Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, 43183 Gothenburg, Sweden.
Molecular AI, Discovery Sciences, R&D, AstraZeneca, 43183 Gothenburg, Sweden.
J Chem Inf Model. 2024 Nov 25;64(22):8481-8494. doi: 10.1021/acs.jcim.4c01475. Epub 2024 Nov 1.
In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. Traditionally, methods like X-ray crystallography and cryo-electron microscopy have been used to unravel these structures, but they are often challenging, time-consuming and costly. Recently, a breakthrough in computational biology has emerged with the development of deep learning algorithms capable of predicting protein structures based on their amino acid sequences (Jumper, J., et al. , , 583. Lane, T. J. , , 170. Kryshtafovych, A., et al. , , 1607). This study focuses on predicting the dynamic changes that proteins undergo upon ligand binding, specifically when they bind to allosteric sites, i.e. a pocket different from the active site. Allosteric modulators are particularly important for drug discovery, as they open new avenues for designing drugs that can target proteins more effectively and with fewer side effects (Nussinov, R.; Tsai, C. J. , , 293). To study this, we curated a data set of 578 X-ray structures comprised of proteins displaying orthosteric and allosteric binding as well as a general framework to evaluate deep learning-based structure prediction methods. Our findings demonstrate the potential and current limitations of deep learning methods, such as AlphaFold2 (Jumper, J., et al. , , 583), NeuralPLexer (Qiao, Z., et al. , , 195), and RoseTTAFold All-Atom (Krishna, R., et al. , , eadl2528) to predict not just static protein structures but also the dynamic conformational changes. Herein we show that predicting the allosteric induce-fit conformation still poses a challenge to deep learning methods as they more accurately predict the orthosteric bound conformation compared to the allosteric induce fit conformation. For AlphaFold2, we observed that conformational diversity, and sampling between the apo and holo state could be increased by modifying the MSA depth, but this did not enhance the ability to generate conformations close to the allosteric induced-fit conformation. To further support advancements in protein structure prediction field, the curated data set and evaluation framework are made publicly available.
在生物医学研究领域，理解蛋白质的复杂结构至关重要，因为这些结构决定了蛋白质在体内的功能以及与潜在药物的相互作用。传统上，使用 X 射线晶体学和低温电子显微镜等方法来揭示这些结构，但这些方法通常具有挑战性、耗时且昂贵。最近，随着能够基于氨基酸序列预测蛋白质结构的深度学习算法的发展，计算生物学领域取得了突破（Jumper, J., et al. ,, 583. Lane, T. J. ,, 170. Kryshtafovych, A., et al. ,, 1607）。这项研究侧重于预测蛋白质在配体结合时发生的动态变化，特别是当它们结合到变构位点时，即与活性位点不同的口袋。变构调节剂对于药物发现尤为重要，因为它们为设计能够更有效地靶向蛋白质且副作用更少的药物开辟了新途径（Nussinov, R.; Tsai, C. J. ,, 293）。为了研究这一点，我们整理了一个由 578 个 X 射线结构组成的数据集，这些结构包含显示正位和变构结合的蛋白质，以及一个评估基于深度学习的结构预测方法的通用框架。我们的研究结果表明，深度学习方法，如 AlphaFold2（Jumper, J., et al. ,, 583）、NeuralPLexer（Qiao, Z., et al. ,, 195）和 RoseTTAFold All-Atom（Krishna, R., et al. ,, eadl2528），具有预测蛋白质结构的潜力和当前局限性，还可以预测动态构象变化。在这里，我们表明，与变构诱导契合构象相比，预测变构结合构象仍然是深度学习方法面临的挑战，因为它们更准确地预测了正位结合构象。对于 AlphaFold2，我们观察到通过修改 MSA 深度可以增加apo 和 holo 状态之间的构象多样性和采样，但这并没有增强生成接近变构诱导契合构象的能力。为了进一步支持蛋白质结构预测领域的进展，我们公开了整理的数据集和评估框架。
