ReviewComputational and structural biotechnology journal2019
Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins.
Review in Computational and structural biotechnology journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Computational structural dynamics and immunoinformatic analysis of EIEC IpaHMolecular diversity · 2026Article
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Immunoinformatics-driven multi-epitope vaccine design targeting PSMA, STEAP1, and B7H3 for prostate cancer.Frontiers in medicine · 2026Article
- Integrating Docking, Dynamics, and Assays to Predict Antimicrobial Peptide Interactions with Mycolic Acid Membranes inACS measurement science au · 2025Article
- PD-L1 targeting in triple negative breast cancer: in silico and in vitro validation of wasp venom peptide MP-1.Medical oncology (Northwood, London, England) · 2025Article
- Coptisine regulates PI3K/AKT pathway to block bladder cancer progression: a study based on network pharmacology, in vitro and in vivo assays.Hereditas · 2025Article
- Molecular dynamics simulations of proteins: an in-depth review of computational strategies, structural insights, and their role in medicinal chemistry and drug development.Biological cybernetics · 2025Review
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Elucidating the interactions between Kinesin-5/BimC and the microtubule: insights from TIRF microscopy and molecular dynamics simulations.Briefings in bioinformatics · 2025Article
- Prediction of SafD adhesin strong binding peptides for pilus proteins assembly suppression in the prevention ofIn silico pharmacology · 2025Article
- Exploring Placental Protein-Target Protein Interactions:Current protein & peptide science · 2025Article
- Adaptive CVgen: Leveraging reinforcement learning for advanced sampling in protein folding and chemical reactions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Characterization and preparation of food-derived peptides on improving osteoporosis: A review.Food chemistry: X · 2024Article
- Diffusivity of COJournal of chemical and engineering data · 2024Review
- Unveiling therapeutic frontiers: DON/DRP-104 as innovative Plasma kallikrein inhibitors against carcinoma-associated hereditary angioedema shocks - a comprehensive molecular dynamics exploration.Cell biochemistry and biophysics · 2024Article
- A Perspective on Protein Structure Prediction Using Quantum Computers.Journal of chemical theory and computation · 2024Review
- Isolation, Characterization, Genome Annotation, and Evaluation of Tyrosinase Inhibitory Activity in Secondary Metabolites ofInternational journal of molecular sciences · 2024Article
- Insight into the Binding Mechanisms of Quartz-Selective Peptides: Toward Greener Flotation Processes.ACS applied materials & interfaces · 2023Article
- Structural Basis of the Binding Mode of the Antineoplastic Compound Motixafortide (BL-8040) in the CXCR4 Chemokine Receptor.International journal of molecular sciences · 2023Article
- Intact Transition Epitope Mapping-Force Differences between Original and Unusual Residues (ITEM-FOUR).Biomolecules · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.
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