Tianyun Liu

Summary

Affiliation: Stanford University
Country: USA

Publications

  1. ncbi Improving the accuracy of template-based predictions by mixing and matching between initial models
    Tianyun Liu
    Department of Microbiology, University of Washington, School of Medicine, Seattle, WA 98195, USA
    BMC Struct Biol 8:24. 2008
  2. ncbi Prediction of calcium-binding sites by combining loop-modeling with machine learning
    Tianyun Liu
    Department of Genetics, Stanford University, Stanford, CA, USA
    BMC Struct Biol 9:72. 2009
  3. ncbi Bioinformatics and variability in drug response: a protein structural perspective
    Jennifer L Lahti
    Department of Bioengineering, Stanford University, Stanford, CA, USA
    J R Soc Interface 9:1409-37. 2012
  4. ncbi Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding
    Tianyun Liu
    Department of Genetics, Stanford University, Stanford, California, United States of America
    PLoS Comput Biol 7:e1002326. 2011
  5. ncbi Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residues
    Shirley Wu
    23andMe, 1390 Shorebird Way, Mountain View, CA, USA
    BMC Struct Biol 10:4. 2010
  6. ncbi Scoring functions for de novo protein structure prediction revisited
    Shing Chung Ngan
    Department of Microbiology, University of Washington School of Medicine, Seattle, WA, USA
    Methods Mol Biol 413:243-81. 2008
  7. ncbi Structural insights into the cellular retinaldehyde-binding protein (CRALBP)
    Tianyun Liu
    Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
    Proteins 61:412-22. 2005
  8. ncbi Comparative modeling: the state of the art and protein drug target structure prediction
    Tianyun Liu
    Department of Bioengineering, Stanford University, 318 Campus Dr, Room S240 Mail code 5448, Stanford, CA 94305, USA
    Comb Chem High Throughput Screen 14:532-47. 2011
  9. ncbi A novel method for predicting and using distance constraints of high accuracy for refining protein structure prediction
    Tianyun Liu
    Department of Genetics, Stanford University, Stanford, California, USA
    Proteins 77:220-34. 2009
  10. ncbi Scaffold proteins and the regeneration of visual pigments
    Maria Nawrot
    Department of Ophthalmology, University of Washington, Seattle, WA, USA
    Photochem Photobiol 82:1482-8. 2006

Collaborators

Detail Information

Publications14

  1. ncbi Improving the accuracy of template-based predictions by mixing and matching between initial models
    Tianyun Liu
    Department of Microbiology, University of Washington, School of Medicine, Seattle, WA 98195, USA
    BMC Struct Biol 8:24. 2008
    ..We investigate the effectiveness of a graph-theoretic clique finding approach to solve this problem...
  2. ncbi Prediction of calcium-binding sites by combining loop-modeling with machine learning
    Tianyun Liu
    Department of Genetics, Stanford University, Stanford, CA, USA
    BMC Struct Biol 9:72. 2009
    ..Methods for recognizing functional sites in these missing loops would be useful for recovering additional functional information...
  3. ncbi Bioinformatics and variability in drug response: a protein structural perspective
    Jennifer L Lahti
    Department of Bioengineering, Stanford University, Stanford, CA, USA
    J R Soc Interface 9:1409-37. 2012
    ..Finally, we highlight tools for analysing protein structures and protein-drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants...
  4. ncbi Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding
    Tianyun Liu
    Department of Genetics, Stanford University, Stanford, California, United States of America
    PLoS Comput Biol 7:e1002326. 2011
    ..Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway...
  5. ncbi Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residues
    Shirley Wu
    23andMe, 1390 Shorebird Way, Mountain View, CA, USA
    BMC Struct Biol 10:4. 2010
    ..Unfortunately, our ability to analyze these proteins is restricted by the limited catalog of known molecular functions and their associated 3D motifs...
  6. ncbi Scoring functions for de novo protein structure prediction revisited
    Shing Chung Ngan
    Department of Microbiology, University of Washington School of Medicine, Seattle, WA, USA
    Methods Mol Biol 413:243-81. 2008
    ..We discuss the implementation and use of some of the scoring functions from these two classes for de novo structure prediction in this chapter...
  7. ncbi Structural insights into the cellular retinaldehyde-binding protein (CRALBP)
    Tianyun Liu
    Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA
    Proteins 61:412-22. 2005
    ..We conclude that the binding and release of retinoid involve large conformational changes in the lipid-exchange loop at the entrance of the ligand-binding cavity...
  8. ncbi Comparative modeling: the state of the art and protein drug target structure prediction
    Tianyun Liu
    Department of Bioengineering, Stanford University, 318 Campus Dr, Room S240 Mail code 5448, Stanford, CA 94305, USA
    Comb Chem High Throughput Screen 14:532-47. 2011
    ..Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families...
  9. ncbi A novel method for predicting and using distance constraints of high accuracy for refining protein structure prediction
    Tianyun Liu
    Department of Genetics, Stanford University, Stanford, California, USA
    Proteins 77:220-34. 2009
    ..Our procedure represents a significant step in solving the protein structure prediction and refinement problem, by enabling the use of consensus constraints, RAPDF, and rTAD for protein structure modeling and refinement...
  10. ncbi Scaffold proteins and the regeneration of visual pigments
    Maria Nawrot
    Department of Ophthalmology, University of Washington, Seattle, WA, USA
    Photochem Photobiol 82:1482-8. 2006
    ..The ligand absorption spectrum of the complex was identical with that of CRALBP x 11-cis-retinal, demonstrating that complex formation did not perturb the ligand-binding domain of CRALBP...
  11. ncbi Predicting drug side-effects by chemical systems biology
    Nicholas P Tatonetti
    Training Program in Biomedical Informatics, Stanford University, Stanford, CA 94305, USA
    Genome Biol 10:238. 2009
    ..New approaches to predicting ligand similarity and protein interactions can explain unexpected observations of drug inefficacy or side-effects...
  12. ncbi The effect of experimental resolution on the performance of knowledge-based discriminatory functions for protein structure selection
    Tianyun Liu
    Department of Microbiology, University of Washington, School of Medicine, Seattle, WA 98195, USA
    Protein Eng Des Sel 19:431-7. 2006
    ..This indicates that using high-resolution structural datasets after filtering out unfavorable contacts can improve the performance of knowledge-based discriminatory functions...
  13. ncbi PROTINFO: new algorithms for enhanced protein structure predictions
    Ling-Hong Hung
    Computational Genomics Group, Department of Microbiology, University of Washington School of Medicine, Seattle, WA 98195, USA
    Nucleic Acids Res 33:W77-80. 2005
    ..These two modules allow a user to submit a protein sequence and return atomic coordinates representing the tertiary structure of that protein. The PROTINFO server is available at http://protinfo.compbio.washington.edu...
  14. ncbi CRALBP ligand and protein interactions
    Zhiping Wu
    Cole Eye Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
    Adv Exp Med Biol 572:477-83. 2006