Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement

By Ambrish Roy, Yang Zhang. 2012

115 citations recorded by the source corpus.

SUMMARY Proteins perform functions through interacting with other molecules. However, structural details for most of the protein-ligand interactions are unknown. We present a new comparative approach (COFACTOR) to recognize functional sites of protein-ligand interactions using low-resolution protein structural models, based on a novel global-to-local sequence and structural comparison algorithm. COFACTOR was tested on 501 proteins, which harbor 582 natural and drug ligand molecules. Starting from I-TASSER structure predictions, the method successfully identifies ligand-binding pocket locations for 65% of apo receptors with an average distance error 2 Å. The average precision of binding-residue assignments is 46% and 137% higher than that by FINDSITE and ConCavity. In CASP9, COFACTOR achieved a binding-site prediction precision 72% and Matthews correlation coefficient 0.69 for 31 blind test proteins, which was significantly higher than all other participating methods. These data demonstrate the power of structure-based approaches to protein-ligand interaction predictions applicable for genome-wide structural and functional annotations.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Jianyi Yang
2. Yang Zhang
3. Ambrish Roy
4. Chengxin Zhang
5. Zhenling Peng
6. Fabio Polticelli
7. Daniele Toti
8. Gabriele Macari

In Molecular Biology

1. Jianyi Yang
2. Yang Zhang
3. Ambrish Roy
4. Robin Pearce
5. Chengxin Zhang
6. Yang Li
7. Wei Zheng
8. S. Mortuza

Citations in the corpus

• I-TASSER server: new development for protein structure and function predictions (2015, methodology): “Starting from the computational structure modeling, a number of methods have been proposed to annotate the biological function of protein molecules, with examples including COFACTROR (8,9), COACH (10), ConCavity (11), FINDSITE (12), Firestar (13) and 3DLigandSite (14), among many others.”
• Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment (2013, methodology): “COFACTOR (Roy et al., 2012; Roy and Zhang, 2012) is a structural comparison approach which identifies ligand-binding residues by global and local matches of the query and template structures.”
• Protein Structure and Function Prediction Using I-TASSER (2015, background): “…binding, enzyme commission, and gene ontology, are inferred from the BioLiP templates that are ranked based on a composite scoring function combining global and local structural similarity, chemical feature conservation, and sequence profile alignments (Roy and Zhang, 2012; Yang et al., 2013b).”
• COFACTOR: an accurate comparative algorithm for structure-based protein function annotation (2012, methodology): “Although the global structural similarity is important for functional inference, we have witnessed a number of examples in both the benchmark and the CASP experiments, where COFACTOR successfully identified the correct functional homologs, which have different global folds but with similar binding s”