Lucas Turano

Frustration Quenching and Network Topology of the Energy Landscape as Primary Determinants of Protein-Ligand Binding Pose Prediction by Deep Learning Models

This paper shows that deep learning models' performance in predicting protein-ligand binding poses depends on the energy landscape's frustration quenching and network topology, not algorithmic flaws.

Localized Rigidification and Allosteric Modulation Mechanisms of SARS-CoV-2 Spike Neutralization by Class 3 and Class 4 Antibodies at Atomic Resolution: An Integrated Computational Study of Binding, Dynamics, and Allostery

This study uses computational methods to analyze class 3 and class 4 antibodies' mechanisms of neutralizing SARS-CoV-2, revealing distinct strategies for binding, dynamics, and immune escape.

Frustration Landscapes of Broadly Neutralizing SARS-CoV-2 Spike Antibodies Targeting Conserved Epitopes Reveal Energetic Logic of Escape-Proof and Escape-Prone Mechanisms

This paper analyzes the molecular mechanisms of broadly neutralizing SARS-CoV-2 antibodies by combining computational methods to reveal how conserved epitopes resist immune escape.

Decoding Allosteric Grammar with Explainable AI Integrating Protein Language Models and Energy Landscape Analysis: Neutral Frustration at Allosteric Binding Sites Encodes Regulatory Versatility in Protein Kinases

This study uses explainable AI to analyze the energy landscape of protein kinases, revealing that allosteric sites are in neutrally frustrated regions, explaining their detection challenges.

Decoding the Allosteric Paradox: A Dual Framework Integrating AI Cofolding Models with Landscape-Guided Interpretable AI Framework of Ligand-Protein Binding

This study identifies a universal failure in AI models to predict allosteric ligand binding, linking it to biophysical constraints in energy landscapes.

Protein Language Models and Structure-Based Machine Learning for Prediction of Allosteric Binding Sites in Protein Kinases: An Explainable AI Framework Grounded in Energy Landscape-Encoded Frustration

This paper compares sequence-based and structure-based methods for predicting allosteric binding sites in protein kinases, finding that sequence-based models perform poorly on allosteric sites despite good performance on orthosteric ones.