Claudio Mirabello

A generalisable framework to inject distance information into Alphafold-like structure predictors

This paper introduces a method to inject distance information into AlphaFold-like structure predictors to improve structure prediction accuracy.

The aging transcriptome: a condition-dependent response to a deteriorating soma

This study shows that gene expression changes in aging Drosophila are similar to those caused by reduced physiological condition, suggesting aging is a plastic response to somatic decline.

Mettl15-Mettl17 modulates the transition from early to late pre-mitoribosome

The paper investigates the role of Mettl15 and Mettl17 in mitoribosome assembly, revealing their cooperative mechanism in transitioning from early to late stages of small subunit formation.

DockQ v2: Improved automatic quality measure for protein multimers, nucleic acids, and small molecules

The paper introduces DockQ v2, an improved tool for assessing the quality of modeled biomolecules, including protein interfaces, nucleic acids, and small molecules.

Towards understanding of allostery in MALT1: a possible role of interdomain motions as revealed by NMR and AlphaFold

The paper investigates the dynamic behavior of MALT1 using NMR and AlphaFold to reveal interdomain motions involved in allosteric regulation.

aMeta: an accurate and memory-efficient ancient Metagenomic profiling workflow

aMeta is a metagenomic profiling workflow that improves accuracy and reduces memory usage in ancient microbiome analysis.

Unmasking AlphaFold: integration of experiments and predictions in multimeric complexes

This study introduces AF_unmasked, a method that integrates experimental data with AlphaFold predictions to improve the accuracy of large protein complex structures.

dgram2dmap: Extraction, visualisation and formatting of distance constraints from AlphaFold distograms

dgram2dmap converts AlphaFold distograms into distance maps for visualization and downstream analysis.

InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network

InterPepRank ranks peptide-protein docking decoys using a deep graph network, improving the selection of high-quality models.

Critical Assessment of Protein Intrinsic Disorder Prediction

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.

InterLig: a fast and accurate software for ligand-based virtual screening

InterLig is a software for ligand-based virtual screening that compares small molecules using 3D atom alignments, outperforming existing methods on a benchmark.

InterPep2: Global Peptide-Protein Docking with Structural Templates

InterPep2 predicts peptide-protein interaction structures using structural templates, outperforming existing methods on a benchmark dataset.

rawMSA: End-to-end Deep Learning Makes Protein Sequence Profiles and Feature Extraction Obsolete

The paper introduces rawMSA, a deep learning approach that eliminates the need for sequence profiles and features by directly processing raw multiple sequence alignments to predict protein structural features.

Predicting protein-peptide interaction sites using distant protein complexes as structural templates

This paper introduces InterPep, a computational pipeline that predicts protein-peptide interaction sites using structural templates and machine learning, achieving 50.7% rank 1 accuracy and 69.3% top five accuracy.

Topology independent structural matching discovers novel templates for protein interfaces

The paper introduces InterComp, a topology and sequence-order independent method for structural comparisons of protein interfaces, showing it can identify similar interfaces in 283 of 586 targets.

Methods For Estimation Of Model Accuracy In CASP12

The paper describes methods for estimating model accuracy in protein structure predictions, showing improvements in single model accuracy over previous methods.

InterPred: A pipeline to identify and model protein-protein interactions

InterPred predicts and models protein-protein interactions from sequence using structural modeling and a novel random forest classifier, outperforming existing methods.