : a publication of the Protein Society · 2025Article
- Selenoprotein K Is a Peripheral ER Membrane Protein.Biochemistry · 2025Article
- MSF-CPMP: A novel multi-source feature fusion model for prediction of cyclic peptide membrane permeability.Computational and structural biotechnology journal · 2025Article
- TBM preferred to AlphaFold 3 for functional models of insect odorant receptors.Computational and structural biotechnology journal · 2025Article
- A Survey of Current Status in AI-Based Topology Prediction of Transmembrane Proteins.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Recent research progress in glycosylphosphatidylinositol-anchored protein biosynthesis, chemical/chemoenzymatic synthesis, and interaction with the cell membrane.Current opinion in chemical biology · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.