Brandon Foley

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.

Neutral Frustration Landscape Architecture of SARS-CoV-2 Spike -Antibody Interfaces Shapes Immune Evasion Mechanisms for Ultrapotent Neutralizing Antibodies and Determines Pathways of Viral Adaptation: Insights from Integrative Computational Approach

This study identifies how ultrapotent antibodies neutralize SARS-CoV-2 by distributing binding energy across rigid interfaces, enabling tolerance to mutations and informing therapeutic design.

Deciphering the Mechanistic Continuum of Broadly Neutralizing Class 4 Antibodies Targeting Conserved Cryptic Epitopes of the SARS-CoV-2 Spike Protein : Operating at the Intersection of Binding, Allostery and Immune Escape

This study uses computational methods to analyze how class 4 antibodies neutralize SARS-CoV-2 by targeting cryptic epitopes, revealing mechanisms involving allostery, binding energetics, and immune escape.

Exploring Dynamic Modulation of Binding, Allostery and Immune Resistance in the SARS-CoV-2 Spike Complexes with Classes of Antibodies Targeting Cryptic Binding Sites: Antibody-Specific Augmentations of Conserved Allosteric Architecture Can Influence Evolution of Viral Escape

This study investigates how Class 4 and Class 5 antibodies modulate the conformational dynamics and immune escape of SARS-CoV-2 spike protein through allosteric effects and mutational scanning.

Conformational Landscaping and Dynamic Mutational Profiling of Binding Interactions and Immune Escape for Broadly Neutralizing Class I Antibodies with SARS-CoV-2 Spike Protein: Distributed Binding Hotspot Networks Underlie Mechanism of Viral Resistance Against Existing Variants

This study analyzes how different antibodies interact with SARS-CoV-2 spike protein using computational methods to identify binding hotspots and resistance mechanisms against variants.

A Conserved Architecture of Allosteric Communications and Regulatory Hotspots in the KRAS Complexes with Diverse Binding Proteins Controls Mechanisms of Effector Mimicry and Allosteric Modulation : Atomistic Revelations from Molecular Simulations and Mutational Scanning of Binding Energetics and Allostery

This study identifies conserved allosteric hotspots in KRAS that mediate effector mimicry and allosteric modulation through molecular simulations and mutational analysis.

Exploring Diverse Binding Mechanisms of Broadly Neutralizing Antibodies S309, S304, CYFN-1006 and VIR-7229 Targeting SARS-CoV-2 Spike Omicron Variants: Integrative Computational Modeling Reveals Balance of Evolutionary and Dynamic Adaptability in Shaping Molecular Determinants of Immune Escape

This study uses computational modeling to analyze binding mechanisms of four antibodies against SARS-CoV-2 Omicron variants, revealing distinct dynamic and evolutionary adaptations that influence immune escape.

Atomistic Profiling of KRAS Interactions with Monobodies and Affimer Proteins Through Ensemble-Based Mutational Scanning Unveils Conserved Residue Networks Linking Cryptic Pockets and Regulating Mechanisms of Binding, Specificity and Allostery

This study identifies conserved allosteric hotspots in KRAS through computational analysis of interactions with monobodies and affimers, revealing structural and functional networks that govern binding and allostery.

Quantitative Characterization and Prediction of the Binding Determinants and Immune Escape Hotspots for Groups of Broadly Neutralizing Antibodies Against Omicron Variants: Atomistic Modeling of the SARS-CoV-2 Spike Complexes with Antibodies

This study identifies immune escape hotspots and binding determinants for broadly neutralizing antibodies against Omicron variants using molecular dynamics and deep mutational scanning.

Assessing Structures and Conformational Ensembles of Apo and Holo Protein States Using Randomized Alanine Sequence Scanning Combined with Shallow Subsampling in AlphaFold2 : Insights and Lessons from Predictions of Functional Allosteric Conformations

This study evaluates AlphaFold2's ability to predict apo and holo protein conformations using randomized alanine masking and subsampling, finding it effective for minor changes but less so for large domain movements.