Protein storytelling through physics.
Protein storytelling through physics.
复制标题
通过物理学讲蛋白质故事。
DOI：
10.1126/science.aaz3041
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发表时间：
2020-11-27
期刊：
影响因子：
--
通讯作者：
Dill K
中科院分区：
文献类型：
--
作者：
Brini E;Simmerling C;Dill K
理解生物学，尤其是在可行的药物发现水平上，通常是开发有关蛋白质的准确故事，这需要理解系统的物理学，而基于物理的计算机建模是一个主要的工具蛋白质的计算分子物理学（CMP）以前过于昂贵且大量的公共超级计算资源。除其他事项外，蛋白质建模驱动了主要的计算机硬件进步，例如IBM的蓝色基因和De Shaw的Anton计算机，蛋白质建模迅速发展了50年，甚至比Moore的定律更快。这是围绕蛋白质建模的：他们范围内的盲人竞争。
50年来，计算机建模的两种方法一直是开发有关蛋白质分子及其生物学作用的故事（i）从结构 - 专业关系中的推论：基于蛋白质的作用取决于其形状的原理使用已知蛋白的数据库来了解未知的蛋白质（II）计算分子物理学使用分子动力学（MD）取样的原子 - 原子相互作用的力场来发展满足化学和热力学的原理我们在计算上很昂贵，仅研究小蛋白的简单作用，但CMP最近已升级（i）。快速，CMP能够准确地模拟时间尺度的蛋白质作用，比微秒更长，有时甚至是毫秒就像赛车对汽车制造商的竞争一样，诸如蛋白质结构预测事件之类的公共盲验正在为蛋白质建模者提供一个共同的评估场地，以改善我们的方法。利用外部信息（例如实验性结构数据），以加速CMP，尤其是在保留适当的物理学的同时。
我们在一件事上学习什么，一个长期的假设是蛋白质通过多个不同的显微镜路线折叠，这个故事太细粒了，无法从实验中学习。通过蛋白质，CMP现在有助于物理化学药物设计。但是，这种方法并未揭示一些最重要的物理特性，即伴侣的伴侣和外率与蛋白质的结合第三个例子显示了严重的急性呼吸综合征2（SARS-COV-2）的峰值蛋白，这是当今的冠状病毒疾病的病因（Covid-19）。这种相当的蛋白质是病毒进入并感染人类细胞所需的关键作用。这项运动的三个动态状态。
细胞的行为是由于其数千种不同的蛋白质的作用。它正在攻击越来越大的蛋白途中的时间，会议分布以及重要的物理量，例如自由能，速率和平衡常数。■
COVID-19的CMP模型感染了人类细胞。结合域（左）。尖峰蛋白，导致病毒融合到人类宿主细胞。
每个蛋白质都有一个故事 - 它如何折叠，它的生物学作用以及它在衰老或疾病中的不良行为。分子物理学（CMP）植根于驱动力的主要物理学，并在时空中揭示了构象种群的颗粒状细节。用原子物理学的语言讲述。
Understanding biology, particularly at the level of actionable drug discovery, is often a matter of developing accurate stories about how proteins work. This requires understanding the physics of the system, and physics-based computer modeling is a prime tool for that. However, the computational molecular physics (CMP) of proteins has previously been much too expensive and slow. A large fraction of public supercomputing resources worldwide is currently running CMP simulations of biologically relevant systems. We review here the history and status of this large and diverse scientific enterprise. Among other things, protein modeling has driven major computer hardware advances, such as IBM’s Blue Gene and DE Shaw’s Anton computers. Further, protein modeling has advanced rapidly over 50 years, even slightly faster than Moore’s law. We also review an interesting scientific social construct that has arisen around protein modeling: community-wide blind competitions. They have transformed how we test, validate, and improve our computational models of proteins. For 50 years, two approaches to computer modeling have been mainstays for developing stories about protein molecules and their biological actions. (i) Inferences from structure-property relations: Based on the principle that a protein’s action depends on its shape, it is possible to use databases of known proteins to learn about unknown proteins. (ii) Computational molecular physics uses force fields of atom-atom interactions, sampled by molecular dynamics (MD), to develop biological action stories that satisfy principles of chemistry and thermodynamics. CMP has traditionally been computationally costly, limited to studying only simple actions of small proteins. But CMP has recently advanced enormously. (i) Force fields and their corresponding solvent models are now sufficiently accurate at capturing the molecular interactions, and conformational searching and sampling methods are sufficiently fast, that CMP is able to model, fairly accurately, protein actions on time scales longer than microseconds, and sometimes milliseconds. So, we are now accessing important biological events, such as protein folding, unbinding, allosteric change, and assembly. (ii) Just as car races do for auto manufacturers, communal blind tests such as protein structure-prediction events are giving protein modelers a shared evaluation venue for improving our methods. CMP methods are now competing and often doing quite well. (iii) New methods are harnessing external information—like experimental structural data—to accelerate CMP, notably, while preserving proper physics. What are we learning? For one thing, a long-standing hypothesis is that proteins fold by multiple different microscopic routes, a story that is too granular to learn from experiments alone. CMP recently affirmed this principle while giving accurate and testable microscopic details, protein by protein. In addition, CMP is now contributing to physico-chemical drug design. Structure-based methods of drug discovery have long been able to discern what small-molecule drug candidates might bind to a given target protein and where on the protein they might bind. However, such methods don’t reveal some all-important physical properties needed for drug discovery campaigns—the affinities and the on- and off-rates of the ligand binding to the protein. CMP is beginning to compute these properties accurately. A third example is shown in the figure. It shows the spike protein of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2), the causative agent of today’s coronavirus disease 2019 (COVID-19) pandemic. A large, hinge-like movement of this sizable protein is the critical action needed for the virus to enter and infect the human cell. The only way to see the details of this motion—to attempt to block it with drugs—is by CMP. The figure shows CMP simulation results of three dynamical states of this motion. A cell’s behavior is due to the actions of its thousands of different proteins. Every protein has its own story to tell. CMP is a granular and principled tool that is able to discover those stories. CMP is now being tested and improved through blind communal validations. It is attacking ever larger proteins, exploring increasingly bigger and slower motions, and with ever more accurate physics. We are reaching a physical understanding of biology at the microscopic level as CMP reveals causations and forces, step-by-step actions in space and time, conformational distributions along the way, and important physical quantities such as free energies, rates, and equilibrium constants.■ CMP modeling of COVID-19 infecting the human cell. SARS-CoV-2 spike glycoprotein (green, with its glycan shield in yellow) attaching to the human angiotensin-converting enzyme 2 (ACE2) receptor protein (purple) through its spike receptor-binding domain (red). (Left) The receptor binding domain (RBD) is hidden. (Middle) The RBD is open and accessible. (Right) The RBD binds human ACE2 receptor. This is followed by a cascade of larger conformational changes in the spike protein, leading to viral fusion to the human host cell. Every protein has a story—how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein’s shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology’s stories to be told in the language of atomistic physics.
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