Shiva Shankar S @BioShankar
| Structural biology and Mass Spectrometry enthusiast | Protein engineering | PhD student @csir_ncl Pune, India Joined August 2015-
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Shiva Shankar S retweeted
Can machine learning design functional antibodies without specialized training, and at what success rates?@ManifoldBio @biorxivpreprint "mBER: Controllable de novo antibody design with million-scale experimental screening" • Antibody therapeutics dominate protein drug…
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Shiva Shankar S retweeted
Targeting hepatocyte-specific SLC2A8blocks hepatic steatosis and dissociates TCA cycle flux inhibition from glutamine anaplerosis #MASLD @HepCommJournal journals.lww.com/hepcomm/fullte…
Shiva Shankar S retweeted
2024 #NobelPrize laureate in chemistry David Baker has succeeded with the almost impossible feat of building entirely new kinds of proteins. In recent years, one incredible protein creation after the other has emerged from Baker’s laboratory. They range from new nanomaterials…
Shiva Shankar S retweeted
ProteinDJ: a high-performance and modular protein design pipeline biorxiv.org/content/10.110… 🧬🖥️🧪 github.com/PapenfussLab/p…
Shiva Shankar S retweeted
Decoding Protein Stabilization: Impact on Aggregation, Solubility, and Unfolding Mechanisms #MolecularDynamics pubs.acs.org/doi/10.1021/ac… @KunkaAntonin @JiriDamborsky #JCIM Vol65 Issue16 #compchem
Shiva Shankar S retweeted
Excited to share this work from the Garcia Lab out today in @NatureComms! We engineer "cytokine adaptors", molecular switches that simultaneously block a target cytokine while inducing local activation of alternative cytokine receptors. rdcu.be/ec7GS
Shiva Shankar S retweeted
Scientists have designed an interleukin-21 mimic with increased stability and capacity to induce more potent #antitumor activity than the native protein, which could inform improved #immunotherapies. @UWproteindesign Learn more in Science #Immunology: scim.ag/3KsvCy9
Shiva Shankar S retweeted
The CAGE complex: a hollow, megadalton, protein assembly in prokaryotic and eukaryotic microbes biorxiv.org/content/10.110…
Shiva Shankar S retweeted
Online now! Flexibility or uncertainty? A critical assessment of AlphaFold 2 pLDDT dlvr.it/TNHb9k
Shiva Shankar S retweeted
Efficient protein structure generation with sparse denoising models Designing new proteins usually means stitching together a 3D backbone, then finding sequences that will actually fold into that shape. Today’s best generators struggle with big proteins and often need retraining…
Shiva Shankar S retweeted
New preprint & open-source! 🚨 “SimpleFold: Folding Proteins is Simpler than You Think” (arxiv.org/abs/2509.18480). We ask: Do protein folding models really need expensive and domain-specific modules like pair representation? We build SimpleFold, a 3B scalable folding model solely…
Shiva Shankar S retweeted
The ability to design antibodies against any protein of interest has major implications for medicine, biotech, and basic science. Today, we introduce Germinal, a pipeline for epitope-targeted de novo antibody design achieving 4–22% success rates with efficient experimental…
Shiva Shankar S retweeted
🚀🚀David Baker paper is here from @Nature How can scientists engineer proteins to rapidly turn off cellular signaling, like having a molecular "kill switch" for cytokines? @UWproteindesign "Design of facilitated dissociation enables timing of cytokine signalling" • Protein…
Shiva Shankar S retweeted
Design of facilitated dissociation enables timing of cytokine signalling: nature.com/articles/s4158…
Shiva Shankar S retweeted
AI-based Methods for Simulating, Sampling, and Predicting Protein Ensembles 1. This review article explores the latest advancements in using artificial intelligence to predict protein ensembles. It highlights the potential of AI to transform our understanding of protein dynamics…
Shiva Shankar S retweeted
Single-sequence deep learning delivers crystal-quality models of covalent K-Ras G12 hotspot complexes 1. A novel study introduces Chai-1, a publicly available structure prediction tool that accurately predicts covalent K-Ras(G12C) complexes without using multiple sequence…
Shiva Shankar S retweeted
ProTDyn: A Foundation Protein Language Model for Thermodynamics and Dynamics Generation 1. ProTDyn is a groundbreaking protein language model that unifies conformational ensemble generation and multi-timescale dynamics modeling within a single framework. Unlike traditional…
Shiva Shankar S retweeted
Research progress on the mechanism of exercise against anxiety disorder sciencedirect.com/science/articl…
Shiva Shankar S retweeted
Computational design of sequence-specific DNA-binding proteins nature.com/articles/s4159…
Shiva Shankar S retweeted
#Throwback REVIEW | T Nguyen & @CorveraLab Adipose tissue as a linchpin of organismal ageing nature.com/articles/s4225…
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