(4fy) Deciphering Sequence-Dynamics-Rheology Relationships of Biomolecular Condensates

Many intrinsically disordered regions (IDRs) or proteins (IDPs) in the human proteome are increasingly found to drive spatiotemporally defined condensed assemblies without physical membranes in cells. The functional ability of the condensates is intricately tied to the conformations, dynamics, and rheology of IDPs. Over the last decade, biophysicists using chemical engineering principles have uncovered how various amino acids within an IDP sequence initiate condensate formation. However, research on finding relations between sequence and dynamical evolution of condensates after formation, displaying liquid-like (functional) to solid-like (pathological) behaviors is in its early stages. Such relationships can lead to exciting opportunities for engineering condensates with new cellular functions, developing therapies to bypass condensate-linked disorders, and their rational use as drug-delivery mediums.

In the first part of my talk, I will show how we use physics-based computational models to uncover these relations for model and naturally occurring charged IDPs. Specifically, I will focus on how a biologically-observed sequence feature such as the arrangement of charged amino acids within the IDPs influences their single-chain properties and condensate biophysical properties. At the single-chain level, we find that the chain-level dynamics correlate with changes in conformations with increasing charge segregation. Importantly, the chain-level and segmental conformations and dynamics conform to simple homopolymer scaling only for uniformly charge-patterned sequences. Within the condensates, we find diffusion, viscosity, and interfacial tension to change significantly with increasing charge segregation, but the changes are surprisingly similar across different IDP sequence compositions. Our multiscale simulations reveal that molecular contacts within the condensates are highly similar to those within a single chain. Remarkably, the condensate material properties of charged IDPs are strongly correlated with their dense phase contact dynamics and single-chain conformations. Two important conclusions emerge: (1) the need for caution in inferring IDP properties in dilute conditions using homopolymer scaling laws as is often done experimentally and (2) the potential utility of single-protein simulations in rapidly exploring varied sequences, thus expediting the design and prototyping of biopolymers with desired material properties in future.

In the second part of my talk, I will discuss how we use innovative computational methods to unravel sequence-encoded spatiotemporal viscoelasticity (i.e., liquid-like and solid-like behaviors) of condensates. I will demonstrate that a passive probe microrheology technique, coupled with fluid mechanics theories, can accurately quantify the viscoelastic properties of single-component and multi-component IDP condensates and is superior to conventional rheology techniques. We find that the technique describes the elastic and viscous modulus, terminal viscosity, and relaxation time of heteropolymeric IDPs that differ either in sequence charge patterning or sequence hydrophobicity. Further, we accurately quantify the spatial dependence of viscoelasticity in heterogeneous condensates formed by a pair of IDPs through spatially controlled probe motion. Computational microrheology has implications for investigating biomolecular architectures, resulting in sequence-dynamics-rheology-function relationships for condensates.

Research Interests:

Vision: It is well-known that biochemical alterations, namely mutations or post-translational modifications in a protein sequence can lead to pathological aggregates, implicated in neurodegenerative diseases and cancer development. My vision for a future career in STEM as a tenure-track faculty will focus on deciphering novel insights into the dynamical evolution of biomolecular condensates, with an eventual goal to establish sequence-dynamics-(dys)function relationships for condensates.

Research contributions: Proteins across the proteome are rich in IDPs (lack a stable three-dimensional structure) with a preference for charged amino acids, critical for condensate formation. My postdoctoral research focused on the patterning of charged amino acids within an IDP sequence to computationally illustrate its role in dictating their biophysics at different length scales ranging from dilute (single-chain) phase to condensed phase. To this end, my first work elucidated the role of charge patterning in dictating the single-chain size and dynamics of IDPs, and how they cannot always be indirectly inferred through scaling laws derived from polymer theories. Accurate measurements of single-chain IDPs have become powerful indicators of whether they will thermodynamically form a condensate: a collapsed IDP favors condensation as opposed to an expanded IDP. This work highlighted that direct measurements of single-chain size of charged IDPs are necessary and caution is required with indirect inferences. My next work unmasked a novel, universal power-law correlation between the dilute phase conformations and material properties of condensates, formed by IDPs of varied compositions. This pivotal discovery has enormous implications for high-throughput screening of protein sequences with targeted mechanical properties.

Current work: My doctoral research involved developing a novel microrheology technique to uncover the physics behind the time-dependent viscoelasticity of polymer melts and colloidal suspensions. In my current work, I have translated my knowledge as a soft matter scientist to biophysical problems. Specifically, we demonstrate, for the first time, that the computational microrheology method can accurately reveal the sequence-encoded spatiotemporal viscoelasticity of single-component and multi-component heteropolymeric IDP condensates. We believe that the findings from this work will generate a huge interest in the biophysics community to use computational microrheology for investigating the rheological evolution of condensates, revealing how the protein sequence shapes the functional abilities of condensates.

Future research goals: In the pursuit of my long-term career goal to define the dynamical and functional landscape of biological condensates, I propose my short-term (2-4 years) research objectives in what follows. (1) Role of dynamics in modulating the co-mixing of different IDPs: Efficient co-mixing of charge-rich IDPs because of their specific charge distribution characteristics enables gene activation within cells. In this project, we hypothesize that global and local charge distributions in IDP sequences can be manipulated to achieve similar IDP dynamics that enable co-mixing or dissimilar dynamics resulting in discrete phases. We envision that our findings will help define general rules regarding the co-phase separation of IDPs responsible for gene activation. (2) Interplay between heterochromatin 1 (HP1) proteins and DNA dynamics in forming chromatin condensates: HP1 is a multidomain protein that has been implicated in cancer progression. In this project, we aim to uncover how the molecular interactions between charged domains in HP1 and DNA regulate the dynamics of chromatin condensates, aiding in genome organization. Our preliminary findings suggest a complex interplay between HP1 and DNA diffusion that are of different timescales at low DNA concentrations but exhibit a non-monotonic trend and dynamic coupling with increasing DNA concentrations. Deciphering how the DNA length and the relative ratios of protein-DNA concentrations affect the dynamic coupling, leading to diverse organizations of DNA within these condensates is our primary interest. Further, we aim to uncover how biochemical sequence modifications in heterochromatin proteins shape their electrostatic interactions with DNA, thereby modulating the dynamics of these chromatin condensates. The findings from this work will serve as an impetus to search for a general sequence grammar that would comprehensively define the dynamical and rheological landscape of protein-nucleic acid condensates across the proteome, responsible for functions such as DNA repair. (3) Relevance of uncharged residues in dictating the dynamics of biocondensates: I have extensive experience in using minimal molecular models for deciphering the biophysics of condensate formation. To this end, we aim to utilize minimal models and data-driven machine learning models to uncover sequence features beyond charged residues (e.g., hydrophobic and aromatic residues) that dictate condensate dynamics and their physical aging into aggregates.

Teaching Interests:

It is my fundamental belief that teaching is a transformative profession, paving the way for scientific and societal progress in all educational disciplines. This conviction solidified when I witnessed the happiness in my students upon grasping the concepts I taught as a Lecturer and a postdoc, creating a fulfilling experience. I aspire to pursue an academic profession, where it is incumbent upon educators like me, to put forward innovative teaching and research methods to ensure continued scientific advancement. As a Professor, I aspire to connect my students and colleagues, particularly from historically underrepresented groups, with opportunities for recognition. I aim to foster an environment of open communication and inclusivity.

My vision for a future career in STEM focuses on uncovering novel biophysical insights about proteins that partake in cellular functions/diseases. I envision training the next generation of scientists in quantitative biophysics as an integral part of my research mission. I am deeply passionate about leading a research lab that develops computational scientists who can perform cutting-edge research in their independent careers. I aspire to continue gaining new skills through teaching classes and attending workshops and conferences. This will enable my research group to solve problems in diverse areas through interdisciplinary collaborations and representing my institution worldwide. Also, I wish to develop training modules to assist undergraduates and graduates in sharpening skills along with preparatory tools for their professional development.

I will incorporate data-driven approaches in my research program to understand biological physics. As a Lecturer, I have developed and taught new curricula on computational thinking and machine learning to diverse Engineering majors. I have mentored Ph.D. students and published computational research in prestigious peer-reviewed journals. I strongly believe that my unique experiences in extensive full-time teaching and research, combined with my objective to develop future scientists, will help me to successfully contribute to the academic missions of teaching, research, and service.