Ronan M. Keegan

A validation approach for RNA structures based on deep-learning 3D-structure predictions

This paper extends a protein structure validation method to RNA, showing it can detect errors in low-resolution structures where map-based methods fail.

Structure and signaling mechanism of Helicobacter pylori transducer-like protein D

The paper presents high-resolution structures of Helicobacter pylori TlpD, revealing how ligand-binding perturbations propagate through coiled-coils to regulate kinase interfaces.

Predicting Protein Crystal Solvent Content from Patterson Maps Using Machine Learning

This study uses machine learning to predict protein crystal solvent content from Patterson maps, achieving over 50% error reduction compared to existing methods.

In the AlphaFold era, when is experimental phasing of protein crystals still required?

The paper assesses how many protein structures still require experimental phasing when using AlphaFold 2 models, finding that most can be solved with Molecular Replacement.

Using deep learning predictions reveals a large number of register errors in PDB deposits

The paper identifies thousands of register errors in PDB structures using deep learning predictions and shows corrections improve refinement statistics.

Using deep learning predictions of inter-residue distances for model validation

The paper introduces validation methods using deep learning-predicted inter-residue distances to detect and correct sequence register errors in protein models.

Slice'N'Dice: Maximising the value of predicted models for structural biologists

SliceNDice improves Molecular Replacement by splitting and repositioning predicted structural models to enhance accuracy in domain orientations.

MrParse: Finding homologues in the PDB and the EBI AlphaFold database for Molecular Replacement and more

MrParse streamlines Molecular Replacement by consolidating homologue searches and rankings from PDB and EBI AlphaFold, using eLLG and structural predictions.

Identification of unknown proteins in X-ray crystallography and cryo-EM

The paper presents an automated pipeline to identify protein sequences from cryo-EM and crystallographic data, demonstrating its application on an unknown snake venom protein.

Assessing the utility of CASP14 models for molecular replacement

This paper evaluates CASP14 models for molecular replacement using a new metric, reLLG, which is independent of diffraction data and shows AlphaFold2 models outperform others.

Helical ensembles out-perform ideal helices in Molecular Replacement

This paper introduces helical ensembles as improved search models for molecular replacement, achieving a 30% increase in solutions and faster convergence compared to ideal helices.