Sebastian Kmiecik

This paper analyzes RNA structure prediction challenges in CASP16, showing that accuracy decreases from local helices to multimer assembly due to monomer errors and interface modeling issues.

This study shows antagonist binding disrupts IL1R1 dynamics to block co-receptor recruitment, contrasting with agonist-induced stabilization of signaling conformations.

The paper uses molecular simulations to show that address-domain engagement before message-domain binding increases the success rate of β-endorphin binding to the μ-opioid receptor.

This paper evaluates blind predictions of dynamic water and ion ensembles around RNA using cryo-EM densities from CASP16, finding that molecular dynamics approaches perform best.

The paper presents an enhanced CABS-dock protocol for flexible docking of cyclic peptides to proteins, showing it can sample near-native solutions on benchmark complexes.

The paper evaluates the effectiveness of molecular dynamics simulations in refining RNA models from CASP15, finding that they improve high-quality models but not poor ones, and provides guidelines for their use.

This study investigates A4galt acceptor specificity by analyzing its dimerization with other GTs and using structural prediction to understand specificity toward GSLs and GPs.

This paper introduces a method for targeted protein degradation in bacteria using degrader peptides that exploit the ClpXP protease, demonstrating its effectiveness on the essential GroEL protein.

The paper presents a database of structure-based protein aggregation predictions for the human proteome using AlphaFold structures and the A3D tool.

This paper updates A3D to predict and optimize protein solubility using a computational pipeline and RESTful service for handling large protein collections.

The paper introduces Aggrescan3D, a tool for designing protein variants with improved solubility and reduced aggregation using 3D structures, and demonstrates its application on therapeutic proteins.

This paper introduces a new protein-protein docking method that allows for large-scale backbone flexibility using coarse-grained Monte-Carlo simulations, achieving acceptable model quality in proof-of-concept tests.

The paper describes a three-stage protocol for docking peptides to GPCRs using CABS-dock, PD2, and Rosetta FlexPepDock, showing improved accuracy in modeling peptide-GPCR complexes.

The paper describes a workflow to refine protein-peptide complex structures from C-alpha models to high-resolution all-atom models using MODELLER and RosettaFlexPepDock.

The paper presents protocols for simulating protein structure flexibility using CABS-flex and SURPASS, showing CABS-flex's high accuracy compared to experimental and MD data.