BaNDyT: Bayesian Network modeling of molecular Dynamics Trajectories 1. BaNDyT introduces a Bayesian Network (BN) approach to molecular dynamics (MD) simulation data, offering a powerful tool to uncover dynamic dependencies between protein residues. Unlike traditional proximity-based models, BaNDyT identifies both local and allosteric interactions, bringing a new perspective on protein function. 2. The software enables fully data-driven insights into molecular dynamics, moving beyond user-based bias. BaNDyT leverages Bayesian networks to interpret MD trajectories, making it a unique resource for researchers analyzing complex protein systems like G protein-coupled receptors (GPCRs). 3. A significant feature of BaNDyT is its dual capability to analyze single residues and inter-residue pairs. This enables in-depth modeling of protein-protein interfaces, key for understanding interaction dynamics in GPCR:G protein complexes. 4. BaNDyT provides scalability for large systems and long timescale simulations, supporting researchers with MD trajectories across varied biomolecular structures, from small proteins to polymeric materials. 5. The software offers an interpretable, unsupervised machine learning model, with a Python interface that allows researchers to fine-tune MD data analysis and visualize networks using Cytoscape, facilitating comprehensive protein interaction studies. 6. Key advantages include the capacity to infer both direct and indirect dependencies within proteins, allowing insights into functional residues that play roles in allosteric regulation and stability, potentially informing targeted therapeutic design. 7. The BN output reveals critical nodes and network properties such as weighted degree, helping researchers identify high-impact residues and interactions that influence protein function and allosteric communication. 8. By modeling GPCR interactions with G proteins, BaNDyT has revealed new insights into selective coupling mechanisms, showing promise for wider applications in protein complex analysis and drug discovery. @emukhaleva @Vaidehilab 💻Code: github.com/bandyt-group/band… 📜Paper: biorxiv.org/content/10.1101/… #BayesianNetwork #MolecularDynamics #ProteinInteractions #GPCR #MachineLearning #ComputationalBiology

This is a star team of oncologists. Anyone would be fortunate to join this team.

Very stimulating @AACR session. Here is a summary to check out

Come and join us at #AACR23 @AACR in Orlando. Representing @cityofhope, I am organizing a major symposium “Advances in Quantitative Sciences in Cancer: From Atoms to Patients” impact of Comp & Quant Sci on challenges: next-gen drug design, early detection and image analysis.

Truly exciting to be part of this work

G proteins, just as their partners GPCRs, exhibit a continuum of conformational states as revealed by mutants of conserved glutamine. Important for functional annotation of disease associated mutations. #biophysics, @UNC_PHCO, @cityofhope. Great teamwork.

We are #hiring a postdoctoral fellow on a very exciting project using Bayesian network Systems Biology techniques to #MDSims for GPCRs. Please send your cv to Dr. Vaidehi at nvaidehi@coh.org Here is the link to the posting cityofhopejobs.org/job/postd…

Proud of this work @NatureComms showing persistence of GPCR-G protein interface interactions regulate promiscuity and selectivity of coupling. Impactful contribution by @M5andhu. Thanks to @m_madan_babu and @Laporte001 for great collaboration. nature.com/articles/s41467-0…

If you’re interested in GPCRs, Angiotensin Receptor (AT1R), peptide ligands or the effect of biased agonists on receptor conformational ensembles, check out our newest work here: sciencedirect.com/science/ar… Happy to see this work from my postdoc published! @VaidehiLab

(1/3) See the latest paper from MFI scientist @AnitaKNivedha on predicting bias in GPCRs, “Biased agonists differentially modulate the receptor conformation ensembles in Angiotensin II type 1 receptor”, research she lead at @cityofhope with @VaidehiLab sciencedirect.com/science/ar…

This is so true. Love @CarolynBertozzi enthusiasm.

Congratulations Dr. Paulekas. We are proud of you.

Insightful piece of work with deep dive into dynamic behavior of partial agonists

Cannot agree with this more

Graduate student from our group, Elizaveta (Eli) Mukhaleva, will be presenting the novel approach for studying GPCR-G protein coupling, bridging structural (MD simulations) and systems biology (Bayesian network modeling) methods. Please visit her at poster 2015 @KeystoneSymp!

Wijnand J.C. Van Der Welden, tonight at @KeystoneSymp, poster 2045, will be elaborating on different structural nanodomains within AT1R that regulate functional selectivity and allosteric communication, which information can be used for the design of allosteric modulators.