小柯机器人

机器学习可从测序数据中预测蛋白质与配体的结合亲和力
2022-05-26 23:44

美国哥伦比亚大学Harmen J. Bussemaker课题组发现,机器学习可从测序数据中预测蛋白质与配体的结合亲和力。这一研究成果于2022年5月23日在线发表在国际学术期刊《自然—生物技术》上。

研究人员报道了一种灵活的机器学习方法,称为ProBound,它可以准确地以平衡结合常数或动力学速率来定义序列识别。这是通过一个多层最大似然框架实现的,该框架对分子相互作用和数据生成过程进行建模。结果表明,ProBound对转录因子(TF)的行为进行了量化,其模型预测的结合亲和力范围超过了以前的资源;捕捉到了DNA修饰和多TF复合物的构象灵活性的影响;并直接从体内数据(如ChIP-seq)中推断出特异性,而无需调用峰值。

当与一种称为KD-seq的检测方法结合时,它可以确定蛋白质-配体相互作用的绝对亲和力。研究人员还应用ProBound来分析激酶-底物相互作用的动力学。ProBound为解码生物网络和合理地设计蛋白质-配体相互作用开辟了新的途径。

据了解,蛋白质-配体的相互作用越来越多地通过亲和力选择和大规模平行测序进行高通量分析。然而,这些检测方法没有提供最严格地量化分子相互作用的生物物理参数。

附:英文原文

Title: Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning

Author: Rube, H. Tomas, Rastogi, Chaitanya, Feng, Siqian, Kribelbauer, Judith F., Li, Allyson, Becerra, Basheer, Melo, Lucas A. N., Do, Bach Viet, Li, Xiaoting, Adam, Hammaad H., Shah, Neel H., Mann, Richard S., Bussemaker, Harmen J.

Issue&Volume: 2022-05-23

Abstract: Protein–ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called KD-seq, it determines the absolute affinity of protein–ligand interactions. We also apply ProBound to profile the kinetics of kinase–substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein–ligand interactions.

DOI: 10.1038/s41587-022-01307-0

Source: https://www.nature.com/articles/s41587-022-01307-0

Nature Biotechnology:《自然—生物技术》,创刊于1996年。隶属于施普林格·自然出版集团,最新IF:68.164
官方网址:https://www.nature.com/nbt/
投稿链接:https://mts-nbt.nature.com/cgi-bin/main.plex


本期文章:《自然—生物技术》:Online/在线发表

分享到:

0