Functional classification of protein structures by local structure matching in graph representation

Abstract

As a result of high‐throughput protein structure initiatives, over 14,400 protein structures have been solved by Structural Genomics (SG) centers and participating research groups. While the totality of SG data represents a tremendous contribution to genomics and structural biology, reliable functional information for these proteins is generally lacking. Better functional predictions for SG proteins will add substantial value to the structural information already obtained. Our method described herein, Graph Representation of Active Sites for Prediction of Function (GRASP‐Func), predicts quickly and accurately the biochemical function of proteins by representing residues at the predicted local active site as graphs rather than in Cartesian coordinates. We compare the GRASP‐Func method to our previously reported method, Structurally Aligned Local Sites of Activity (SALSA), using the Ribulose Phosphate Binding Barrel (RPBB), 6‐Hairpin Glycosidase (6‐HG), and Concanavalin A‐like Lectins/Glucanase (CAL/G) superfamilies as test cases. In each of the superfamilies, SALSA and the much faster method GRASP‐Func yield similar correct classification of previously characterized proteins, providing a validated benchmark for the new method. In addition, we analyzed SG proteins using our SALSA and GRASP‐Func methods to predict function. Forty‐one SG proteins in the RPBB superfamily, nine SG proteins in the 6‐HG superfamily, and one SG protein in the CAL/G superfamily were successfully classified into one of the functional families in their respective superfamily by both methods. This improved, faster, validated computational method can yield more reliable predictions of function that can be used for a wide variety of applications by the community.

CHARMM-GUI LBS Finder & Refiner for Ligand Binding Site Prediction and Refinement

“…A protein LBS is a key location that should be identified and characterized properly for a successful structure-based drug design campaign . In addition, locating LBS on a protein can facilitate functional annotation through their LBS structural similarity comparisons. − This is particularly useful for the characterization of a large number of protein structures solved via high-throughput methods from structural genomics …”

Characterisation of a novel laminarinase from Microbulbifer sp. ALW1 and the antioxidant activity of its hydrolysates

“…The translated product was predicted to contain a signal peptide consisting of the first 18 aa located at the N‐terminus. Protein domain analysis revealed that MaLamNA contained a C‐terminal laminin‐G3 domain specified for concanavalin A‐like lectin/glucanase superfamily, suggesting a potential role of MaLamNA in biomass degradation through hydrolysis (Mills et al ., 2018). Phylogenetic analysis of MaLamNA with the characterised bacterial laminarinases of different GH families of EC 3.2.1.39 indicated that MaLamNA was not closely grouped with other members in a same family branch (Fig.…”

Enzyme Substrate Prediction from Three-Dimensional Feature Representations Using Space-Filling Curves

“…One common approach uses three-dimensional (3D) convolutional neural networks. − Concentric shells surrounding specific protein sites can also be used to represent the spatial distribution of biochemical properties . Other methods use graph representations of enzymes and their active sites. , For example, Gligorijević et al generate amino contact maps from protein structures and use the resulting adjacency matrix as input for a graph convolutional network. The distribution of torsion angles and pairwise distances, extracted for each amino acid (AA) type separately, can also generate feature maps for downstream protein or enzyme functional prediction. , …”