Main dataset for the Large-scale analysis of the β-lactamase sequence space with protein language models
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The main dataset for the publication "Large-scale analysis of the β-lactamase sequence space with protein language models". This dataset contains 29,445 rows and 82 columns and is provided in parquet format. The rows represent all sequences retrieved from the BLDB. The columns contain information processed from the BLDB, including their taxonomy annotated against the Genome Taxonomy Database (GTDB RS207), the per-protein embeddings derived from five protein language models (ESM-1b, ESM2-650, ESM2-3b, CARP-640M, ProtTrans-t5-xl-u50), functional annotations estimated with Biopython, sequence quality filters applied to select sequences for the analysis, annotations from the AlphaFold Database (AFDB) for the available structures, and the secondary structure annotations generated from the predicted structures by AlphaFold2 using pyDSSP. The 2-dimensional representations of PCA, t-SNE, and UMAP for the evaluated protein language models are provided as datasets in CSV and Parquet formats. The algorithm used and the specific set of beta-lactamases are indicated at the beginning of the filename: sbl for serine beta-lactamases and mbl for metallo-beta-lactamases. For more information, consult the following Github repository https://github.com/miangoar/Betalactamase-analysis-with-machine-learning
本数据集为论文《基于蛋白质语言模型的β-内酰胺酶序列空间大规模分析》（Large-scale analysis of the β-lactamase sequence space with protein language models）的核心数据集。 本数据集包含29445行与82列，以Parquet格式存储。数据行对应从β-内酰胺酶数据库（BLDB）中获取的全部序列；列则包含从BLDB处理得到的各类信息，包括基于基因组分类数据库（Genome Taxonomy Database, GTDB RS207）注释的分类学信息、由5种蛋白质语言模型（protein language model）生成的单蛋白质嵌入向量（ESM-1b、ESM2-650、ESM2-3b、CARP-640M、ProtTrans-t5-xl-u50）、通过Biopython预测得到的功能注释、用于筛选分析序列的序列质量过滤结果、对应可用结构的AlphaFold数据库（AlphaFold Database, AFDB）注释，以及通过AlphaFold2预测的结构经pyDSSP生成的二级结构注释。 针对本次评估的蛋白质语言模型，其PCA、t-SNE与UMAP二维降维表征以数据集形式提供，格式涵盖CSV与Parquet。数据集文件名前缀会标明所用算法与对应的β-内酰胺酶类型：sbl代表丝氨酸β-内酰胺酶，mbl代表金属β-内酰胺酶。 如需获取更多信息，请访问以下GitHub仓库：https://github.com/miangoar/Betalactamase-analysis-with-machine-learning
