Ruth Nussinov

Single-Cell Mapping of Malignant Signaling Networks Guides Drug Combinations

The paper identifies recurrent signaling pathways in breast cancer using single-cell data and suggests drug combinations targeting these pathways.

The structural basis of RanGAP1 regulation and catalysis in nuclear transport

This study reveals the structural basis of RanGAP1 regulation and catalysis in nuclear transport, showing how sumoylation and conformational changes coordinate GTP hydrolysis.

How Functional Variants Reconfigure the Rac2 Conformational Landscape

This study investigates how mutations in Rac2 alter its conformational dynamics and signaling, revealing distinct inactive and overactive states that impact cellular function.

Oncogenic PI3Kα variants reveal graded conformational spectrum with mutation-specific cryptic pockets

The paper uses molecular dynamics simulations to show that double mutations in PI3K variants lead to more active conformations and drug-targetable cryptic pockets, suggesting a conformational selection strategy for drug development.

Cellular Mechanisms Underlying Melanoma Brain Metastasis

The paper identifies key molecular pathways and proteins associated with melanoma brain metastasis through genomic analysis and suggests therapeutic strategies targeting these pathways.

ERK Allosteric Activation: The Importance of Two Ordered Phosphorylation Events

This study uses molecular dynamics simulations to show that ERK activation requires ordered phosphorylation events, with threonine phosphorylation as the second step being essential for catalytic activity.

Distinct allosteric networks in CDK4 and CDK6 in the cell cycle and in drug resistance

This study identifies distinct allosteric networks in CDK4 and CDK6 that explain their differential kinase activity, inhibitor binding, and roles in cell cycle regulation and drug resistance.

mTOR variants activation discovers PI3K-like cryptic pocket, expanding allosteric, mutant-selective inhibitor designs

The paper identifies a cryptic pocket in mTOR oncogenic variants through molecular dynamics simulations and suggests designing PI3K-like inhibitors to target this pocket for therapeutic benefit.

Anticancer Target Combinations: Network-Informed Signaling-Based Approach to Discovery

This paper develops a signaling-based method to identify optimal protein combinations for anticancer drug therapy, validated using patient-derived data and xenografts.

Co-occurring Mutations in Different Genes Can Fuel Oncogenic Signaling and Serve as Metastatic Tumor Markers

The paper identifies co-occurring mutation pairs in cancer genomes that are associated with metastasis and provides a tool for predicting metastatic potential.

Neurodevelopmental disorders and cancer networks share pathways; but differ in mechanisms, signaling strength, and outcome

This study compares neurodevelopmental disorders and cancer, finding shared pathways but differing in signaling strength and outcomes.

Cell cycle progression mechanisms: slower cyclin-D/CDK4 activation and faster cyclin-E/CDK2

The paper investigates why cyclin-D/CDK4 drives the longer G1 phase while cyclin-E/CDK2 regulates the short G1/S transition, proposing slower activation for cyclin-D/CDK4 and faster for cyclin-E/CDK2, with implications for CDK4 inhibitor design.

Comprehensive characterization of multi-omic landscapes between gut-microbiota metabolites and the G-protein-coupled receptors in Alzheimer's disease

This study identifies GPCR targets of gut microbiota metabolites in Alzheimer's disease using multi-omics and machine learning.

Novel MAPK/AKT-impairing germline NRAS variant identified in a melanoma-prone family

The paper identifies a novel NRAS variant in a melanoma-prone family and shows it impairs MAPK/AKT signaling while enhancing PI3K signaling.

The mechanism of full activation of tumor suppressor PTEN at the phosphoinositide-enriched membrane

This study uses explicit solvent simulations to reveal how PTEN interacts with phosphoinositide-enriched membranes, elucidating its mechanism of activation and membrane anchoring.

Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data Reveal their Clinical Impact

The paper identifies latent driver double mutations in pan-cancer data and links them to clinical outcomes and drug response.

A deep learning framework for elucidating whole-genome chemical interaction space

This paper introduces DISAE, a deep learning framework that predicts ligands for unannotated proteins and applies it to assign ligands to human orphan GPCRs.

Genome-wide Prediction of Small Molecule Binding to Remote Orphan Proteins Using Distilled Sequence Alignment Embedding

This paper introduces DISAE, a protein pre-training method for deorphanization, which improves generalizability and outperforms existing methods in predicting small molecule binding to orphan proteins.