构建抗体理解的表征学习模型。

IF 6.9 2区 生物学 Q1 CELL BIOLOGY
Justin Barton, Aretas Gaspariunas, Jacob D Galson, Jinwoo Leem
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引用次数: 0

摘要

抗体是一种多功能蛋白质,具有结合广泛靶标的能力,并且是最成功的治疗方法之一。然而,开发新的抗体疗法是一个漫长而昂贵的过程。仅从氨基酸序列预测抗体的功能和生物物理特性是具有挑战性的,需要大量的实验来充分表征。机器学习,特别是深度表示学习,已经成为一系列可以补充湿实验室方法并加速整体发现和工程过程的方法。在这里,我们回顾了抗体序列表示学习的进展,以及它如何改进抗体结构预测和促进抗体优化。我们讨论了开发和实施这些模型所面临的挑战,例如缺乏公开可用的、精心策划的抗体功能数据,并强调了改进的机会。抗体序列机器学习的这些和未来的进展有可能提高开发新疗法的成功率,从而更广泛地获得变革性药物并改善患者的治疗效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building Representation Learning Models for Antibody Comprehension.

Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. It is challenging to predict the functional and biophysical properties of antibodies from their amino acid sequence alone, requiring numerous experiments for full characterization. Machine learning, specifically deep representation learning, has emerged as a family of methods that can complement wet lab approaches and accelerate the overall discovery and engineering process. Here, we review advances in antibody sequence representation learning, and how this has improved antibody structure prediction and facilitated antibody optimization. We discuss challenges in the development and implementation of such models, such as the lack of publicly available, well-curated antibody function data and highlight opportunities for improvement. These and future advances in machine learning for antibody sequences have the potential to increase the success rate in developing new therapeutics, resulting in broader access to transformative medicines and improved patient outcomes.

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来源期刊
CiteScore
15.00
自引率
1.40%
发文量
56
审稿时长
3-8 weeks
期刊介绍: Cold Spring Harbor Perspectives in Biology offers a comprehensive platform in the molecular life sciences, featuring reviews that span molecular, cell, and developmental biology, genetics, neuroscience, immunology, cancer biology, and molecular pathology. This online publication provides in-depth insights into various topics, making it a valuable resource for those engaged in diverse aspects of biological research.
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