背景:机器学习技术在选择和优化治疗分子方面变得越来越重要,以及用于配方成分的选择和长期稳定性的预测。与第一原理模型相比,机器学习技术更容易实现,并且可以识别在机械层面上很难描述的相关性,但强烈依赖高质量的输入训练数据。这里,我们评估了“混沌游戏”表示为机器学习模型提供输入数据的潜力。混沌博弈是最初为产生分形结构而开发的算法,后来也应用于生物序列的表示,如基因和蛋白质。我们的结果表明,混沌游戏表示与卷积神经网络的组合导致与其他机器学习方法相当的准确性。因此表明,混沌游戏表示可能是生物序列机器学习模型的现有特征化策略的有效替代方案。
方法:我们在Python3.8.10中实现了混沌游戏,并使用它来产生蛋白质序列的分形以及新颖的扩展表示。然后,我们将生成的图像提供给卷积神经网络,内置Python3.8.10,使用TensorFlow2.9.1、Keras2.9.0和scikit-learn1.1.1包。我们选择最近发表的抗体伊米贝珠单抗的数据集作为案例研究,目的是共同优化具有高亲和力和低非特异性结合的抗体变体。
BACKGROUND: Machine learning techniques are becoming increasingly important in the selection and optimization of therapeutic molecules, as well as for the selection of formulation components and the prediction of long-term stability. Compared to first-principle models, machine learning techniques are easier to implement, and can identify correlations that would be hard to describe at a mechanistic level, but strongly rely on high-quality input training data. Here, we evaluate the potential of the \"chaos game\" representation to provide input data for machine learning models. The chaos game is an algorithm originally developed for the production of fractal structures, and later on applied also to the representation of biological sequences, such as genes and proteins. Our results show that the combination of the chaos game representation with convolutional neural networks results in comparable accuracy to other machine learning approaches, thus indicating that chaos game representations could be a valid alternative to existing featurization strategies for machine learning models of biological sequences.
METHODS: We implement the chaos game in Python 3.8.10, and use it to produce fractal as well as novel expanding representations of protein sequences. We then feed the resulting images to a convolutional neural network, built in Python 3.8.10, using TensorFlow 2.9.1, Keras 2.9.0, and the scikit-learn 1.1.1 packages. We select as
case study a recently published dataset for the antibody emibetuzumab, with the objective of co-optimizing antibodies variants with both high affinity and low non-specific binding.