CREMP：用于机器学习的大环肽构象体-旋转异构体集合
CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning
AI总结：
本文发布 CREMP 数据集，提供数万种大环肽的 CREST 构象集合、xTB 能量及渗透性数据，以支持机器学习建模与治疗性肽设计。
摘要：
对大环肽构象景观进行建模的计算和机器学习方法，有潜力实现合理的设计与优化。然而，用于建模大环几何结构的准确、快速且可扩展的方法仍然难以获得。近期的深度学习方法显著加快了蛋白质结构预测和小分子构象集合生成，但由于大环肽具有独特性质，类似进展尚未在其身上实现。在此，我们介绍 CREMP，这是为快速开发和评估大环肽机器学习模型而生成的一项资源。CREMP 包含 36,198 种独特大环肽及其使用 Conformer-Rotamer Ensemble Sampling Tool（CREST）生成的高质量结构集合。总体而言，这一新数据集包含近 3,130 万种独特大环几何结构，每一种都标注有源自半经验扩展紧束缚（xTB）DFT 计算的能量。此外，我们还纳入了 3,258 个具有已报告被动渗透性数据的大环，以将构象集合与实验联系起来。我们预计该数据集将推动机器学习模型的发展，从而改进用于新型疗法的肽设计与优化。
英文摘要：
Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning approaches have significantly accelerated protein structure prediction and the generation of small-molecule conformational ensembles, yet similar progress has not been made for macrocyclic peptides due to their unique properties. Here, we introduce CREMP, a resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this new dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations. Additionally, we include 3,258 macrocycles with reported passive permeability data to couple conformational ensembles to experiment. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics.
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