ArticleBiomolecules2022
Benchmarking the Accuracy of AlphaFold 2 in Loop Structure Prediction.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Computational screening of Alternaria metabolites as potential DPP-4 inhibitors: ADMET, molecular docking, molecular dynamics, and network pharmacology analysis.Molecular diversity · 2026Article
- Comprehensive evaluation of AlphaFold/OpenFold prediction of experimentally unresolved proteins through novel metrics.Briefings in bioinformatics · 2026Article
- Sketching microprotein portraits.Protein science : a publication of the Protein Society · 2026Review
- Balancing under constraint: Structural insights into norovirus evolution and antigenic innovation.PLoS pathogens · 2026Article
- Cancer-associated TRF1 mutations alter PARP1 interaction dynamics: an in silico study.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
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- Crystal structure of Echinococcus multilocularis cystatin B reveals a novel feature in classical stefins.Scientific reports · 2026Article
- Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms.Computational and structural biotechnology journal · 2026Review
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- In Silico Characterization of ADAR1: Structure, Dynamics, and Functional Implications.Current issues in molecular biology · 2025Article
- The molecular complexity of deep vein thrombosis was preliminarily explored based on the ceRNA network, scRNA-seq and AlphaFold 2.BMC medical genomics · 2025Article
- Structure of the central Staphylococcus aureus AAA+ protease MecA/ClpC/ClpP.Communications biology · 2025Article
- Leveraging AlphaFold2 structural space exploration for generating drug target structures in structure-based virtual screening.Biochemistry and biophysics reports · 2025Article
- CASTER-DTA: equivariant graph neural networks for predicting drug-target affinity.Briefings in bioinformatics · 2025Article
- Resurgence of magnetic resonance techniques in the era of AlphaFold.Biophysical reviews · 2025Review
- Article
- Message hidden in α-helices-toward a better understanding of plant ABCG transporters' multispecificity.Plant physiology · 2025Article
- ExploreTurns: A web tool for the exploration, analysis, and classification of beta turns and structured loops in proteins; application to beta-bulge and Schellman loops, Asx helix caps, beta hairpins, and other hydrogen-bonded motifs.Protein science : a publication of the Protein Society · 2025Article
- Bridging prediction and reality: Comprehensive analysis of experimental and AlphaFold 2 full-length nuclear receptor structures.Computational and structural biotechnology journal · 2025Article
- AlphaFold2, SPINE-X, and Seder on Four Hard CASP Targets.Methods in molecular biology (Clifton, N.J.) · 2025Article
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2 authors.
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Abstract
The inhibition of protein-protein interactions is a growing strategy in drug development. In addition to structured regions, many protein loop regions are involved in protein-protein interactions and thus have been identified as potential drug targets. To effectively target such regions, protein structure is critical. Loop structure prediction is a challenging subgroup in the field of protein structure prediction because of the reduced level of conservation in protein sequences compared to the secondary structure elements. AlphaFold 2 has been suggested to be one of the greatest achievements in the field of protein structure prediction. The AlphaFold 2 predicted protein structures near the X-ray resolution in the Critical Assessment of protein Structure Prediction (CASP 14) competition in 2020. The purpose of this work is to survey the performance of AlphaFold 2 in specifically predicting protein loop regions. We have constructed an independent dataset of 31,650 loop regions from 2613 proteins (deposited after the AlphaFold 2 was trained) with both experimentally determined structures and AlphaFold 2 predicted structures. With extensive evaluation using our dataset, the results indicate that AlphaFold 2 is a good predictor of the structure of loop regions, especially for short loop regions. Loops less than 10 residues in length have an average Root Mean Square Deviation (RMSD) of 0.33 Å and an average the Template Modeling score (TM-score) of 0.82. However, we see that as the number of residues in a given loop increases, the accuracy of AlphaFold 2's prediction decreases. Loops more than 20 residues in length have an average RMSD of 2.04 Å and an average TM-score of 0.55. Such a correlation between accuracy and length of the loop is directly linked to the increase in flexibility. Moreover, AlphaFold 2 does slightly over-predict α-helices and β-strands in proteins.
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