KinForm: Trained models, protein embeddings, binding-site scores, and sequence ID mapping

This dataset provides all runtime assets needed to reproduce KinForm results and generate predictions offline using the repository’s code. It includes pre-trained models, precomputed protein embeddings, optional binding-site probability scores, and the sequence ID mapping used across assets. Contents results/sequence_id_to_sequence.pkl A pickled dict mapping stable sequence IDs (e.g., “Sequence 11894”) to amino-acid sequences. Used to align embeddings and binding-site scores to IDs. results/trained_models/ Serialized scikit-learn regressors trained for kcat and KM across KinForm configurations (e.g., UniKP, KinForm-L, KinForm-H). Ready for use with main.py to train/evaluate/predict. results/binding_sites/ Pseq2Sites per-residue binding-site probability outputs in TSV format. Columns: PDB: sequence ID (matches the keys in sequence_id_to_sequence.pkl) Pred_BS_Scores: stringified list of length L (sequence length), where the i-th value is the probability that residue i is in the binding site. results/protein_embeddings/ Precomputed mean and weighted protein embeddings used as model inputs, including ESM2 (layers 26 and 29), ESMC (layers 24 32), and ProtT5 (layer 19 and last layer). For details on methods and benchmarks, see the preprint: https://arxiv.org/abs/2507.14639. Use with https://github.com/Digital-Metabolic-Twin-Centre/KinForm.本数据集提供了复现KinForm研究结果，并通过对应代码仓库离线生成预测结果所需的全部运行时资源，涵盖预训练模型、预计算蛋白质嵌入向量、可选的结合位点概率评分，以及贯穿所有资源使用的序列ID映射表。 ### 数据集内容 #### results/sequence_id_to_sequence.pkl 一个经序列化存储的字典，将稳定序列ID（例如"Sequence 11894"）映射至氨基酸序列，用于将嵌入向量与结合位点评分对齐至对应ID。 #### results/trained_models/ 针对KinForm不同配置（如UniKP、KinForm-L、KinForm-H）训练得到的序列化scikit-learn回归器，分别用于预测催化常数kcat与米氏常数KM值，可直接与main.py配合开展训练、评估与预测任务。 #### results/binding_sites/ 采用TSV格式存储的Pseq2Sites逐残基结合位点概率输出结果，各列说明如下： - PDB：序列ID（与sequence_id_to_sequence.pkl中的键值一致） - Pred_BS_Scores：长度为L（序列总长度）的字符串化列表，其中第i个数值代表第i号残基位于结合位点的概率。 #### results/protein_embeddings/ 预计算得到的均值与加权蛋白质嵌入向量，用作模型输入，涵盖ESM2（第26、29层）、ESMC（第24、32层）以及ProtT5（第19层与最后一层）。 如需了解方法与基准测试详情，请参阅预印本：https://arxiv.org/abs/2507.14639。配套代码仓库地址：https://github.com/Digital-Metabolic-Twin-Centre/KinForm。