Testing and improving alchemical techniques for predicting protein-ligand binding

描述(申请人提供)：药物发现需要花费大量的时间和金钱。现在，将每种新药推向市场的资金平均约为10亿美元。这一成本中的一个重要因素是找到调节生物分子功能的初始“命中”，然后将其提炼成与生物分子靶标和其他所需特性具有足够亲和力的“引线”所涉及的试错量。理想情况下，计算方法将极大地减少所涉及的试错量，在实验之前提出命中建议，并通过预测化学变化来指导改进过程，以在保持药物性质的同时提高亲和力。目前的计算方法不够可靠，不足以促进药物发现过程中的这种根本性变化。这项建议将PI在基于计算机模拟预测结合亲和力方面的最新创新应用于几个增加生物相关性的不同系统，[并在定向、构象和侧链采样方面提出了进一步的创新]。这将导致该方法的进一步改进，[以及对其准确性的测试]，使其更接近于将其应用于药物发现的背景。提出的方法使用了基于分子动力学模拟的“炼金术”绝对自由能计算。这是可用的物理上最现实的方法之一，也是精度方面最有希望的方法之一。该项目的目标是(1)提高结合自由能技术在T4溶菌酶的极性模型结合部位的准确性，使用炼金术和新算法来克服采样问题；(2)在盲法测试中预测胰蛋白酶抑制剂的结合亲和力，随后进行大量分析和额外计算；以及(3)计算广泛的DNA旋转酶抑制剂的结合亲和力，对照实验数据进行测试，并使观察到的趋势合理化。]在每个项目中，我们都将计算所研究的每个潜在抑制剂的绝对[或相对]结合亲和力，从将潜在抑制剂对接到一个现有的蛋白质未结合结构中产生的一组可能的结合模式开始。使用这组可能的结合模式作为模拟的起点意味着不需要事先知道单个抑制剂的结合结构以获得准确性。最终，这项工作将在改进的亲和度计算方法中得到回报。一旦精确度、速度和可靠性足够高，这些将被应用于药物发现中的问题。这项工作将在实现必要的准确性和可靠性方面发挥关键作用。 与公共健康相关：药物发现通过对常见疾病和障碍的新的和改进的治疗方法产生显著的公共健康好处，但这是一个昂贵、耗时的过程，涉及许多试验和错误，失败是常见的。这项提议改进并应用计算机模拟技术来预测蛋白质、生命的分子机器和潜在的药物制剂之间的联系。这项工作有可能彻底改变药物发现过程的早期阶段，帮助新药的开发并支付公共健康回报！

DESCRIPTION (provided by applicant): Pharmaceutical drug discovery costs a tremendous amount of time and money. Now roughly $1 billion goes into bringing each new drug to market, on average [1]. An important factor in this cost is the amount of trial and error involved in finding initial "hits" which modulate the function of a biomolecule, and then refining these into "leads" which have adequate affinity for the biomolecular target and other desirable properties. Ideally, computational methods would drastically reduce the amount of trial and error involved, suggesting hits in advance of experiment, and guiding the refinement process by predicting chemical changes to improve affinity while maintaining drug-like properties. Current computational methods are not reliable enough to facilitate this radical change in the drug discovery process. This proposal applies the PI's recent innovations in predicting binding affinities based on computer simulations to several different systems of increasing biological relevance, [and proposes further innovations in orientational, conformational, and sidechain sampling]. This will result in further improvements in the approach, [and tests of its accuracy], bringing it closer to the point where it will be applied in a drug discovery context. The proposed approach uses "alchemical" absolute free energy calculations based on molecular dynamics simulations. This is one of the most physically realistic approaches available, and one of the most promising in terms of accuracy. This project's aims are to (1) improve the accuracy of binding free energy techniques in a polar model binding site in T4 lysozyme, using alchemical techniques with new algorithms to overcome sampling problems; [(2) predict binding affinities of trypsin inhibitors in a blind test, following this up with substantial analysis and additional calculations; and (3) compute binding affinities of an extensive set of DNA gyrase inhibitors, testing against experimental data and rationalizing observed trends.] In each of these projects, we will compute absolute [or relative] binding affinities for each potential inhibitor studied, beginning from a set of possible binding modes generated by docking potential inhibitors into one existing unbound structure of the protein. Using this set of possible binding modes as starting points for simulation means that bound structures of the individual inhibitors do not need to be known in advance for accuracy. Ultimately, this work will pay off in improved methods for affinity calculation. These will be applied to problems in drug discovery once the accuracy, speed, and reliability are sufficiently high. This work will play a key role in achieving necessary levels of accuracy and reliability. PUBLIC HEALTH RELEVANCE: Pharmaceutical drug discovery produces dramatic public health benefits through new and improved treatments for common diseases and disorders, but it is an expensive, time-consuming process involving much trial and error, and failure is common. This proposal refines and applies computer simulation techniques to predicting association between proteins, life's molecular machines, and potential pharmaceutical agents. This work has the potential to drastically change the early stages of the drug discovery process, aiding the development of new drugs and paying public health rewards!