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1. MPBind: a multitask protein binding site predictor using protein language models and equivariant GNNs

   https://doi.org/10.1093/bioinformatics/btaf589  

   Wang, Yanli; Boadu, Frimpong; Cheng, Jianlin (November 2025, Bioinformatics)

   Cowen, Lenore (Ed.)

   Motivation. Proteins interact with a variety of molecules, including other proteins, DNAs, RNAs, ligands, ions, and lipids. These interactions play a crucial role in cellular communication, metabolic regulation, gene regulation, and structural integrity, making proteins fundamental to nearly all biological functions. Accurately predicting protein interaction (binding) sites is essential for understanding protein interaction and function. ResultsIn this work, we introduce MPBind, a multitask protein binding site prediction method, which integrates protein language models (PLMs) that can extract structural and functional information from sequences and equivariant graph neural networks (EGNNs) that can effectively capture geometric features of 3D protein structures. Through multitask learning, it can predict binding sites on proteins that interact with five key categories of binding partners: proteins, DNA/RNA, ligands, lipids, and ions. MPBind generalizes across the five molecular classes with state-of-the-art accuracy, achieving AUROC scores of 0.83 and 0.81 for protein–protein and protein–DNA/RNA-binding site prediction, respectively. Moreover, MPBind outperforms both general and task-specific binding site prediction methods, making it a useful, versatile tool for protein binding site prediction. Availability and implementationThe source code of MPBind is available at the GitHub repository: https://github.com/jianlin-cheng/MPBind. 

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2. ViralMSA: massively scalable reference-guided multiple sequence alignment of viral genomes

   https://doi.org/10.1093/bioinformatics/btaa743  

   Moshiri, Niema (August 2020, Bioinformatics)

   Robinson, Peter (Ed.)

   Abstract Motivation In molecular epidemiology, the identification of clusters of transmissions typically requires the alignment of viral genomic sequence data. However, existing methods of multiple sequence alignment (MSA) scale poorly with respect to the number of sequences. Results ViralMSA is a user-friendly reference-guided MSA tool that leverages the algorithmic techniques of read mappers to enable the MSA of ultra-large viral genome datasets. It scales linearly with the number of sequences, and it is able to align tens of thousands of full viral genomes in seconds. However, alignments produced by ViralMSA omit insertions with respect to the reference genome. Availability and implementation ViralMSA is freely available at https://github.com/niemasd/ViralMSA as an open-source software project. Contact niema@ucsd.edu Supplementary information Supplementary data are available at Bioinformatics online. 

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3. eCOMPASS: evaluative comparison of multiple protein alignments by statistical score

   https://doi.org/10.1093/bioinformatics/btab374  

   Neuwald, Andrew F; Kolaczkowski, Bryan D; Altschul, Stephen F (May 2021, Bioinformatics)

   Ponty, Yann (Ed.)

   Abstract Motivation Detecting subtle biologically relevant patterns in protein sequences often requires the construction of a large and accurate multiple sequence alignment (MSA). Methods for constructing MSAs are usually evaluated using benchmark alignments, which, however, typically contain very few sequences and are therefore inappropriate when dealing with large numbers of proteins. Results eCOMPASS addresses this problem using a statistical measure of relative alignment quality based on direct coupling analysis (DCA): To maintain protein structural integrity over evolutionary time, substitutions at one residue position typically result in compensating substitutions at other positions. eCOMPASS computes the statistical significance of the congruence between high scoring directly coupled pairs and 3D contacts in corresponding structures, which depends upon properly aligned homologous residues. We illustrate eCOMPASS using both simulated and real MSAs. Availability and Implementation The eCOMPASS executable, C ++ open source code and input data sets are available at https://www.igs.umaryland.edu/labs/neuwald/software/compass. Supplementary information Supplementary data are available at Bioinformatics online. 

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4. The gift of novelty: repeat-robust k-mer-based estimators of mutation rates

   https://doi.org/10.1093/bioinformatics/btag234  

   Wu, Haonan; Medvedev, Paul (July 2026, Bioinformatics)

   Abstract MotivationEstimating mutation rates between evolutionarily related sequences is a central problem in molecular evolution. Due to the rapid expansion of datasets, modern methods avoid costly alignment and instead compare sketches of sets of constituent k-mers. While these methods perform well on many sequences, they are not robust to highly repetitive sequences such as centromeres. ResultsWe present three new estimators that are robust to the presence of repeats. The estimators are applicable in different settings, depending on whether count information is available from zero, one, or both sequences. We evaluate our estimators empirically using highly repetitive alpha satellite sequences. Each estimator performs best within its class, and our strongest estimator outperforms all other tested estimators. Availability and implementationOur software is open-source and freely available at https://github.com/medvedevgroup/Accurate_repeat-aware_kmer_based_estimator. 

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5. Protein intrinsically disordered regions have a non-random, modular architecture

   https://doi.org/10.1093/bioinformatics/btad732  

   McConnell, Brendan S; Parker, Matthew W (December 2023, Bioinformatics)

   Elofsson, Arne (Ed.)

   Abstract MotivationProtein sequences can be broadly categorized into two classes: those which adopt stable secondary structure and fold into a domain (i.e. globular proteins), and those that do not. The sequences belonging to this latter class are conformationally heterogeneous and are described as being intrinsically disordered. Decades of investigation into the structure and function of globular proteins has resulted in a suite of computational tools that enable their sub-classification by domain type, an approach that has revolutionized how we understand and predict protein functionality. Conversely, it is unknown if sequences of disordered protein regions are subject to broadly generalizable organizational principles that would enable their sub-classification. ResultsHere, we report the development of a statistical approach that quantifies linear variance in amino acid composition across a sequence. With multiple examples, we provide evidence that intrinsically disordered regions are organized into statistically non-random modules of unique compositional bias. Modularity is observed for both low and high-complexity sequences and, in some cases, we find that modules are organized in repetitive patterns. These data demonstrate that disordered sequences are non-randomly organized into modular architectures and motivate future experiments to comprehensively classify module types and to determine the degree to which modules constitute functionally separable units analogous to the domains of globular proteins. Availability and implementationThe source code, documentation, and data to reproduce all figures are freely available at https://github.com/MWPlabUTSW/Chi-Score-Analysis.git. The analysis is also available as a Google Colab Notebook (https://colab.research.google.com/github/MWPlabUTSW/Chi-Score-Analysis/blob/main/ChiScore_Analysis.ipynb). 

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