A. Berçin Barlas

academic.oup.comARTS-DB 2.0: an expanded database for target-directed genome mining across bacteria and fungiAbstract. The increasing prevalence of antimicrobial resistance requires efficient strategies to prioritize biosynthetic gene clusters (BGCs) that may enco

🎉 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...

A. Berçin Barlas reposted

Turgut Mesut Yılmaz

@turgutmesut.bsky.social

📢 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

Nadine Ziemert@nadineziemert.bsky.social

Congrats to @turgutmesut.bsky.social @canerbagci.bsky.social and Semih Erdoğmuş for the update and major improvement of ARTs-DB academic.oup.com/database/art... 🥳 Now also including fungal data and a much improved and more intuitive interface!! happy #targetdirectedgenomemining #secmet

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youtube.comBioexcel Webinar #95: DynaPIN: A Tool for Characterizing Dynamic Protein InterfacesYouTube video by BioExcel CoE

To 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_...

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

Interested 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...

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DynaPIN 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

Do 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.

Why 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.

bioexcel.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...

🎯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.

A. Berçin Barlas reposted

BioExcel CoE

@bioexcelcoe.bsky.social

27 Apr

Our 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 #molecularmodeling

A. Berçin Barlas reposted

Ezgi Karaca

@ezgikaraca.bsky.social

27 Mar

Only 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!

BioExcel CoE@bioexcelcoe.bsky.social

🎗️ reminder that our #webinar on characterising dynamic #protein interfaces with the DynaPIN #software will take place next week; 🗓️ 31 March 2026, 15:00 CET ✍️ bioexcel.eu/lgmq @ezgikaraca.bsky.social #webinar #ComputerSimulation #protein #molecularmodeling

A. Berçin Barlas reposted

Büşra Savaş

@busrasavas.bsky.social

9 Feb

🚀 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 👇

Büşra Savaş@busrasavas.bsky.social

🚨 Super excited for our new preprint on flexibility cues in AlphaFold! Together with @ezgikaraca.bsky.social and @aysebercinb.bsky.social, we found that distograms of AF2.3 and AF3 mirror MD sampling by predicting the extent of a novel conformational change! 🤯 For more👇

A. Berçin Barlas reposted

Büşra Savaş

@busrasavas.bsky.social

11 Dec 2025

364 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

A. Berçin Barlas reposted

Ezgi Karaca

@ezgikaraca.bsky.social

6 Oct 2025

And the legacy continues! 😊 @amjjbonvin.bsky.social @bioinfo.se @lindorfflarsen.bsky.social #EMBOIntegMod25 ! 🍀🧿

Ezgi Karaca@ezgikaraca.bsky.social

The legendary EMBO Integrative Modeling Course is back! If you want to be in this picture in the 2025 edition, just go ahead and register at meetings.embo.org/event/25-bio... Organized by myself & @amjjbonvin.bsky.social & @arneelof.bsky.social & @lindorfflarsen.bsky.social

nature.comSo close yet so far apart: distinct flanking sequence recognition by DNMT3A and DNMT3B - Communications BiologyComparative dynamics analysis identifies key residues driving sequence specificity differences between human DNA methyltransferases DNMT3A and DNMT3B.

🚀 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? 🧬✨

Communications Biology@commsbio.nature.com

Comparative dynamics analysis identifies key residues driving sequence specificity differences between human DNA methyltransferases DNMT3A and DNMT3B. @aysebercinb.bsky.social‬ @ezgikaraca.bsky.social‬ www.nature.com/articles/s42...

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Altogether, ✅ 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.

This 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! 🌱

Communications Biology@commsbio.nature.com

To 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