2025 Hyper Recent •CC0 1.0 Universal

This work is dedicated to the public domain. No rights reserved.

Access Preprint From Server
April 2nd, 2025
Version: 1
Computer-Aided Molecular Engineering, Department of Oncology UNIL-CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
bioinformatics
biorxiv

Novel universal domain-centric method for protein classification

Fadaei, S.Open in Google Scholar•Krebs, F. S.Open in Google Scholar•Zoete, V.Open in Google Scholar

Human protein kinases constitute a large superfamily of about 500 genes, historically classified into subfamilies based on phylogenetic relationship. However, many kinases remain unclassified. Phylogeny is typically based on multiple sequence alignments, and neglects the physico-chemical properties of residues at each position of the sequence. By incorporating these properties, we can gain deeper insights beyond basic alignments. Here we use, for the first time, a detailed physico-chemical description of kinases to identify class-specific structural regions, supporting an unsupervised classification method capable of classifying previously unlabeled kinases. This novel approach aligns with existing phylogeny-based classifications while offering refinements and enhanced accuracy. Ultimately, we use machine learning techniques to classify unlabeled kinases, validated by analyzing class-specific structural regions. This new classification approach goes beyond current rankings and can be applied to any type of protein, such as immunoglobulins and G protein-coupled receptors.

Similar Papers

biorxiv
Thu Apr 03 2025
Sketch, capture and layout phylogenies
Phylogenetic trees and networks play a central role in biology, bioinformatics, and mathematical biology, and producing clear, informative visualizations of them is an important task. We present new algorithms for visualizing rooted phylogenetic networks as either combining or transfer networks, in both cladogram and phylogram style. In addition, we introduce a layout algorithm that aims to improv...
Huson, D.
biorxiv
Thu Apr 03 2025
BioPKS Pipeline: An integrated platform for merging the computational design of chimeric type I polyketide synthases with enzymatic pathways for chemical biosynthesis
Synthetic biology offers the promise of manufacturing chemicals more sustainably than petrochemistry. Yet, both the rate at which biomanufacturing can synthesize these molecules and the net chemical accessible space are limited by existing pathway discovery methods which can often rely on arduous literature searches. Here, we present an automated retrobiosynthesis tool, BioPKS Pipeline, that simul...
Chainani, Y.
•
Diaz, J.
•
Guilarte-Silva, M.
•
Blay, V.
...•
Backman, T. W. H.
biorxiv
Thu Apr 03 2025
Differential T cell receptor gene expression analysis using the Wilcoxon test with similarity-based weighting
To better understand immune responses, comparing the abundance of T cell receptors (TCRs) between conditions can provide insights into which T cells have proliferated or were involved in immune activation. This requires methods that can accurately identify significant differences in TCR-seq data. For conventional RNA-seq data, well-established differential gene expression (DGE) analysis tools such...
Buytenhuijs, F.
•
Ankan, A.
•
Textor, J.
biorxiv
Wed Apr 02 2025
Serotonergic Modulation of Motoneuronal Excitability and Its Impact on Muscle Force Generation: A Computational Study
This study investigates the impact of serotonin (5-HT) on motoneuron electrical activity and muscle force generation. Using a computational model, we explore how 5-HT receptors influence motoneuron excitability and muscle function at different stimulation frequencies. Our results demonstrate that physiological 5-HT release increases motoneuron excitability, particularly at higher frequencies (40 H...
Melendez-Gallardo, J.
•
Plada-Delgado, D.
biorxiv
Wed Apr 02 2025
Building A Unified Model for Drug Synergy Analysis Powered by Large Language Models
Drug synergy prediction is a challenging and important task in the treatment of complex diseases including cancer. In this manuscript, we present a novel unified Model, known as BAITSAO, for tasks related to drug synergy prediction with a unified pipeline to handle different datasets. We construct the training datasets for BAITSAO based on the context-enriched embeddings from Large Language Models...
Liu, T.
•
Chu, T.
•
Luo, X.
•
Zhao, H.
biorxiv
Wed Apr 02 2025
Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states
Single-cell RNA-seq (scRNA-seq) has become a prominent tool for studying human biology and disease. The availability of massive scRNA-seq datasets and advanced machine learning techniques has recently driven the development of single-cell foundation models that provide informative and versatile cell representations based on expression profiles. However, to understand disease states, we need to con...
Liu, T.
•
De Brouwer, E.
•
Kuo, T.
•
Diamant, N.
...•
Heimberg, G.
biorxiv
Wed Apr 02 2025
FUSE-PhyloTree: Linking functions and sequence conservation modules of a protein family through phylogenomic analysis
Summary: FUSE-PhyloTree is a phylogenomic analysis software for identifying local sequence conservation associated with the different functions of a multi-functional (e.g., paralogous or multi-domain) protein family. FUSE-PhyloTree introduces an original approach that combines advanced sequence analysis with phylogenetic methods. First, local sequence conservation modules within the family are ide...
Dennler, O.
•
Chenel, E.
•
Coste, F.
•
Blanquart, S.
...•
Theret, N.
biorxiv
Wed Apr 02 2025
Predicting microbial transcriptome using genome sequence
We present TXpredict, a transformer-based framework for predicting microbial transcriptomes using annotated genome sequences. By leveraging information learned from a large protein language model, TXpredict achieves an average Spearman correlation of 0.53 and 0.62 in predicting gene expression for new bacterial and fungal genomes. We further extend this framework to predict transcriptomes for 2,68...
FU, G.
•
YAN, Y.
•
CHEN, Y.
•
SHAO, B.
biorxiv
Wed Apr 02 2025
In-Silico Stability Predictors: Investigation of Performance Towards balanced Experimental Data
Change in protein stability, quantified as the change in Gibbs free energy of folding ({Delta}{Delta}G) in kcal/mol, plays a crucial role in functional alterations of proteins, with misfolding and destabilization commonly associated with pathogenicity. The past two decades have brought the development of bioinformatics tools leveraging evolutionary knowledge, empirical force fields, and machine le...
Degn, K.
•
Utichi, M.
•
Besora, P. S.-I.
•
Tiberti, M.
•
Papaleo, E.