Alexander Miguel Monzón

AlphaFlex generates atomistic conformational ensembles for human proteins with intrinsically disordered regions, integrating AlphaFold models and implicit bilayers for transmembrane proteins.

GeomeTRe calculates geometric descriptors for tandem repeat proteins, improving their classification by analyzing structural symmetry and variations in spatial arrangements.

STRPsearch detects structured tandem repeat proteins by aligning protein structures to a curated database and mapping repeat units efficiently.

The paper evaluates AlphaFold2's ability to predict intrinsically disordered regions and conditionally folded regions using the CAID dataset, finding it highly competitive.

CoDNaS-Q is a database of conformational diversity in homooligomeric proteins with quaternary structure, providing curated structures and annotations for research.

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

The paper shows that evolutionary rates of intrinsically disordered proteins correlate with conformational diversity, suggesting that structural constraints from selected conformers influence rate heterogeneity.

The paper analyzes experimental evidence for long intrinsically disordered protein regions in the PDB, showing increased coverage of intrinsic disorder over the last decade.

The paper evaluates hydroxylation site predictors using independent datasets, finding they perform no better than random, highlighting issues with self-reported performance and generalization.

The paper compares structural constraints in ordered and disordered protein ensembles, finding similar proportions of constrained sites and long-range contacts.

This study shows that structural divergence affects conservation and covariation estimates in protein families, suggesting that using multiple structures improves the interpretation of evolutionary signals.

This paper investigates how conformational diversity affects the correlation between sequence and structural divergence in homology modeling, finding that it reduces correlation and that order/disorder can improve model performance.