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Dr. Anita Nivedha: Computational Dynamics of Ligand Bias in GPCR Signaling

Scientific Abstract

G protein–coupled receptors (GPCRs) transmit extracellular signals through conformational changes that propagate from ligand-binding sites to intracellular signaling partners. Understanding how ligand binding leads to specific signaling outcomes—particularly ligand bias toward G protein or β-arrestin pathways—remains a major challenge in GPCR pharmacology and drug discovery.

In this conversation, Dr. Anita Nivedha discusses computational approaches to decoding signal propagation within GPCR structures. Using molecular dynamics simulations combined with network analysis of residue communication pathways, Dr. Nivedha describes how allosteric signaling within receptors can be quantified at atomic resolution. During her postdoctoral work, she developed a computational framework that maps communication pathways from ligand-binding pockets to intracellular effector interfaces, enabling prediction of ligand bias directly from structural simulations.

The discussion explores how microscopic structural communication within receptors correlates with experimentally observed signaling outcomes, providing a bridge between structural biology and pharmacology. Dr. Nivedha also reflects on broader implications for receptor subtype selectivity, peptide receptor pharmacology, and the evolving role of computational pharmacology in modern drug discovery.

Together, these perspectives illustrate how simulations can move beyond visualization to become quantitative tools for understanding receptor activation and guiding GPCR-targeted therapeutics.

About the Guest

Dr. Nivedha is a computational biologist specializing in molecular simulations and computational pharmacology. Her work focuses on understanding how ligand binding propagates structural signals through GPCRs using molecular dynamics simulations and network-based analysis of residue communication pathways.

During her postdoctoral research, Dr. Nivedha developed computational approaches to quantify ligand bias by analyzing communication routes between extracellular ligand-binding sites and intracellular signaling interfaces. Her work has also explored receptor subtype selectivity and peptide-binding GPCRs, connecting structural dynamics to pharmacological outcomes relevant for drug discovery.

Scientific Themes of the Conversation

Allosteric communication networks within GPCR structures
Computational quantification of ligand bias
Molecular dynamics simulations of receptor activation
Structural determinants of GPCR subtype selectivity
Integrating computational pharmacology with experimental GPCR biology
Translating structural dynamics into drug discovery insights

Key Insights from the Conversation

Computational Pathways Can Reveal Ligand Bias

Dr. Nivedha describes how communication pathways inside GPCR structures can be mapped using molecular dynamics simulations. By counting signaling routes connecting ligand-binding sites to intracellular interfaces, simulations can reproduce experimentally measured ligand bias.

Allosteric Communication Explains Signal Propagation

Ligand binding does not directly trigger signaling. Instead, information travels through networks of interacting amino acids across the receptor structure, forming allosteric communication pathways that connect extracellular ligand binding to intracellular signaling outcomes.

Simulations Provide Dynamic Views of Receptor Function

Static structures capture only one moment in receptor activation. Molecular dynamics simulations allow researchers to observe proteins in motion and analyze conformational transitions that drive receptor signaling.

Structural Communication Reveals Functional Residues

Mapping communication networks identifies key residues involved in signal propagation. These residues may represent potential targets for allosteric modulators that reshape receptor signaling pathways.

Computational Methods Complement Experimental Pharmacology

Experimental assays measure pathway activation, but they do not directly reveal the structural mechanisms underlying signaling bias. Computational approaches provide a mechanistic bridge between receptor structure and pharmacological output.

Subtype Selectivity Emerges from Subtle Structural Differences

Even closely related receptor subtypes can display different ligand affinities. Structural simulations can reveal how binding pocket geometry and receptor dynamics contribute to subtype selectivity.

Scientific Careers Often Evolve Unexpectedly

Dr. Nivedha reflects on how her career in GPCR biology began unexpectedly during her postdoctoral work. What started as a general interest in protein dynamics evolved into a deep engagement with the GPCR field.

Episode Timeline

00:00 — Introduction: From ligand binding to receptor signaling Overview of ligand bias and the challenge of tracing signal propagation within GPCR structures.

02:00 — Scientific background of Dr. Nivedha Bioinformatics training, computational chemistry, and early research in carbohydrate–protein interactions.

07:30 — First encounters with GPCR structural biology Transition from studying general protein dynamics to the complexities of GPCR signaling.

16:45 — Studying allosteric communication in GPCRs Introduction to computational tools for mapping communication pathways inside proteins.

18:30 — Quantifying ligand bias using molecular simulations Development of a computational framework linking communication pathways to signaling outcomes.

20:00 — Applications to subtype selectivity and receptor pharmacology Using structural communication analysis to explain differences in ligand affinity across receptor subtypes.

26:00 — Academic–industry collaborations in GPCR drug discovery Insights from collaborative research with pharmaceutical partners.

46:00 — Scientific turning points and career insights Key moments that shaped Dr. Nivedha’s research direction.

52:00 — Reflections on GPCR research and future directions Perspectives on the diversity and continuing importance of GPCR biology.

Selected Quotes

“Communication pathways connect the ligand-binding site to the intracellular signaling interface, and those pathways are made of residues along the way.”

“By counting the number of communication pathways toward G proteins versus arrestins, we began to see correlations with experimental ligand bias.”

“Molecular dynamics simulations allow us to simulate proteins in motion and understand the conformational changes that drive receptor activation.”

“Simulations stopped being just visualizations—they became measurements that could inform predictions for drug discovery.”

Full Transcript

(Formatted for readability)

Introduction

Yamina Berchiche:

Hello listeners, Yamina here. Welcome to the Dr. GPCR podcast.

If you’ve ever wanted to measure ligand bias, you’ve probably looked at the activation of different signaling pathways. But how does the binding of a ligand at the extracellular pocket of a receptor redirect residues within the receptor to activate signaling? That remains difficult to trace today.

My guest today is Dr. Anita Nivedha, a computational biologist studying allosteric communication in GPCRs. She uses molecular dynamics and network analysis to map signaling pathways through receptors.

During her postdoctoral work on GPCR structure and function, one problem stood out: can simulations quantify ligand bias inside receptor structures?

One evening running analyses alone in the lab, a pattern appeared. Communication routes toward G proteins differed from those toward arrestins. When she counted those pathways, the numbers tracked experimental bias.

A computational framework for predicting ligand bias began to emerge. Those calculations linked microscopic communication within the receptor to macroscopic signaling outcomes.

Suddenly, simulations were not just visualizations anymore—they were measurements. And measurement meant predictions for drug discovery.

Before we dive into this episode, I invite you to check out the relevant links in the show notes.

And now, let’s dive into this conversation with Dr. Nivedha.

Guest Introduction

Yamina Berchiche:

Hello everyone, this is Yamina from Dr. GPCR. And I’m very excited today because this has been a long time in the works to have with me Dr. Anita Nivedha.

Dr. Nivedha:

Thank you so much for having me. I’m really excited for our conversation today.

Yamina Berchiche:

Let’s start at the beginning. Could you introduce yourself and tell our audience who you are and what you do?

Dr. Nivedha:

Absolutely. My name is Anita. I’m currently a senior scientist at a biotech in Toronto, Canada. Previously I worked at another company in Montreal offering computer-aided drug discovery services.

Before that I was doing my postdoc in California in the lab of Dr. Nagarajan Vaidehi at City of Hope. That’s where I first got introduced to GPCRs.

My PhD was in Georgia in the lab of Dr. Robert Woods, where I worked on carbohydrate ligands and docking scoring functions. My undergraduate degree was in bioinformatics in India, which is where my interest in computational approaches to biology started.

Yamina Berchiche:

Wonderful. And we did have Dr. Nagarajan on the podcast and I love chatting with her. She’s wonderful.

Dr. Nivedha:

Yeah, I actually listened to that podcast. I was very excited to see her on your podcast.

Yamina Berchiche:

She is wonderful. Thank you so much for that introduction. I have to ask—how did you get interested in science in general?

Dr. Nivedha:

Science was something I was interested in since middle school. I was always interested in biology, especially studying anatomy. At one point I was interested in becoming a medical doctor when I was younger.

But as I grew up and learned more about biotechnology and the possibilities that existed, my interests diversified. Around high school I was introduced to programming. I started learning C and C++ programming and I really loved it.

When it came time to choose my major for undergrad, I wanted something that combined both biology and computers. I didn’t even know such a field existed. But when I came across bioinformatics, I thought, “This is it.” It combined both things I liked.

So my interest in science and computers kind of merged there, and that’s what I still do today.

Women in Computational Science

Yamina Berchiche:

I love it. And I think other than you and Dr. Vaidehi, I don’t remember ever having female computational scientists on the podcast. And today is International Women’s Day, so we’re revealing when this podcast is being recorded.

I was wondering if you could speak to your experience being a computational scientist. What’s the ratio of female versus male computational scientists in your experience?

Dr. Nivedha:

That’s a good question. When you mentioned that only a few female computational scientists have been on the podcast, I’m surprised—but also not surprised.

During my PhD there was a better balance of men and women, although the lab was not purely computational. But since then, I’ve often been outnumbered.

When I joined Dr. Vaidehi’s lab, I was the only woman apart from her initially. Later more women joined the group. But overall, I would say women are still underrepresented in computational fields.

Discovering GPCRs

Yamina Berchiche:

You mentioned that you joined Dr. Vaidehi’s lab and that’s when you were first exposed to GPCRs. Do you remember the first time you looked at a GPCR on a computer?

Was it just another protein target, or did you immediately feel it was something special?

Dr. Nivedha:

I’m trying to remember exactly when I first saw one. It was probably in VMD or another visualization tool we commonly use.

At the time, I didn’t realize the full scope of GPCR biology. I didn’t yet appreciate how interesting and diverse they are, or how important they are as drug targets.

Before that, my PhD focused on carbohydrate ligands interacting with proteins. So the proteins were more like partners for the glycans we studied.

When I applied for postdocs, I was mostly interested in studying proteins in general—their structure, dynamics, and function. GPCRs were not specifically what I was looking for.

But once I started working on them, I appreciated them more and more. Over time I realized how vast and exciting the field is.

Postdoctoral Research Questions

Yamina Berchiche:

Let’s talk about your postdoc research. What were the main questions you were trying to answer?

Dr. Nivedha:

One of the main topics I worked on was allosteric communication in proteins.

In GPCRs, a ligand binds at the extracellular side of the receptor. The result is activation of intracellular signaling partners like G proteins or β-arrestins.

But how does the signal travel from the extracellular binding pocket to the intracellular interface?

That transmission happens through communication pathways across the receptor structure. This is what we call allosteric communication.

The lab had developed software to analyze these communication pathways. Using that framework, I worked on quantifying ligand bias computationally.

We used molecular dynamics simulations and extracted communication pathways connecting the ligand binding site to intracellular interfaces. Then we compared pathways leading to G proteins versus β-arrestins.

We applied this method to GPCR systems where experimental bias values were known. And interestingly, the computational results correlated with experimental measurements.

That project became a collaboration with Boehringer Ingelheim and eventually led to a publication.

Applications to Receptor Selectivity

Dr. Nivedha:

After that, I extended this work to study subtype selectivity.

Some GPCR families contain closely related subtypes. Ligands may bind one subtype strongly but another weakly.

Using communication pathway analysis and simulations, we explored how structural differences between receptor subtypes influence ligand affinity.

I also worked on collaborative projects involving peptide-binding receptors, including angiotensin receptors.

Industry Collaboration

Yamina Berchiche:

Your work involved collaboration with industry. What did you learn from that experience?

Dr. Nivedha:

It was actually my first interaction with industry researchers.

Our collaborator at Boehringer Ingelheim was also a computational scientist with deep GPCR knowledge. That made the collaboration really productive.

They provided suggestions that were directly relevant to our work. It was a great learning experience.

It also showed me how important GPCR research is in pharmaceutical development. Seeing how computational methods could be applied to real drug discovery questions was very motivating.

That experience influenced my later decision to pursue industry roles.

Career Reflections and Scientific Turning Points

Yamina Berchiche:

I’d love to hear about some “aha moments” in your career.

Dr. Nivedha:

One moment happened very early when I chose bioinformatics as my undergraduate major. I remember standing in a hall reading a pamphlet describing the program.

When I saw that bioinformatics combined biology and computing, I knew immediately that this was what I wanted to do.

Another moment came during my postdoc while working on the ligand bias project.

One Friday evening I was alone in the lab running analyses. I decided to compare the number of communication pathways leading to G protein versus arrestin interfaces.

When I saw that those numbers tracked experimentally observed ligand bias, I realized we might have discovered something important.

That moment led to the development of the computational method we later published.

Favorite GPCRs

Yamina Berchiche:

Do you have a favorite GPCR?

Dr. Nivedha:

That’s a difficult question.

I don’t think I have one favorite receptor. It often depends on what I’m studying at the time.

For example, I became fascinated with adhesion GPCRs when I learned about them. I also find olfactory receptors fascinating because of how challenging they are to study.

And now that I work with peptide drug modalities, I’m interested in peptide-binding GPCRs.

So I keep discovering new receptors that do interesting things. That’s one of the reasons the GPCR field is so exciting.