There is a large group of bacterial FtsZ inhibitors whose biological activity has been confirmed biochemically. However, the sites of protein–ligand interaction for most of them remain unknown, significantly complicating the further search and combinatorial design of FtsZ inhibitors. This study presents the results of bioinformatic analysis of bacterial FtsZ effectors, the interaction of which has been proven and documented in the ChEMBL database of biologically active molecules. With an integrated approach based on chemo- and bioinformatic methods and AI-based predictions, 23 inhibitors of nucleotide-binding site (NBS), as well as 16 new effectors of the interdomain cleft (IDC), were identified.
SciTech Chronicles. . . . . . . . .Mar 5th, 2025
#cornea #epithelial #transplantation #18-months #sunspot #earthquake #Prediction #tectonic #hydrosilylation #platinum #silicon #ligand #AI #entanglement #communication #photons #LiDAR #Autonomous #Farming #odometry
My favorite molecular #protein-#ligand #docking method, #DiffDock, has been updated! The new DiffDock-L, provides a significant improvement in performance and generalization capacity.
Importantly., this new method comes with the new #DockGen benchmark, aiming to provide better evaluation metrics and help improve #generalization of #ML docking models by accounting for sequence-dissimilar proteins with very similar binding pockets in training/test splits.
Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Existing benchmarks, however, fail to rigorously assess generalizability. Therefore, we develop DockGen, a new benchmark based on the ligand-binding domains of proteins, and we show that existing machine learning-based docking models have very weak generalization abilities. We carefully analyze the scaling laws of ML-based docking and show that, by scaling data and model size, as well as integrating synthetic data strategies, we are able to significantly increase the generalization capacity and set new state-of-the-art performance across benchmarks. Further, we propose Confidence Bootstrapping, a new training paradigm that solely relies on the interaction between diffusion and confidence models and exploits the multi-resolution generation process of diffusion models. We demonstrate that Confidence Bootstrapping significantly improves the ability of ML-based docking methods to dock to unseen protein classes, edging closer to accurate and generalizable blind docking methods.
New, and promising, #SurfDock diffusion generative model for #protein-#ligand #docking
https://www.biorxiv.org/content/10.1101/2023.12.13.571408v2.full.pdf+html
Unfortunately, code will only be available after the paper has been published 🙃
https://github.com/CAODH/SurfDock
Has anyone looked at CINS (https://doi.org/10.1371/journal.pcbi.1010468)?
Learns a Bayesian network of cell type dependencies from changes in cell type proportions (#scRNAseq) in case-control studies. Causal ligands from cell-cell edges are predicted by LASSO regression of #ligand and predicted response genes (using NicheNet's ligand-reponse network).
Cool idea, weird lack of validation in their paper... But I guess that's the challenge in #CellCellInteraction prediction - validation is _hard_
Author summary Single cell transcriptomics has emerged as a leading technology for studying the composition of organs and tissues in the human body, development and several other biological processes. More recent studies, including studies of various diseases (such as cancer), treatment—response studies and aging studies aim at comparing samples at the single cell level. To date, such analysis mainly focused on the differences in expression of genes in the different cell types. However, in addition to differences in expression such studies also provide information on the differences of cell type proportions between the conditions. To use such information for inferring cell interactions we developed a new computational framework termed CINS. CINS combines Bayesian network learning (which is used to infer cell type–cell type interactions) with constrained regression analysis (used to infer the specific proteins involved in such interactions). We applied CINS to a number of different case-control scRNA-Seq datasets including a lung disease and an aging study. By analyzing public data and profiling new scRNA-Seq data we show that CINS is able to correctly identify several proteins as playing a significant role in interactions controlling disease progression and aging, improving on prior methods suggested for this task.
Finally read the REMI paper by Alice Yu (aliceomics@twitter) et al. Calculating the partial correlation structure of #ligand #receptor interactions across #scRNAseq samples (cancer, in their case) to _more_specifically_ identify context-dependent interactions. #CellCellInteraction prediction has a specificity problem, and this method outperforms NicheNet, which uses predicted transcriptional response to improve specificity of predictions.
https://twitter.com/PlevritisLab/status/1509196799520620544?s=20&t=6jB2saz7rcO2u-4Gh54C4Q
A nice new #paper where I have played a small part. An extremely surprising finding where mutants in the 5S #ribosomal #RNA #transcription factor #GTF3A causes enhanced #susceptibility to #HSV #virus #infection, leading to #encephalitis. A #pseudogene #transcribed is a #ligand activating #RIG-I.
https://pubmed.ncbi.nlm.nih.gov/36399538/
#fediscience #immunology #science #PID #genetics #signaling #herpes #health
Herpes simplex virus 1 (HSV-1) infects several billion people worldwide and can cause life-threatening herpes simplex encephalitis (HSE) in some patients. Monogenic defects in components of the type I interferon system have been identified in patients with HSE, emphasizing the role of inborn errors …
Just joined and testing this new platform, time for an #introduction!
We are a #biochemistry and #structuralbiology laboratory based in #Pavia, #Italy.
#wemakeproteins and are fascinated by the #extracellular space: we study how #collagen contributes to the assembly of the #ECM and we perform #drugdiscovery studies on #enzymes of collagen #biosynthesis, plus we investigate various #ligand - #receptor #interactions leading to #signaling in humans and in insects.
Always open to #collaboration.
Check our website and help us grow: http://fornerislab.unipv.it/armenise_lab/
Getzler Lab at KenyonCollege
Cytology and Genetics
John Vaccaro (johniac)
Elly Poretsky
Brendan Innes
Savani Anbalagan
Jens Staal
Forneris Lab