When does MD improve RNA models?
Study on 61 CASP15 #RNA with #Amber#χOL3 shows when MD refines vs reveals instability, with practical #guidelines. Use #MD 𝗮𝘀 𝗮 #𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝘁𝗼𝗼𝗹, 𝗻𝗼�� 𝗮 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗳𝗶𝘅𝗲𝗿.
Full article at: https://t.co/qbHQXsKCaG
@MissAgataStar@m_gorna W regulaminie szkoly dr nauk scislych i przyrodniczych UW jest mozliwosc zwolnienia z posiadania mgr w szczegolnych sytuacjach jakichs osiagniec, i komisja to klepnela. Co ciekawe innej szkole dr UW, miedzydziedzinowej, nie bylo takiej mozliwosci
💡 CABS-flex 3.0 offers 4 flexibility modes:
🔹 Rigid – minimal motion, close to the native structure
🔹 Flexible – loops & unstructured regions can move
🔹 Rigid-pLDDT – adapts flexibility based on AlphaFold confidence
🔹 Unleashed – no restraints, full conformational freedom
What you get after simulation:
🔍 Interactive 3D viewer (Mol*) – explore structure, RMSF, pLDDT, and more
📊 Fluctuation plots – residue-level RMSF with secondary structure overlays
🧩 Contact maps – dynamic, clickable, chain-by-chain
📦 10-model ensemble download (PDB & trajectory)
All structures reconstructed with AI-based cg2all = high-quality atom-level detail.
Need to assess protein flexibility or model peptide structures? Check out CABS-flex 3.0 — a fast, intuitive web server, now published in Nucleic Acids Research.
🖥️ Free and browser-based:
https://t.co/C3zagys3iQ
#CABSflex#ProteinDynamics#PeptideModeling #StructuralBioinformatics
Want to see where your protein is flexible?
Upload any @rcsbPDB structure and get nice flexibility visualizations — straight from your browser.
🚀CABS-flex 3.0 is the third generation of our web server for protein flexibility simulation and peptide modeling — now published in @NAR_Open!
🔗https://t.co/wY0T2WPigR
📖https://t.co/C3zagys3iQ
Compatible with structure data from @PDBeurope and AlphaFold DB (@emblebi), developed at @UniWarszawski
#CABSflex #ProteinDynamics #Bioinformatics #StructuralBioinformatics #AlphaFold
Aggrescan4D: A comprehensive tool for pH-dependent analysis and engineering of protein aggregation propensity
1/ Aggrescan4D (A4D) introduces a novel dimension in protein aggregation prediction by incorporating pH into structural analysis, allowing scientists to explore how environmental pH affects protein stability and aggregation propensity.
2/ Building upon the successful Aggrescan3D tool, A4D goes further by integrating dynamic mode simulations and structural flexibility, offering a more realistic and nuanced understanding of protein aggregation.
3/ A4D's pH-dependent analysis is a game-changer, especially for studying biotechnological applications where pH fluctuations impact protein solubility and disease mechanisms like Alzheimer's and Parkinson's.
4/ A4D also supports advanced protein engineering. Users can simulate and propose mutations that enhance solubility without compromising protein structure, paving the way for more effective therapeutic protein design.
5/ The tool has demonstrated superior accuracy in comparison to other state-of-the-art structure-based prediction tools, like CamSol and SolubiS, with an impressive track record in predicting mutation-driven solubility and aggregation changes.
@sekmi@pfdlab@PPMC_UAB@OriolBarcenas
💻Code: https://t.co/CvmNacoa8l
📜Paper: https://t.co/MOwWMb7DvF
Excited to share our new paper with @sekmi lab! Dive into the details and possibilities of Aggrescan4D, the latest A3D update featuring pH-dependent #protein#aggregation prediction! Now in Protein Science!
@ProteinSociety@UABBarcelona@IBB_UAB@I3ptT
https://t.co/GpLo9yNnhE
Another important paper from @k_mikolajczyk and
@sekmi: how heterodimerization with β1,4-galactosyltransferases 1 and 5 (B4galt1/5) influences dual acceptor specificity (GP vs GSL) of Gb3/CD77 synthase (human α1,4-galactosyltransferase, A4galt)
https://t.co/WUm6WSRsow
#glycotime
We are thrilled to announce the publication of our collaborative work with Prof. Sebastian Kmiecik, featured in Nucleic Acids Research! 🎉
This article confirms our unique expertise in the area of development of new drugs modulating mRNA function 🔬
➡️ https://t.co/3QOpj2Afr7
Our new study compares RNA 3D structure prediction methods, focusing on RNA-small molecule binding sites. AlphaFold 3 shows potential on the level of other ML-based methods despite some accuracy challenges. With @molecure_sa#AlphaFold#RNA#RNAstructure
https://t.co/6Q0oFrxuOB
🚀 Excited to publish Aggrescan4D (A4D) in @NAR_Open!!! Building on the popular A3D server, A4D predicts pH-dependent protein aggregation and offers automated mutation protocols to enhance protein solubility. Explore A4D advanced features! #ProteinResearch
https://t.co/JWcIoIJCCd
🔬 Studying protein aggregation in S. cerevisiae just got easier! 🍞🍺 Introducing A3DyDB, your go-to resource for pre-computed predictions on yeast protein structural aggregation, with @sekmi. 📊 #ProteinAggregation#YeastResearch#A3DyDB
https://t.co/9HjNXV1ucc