These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

325 related articles for article (PubMed ID: 38739759)

  • 1. A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.
    Jia P; Zhang F; Wu C; Li M
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38739759
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Review and comparative assessment of sequence-based predictors of protein-binding residues.
    Zhang J; Kurgan L
    Brief Bioinform; 2018 Sep; 19(5):821-837. PubMed ID: 28334258
    [TBL] [Abstract][Full Text] [Related]  

  • 3. SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.
    Zhang B; Hou Z; Yang Y; Wong KC; Zhu H; Li X
    Commun Biol; 2024 Jun; 7(1):679. PubMed ID: 38830995
    [TBL] [Abstract][Full Text] [Related]  

  • 4. SCOWLP update: 3D classification of protein-protein, -peptide, -saccharide and -nucleic acid interactions, and structure-based binding inferences across folds.
    Teyra J; Samsonov SA; Schreiber S; Pisabarro MT
    BMC Bioinformatics; 2011 Oct; 12():398. PubMed ID: 21992011
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Accurate structure prediction of biomolecular interactions with AlphaFold 3.
    Abramson J; Adler J; Dunger J; Evans R; Green T; Pritzel A; Ronneberger O; Willmore L; Ballard AJ; Bambrick J; Bodenstein SW; Evans DA; Hung CC; O'Neill M; Reiman D; Tunyasuvunakool K; Wu Z; Žemgulytė A; Arvaniti E; Beattie C; Bertolli O; Bridgland A; Cherepanov A; Congreve M; Cowen-Rivers AI; Cowie A; Figurnov M; Fuchs FB; Gladman H; Jain R; Khan YA; Low CMR; Perlin K; Potapenko A; Savy P; Singh S; Stecula A; Thillaisundaram A; Tong C; Yakneen S; Zhong ED; Zielinski M; Žídek A; Bapst V; Kohli P; Jaderberg M; Hassabis D; Jumper JM
    Nature; 2024 Jun; 630(8016):493-500. PubMed ID: 38718835
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Importance of molecular computer modeling in anticancer drug development.
    Geromichalos GD
    J BUON; 2007 Sep; 12 Suppl 1():S101-18. PubMed ID: 17935268
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Possibilities of the method of step-by-step complication of ligand structure in studies of protein--nucleic acid interactions: mechanisms of functioning of some replication, repair, topoisomerization, and restriction enzymes.
    Bugreev DV; Nevinsky GA
    Biochemistry (Mosc); 1999 Mar; 64(3):237-49. PubMed ID: 10205294
    [TBL] [Abstract][Full Text] [Related]  

  • 8. DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning.
    Zhang F; Zhao B; Shi W; Li M; Kurgan L
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34905768
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Functional structural motifs for protein-ligand, protein-protein, and protein-nucleic acid interactions and their connection to supersecondary structures.
    Kinjo AR; Nakamura H
    Methods Mol Biol; 2013; 932():295-315. PubMed ID: 22987360
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains.
    Zhang J; Ma Z; Kurgan L
    Brief Bioinform; 2019 Jul; 20(4):1250-1268. PubMed ID: 29253082
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.
    Pandurangan AP; Blundell TL
    Protein Sci; 2020 Jan; 29(1):247-257. PubMed ID: 31693276
    [TBL] [Abstract][Full Text] [Related]  

  • 12. fingeRNAt-A novel tool for high-throughput analysis of nucleic acid-ligand interactions.
    Szulc NA; Mackiewicz Z; Bujnicki JM; Stefaniak F
    PLoS Comput Biol; 2022 Jun; 18(6):e1009783. PubMed ID: 35653385
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Dynamics of Ionic Interactions at Protein-Nucleic Acid Interfaces.
    Yu B; Pettitt BM; Iwahara J
    Acc Chem Res; 2020 Sep; 53(9):1802-1810. PubMed ID: 32845610
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.
    Vora DS; Kalakoti Y; Sundar D
    Methods Mol Biol; 2023; 2553():285-323. PubMed ID: 36227550
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Optimizing scoring function of protein-nucleic acid interactions with both affinity and specificity.
    Yan Z; Wang J
    PLoS One; 2013; 8(9):e74443. PubMed ID: 24098651
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Thermophoresis for characterizing biomolecular interaction.
    Asmari M; Ratih R; Alhazmi HA; El Deeb S
    Methods; 2018 Aug; 146():107-119. PubMed ID: 29438829
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.
    Cang Z; Mu L; Wei GW
    PLoS Comput Biol; 2018 Jan; 14(1):e1005929. PubMed ID: 29309403
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Docking and scoring for nucleic acid-ligand interactions: Principles and current status.
    Feng Y; Yan Y; He J; Tao H; Wu Q; Huang SY
    Drug Discov Today; 2022 Mar; 27(3):838-847. PubMed ID: 34718205
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Thermodynamic database for protein-nucleic acid interactions (ProNIT).
    Prabakaran P; An J; Gromiha MM; Selvaraj S; Uedaira H; Kono H; Sarai A
    Bioinformatics; 2001 Nov; 17(11):1027-34. PubMed ID: 11724731
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An Overview of Computational Tools of Nucleic Acid Binding Site Prediction for Site-specific Proteins and Nucleases.
    Wan H; Li JM; Ding H; Lin SX; Tu SQ; Tian XH; Hu JP; Chang S
    Protein Pept Lett; 2020; 27(5):370-384. PubMed ID: 31746287
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 17.