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10.1101/2025.03.12.642791 | ABCFold: easier running and comparison of AlphaFold 3, Boltz-1 and Chai-1 | Elliott, L. G.; Simpkin, A. J.; Rigden, D. J. | Daniel J Rigden | University of Liverpool | 2025-03-17 | 1 | new results | cc_by | bioinformatics | https://www.biorxiv.org/content/early/2025/03/17/2025.03.12.642791.source.xml | Motivation: The latest generation of deep learning-based structure prediction methods enable accurate modelling of most proteins and many complexes. However, preparing inputs for the locally installed software is not always straightforward, and the results of local runs are not always presented in an ideally accessible fashion. Furthermore, it is not yet clear whether the latest tools perform equivalently for all types of target. Results: ABCFold facilitates the use of AlphaFold 3, Boltz-1, and Chai-1 with a standardised input to predict atomic structures, with Boltz-1 and Chai-1 being installed on runtime (if required). MSAs can be generated internally using either the JackHMMER MSA search within AlphaFold 3, or with the MMseqs2 API. Alternatively, users can provide their own custom MSAs. This therefore allows AlphaFold 3 to be installed and run without downloading the large databases needed for JackHMMER. There are also straightforward options to use templates, including custom templates. Results from all packages are treated in a unified fashion, enabling easy comparison of results from different methods. A variety of visualisation options are available which include information on steric clashes. Availability and implementation: ABCFold is coded in Python and JavaScript. All scripts and associated documentation are available from https://github.com/rigdenlab/ABCFold or https://pypi.org/project/ABCFold/1.0.0/. | NA | biorxiv | 90 |
10.1101/2025.03.14.643225 | Cluster Analysis for Protein Sequences | Jani, M. R. | Md Rafsan Jani | Drexel University | 2025-03-17 | 1 | contradictory results | cc_by | bioinformatics | https://www.biorxiv.org/content/early/2025/03/17/2025.03.14.643225.source.xml | This paper presents a comprehensive analysis of MMseqs2 clusters and traditional machine learning (ML) clustering algorithms, including KMeans and Hierarchical clusterings, in terms of protein sequences. The analyses are validated experimentally. The cluster analyses have been performed in the A STRAL Compendium protein sequences dataset hosted in the SCOPe database. The dataset is embedded using two pre-trained transformer models using Evolutionary Scale Modeling (ESM) to perform KMeans and Hierarchical clustering algorithms. Afterward, those four clusters are compared with MMseqs2/Linclust and MMseqs2/easy-cluster methods. After performing the experiment, MMseqs2/Linclust and MMseqs2/easy-cluster outperform traditional machine learning cluster algorithms by a considerable margin. This analysis demonstrates the superiority of the MMseqs2 clustering techniques over conventional machine learning clustering algorithms. The source code of the experiment is publicly available and readily accessible through: https://github.com/mrzResearchArena/protein-clustering. | NA | biorxiv | 91 |
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