蛋白质结构预测模型AlphaFold2的应用进展

蛋白质结构预测是生命科学和医学的重要研究领域,也是人工智能在科学研究中的重要应用场景。AlphaFold2是由DeepMind开发的一种基于深度学习的蛋白质结构预测系统,可以从氨基酸序列中高效地生成原子级精度的蛋白质空间结构。由于AlphaFold2优越的性能,自问世以来对蛋白质结构预测方面的研究提供了前所未有的助力,因此备受关注和研究。本文介绍了AlphaFold2的模型架构、亮点、局限性和应用进展,列举了几种其他类型的蛋白质结构预测模型,并讨论了其能力、优势及局限性并思考该蛋白质结构预测模型的未来发展方向。     

AlphaFold结构预测的重大突破及其对蛋白质研究的影响与挑战

近年来,基于深度学习的方法研究在蛋白质结构预测领域实现了重大突破。AlphaFold 2（AF2）于2021年开源发布,实现了蛋白质暨蛋白质复合物三维结构的高精度预测,使得研究人员能够快速获取可靠的三维结构信息,显著加速了蛋白质结构与功能研究的进展。2024年发布的AlphaFold 3（AF3）更进一步,能对蛋白质-核酸、蛋白质-小分子等生物复合物的三维结构进行精准预测。AF3采用改进的算法与更高效的模型,大幅提升了预测准确度,特别是在抗原-抗体复合物、蛋白质-小分子复合物等方面展现出卓越的性能。AlphaFold的成功不仅为结构生物学带来了革命性进展,还在药物研发、蛋白质设计、分子功能机制研究等领域展示了巨大的应用潜力,推动了生物医学研究的革新。本文将回顾AlphaFold及相关蛋白质结构预测方法的研发历史,概述其关键技术和当下应用,并结合其局限性,展望未来的研究方向和应用。     

The energy landscape of modular repeat proteins: topology determines folding mechanism in the ankyrin family

Proteins consisting of repeating amino acid motifs are abundant in all kingdoms of life, especially in higher eukaryotes. Repeat-containing proteins self-organize into elongated non-globular structures. Do the same general underlying principles that dictate the folding of globular domains apply also to these extended topologies? Using a simplified structure-based model capturing a perfectly funneled energy landscape, we surveyed the predicted mechanism of folding for ankyrin repeat containing proteins. The ankyrin family is one of the most extensively studied classes of non-globular folds. The model based only on native contacts reproduces most of the experimental observations on the folding of these proteins, including a folding mechanism that is reminiscent of a nucleation propagation growth. The confluence of simulation and experimental results suggests that the folding of non-globular proteins is accurately described by a funneled energy landscape, in which topology plays a determinant role in the folding mechanism.     

Definition of a New Information-Based Per-Residue Quality Parameter

For biomolecular NMR structures typically only a poor correspondence is observed between statistics derived from the experimental input data and structural quality indicators obtained from the structure ensembles. Here, we investigate the relationship between the amount of available NMR data and structure quality. By generating datasets with a predetermined information content and evaluating the quality of the resulting structure ensembles we show that there is, in contrast to previous findings, a linear relation between the information contained in experimental data and structural quality. From this relation, a new quality parameter is derived that provides direct insight, on a per-residue basis, into the extent to which structural quality is governed by the experimental input data.     

Oligopeptide biases in protein sequences and their use in predicting protein coding regions in nucleotide sequences

We have examined oligopeptides with lengths ranging from 2 to 11 residues in protein sequences that show no obvious evolutionary relationship. All sequences in the Protein Identification Resource database were carefully classified by sensitive homology searches into superfamilies to obtain unbiased oligopeptide counts. The results, contrary to previous studies, show clear prejudices in protein sequences. The oligopeptide preferences were used to help decide the significance of sequence homologies and to improve the more general methods for detecting protein coding regions within nucleotide sequences.     

A method to detect nonproline cis peptide bonds in proteins

Based on the geometrical parameters around seventeen incorrectly assigned trans conformations of peptide bonds in protein structures and their correct cis counterparts, we have devised an algorithm that is capable of detecting these sites. The algorithm was optimized to reliably find all of the seventeen test cases. It can be used to quickly scan an atomic coordinate file or the complete Brookhaven Protein Data Base for more likely candidates for non‐Pro cis peptide bonds. Also, it can be of help to guide the crystallographer in intermediate stages of structure determination towards suspect areas. © 1999 John Wiley & Sons, Inc. Biopoly 50: 536–544, 1999     

Joint refinement as a tool for thorough comparison between NMR and X-ray data and structures of HU protein

Joint refinement, i.e., the simultaneous refinement of a structure against both nuclear magnetic resonance (NMR) spectroscopic and X-ray crystallographic data, was performed on the HU protein from Bacillus stearothermophilus (HUBst). The procedure was aimed at investigating the compatibility of the two data sets and at identifying conflicting information. Wherever important differences were found, such as peptide flips in the main-chain conformation, the data were further analyzed to find the cause. The NMR data showed some errors arising either from the manual interpretation of the spectra or from the incorrect account for spin diffusion. The most important artefact inherent to the X-ray data is the crystal packing of the molecules: the effects range from the limitation of the freedom of the flexible parts of the HUBst molecule to possibly one of the peptide flips.