Burak Erman

Kirchhoff matrix-tree evaluation of residue-contact spanning-tree ensembles yields exact allosteric thermodynamics; KRAS G12D conserves global and channel heat capacity but reroutes paths via Q61 and F156.

The paper introduces a matrix framework to map directional entropy transfer in proteins by incorporating continuous distance-dependent weighting into the Kirchhoff matrix, applied to KRAS to identify allosteric communication pathways.

This paper introduces a mathematical framework to analyze allostery in proteins, showing how mutations affect pathway correlations and communication through intermediary residues.

This paper generalizes the Gaussian Network Model using spanning-tree thermodynamics to show that KRAS activation is driven by entropy-enthalpy compensation.

This study maps the peptide interaction fingerprint of HLA-B*51:01 using MD simulations to understand its role in Behcet's disease and guide therapeutic peptide design.

The paper introduces a framework using dynamic distance, edge centrality, and entropy sensitivity to analyze allosteric communication in KRAS, identifying key residue pairs that function as bottlenecks.

The paper quantifies noise suppression in protein allostery by analyzing communication pathways and their topology-aware weighting across multiple proteins.

This paper improves the Gaussian Network Model by integrating local and allosteric factors to better predict residue-residue correlations and B-factors in proteins.

This study uses molecular dynamics simulations to show how the G12D mutation in KRAS increases entropy and dynamic coupling, altering its conformational landscape and function.

This paper analyzes how residue fluctuations in proteins influence allosteric communication by quantifying synergy, redundancy, and noise using computational methods.

This paper introduces a method using mutual information profile shifts to assess the pathogenicity of Pyrin mutations in familial Mediterranean fever, showing how negative shifts indicate severe functional effects.

The paper introduces DP-GNM to identify directional information flow in proteins, revealing driver residues and synchronized dynamics in Cyclophilin A.

The paper evaluates ZDOCK decoys for nanobody-protein complexes using the Dreiding Force Field, finding translations sufficient for accurate docking and identifying salt bridges as essential for recognition.

The paper revisits the Gaussian Network Model (GNM) to improve its applicability to mutation and ligand binding studies by incorporating atomic details through modifications to the Kirchoff Adjacency Matrix.

The paper quantifies information transfer between protein residues using transfer entropy, showing significant long-range information transfer and high transfer rates in PSD-95.