Da Cui 崔达

Preprint: The tumour suppressor RBM5 activates the helicase DHX15 to regulate splicing researchsquare.com/article/rs-867… @UCLA

ROCKET makes AlphaFold context-aware. We iteratively steer prediction to agree with experiment (cryo-EM, crystallography) at inference time, no retraining. Structure determination becomes a search where ML priors and experiment productively combine and inform each other. 2/15

New online! RNA damage signaling primes transcription-coupled repair dlvr.it/TRq6f0

Nature research paper: Pre-incision structures reveal principles of DNA nucleotide excision repair go.nature.com/4tv0bFB

This is cool

BRD2 bridges TFIID and MOF-H4K16ac-containing nucleosomes to promote transcriptional initiation dlvr.it/TQVqNL

Contrastive Geometric Learning Unlocks Unified Structure and Ligand-Based Drug Design 1. A novel approach in computational drug design, ConGLUDe integrates structure-based and ligand-based training into a single model, overcoming traditional limitations of disjoint data sources. This unified method significantly enhances the efficiency and accuracy of drug discovery tasks. 2. ConGLUDe leverages a geometric protein encoder that predicts whole-protein representations and implicit binding site embeddings, removing the need for predefined pockets. This innovation allows for ligand-conditioned pocket prediction, virtual screening, and target fishing—all within a unified framework. 3. The model achieves state-of-the-art performance in zero-shot virtual screening without binding pocket information, outperforming existing methods on challenging target fishing tasks. It also demonstrates competitive results in ligand-conditioned pocket selection, highlighting its versatility. 4. ConGLUDe's training involves alternating between structure-based and ligand-based batches, utilizing a novel three-way InfoNCE loss function. This approach aligns protein, ligand, and binding site embeddings, enabling efficient large-scale screening. 5. The study evaluates ConGLUDe across diverse benchmarks, demonstrating superior performance in tasks like virtual screening and target fishing. The model's ability to generalize across datasets underscores its potential as a foundational tool for drug discovery. 6. Limitations include uncertainty in performance on proteins with predicted structures and the current lack of support for phenotypic assays. However, the potential for future extensions, such as integrating generative models for ligand design, suggests exciting prospects for this approach. 📜Paper: arxiv.org/abs/2601.09693… #ContrastiveLearning #DrugDesign #ComputationalBiology #AIinPharma #ProteinLigandInteractions

New online! Interplay between cohesin and RNA polymerase II in regulating chromatin interactions and gene transcription dlvr.it/TQKgsH

New Online! Organization of replicated chromosomes by DNA loops and sister chromatid cohesion bit.ly/4jiRJV8

A SWI/SNF-specific Ig-like domain, SWIFT, is a transcription factor binding platform science.org/doi/10.1126/sc… @ScienceMagazine @CKadoch @DanaFarber @harvardmed @broadinstitute @hhminews

CryoDDM: CryoEM denoising diffusion model for heterogeneous conformational reconstruction 1. CryoDDM is a novel denoising diffusion model designed to enhance cryo-EM single-particle analysis by preserving high-frequency structural information while removing noise, significantly improving the accuracy of protein conformational heterogeneity classification and reconstruction. 2. The model introduces a two-phase diffusion process tailored for cryo-EM images, overcoming the limitations of Gaussian noise assumptions and reducing computational costs by optimizing diffusion steps. 3. CryoDDM outperforms existing methods by enabling high-resolution reconstruction of diverse proteins, including a proteasome, a membrane protein, and a spike protein, revealing previously undetected conformational states and motions. 4. The study demonstrates CryoDDM's ability to enhance downstream analysis, such as particle picking and 3D classification, by providing cleaner images without sacrificing structural details, thus advancing structural biology research. 5. CryoDDM's effectiveness is validated across multiple datasets, consistently showing superior performance in capturing dynamic protein behaviors and improving reconstruction quality, making it a valuable tool for cryo-EM studies. 📜Paper: biorxiv.org/content/10.648… #CryoEM #Denoising #DiffusionModel #ProteinStructure #CryoDDM #StructuralBiology

An RNA splicing system that excises DNA transposons from animal mRNAs | Nature nature.com/articles/s4158…

Rethinking what pLDDT really tells us about protein flexibility dlvr.it/TPkG8L

NEURIPS! I just arrived! Check out our group's papers and find me + my students to talk science! - CryoBoltz ❄️⚡️: Multiscale guidance of protein structure prediction with heterogeneous cryo-EM data @rishwanth_raghu arxiv.org/abs/2506.04490 - ChefNMR 🧑‍🍳⚛️: Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra @zyxiong123 arxiv.org/abs/2512.03127 Workshop papers @workshopmlsb: - CryoNOO❄️🚫: Separating signal from noise: a self-distillation approach for amortized heterogeneous cryo-EM reconstruction @MinkyuJeon19791 @jeffhygu - CryoHype❄️🙌: Reconstructing a thousand cryo-EM structures with transformer-based hypernetworks @jeffhygu @MinkyuJeon19791 Really excited to share these works. Separate tweet threads soon!

New online! Gene regulation through exon junction complex modularity bit.ly/448By6y

New online! DNA topoisomerase I acts as supercoiling sensor for bacterial transcription elongation bit.ly/48IBsoF

New online! Kinetic control of mammalian transcription elongation bit.ly/4ipj2wp

Excited to release BoltzGen which brings SOTA folding performance to binder design! The best part of this project has been collaborating with many leading biologists who tested BoltzGen at an unprecedented scale, showing success on many novel targets and pushing its limits! 🧵..

🎉🎉🎉 Today, we have a huge announcement to make: SAMSON is now free for non-commercial use! This includes all extensions for docking, simulating, animating, scripting, and much, much more. Precisely, we are making the entire SAMSON molecular design platform - SAMSON + every SAMSON Extension on SAMSON Connect - free for non-commercial use. This means you can now use SAMSON at no cost in academic and nonprofit settings for: - Education (teachers, students, classrooms) - Academic & publicly funded research - Personal projects (no revenue, no paid consulting) If this is your case, you can activate your free non-commercial license yourself when you sign up at samson-connect.net. This will grant you a free Expert plan and make all SAMSON Extensions free to add on SAMSON Connect. (as you may know, most features run locally, but some optional calculations run in the cloud, such as structure prediction and cloud simulations - these require computing credits). When your work involves paid services, consulting, product development, or commercial R&D, just visit the Pricing page and select one of the commercial plans. You can later revert to non-commercial use. If you are unsure whether you are eligible to a free non-commercial license, please just contact us and we'll work it out with you. Of course, feel free to share the news with your friends, students, and colleagues (and everyone else 😊). #SAMSON #Community

@OliBClarke It works great for my 200kda complex. Great method! Two weeks ago I was still playing with “high res” blobs but now I’m building sidechains already!