Sergio Branciamore

Allosteric pathways govern Gα protein coupling selectivity at promiscuous GPCRs

The paper identifies allosteric pathways that determine G protein coupling selectivity in promiscuous GPCRs using a combination of mutagenesis, simulations, and machine learning.

Interpretable Machine Learning Model of Receptor Dynamics Reveals AT1R Allostery and a Negative Allosteric Modulator

The paper develops an interpretable machine learning model to identify allosteric modulators of the AT1R receptor and discovers a negative allosteric modulator.

Multiomic State-Transitions Reveal Post-Treatment Transcriptome Desynchronization in Acute Myeloid Leukemia

The paper uses a state-transition model to study transcriptome dynamics in AML, finding delayed miRNA responses and linking the Dlk1-Dio3 locus to treatment-induced desynchronization.

Longitudinal single cell RNA-sequencing reveals evolution of micro- and macro-states in chronic myeloid leukemia

The paper shows that clinically relevant leukemia phenotypes emerge at the macrostate level from single cell data, revealing how disease states aggregate from noisy single cell transcriptomes.

Conserved Residues in the Gα interface show subtype specificity in Gβγ coupling

The paper identifies subtype-specific conserved residues in G protein subtypes that modulate Gβγ coupling, showing conservation does not guarantee functional equivalence.

DRUMBEAT: Temporally resolved interpretable machine learning model for characterizing state transitions in protein dynamics

DRUMBEAT is a machine learning model that identifies time-resolved cooperative events in protein dynamics, revealing folding pathways and critical residues.

Integrating computational chemistry and machine learning to predict KRAS mutation-induced resistance

This paper develops a computational framework combining molecular dynamics and machine learning to predict KRAS mutation-induced drug resistance with over 90% accuracy.

Allostery between distant structural regions dictates selectivity in GPCR:G protein coupling.

The paper uses machine learning and simulations to show that distant residue interactions in the G protein core influence GPCR selectivity and can be engineered to change signaling preferences.

Temporally Resolved and Interpretable Machine Learning Model of GPCR Conformational Transition

This paper develops an interpretable machine learning model to identify residue communities involved in GPCR conformational transitions, with applications to dopamine receptor subtype-specific drug design.

Ligand Discrimination in Immune Cells: Signal Processing Insights into Immune Dysfunction in ER+ Breast Cancer

The paper measures ligand discrimination errors and molecular noise in immune cells of ER+ breast cancer patients compared to healthy controls using a communication model.

CAR T-cell and oncolytic virus dynamics and determinants of combination therapy success for glioblastoma

This study uses a mathematical model to analyze CAR T-cell and oncolytic virus dynamics in glioblastoma, identifying virus bursting size as a key determinant of combination therapy efficacy.

Protein Frustration Reveals Active Sites in Co-Evolved GPCR:G Protein Complexes and in Engineered Targeted Degrader Complexes

The paper identifies frustrated residues in GPCR:G protein interfaces and engineered degrader complexes, showing higher frustration in engineered interfaces.

Overcoming treatment resistance mediated by the bone marrow vascular niche in acute myeloid leukemia

This study develops a computational model of the AML bone marrow microenvironment and shows that combining TKIs with miR-126 inhibition can overcome treatment resistance by disrupting the protective vascular niche.

Lymphocytes and monocytes undergo swift suppression of IL-10R, IL-6R, and IL-2Rβγ signaling under high concentrations of different cytokines

The paper shows that lymphocytes and monocytes suppress IL-10R, IL-6R, and IL-2Rβγ signaling under high cytokine concentrations, mediated by cytokines that do not induce their own pSTATs.

BaNDyT: Bayesian Network modeling of molecular Dynamics Trajectories

BaNDyT uses Bayesian network modeling to analyze molecular dynamics trajectories, providing data-driven insights into protein function and interactions.

Characterization of DNA extrusion scenarios underlying aggregate Hi-C patterns by a new computational framework

The paper introduces a computational framework to reconstruct DNA extrusion scenarios from aggregate Hi-C data and demonstrates how DNA extrusion explains directional biases and dynamic promoter-enhancer interactions.

Synthetic data generation with probabilistic Bayesian Networks

This study introduces a probabilistic Bayesian Network simulation framework to generate synthetic data that better aligns with real biological data and network models.

New analysis framework incorporating mixed mutual information and scalable Bayesian networks for multimodal high dimensional genomic and epigenomic cancer data

The paper introduces a two-stage framework using mixed mutual information and scalable Bayesian networks to analyze multimodal cancer data, identifying 11 candidate genes with 6 validated in literature.