Computational Design of Fusion Inhibitors Targeting Drug-resistant HIVgp41

我们研究的长期目标是开发急需的小分子药物，以帮助治疗全球约3300万艾滋病毒感染者。针对目前批准的疗法的有害耐药突变的出现需要针对HIV生命周期事件的新策略，包括互补抑制机制和利用具有高序列保守性的区域。我们实验室最近开发的一种创新方法，旨在利用原子级分子足迹所固有的丰富的能量和结构信息-病毒蛋白质上可靶向口袋内发生的相互作用图-合理地识别，开发和设计针对病毒蛋白gp 41的新型小分子抑制剂。如下所述，我们有强有力的证据表明，来自gp 41区域的足迹编码关于哪些残基对配体结合最关键的信息，并且我们开发了一种新的计算方法来利用这些信息，这是一种潜在的突破性方式来筛选或从头开始设计小分子融合抑制剂。在这里，我们的目标是利用足迹的力量来合理设计特异性结合gp 41并抑制膜融合的小分子。我们发表的具有gp 41的肽抑制剂（C34和T20/Fuzeon）的原子模型将使我们能够靶向已知的疏水口袋以及到目前为止尚未开发的其他区域。我们的中心假设是，与gp 41相互作用的有机小分子，以与已知肽抑制剂的关键侧链类似的方式，将成为治疗的有效先导。此外，通过在鉴定和开发阶段包括源自保守gp 41区域内发生的相互作用的足迹，我们假设化合物将更有可能具有稳健的耐药谱。这一假设是基于几个观察，包括我们确定的七个实验验证的抑制剂使用一个新的虚拟筛选协议定制占足迹为基础的相似性。我们还使用足迹结合分子动力学模拟来解释T20耐药性的起源，并表明C-螺旋肽抑制剂的结合可以仅由保守的gp 41口袋内的变化驱动，支持保守的口袋区域是重要的药物靶位点的前提。有希望的初步结果，其中我们已经集成了足迹与计算从头设计方法，有利于，第一次，从零开始的小分子定制建设，使特定的足迹模式与gp 41以类似的方式作为一个已知的参考。在目标#1中，我们将定制设计专门针对gp 41上能量有利界面的小分子抑制剂。在目标#2中，我们将开发实验验证的gp 41导致具有改进的gp 41活性。在目标#3中，我们将鉴定gp 41前发夹模型中的其他可靶向事件。

DESCRIPTION (provided by applicant): The long-term goal of our research is to develop critically-needed small molecule drugs to help treat the approximately 33 million people worldwide living with HIV. The emergence of deleterious drug-resistance mutations against currently-approved therapies necessitates new strategies for targeting HIV life-cycle events that include complementary inhibition mechanisms and exploitation of regions with high sequence conservation. An innovative approach, recently developed in our lab, aims to leverage the wealth of energetic and structural information inherent to atomic-level molecular footprints - interaction maps occurring within targetable pockets on viral proteins - to rationally identify, develop, and design novel small molecule inhibitors against the viral protein gp41. As outlined below, we have strong evidence suggesting that footprints derived from regions on gp41 encode information regarding which residues are most critical for ligand binding and that our development of a novel computational means to harness this information is a potentially ground-breaking way to screen for, or alternatively, design-from-scratch small molecule fusion inhibitors. Here, the objectives are to utilize the power of footprints to rationally design small molecules that specifically bind to gp41 and inhibit membrane fusion. Our published atomic models for peptide inhibitors (C34 and T20/Fuzeon) with gp41 will enable us to target the known hydrophobic pocket in addition to other regions that, until now, have yet to be exploited. Our central hypothesis is that small organic molecules which make interactions with gp41, in an energetically similar manner as key side-chains on known peptide inhibitors, will make effective leads for therapeutics. Furthermore, by including footprints derived from interactions occurring within conserved gp41 regions during the identification and development stages, we postulate compounds will be more likely to have robust resistance profiles. This hypothesis is based on several observations, including our identification of seven experimentally-verified inhibitors usin a new virtual screening protocol tailored to account for footprint-based similarity. We have also used footprints in conjunction with molecular dynamics simulations to explain the origins of T20 drug resistance as well as show that association of C-helix peptide inhibitors can be driven solely by changes within the conserved gp41 pocket, supporting the premise that the conserved pocket region is an important drug target site. Promising preliminary results, in which we have integrated footprints with computational de novo design methods, facilitates, for the first time, custom construction from scratch of small molecules which make specific footprint patterns with gp41 in a similar way as a known reference. In Aim #1 we will custom-design small molecule inhibitors specifically tailored to energetically favorable interfaces on gp41. In Aim #2 we will develop experimentally verified gp41 leads to have improved gp41 activity. In Aim #3 we will identify other targetable events in the gp41 pre-hairpin model.