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PubMed2026

Comprehensive evaluation of AlphaFold/OpenFold prediction of experimentally unresolved proteins through novel metrics.

Predicting accurate protein structures is essential for understanding molecular mechanisms, interpreting the impact of sequence variation, and supporting translational applications ranging from drug discovery to clinical genomics. Recent advances in deep-learning-based predictors such as AlphaFold2, OpenFold, and AlphaFold3 have transformed structural biology, enabling routine in silico modeling even for challenging or previously uncharacterized proteins. However, systematic benchmarking of these tools-especially for novel targets and single amino acid variants-remains limited. Conventional global metrics often fail to capture biologically meaningful discrepancies. By evaluating multiple implementations of AlphaFold2 and OpenFold, together with ColabFold and the AlphaFold3 server, across 10 different proteins and 222 single amino acid protein variants encompassing a wide range of sizes, structures, and functions, we show that although widely used global indicators-like mean pLDDT, pTM-score, and RMSD-frequently suggest comparable performance, substantial local-level differences remain elusive. To address this gap, we introduce a comparative framework leveraging Bland-Altman agreement analysis, to evaluate per-residue Cα-confidence differences and Per-Residue profiles (PRPs), complemented by Uniform Manifold Approximation and Projection (UMAP). This approach reveals marked localized divergences, particularly within flexible or intrinsically disordered regions, where both predictor choice and single-residue substitutions trigger the largest conformational shifts. We further demonstrate that using reduced homology databases has minimal impact on predicted structural quality, offering computationally efficient alternatives. Collectively, our findings underscore the importance of integrating global and residue-specific evaluations to more accurately assess robustness, agreement, and practical usability across contemporary protein structure prediction methods.

Proteins↗

PubMed2026

Uncovering viral protein acquisition events and human-specific folds with pairwise comparisons of predicted protein structures.

Pairwise sequence comparisons are at the center of molecular evolutionary analyses. However, viral pairwise comparisons are challenging because extreme mutation rates and evolutionary pressure cause genomes to diverge rapidly, limiting detectable sequence similarity to fewer than 3% of virus pairs. To overcome these limitations, we compared viruses based on structural similarity, using predicted protein structures from ColabFold and Foldseek to define protein fold clusters. We represented each virus genome by its protein structural content. Pairwise similarities between viruses were then quantified using the Jaccard index based on the presence or absence of protein fold clusters. Using a recently established viral protein fold database, we compared all pairs of eukaryotic viruses in RefSeq. This approach increased the proportion of comparable viral genome pairs from 2.4% to 16.5%. Using this protein-fold representation of viruses, we were able to accurately predict viral families with an average sensitivity of 85.9%. Investigation of viral families showing limited sensitivity with this approach uncovered a laterally transferred structural cluster (Rep/NS1) broadly shared across diverse viral families and found in the avian lineage of adenoviruses. Sequence homology suggests that this Rep was acquired from Parvoviridae, but the protein is mutant in the ATPase active site, indicating possible exaptation toward a purely DNA-binding function. In Gammapapillomaviruses, several E4 clusters were associated with human tropism. In summary, by representing viruses with structural protein clusters, we can classify highly divergent viruses, trace lateral gene transfer, and uncover features associated with viral host range.

Humans↗

PubMed2025

In silico prediction of the impact of genomic variations in the small conductance calcium activated potassium channel SK3 structure and function.

The small-conductance calcium-activated potassium channel SK3, encoded by the KCNN3 gene, plays a critical role in regulating dopaminergic neuron (DN) firing patterns by modulating after hyperpolarization currents. SK3 dysfunction has been implicated in neuropsychiatric and neurodegenerative disorders. We analyzed structural and functional consequences of KCNN3 splicing and genetic variation. Alternative splicing variants of the KCNN3 gene were retrieved from the Ensembl database and aligned using T-Coffee, manually inspected and curated. Protein domains were identified with Pfam 35.0, SMART 9.0, and InterPro 98.0, and visualized. An AlphaFold2 model of SK3 full-length protein (UniProt: Q9UGI6) used as reference and structural models of its splicing variants were predicted with ColabFold. Functional domains (S1-S6 transmembrane helices, H5 pore loop, and calmodulin-binding) were defined and superimposed onto the AlphaFold2 reference. Domain integrity was assessed based on completeness of all expected residue indices within each functional region. SNPs and CNVs across all coding KCNN3 splicing variants were analyzed, classified, and filtered to isolate pathogenic variants prioritizing non-synonymous amino acid substitutions. Differential variant impacts across splicing isoforms were assessed by mapping variant positions to individual transcript protein sequences and used to predict functional consequences. Two long and two short splicing variants are known. Short variants lack the motif required for potassium channels. Pathogenic variants result from missense mutations resulting in amino acid substitutions. In all cases, the consequential effects depend on the specific location and role of the amino acid being changed.

SK3 channels↗