Structural Protein Descriptors in 1-Dimension and their Sequence-Based Predictions

Abstract

The last few decades observed an increasing interest in development and application of 1-dimensional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods.

MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins

“…the calculation of the features is based on a segment of residues centered over the input (to-be-predicted) residue. The use of the window is a popular approach in the design of similar sequence-based predictors (Kurgan and Disfani, 2011). In the second step, a vector of 24 features is fed into a linear SVM to calculate propensity of a given input residue to form a MoRF region.…”

“…Real-SPINE3 (Faraggi et al , 2009) and PROFbval (Schlessinger et al , 2006) are used to predict relative solvent accessibility and B-factors, respectively. The choice of the disorder predictors is based on the results from a recent review (Kurgan and Disfani, 2011). These predictions are based on standalone implementations using default parameters.…”

TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

“…An useful intermediate way to address this is to predict one-dimensional structural properties of proteins including secondary structure, solvent accessibility, residue contact number/order, residue depth, and dihedral torsion angles [1]–[12]. For a comprehensive review of recent progress on the development of one-dimensional predictors, refer to Kurgan and Disfani [13]. In the past two decades, most efforts have been made to predict the former three properties of proteins, leading to ongoing improvements in prediction performance [14]–[16].…”

Computational Prediction of Protein Secondary Structure from Sequence

“…Most of the existing SS predictors focus on the 3‐class SS (Table ). Based on the prior comparative reviews and our small‐scale evaluation (Table ), the end user can expect to collect predictions with the average overall accuracy over the three SS types between 70% and 80% (Rost, ; Kurgan and Disfani, ; Zhang et al., ). We also encourage the user to utilize the confidence indices provided by some predictors to judge reliability of predictions for individual residues.…”