Additional file 1: Table S1. of BLAST-based structural annotation of protein residues using Protein Data Bank

STARPDB webserver annotation accuracy of secondary structure, accessible surface area, DNA and RNA modules. Table S2. STARPDB webserver annotation accuracy of tight turns modules. Table S3. STARPDB webserver annotation accuracy of ligand and metal modules. (DOCX 21 kb

Evaluation of Protein Dihedral Angle Prediction Methods

<div><p>Tertiary structure prediction of a protein from its amino acid sequence is one of the major challenges in the field of bioinformatics. Hierarchical approach is one of the persuasive techniques used for predicting protein tertiary structure, especially in the absence of homologous protein structures. In hierarchical approach, intermediate states are predicted like secondary structure, dihedral angles, C^α-C^α distance bounds, etc. These intermediate states are used to restraint the protein backbone and assist its correct folding. In the recent years, several methods have been developed for predicting dihedral angles of a protein, but it is difficult to conclude which method is better than others. In this study, we benchmarked the performance of dihedral prediction methods ANGLOR and SPINE X on various datasets, including independent datasets. TANGLE dihedral prediction method was not benchmarked (due to unavailability of its standalone) and was compared with SPINE X and ANGLOR on only ANGLOR dataset on which TANGLE has reported its results. It was observed that SPINE X performed better than ANGLOR and TANGLE, especially in case of prediction of dihedral angles of glycine and proline residues. The analysis suggested that angle shifting was the foremost reason of better performance of SPINE X. We further evaluated the performance of the methods on independent ccPDB30 dataset and observed that SPINE X performed better than ANGLOR.</p></div

Normal psi angle distribution of Alanine.

<p>Normal psi angle distribution of Alanine.</p

Normal psi angle distribution of glycine.

<p>Normal psi angle distribution of glycine.</p

Additional file 1: Figure S1. of A web server for analysis, comparison and prediction of protein ligand binding sites

The propensity score of residues interacting with various carbohydrates. Figure S2. A web logo of ATP interacting patterns of 21-window length. Table S1. Performance of our propensity based prediction models on 50 major ligands, evaluated on independent datasets. Table S2. List of 824 ligands having more than 30 binding sites in the PDB. Table S3. Minimum, Maximum and Median Resolution of PDBs interacting with 824 ligands. (DOCX 276 kb

Comparison of performance of SPINE X, ANGLOR and TANGLE, in terms of MAE, on different datasets for the prediction of psi dihedral angle.

<p>First row show the name of dataset and second row show the name of methods.</p><p>Comparison of performance of SPINE X, ANGLOR and TANGLE, in terms of MAE, on different datasets for the prediction of psi dihedral angle.</p

Psi angle distribution of glycine after shifting the angles.

<p>Psi angle distribution of glycine after shifting the angles.</p

Length-wise distribution of peptides (B-cell epitopes and non-epitopes), we divided peptides in different bins like peptides having a length less than five residues, having residues between 5 to 10 residues.

<p>Length-wise distribution of peptides (B-cell epitopes and non-epitopes), we divided peptides in different bins like peptides having a length less than five residues, having residues between 5 to 10 residues.</p

The performance of IBk models developed on Lbtope_Fixed dataset using various features.

<p>These models were developed using 5-fold cross-validation on 90% data and tested on remaining 10% data.</p

The performance of our method LBtope on ABCpred dataset and performance of ABCpred on dataset Lbtope_Fixed (fixed length patterns used in this study).

<p>The performance of our method LBtope on ABCpred dataset and performance of ABCpred on dataset Lbtope_Fixed (fixed length patterns used in this study).</p