A deep learning framework to predict binding preference of RNA constituents on protein surface
NucleicNet can serve to provide quantitative fitness of RNA sequences for given binding pockets or to predict potential binding pockets and binding RNAs for previously unknown RNA binding proteins on a diverse set of challenging RNA-binding proteins.
PROTINFO: new algorithms for enhanced protein structure predictions
New algorithms and modules for protein structure prediction available as part of the PROTINFO web server, which allow a user to submit a protein sequence and return atomic coordinates representing the tertiary structure of that protein.
CRALBP ligand and protein interactions.
To better understand the visual cycle, characterizing CRALBP ligand and protein interactions and retinoid trafficking within the RPE is characterizing.
Prediction of calcium-binding sites by combining loop-modeling with machine learning
Limited loop modeling combined with pattern matching algorithms can recover functions and propose putative conformations associated with these functions in protein crystal structures when loops are missing.
Bioinformatics and variability in drug response: a protein structural perspective
- Jennifer L. Lahti, Grace W. Tang, E. Capriotti, Tianyun Liu, R. Altman
- BiologyJournal of the Royal Society Interface
- 7 July 2012
This review summarizes structural characteristics of protein targets and common mechanisms of drug interactions, and describes the impact of coding mutations on protein structures and drug response.
Using Multiple Microenvironments to Find Similar Ligand-Binding Sites: Application to Kinase Inhibitor Binding
An algorithm is introduced that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microen environments, and applies it to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding.
Predicting drug side-effects by chemical systems biology
New approaches to predicting ligand similarity and protein interactions can explain unexpected observations of drug inefficacy or side-effects.
Comparative modeling: the state of the art and protein drug target structure prediction.
- Tianyun Liu, Grace W. Tang, E. Capriotti
- BiologyCombinatorial chemistry & high throughput…
- 30 June 2011
The theoretical basis of comparative modeling, the available automatic methods and databases, and the algorithms to evaluate the accuracy of predicted structures are described, focusing on the G protein-coupled receptor (GPCR) and protein kinase families.
Identification of CRALBP Ligand Interactions by Photoaffinity Labeling, Hydrogen/Deuterium Exchange, and Structural Modeling*
- Zhiping Wu, A. Hasan, Tianyun Liu, D. Teller, J. Crabb
- Chemistry, BiologyJournal of Biological Chemistry
- 25 June 2004
The ligand binding cavity in human recombinant CRALBP was characterized by photoaffinity labeling with 3-diazo-4-keto-11-cis-retinol and by high resolution mass spectrometric topological analyses, which expand to 12 the number of residues proposed to interact with ligand and provide further insight intoCRALBP lig and protein interactions.
Homology modeling of TMPRSS2 yields candidate drugs that may inhibit entry of SARS-CoV-2 into human cells.
This work identified six compounds with predicted high binding affinity in the range of the known inhibitors of TMPRSS2, and showed that a previously published weak inhibitor, Camostat, had a significantly lower binding score than these six compounds.