Browsing by Subject "Binding sites"

Now showing 1 - 4 of 4
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    MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins
    (Oxford University Press, 2012) Disfani, Fatemeh Miri; Hsu, Wei-Lun; Mizianty, Marcin J.; Oldfield, Christopher J.; Xue, Bin; Dunker, A. Keith; Uversky, Vladimir N.; Kurgan, Lukasz; Biochemistry and Molecular Biology, School of Medicine
    Motivation: Molecular recognition features (MoRFs) are short binding regions located within longer intrinsically disordered regions that bind to protein partners via disorder-to-order transitions. MoRFs are implicated in important processes including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains. Results: We introduce a new MoRF predictor, MoRFpred, which identifies all MoRF types (α, β, coil and complex). We develop a comprehensive dataset of annotated MoRFs to build and empirically compare our method. MoRFpred utilizes a novel design in which annotations generated by sequence alignment are fused with predictions generated by a Support Vector Machine (SVM), which uses a custom designed set of sequence-derived features. The features provide information about evolutionary profiles, selected physiochemical properties of amino acids, and predicted disorder, solvent accessibility and B-factors. Empirical evaluation on several datasets shows that MoRFpred outperforms related methods: α-MoRF-Pred that predicts α-MoRFs and ANCHOR which finds disordered regions that become ordered when bound to a globular partner. We show that our predicted (new) MoRF regions have non-random sequence similarity with native MoRFs. We use this observation along with the fact that predictions with higher probability are more accurate to identify putative MoRF regions. We also identify a few sequence-derived hallmarks of MoRFs. They are characterized by dips in the disorder predictions and higher hydrophobicity and stability when compared to adjacent (in the chain) residues. Availability: http://biomine.ece.ualberta.ca/MoRFpred/; http://biomine.ece.ualberta.ca/MoRFpred/Supplement.pdf.
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    Multisite λ-Dynamics for Protein-DNA Binding Affinity Prediction
    (ACS, 2025) Al Masri, Carmen; Vilseck, Jonah Z.; Yu, Jin; Hayes, Ryan L.; Biochemistry and Molecular Biology, School of Medicine
    Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences, playing critical roles in cellular processes and disease pathways. Computational methods, particularly λ-Dynamics, offer a promising approach for predicting TF relative binding affinities. This study evaluates the effectiveness of different λ-Dynamics perturbation schemes in determining binding free energy changes (ΔΔGb) of the WRKY transcription factor upon mutating its W-box binding site (GGTCAA) to a nonspecific sequence (GATAAA). Among the schemes tested, the single λ per base pair protocol demonstrated the fastest convergence and highest precision. Extending this protocol to additional mutants (GGTCCG and GGACAA) yielded ΔΔGb values that successfully ranked binding affinities, showcasing its strong potential for high-throughput screening of DNA binding sites.
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    regSNPs: a strategy for prioritizing regulatory single nucleotide substitutions
    (Oxford University Press, 2012) Teng, Mingxiang; Ichikawa, Shoji; Padgett, Leah R.; Wang, Yadong; Mort, Matthew; Cooper, David N.; Koller, Daniel L.; Foroud, Tatiana; Edenberg, Howard J.; Econs, Michael J.; Liu, Yunlong; Medical and Molecular Genetics, School of Medicine
    Motivation: One of the fundamental questions in genetics study is to identify functional DNA variants that are responsible to a disease or phenotype of interest. Results from large-scale genetics studies, such as genome-wide association studies (GWAS), and the availability of high-throughput sequencing technologies provide opportunities in identifying causal variants. Despite the technical advances, informatics methodologies need to be developed to prioritize thousands of variants for potential causative effects. Results: We present regSNPs, an informatics strategy that integrates several established bioinformatics tools, for prioritizing regulatory SNPs, i.e. the SNPs in the promoter regions that potentially affect phenotype through changing transcription of downstream genes. Comparing to existing tools, regSNPs has two distinct features. It considers degenerative features of binding motifs by calculating the differences on the binding affinity caused by the candidate variants and integrates potential phenotypic effects of various transcription factors. When tested by using the disease-causing variants documented in the Human Gene Mutation Database, regSNPs showed mixed performance on various diseases. regSNPs predicted three SNPs that can potentially affect bone density in a region detected in an earlier linkage study. Potential effects of one of the variants were validated using luciferase reporter assay.
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    Structures of rhodopsin in complex with G protein-coupled receptor kinase 1
    (Springer Nature, 2021) Chen, Qiuyan; Plasencia, Manolo; Li, Zhuang; Mukherjee, Somnath; Patra, Dhableswar; Chen, Chun-Liang; Klose, Thomas; Yao, Xin-Qiu; Kossiakoff, Anthony A.; Chang, Leifu; Andrews, Philip C.; Tesmer, John J. G.; Biochemistry and Molecular Biology, School of Medicine
    G-protein-coupled receptor (GPCR) kinases (GRKs) selectively phosphorylate activated GPCRs, thereby priming them for desensitization1. Although it is unclear how GRKs recognize these receptors2-4, a conserved region at the GRK N terminus is essential for this process5-8. Here we report a series of cryo-electron microscopy single-particle reconstructions of light-activated rhodopsin (Rho*) bound to rhodopsin kinase (GRK1), wherein the N terminus of GRK1 forms a helix that docks into the open cytoplasmic cleft of Rho*. The helix also packs against the GRK1 kinase domain and stabilizes it in an active configuration. The complex is further stabilized by electrostatic interactions between basic residues that are conserved in most GPCRs and acidic residues that are conserved in GRKs. We did not observe any density for the regulator of G-protein signalling homology domain of GRK1 or the C terminus of rhodopsin. Crosslinking with mass spectrometry analysis confirmed these results and revealed dynamic behaviour in receptor-bound GRK1 that would allow the phosphorylation of multiple sites in the receptor tail. We have identified GRK1 residues whose mutation augments kinase activity and crosslinking with Rho*, as well as residues that are involved in activation by acidic phospholipids. From these data, we present a general model for how a small family of protein kinases can recognize and be activated by hundreds of different GPCRs.