Constructor Knowledge Labs’ Post

🧬 AI Reveals How Nature’s Toughest Protein Bonds Activate Under Force! 🤖 A multidisciplinary team from Auburn University and Colorado State University, led by Dr. Rafael C. Bernardi and Dr. Marcelo Melo, has uncovered how catch-bonds; protein interactions that tighten under mechanical stress; activate almost instantly. Using AI-driven molecular simulations, the team decoded the dynamic behavior of these bonds, providing insights for biomaterials, drug design, and tissue resilience. This research highlights how combining artificial intelligence and computational biophysics can solve long-standing questions in protein mechanics. 📖 Read the full study here: https://lnkd.in/eAaHmC4E -Catch-bonds strengthen immediately under force -AI predicts protein resilience from short simulation data -Insights could guide biomaterial design, adhesives, and drug delivery -Demonstrates the integration of AI with computational biophysics How do you see AI reshaping biological research and materials engineering? #AI #ProteinMechanics #Biophysics #Biomaterials #DrugDesign #ComputationalBiology #AuburnUniversity #ColoradoStateUniversity #STEMResearch #Innovation

SAGERank: inductive learning of protein–protein interaction from antibody–antigen recognition Abstract Predicting Antibody–Antigen (Ab–Ag) docking and structure-based design represent significant long-term and therapeutically important challenges in computational biology. We present SAGERank, a general, configurable deep learning framework for antibody design using Graph Sample and Aggregate Networks. SAGERank successfully predicted the majority of epitopes in a cancer target dataset. In nanobody–antigen structure prediction, SAGERank, coupled with a protein dynamics structure prediction algorithm, slightly outperforms Alphafold3. Most importantly, our study demonstrates the real potential of inductive deep learning to overcome the small dataset problem in molecular science. The SAGERank models trained for antibody–antigen docking can be used to examine general protein–protein interaction tasks, such as T Cell Receptor-peptide-Major Histocompatibility Complex (TCR-pMHC) recognition, classification of biological versus crystal interfaces, and prediction of ternary complexes of molecular glues. In the cases of ranking docking decoys and identifying biological interfaces, SAGERank is competitive with or outperforms state-of-the-art methods. https://lnkd.in/eCnUBGud

🧬 AI Reveals How Nature’s Toughest Protein Bonds Activate Instantly! 🤖 Exciting developments in computational biophysics! A research team from Auburn University, led by Dr. Rafael C. Bernardi and in collaboration with Dr. Marcelo Melo (Colorado State University, formerly Auburn), alongside Bikas Bernardi and Jeffrey Hatch, has uncovered how protein catch-bonds; which strengthen under mechanical force; activate almost immediately using AI and molecular simulations. This study provides new insights into how proteins resist mechanical stress, which has implications for biomaterials design, drug development, and understanding cellular mechanics. 📖 Read more about the research here: https://lnkd.in/eiA2VKm8 -AI predicts protein catch-bond activation almost instantly -Combines molecular simulations with machine learning for precise modeling -Insights applicable to biomaterials, adhesives, and drug design How do you see AI shaping the future of computational biology and biomaterials? #AI #ComputationalBiology #ProteinMechanics #MolecularSimulations #AuburnUniversity #STEM #Biomaterials #DrugDesign #Research #Innovation #Science

📢 We are pleased to announce the launch of a new Special Issue in Academia Molecular Biology and Genomics: “The Application of Machine Learning and Artificial Intelligence in Omics Data” Guest Edited by Dr. Yang Zhang and Dr. Shixiang Wang, this Special Issue will explore cutting-edge applications of machine learning and AI in omics, highlighting innovations in multi-omics integration, biomarker discovery, drug response prediction, and single-cell and spatial analysis. We invite contributions that advance computational biology and demonstrate how AI-driven methods accelerate biomedical discovery and precision medicine. 📝 Submit your manuscript: https://lnkd.in/ePfT36G5 📅 Submission deadline: 31 October 2026 #OpenAccess #AcademiaOpenAccess #AcademiaMolecularBiologyandGenomics

Turning Jupyter into a lab, and code & math into tools—simulating neurons, one at a time. 🧪 I recently extended the classic Hodgkin-Huxley model to simulate the effect of different drugs on neuronal activity. By adjusting ion channel dynamics, I could observe how each drug modulates neuronal firing patterns—all in a virtual, in-silico environment. This project merges: - Computational Neuroscience – modeling neuron behavior - Pharmacology – testing drug effects without a wet lab - Simulation & Data Analysis – turning equations into insights The goal? Understanding how drugs interact with neurons at a mechanistic level—an approach sometimes called pharmaNeuroSimulation. Even if you’re not a neuroscientist, the idea is simple: instead of testing on actual neurons, I let code and math do the experiment. #ComputationalNeuroscience #InSilicoExperiments

🔬 𝗥𝗲𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗻𝗴 𝗧𝗶𝘀𝘀𝘂𝗲𝘀 𝗶𝗻 𝟯𝗗: 𝗶𝘀𝗼𝗦𝗧 𝗨𝗻𝗹𝗼𝗰𝗸𝘀 𝗧𝗿𝘂𝗲 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗽𝘁𝗼𝗺𝗶𝗰𝘀 A research team from Beihang University, Fudan University, and Tsinghua University has tackled one of the biggest open questions in spatial biology: 👉 𝘏𝘰𝘸 𝘤𝘢𝘯 𝘸𝘦 𝘳𝘦𝘤𝘰𝘯𝘴𝘵𝘳𝘶𝘤𝘵 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦, 𝘵𝘩𝘳𝘦𝘦-𝘥𝘪𝘮𝘦𝘯𝘴𝘪𝘰𝘯𝘢𝘭 𝘮𝘢𝘱𝘴 𝘰𝘧 𝘨𝘦𝘯𝘦 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘪𝘰𝘯 𝘢𝘤𝘳𝘰𝘴𝘴 𝘦𝘯𝘵𝘪𝘳𝘦 𝘰𝘳𝘨𝘢𝘯𝘴 𝘸𝘩𝘦𝘯 𝘮𝘰𝘴𝘵 𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘵𝘳𝘢𝘯𝘴𝘤𝘳𝘪𝘱𝘵𝘰𝘮𝘪𝘤𝘴 𝘥𝘢𝘵𝘢 𝘤𝘰𝘮𝘦 𝘧𝘳𝘰𝘮 𝘵𝘩𝘪𝘯, 𝘵𝘸𝘰-𝘥𝘪𝘮𝘦𝘯𝘴𝘪𝘰𝘯𝘢𝘭 𝘵𝘪𝘴𝘴𝘶𝘦 𝘴𝘦𝘤𝘵𝘪𝘰𝘯𝘴? 💡 𝗧𝗵𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Traditional spatial transcriptomics gives us beautiful but incomplete 2D snapshots. Rebuilding tissues in 3D from sparse slices often leads to discontinuous, distorted maps that miss essential biological insights — from neuronal layering 🧠 to tumor microenvironment organization 🎗️. 🖥️ 𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗶𝘀𝗼𝗦𝗧 The team introduces isoST, a deep learning framework that uses stochastic differential equations to model smooth biological continuity across tissue depth. - It reconstructs isotropic 3D transcriptomic maps from sparse 2D slices. - It integrates histological imaging to reduce experimental burden. - It even extends to temporal modeling, generating fine-scale spatial–temporal trajectories of development. 🧪 𝗧𝗵𝗲 𝗿𝗼𝗹𝗲 𝗼𝗳 #MERFISH 𝗰𝗵𝗲𝗺𝗶𝘀𝘁𝗿𝘆 To benchmark isoST, the researchers applied it to a #MERFISH-𝗯𝗮𝘀𝗲𝗱 𝗺𝗼𝘂𝘀𝗲 𝗯𝗿𝗮𝗶𝗻 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 consisting of 54 coronal sections and over 1 million cells, with expression of 1,122 genes captured at single-cell resolution. This combination of #MERFISH 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝗹 𝗱𝗮𝘁𝗮 and 𝗶𝘀𝗼𝗦𝗧 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗿𝗲𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻 revealed fine laminar structures in the olfactory bulb and dentate gyrus, faithfully aligned with known anatomical references — showing how experimental and computational innovations together unlock hidden biology. 🌍 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 With isoST and #MERFISH data, researchers can now: 🧠 Explore neural circuits in 3D 💉 Map injury and recovery in the spinal cord 👶 Trace embryonic development with high temporal resolution 💊 Reveal disease-associated gradients that 2D approaches miss 🚀 This work represents a step forward toward building comprehensive 𝟯𝗗 𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗽𝘁𝗼𝗺𝗶𝗰 𝗮𝘁𝗹𝗮𝘀𝗲𝘀 for complex organs and whole organisms. 👉 Read the preprint here: https://lnkd.in/geGUTptP #SpatialTranscriptomics #3DTranscriptomics #MERFISH #isoST #ComputationalBiology #Bioinformatics #TissueReconstruction

Biophysics meets AI in protein engineering Proteins are the molecular machines of life, and designing new ones is a central challenge for medicine, biotechnology, and synthetic biology. In recent years, protein language models (PLMs)—AI systems trained on millions of natural protein sequences—have emerged as powerful tools. They can recognize patterns of evolution and suggest which mutations may help or harm a protein’s function. But these models have a blind spot: they know little about the underlying physics that determines how proteins fold, stabilize, or interact. A new study by Sam Gelman and coauthors bridges this gap by introducing mutational effect transfer learning (METL). Instead of training only on sequence data, METL pretrains models on millions of simulated protein variants, where physics-based calculations capture structural and energetic changes. The model is then fine-tuned with a small number of experimental measurements. The result is an AI system that performs especially well when data are scarce, predicting how mutations affect function even beyond the examples it has seen. In a striking test, the team designed functional green fluorescent protein variants after training on just 64 examples. This combination of physics and AI points to a new generation of methods for rational protein engineering, accelerating the design of enzymes, biosensors, and therapeutic proteins. Paper: https://lnkd.in/dW3A_FH7 #ProteinEngineering #AIforBiology #ProteinLanguageModels #Biophysics #DeepLearning #MachineLearning #SyntheticBiology #AIforScience #MolecularEngineering #RosettaModeling #ProteinDesign #DrugDiscovery #EnzymeEngineering #FluorescentProteins #ComputationalBiology

📢 New Publication Alert 📢 ✒️ Non-invasive maturity assessment of iPSC-CMs based on optical maturity characteristics using interpretable AI in Computational and Structural Biotechnology Journal (Elsevier) 💡Highlights • Non-invasive maturation state evaluation of human iPSC-CMs by analyzing beating characteristics using interpretable AI. • Distinguish immature from more mature iPSC-CMs with high accuracy of 99.5 % using a simple support vector machine. • Methods of explainable artificial intelligence enable the identification of the most relevant beating characteristics . • Optical evaluation of iPSC-CM maturation state may reduce experimental variability and improve the reproducibility of studies. 📃Full text version (open access): https://lnkd.in/gNcVYqj4 by Fabian Scheurer, Alexander Hammer, Mario Schubert, Robert-Patrick Steiner, Oliver Gamm, Kaomei Guan, Frank Sonntag, Hagen Malberg and Martin Schmidt Strong collaboration between the Institute of Biomedical Engineering (TUD School of Engineering Sciences), the Institute of Pharmacology and Toxicology (TUD Faculty of Medicine) and the Fraunhofer IWS. #MaturityAssessment #IPSC-CM #VideoBasedMotionAnalysis #OpticalCharacteristics #InterpretableAI #MachineLearning #NonInvasive

#AINews 🌍 University of Warwick and 3i are pioneering AI-assisted microscopic research to accelerate the pace of cell biology. Research meets innovation as CelFDrive promises to revolutionize the way we analyze cellular phenomena. 🧬 📊 Key Implications for Your Team: • CelFDrive automates rare cellular event detection, boosting discovery speed by 34 times. • The tool focuses on data-rich regions, reducing redundancy and enhancing efficiency. • Partnership with 3i aims to democratize access to top-tier imaging technologies. • Potential to revolutionize research productivity globally and open high-end tools to broader academic and pharmaceutical audiences. 🔍 Strategic Considerations: The integration of AI in microscopy is set to standardize high-level research methodologies and enhance both scientific and commercial outcomes. 💡 My Take: This partnership showcases how academic collaborations can rapidly translate AI innovations into industry-ready solutions, overcoming traditional research workflow challenges. 🤔 How will AI-driven tools like CelFDrive impact your research or development pipelines? #Innovation #AIInResearch #CellBiology #SmarterWithAI

#AINews 🌍 University of Warwick and 3i are pioneering AI-assisted microscopic research to accelerate the pace of cell biology. Research meets innovation as CelFDrive promises to revolutionize the way we analyze cellular phenomena. 🧬 📊 Key Implications for Your Team: • CelFDrive automates rare cellular event detection, boosting discovery speed by 34 times. • The tool focuses on data-rich regions, reducing redundancy and enhancing efficiency. • Partnership with 3i aims to democratize access to top-tier imaging technologies. • Potential to revolutionize research productivity globally and open high-end tools to broader academic and pharmaceutical audiences. 🔍 Strategic Considerations: The integration of AI in microscopy is set to standardize high-level research methodologies and enhance both scientific and commercial outcomes. 💡 My Take: This partnership showcases how academic collaborations can rapidly translate AI innovations into industry-ready solutions, overcoming traditional research workflow challenges. 🤔 How will AI-driven tools like CelFDrive impact your research or development pipelines? #Innovation #AIInResearch #CellBiology #SmarterWithAI