#introduction #biology #biotech #science #academic
Hi all!
I am Akanksha, currently working as a research tech at HHMI Janelia Research Campus, engineering bioluminescent proteins for functional imaging.
My research interests lie at the interface of chemistry, biology, and engineering 🔬. 'A tool developer' describes me best - in the dry lab, I was working on a systems biology project involving detecting hard-to-find nucleotide changes with certain effects from human transcriptome datasets. Transitioning to a wet lab, I have been engineering proteins for various applications.
Being an avid learner, I spend a lot of time reading articles from across various sub-disciplines in bioscience and biotech. I'm happiest when brainstorming ideas in molecular/synthetic biology or new tools that don't exist with a near-perfect cup of coffee☕ ! Always happy to connect over a crazy idea!
Cheers!
Helpful paper unpacking some metrics and intuition behind higher success rate (10-fold?) of designing protein binders using DL and/or physics-based methods alone
Improving de novo protein binder design with deep learning | Nature Communications https://www.nature.com/articles/s41467-023-38328-5
Neat single-molecule experiments!
Two-component molecular motor driven by a GTPase cycle | Nature Physics
https://www.nature.com/articles/s41567-023-02009-3
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RT @stpalli
The mechanism of this new motor is distinct from conventional ones.
Unlike motors such as Kinesin, it runs on GTP not ATP.
Also, the motor has no "power stroke" motion of one protein domain relative to another, but relies on a sudden change in the stiffness of the en…
https://twitter.com/stpalli/status/1654141624933335048
Super cool observations! Not entry, not copy #, but lack of histone modifications affecting transgene expression
The AAV capsid can influence the epigenetic marking of rAAV delivered episomal genomes in a species dependent manner | Nature Communications https://www.nature.com/articles/s41467-023-38106-3
Fun read! Emergence of Cooperative Glucose-Binding Networks in Adaptive Peptide Systems https://pubs.acs.org/doi/10.1021/jacs.3c01620#.ZEXPMrgBLHs.twitter
In vivo bioluminescence imaging of natural bacteria within deep tissues via ATP-binding cassette sugar transporter | Nature Communications https://www.nature.com/articles/s41467-023-37827-9
Genetically encoded barcodes for correlative volume electron microscopy | Nature Biotechnology https://www.nature.com/articles/s41587-023-01713-y
Neoepitope formation through the generation of RNA-derived “editopes” https://www.biorxiv.org/content/10.1101/2023.03.16.532918v1
I want to especially thank my BB405,503 UG course professors @patankar_lab @Banerjeeiitb, who taught molecular biology in a way that probably changed the course of my academic interests ever since.
Also excited to get 'The Eighth Day of Creation' by Judson as my celebratory book
RT @MuirLab
Looking to study chromatin interactomes with proximity labeling? Check out our new paper in @Nature, where we combine our split-intein expertise with uMap technology from the @MacMillan_Lab to map the microenvironment of chromatin state changes.
A Vaccinia-based system for directed evolution of GPCRs in mammalian cells | Nature Communications https://www.nature.com/articles/s41467-023-37191-8
RT @shivam_kajale
Delighted to share that I recently had the chance to talk about my work on development of highly energy-efficient “beyond CMOS” devices, based on magnetics, spintronics and magnetoelectrics, at @TEDxBoston 's Planetary Stewardship forum.
For writing #tootorial, I’ve used the trick where the first toot is Public but the rest are Unlisted. The top post will be the one that shows up in followers timelines, rather than all of them in reverse order.
I think the Unlisted ones can be boosted by others, but they aren’t searchable though, which is a downside.
Papers and patents are becoming less disruptive over time | Nature https://www.nature.com/articles/s41586-022-05543-x
@ypriverol had a conversation with a new data science student looking at a high throughput screen. They asked, how do you approach this as a statistician? I said, make plots, more plots, keep making plots. Raw data, summary stats, typical features/cases, exceptional features/cases. Student asked, can AI help in this process? Me: not really, this is where the human 👁️ and 🧠 are your best tools, to then provide quality data for models to learn meaningful patterns instead of artifacts and bias
For eg, in the last few days,
{I have been listening to talks on BCI, explaining to friends about an experience when I wanted to understand someone’s approach to a problem, thought about watching one of the Harry Potter movies, read about José’s blog embeddings}
..hence,Pensieve?
The key point being that the dissection is in a way that it is immediately obvious to understand your idea/thought/feeling and how you arrived at it at the moment, if you/anyone revisited it in future, and in a way that cannot be corrupted by any biases/assumptions
🚨New preprint posted🚨
Protein turnover is regulated by learning and disease processes. We developed a method (DELTA) to quantify the rate of protein turnover across the whole brain, with single synapse resolution.
https://www.biorxiv.org/content/10.1101/2022.11.12.516226v1
We used knock-in mice in which a protein of interest is fused with HaloTag, a self-labeling enzyme which can capture bright Janelia Fluor dyes. This is the basis of a pulse-chase experiment to estimate turnover rates.
grad student at Caltech Bioengineering | prev research tech at HHMI Janelia | chemistry, systems biology, protein engineering, athlete, episodic poet | MSc Molecular biology, MPI-NAT l BS Chemistry, IIT Bombay https://twitter.com/akankshay58