Reposted by A. Berçin BarlasTurgut Mesut Yılmaz @turgutmesut.bsky.social · 27/09/2026📢 Very happy to see ARTS-DB 2.0 out in Database! 🧬 A lot of work went into bringing bacterial and fungal target-directed genome mining together with updated BGC predictions and an improved interface. Great to finally share it with the community! 🚀 📄: doi.org/10.1093/data... #TDGM #NPs #SecMet 042
A. Berçin Barlas @aysebercinb.bsky.social · 17/07/2026Interested in trying it out for yourself? 🚀 Grab the code, check out the documentation, and start analyzing your own dynamic protein interfaces. We would love to hear your feedback! 💻 Code & documentation: github.com/CSB-KaracaLa...github.comGitHub - CSB-KaracaLab/DynaPIN: An open-source analysis toolkit to characterize dynamic protein interfaces from MD trajectories.An open-source analysis toolkit to characterize dynamic protein interfaces from MD trajectories. - CSB-KaracaLab/DynaPIN 052
A. Berçin Barlas @aysebercinb.bsky.social · 17/07/2026To complement the paper, @ezgikaraca.bsky.social and I recently presented DynaPIN as part of the @bioexcelcoe.bsky.social webinar series, covering the motivation, a live demo, and where it can be applied. 📺 Webinar: www.youtube.com/watch?v=kr5_...youtube.comBioexcel Webinar #95: DynaPIN: A Tool for Characterizing Dynamic Protein InterfacesYouTube video by BioExcel CoE 184
A. Berçin Barlas @aysebercinb.bsky.social · 17/07/2026Do traditional docking benchmarks reflect actual interface flexibility? Not always. 🟢 Using the DynaBench dataset, we showed that DynaPIN’s dynamic descriptors provide a more biologically meaningful picture of protein interactions than static metrics alone. 100
A. Berçin Barlas @aysebercinb.bsky.social · 17/07/2026DynaPIN is designed to be flexible: ✔️ Command-line interface (CLI) for automated workflows ✔️ Python API with Jupyter Notebook integration ✔️ Compatible with MD trajectories (DCD/XTC/TRR), PDB ensembles, AlphaFold models, docking predictions, and experimental structures 100
A. Berçin Barlas @aysebercinb.bsky.social · 17/07/2026🎯A key concept behind DynaPIN: Dynamic Interface (DI) Instead of treating interfaces as static, DI residues are identified based on how persistently they remain at the interface throughout a simulation, while their interface layer (core, rim, or support) is characterized over time. 100
A. Berçin Barlas @aysebercinb.bsky.social · 17/07/2026Why DynaPIN? 🎯 Unified workflow integrating quality control, residue characterization, and interaction profiling into a single automated run. 🎯 Standardized outputs with machine-readable tables and publication-ready figures. 100
A. Berçin Barlas @aysebercinb.bsky.social · 17/07/2026🎉 Finally out!! Our paper, "DynaPIN: A tool for characterizing dynamic protein interfaces" is accepted in JMB! @ezgikaraca.bsky.social (in collaboration with @sacquin-mo.eurosky.social & co.) DynaPIN is an open-source pipeline for analyzing dynamic protein interfaces. 🧵👇 📄 doi.org/10.1016/j.jm... 1115
Reposted by A. Berçin BarlasBioExcel CoE @bioexcelcoe.bsky.social · 27/04/2026Our rescheduled webinar "DynaPIN: a tool for characterising dynamic protein interfaces" is happening tomorrow! 🗓️ 28 April 2026, 15:00 CET ✍️ bioexcel.eu/lgmq @ezgikaraca.bsky.social #webinar #ComputerSimulation #protein #molecularmodelingbioexcel.euWebinar: DynaPIN: A Tool for Characterizing Dynamic Protein Interfaces (2026-04-28)Date: 28 April 2026 Time: 15:00 CET Registration Abstract Static structural models often fail to capture the dynamic mechanisms of protein interactions. To address this, we introduce DynaPIN, an open... 095
Reposted by A. Berçin BarlasEzgi Karaca @ezgikaraca.bsky.social · 27/03/2026Only a couple of days left to introduce our brand new tool, DynaPIN that @aysebercinb.bsky.social developed (in collaboration with @sacquin-mo.bsky.social & co.) to analyze dynamic/ensemble protein interfaces! 084
Reposted by A. Berçin BarlasBüşra Savaş @busrasavas.bsky.social · 09/02/2026🚀 Excited to announce that our perspective piece with @ezgikaraca.bsky.social and @aysebercinb.bsky.social on AlphaFold distograms is now published in @febsletters.bsky.social!! Here is what we did further 👇 143
Reposted by A. Berçin BarlasBüşra Savaş @busrasavas.bsky.social · 11/12/2025364 days a year we use AlphaFold to predict protein structure… But not on Christmas Eve! That’s when Santa does the predictions. But beware, computational structural biologists on the naughty list will only get low pLDDTs #SantaFold #bananapro 092
Reposted by A. Berçin BarlasEzgi Karaca @ezgikaraca.bsky.social · 06/10/2025And the legacy continues! 😊 @amjjbonvin.bsky.social @bioinfo.se @lindorfflarsen.bsky.social #EMBOIntegMod25 ! 🍀🧿 12611
A. Berçin Barlas @aysebercinb.bsky.social · 22/08/2025This is the first systematic dynamic analysis of shape readout in DNMT3 enzymes, showing how small molecular changes can lead to big functional differences --and laying groundwork for engineering paralog-specific protein-DNA interactions. A long journey, but rewarding! 🌱 020
A. Berçin Barlas @aysebercinb.bsky.social · 22/08/2025Altogether, ✅ DNMT3A uses a rigid, precise strategy, while DNMT3B is more flexible and adaptable. In other words, ✅ DNMT3A is a specialist with a pre-organized catalytic loop, while DNMT3B is a generalist with a flexible catalytic loop supporting its adaptability. 120
A. Berçin Barlas @aysebercinb.bsky.social · 22/08/2025We found that subtle amino acid substitutions reshape DNA recognition: ⚡ DNMT3A uses Arg836 → rigid hydrogen bonding + electrostatic anchoring, favors pyrimidines (C/T). 🤸♀️ DNMT3B replaces Arg836 with Lys777 and introduces Asn779 → flexible and cooperative readout with broader substrate tolerance. 120
A. Berçin Barlas @aysebercinb.bsky.social · 22/08/2025To answer this, we ran 16 μs MD sims and built a new framework: Comparative Dynamics Analysis (CDA). CDA integrates two complementary perspectives: ➡️ Base readout: base-specific hydrogen bonds at major groove ➡️ Shape readout: DNA deformation, electrostatics, and hydrophobic contacts at minor groove 120
A. Berçin Barlas @aysebercinb.bsky.social · 22/08/2025🚀 Excited to share that our article with @ezgikaraca.bsky.social is now published in Communications Biology! In this study, we explored DNA readout rules of almost identical DNMT3A and DNMT3B (91% sequence similarity!), and we asked: how can nearly the same proteins “see” DNA so differently? 🧬✨ 173
