ArticleScientific reports2020
Evaluating the significance of contact maps in low-homology protein modeling using contact-assisted threading.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- PatchEpi: patch-aware equivariant learning improves structure-based epitope prediction.Bioinformatics (Oxford, England) · 2026Article
- MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning.Briefings in bioinformatics · 2026Article
- MAKA-Map: Real-Valued Distance Prediction for Protein Folding Mechanisms via a Hybrid Neural Framework Integrating the Mamba and Kolmogorov-Arnold Networks.Biomolecules · 2026Article
- Evaluating the significance of embedding-based protein sequence alignment with clustering and double dynamic programming for remote homology.Scientific reports · 2025Article
- Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.Molecular biotechnology · 2025Review
- An interactive visualization tool for educational outreach in protein contact map overlap analysis.Frontiers in bioinformatics · 2024Article
- Molecular Dynamic Studies of Dye-Dye and Dye-DNA Interactions Governing Excitonic Coupling in Squaraine Aggregates Templated by DNA Holliday Junctions.International journal of molecular sciences · 2023Article
- Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Article
- Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading.Frontiers in molecular biosciences · 2021Review
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The development of improved threading algorithms for remote homology modeling is a critical step forward in template-based protein structure prediction. We have recently demonstrated the utility of contact information to boost protein threading by developing a new contact-assisted threading method. However, the nature and extent to which the quality of a predicted contact map impacts the performance of contact-assisted threading remains elusive. Here, we systematically analyze and explore this interdependence by employing our newly-developed contact-assisted threading method over a large-scale benchmark dataset using predicted contact maps from four complementary methods including direct coupling analysis (mfDCA), sparse inverse covariance estimation (PSICOV), classical neural network-based meta approach (MetaPSICOV), and state-of-the-art ultra-deep learning model (RaptorX). Experimental results demonstrate that contact-assisted threading using high-quality contacts having the Matthews Correlation Coefficient (MCC) ≥ 0.5 improves threading performance in nearly 30% cases, while low-quality contacts with MCC <0.35 degrades the performance for 50% cases. This holds true even in CASP13 dataset, where threading using high-quality contacts (MCC ≥ 0.5) significantly improves the performance of 22 instances out of 29. Collectively, our study uncovers the mutual association between the quality of predicted contacts and its possible utility in boosting threading performance for improving low-homology protein modeling.
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