Ensemble Networks for Allosteric Drug Design

用于变构药物设计的集成网络

基本信息

批准号：
10203327
负责人：
Kelly Marie Thayer
金额：
$ 49.29万
依托单位：
WESLEYAN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10203327
关键词：
Active Sites Advanced Development Affect Algorithms Allosteric Regulation Binding Binding Proteins Biological Process Biology Cancerous Carbon Chemicals Computing Methodologies Coupling Crystallization Development Disease Drug Design Drug Targeting Engineering Evolution Free Energy Goals Health Knowledge Life Link Malignant Neoplasms Maps Medical Membrane Proteins Methods Modeling Modification Molecular Molecular Conformation Monitor Motion Mutation Outcome Peptides Pharmaceutical Preparations Population Probability Protein Conformation Proteins Protocols documentation Public Health Regulation Reporting Research Side Site Specificity Structure System TP53 gene Tactile Testing Therapeutic Tumor Suppressor Proteins Vertebral column Work base cohesion cost design driving force drug discovery functional group innovation insight molecular dynamics mutant novel novel drug class novel therapeutics protein function simulation therapeutic protein vector

项目摘要

Developing allosteric drugs, a novel class of therapeutics mimicking a ubiquitous phenomenon in biology, requires selecting the active protein substate conformation. However, there is a gap in knowledge to link how allosteric effectors select particular conformational substates; this is the key information needed to engineer efficacious allosteric drugs selecting for active substates. The long-term goal is to develop allosteric drugs to modulate and/or restore protein function. The overall objective of this application is to develop a mechanistic understanding of how allosteric effectors select specific conformational substates to control function of the p53 protein. The central hypothesis is that casting the energy landscape as vector tensors provides critical information for rationally designing allosteric effectors to select substates corresponding to desired protein function. The approach will link the allosteric effector to its effects in the energy landscape, leading to substate selection. The three aims of the project will enable allosteric control of proteins. First, the free energy landscape of the protein by residue will be captured in a vector tensor model. The interaction energies between a residue and its neighbors will be computed, and the magnitude and direction of the net force will be mapped to its alpha carbon. Repeating for all residues will produce a field of vectors reporting on the energy landscape. Preliminary results indicate the method is capable of identifying the major conformational substate of a test case. Second, the utility of the vector approach for elucidating functional substates of proteins will be demonstrated using an example of a known allosteric effector of the tumor suppressor protein p53. MD simulations and MD-Markov State Models will identify the desired active conformational substates, and the vector tensors will point out the forces as the drug restores a mutant to a functional conformation. Third, drugs based on alpha helical peptides will bind to allosteric control sites p53 from MD Sectors to rescue cancerous hotspot mutations. The vectors will identify which interactions need to be changed by modifying the side chain composition of alpha helical allosteric effectors to steer the protein into the desired active conformation as identified by Molecular Dynamics-Markov State Models. Using chemical principles, the functional group side chains of the peptide will be iteratively refined to steer the protein following the energy vectors into the desired active substate, thereby selecting functionality. This work will significantly advance the development of a new class of allosteric drugs by creating an algorithm to steer non-functional mutant proteins into an active conformation through the iterative refinement of the functional R-groups of the drug. This new drug design takes the innovative strategy of capturing the free energy landscape itself in a field of vector tensors that will directly point out the forces felt by each protein residue to elucidate required modifications to steer a mutant into an active conformation. This will enable the development of allosteric therapeutics for all allosteric proteins, thereby allowing drugs to be developed for targets that have not yet been drugged. This will lead to cures to diseases such as cancer and more.

开发变构药物，这是一种新颖的疗法，模仿了生物学中普遍存在的现象，需要选择活性蛋白质构象。但是，知识存在差距可以链接变构效应器选择特定的构象取代；这是工程师所需的关键信息有效的变构药物选择活性取代。长期目标是开发变构药调节和/或恢复蛋白质功能。该应用程序的总体目的是开发机械了解变构效应器如何选择特定的构象取代来控制p53的功能蛋白质。中心假设是，作为向量张量提供的能量景观提供关键信息为了合理设计变构效应子以选择对应于所需蛋白质功能的取代。这方法将把变构效应子与其在能量景观中的影响联系起来，从而导致选择。该项目的三个目标将使蛋白质的变构控制。首先，自由能环境残基的蛋白质将在矢量张量模型中捕获。残基和将计算其邻居，并将净力的大小和方向映射到其alpha 碳。重复所有残留物将产生有关能量景观报告的向量领域。初步的结果表明该方法能够识别测试案例的主要构象取代。第二，向量方法阐明蛋白质功能取代的实用性将使用肿瘤抑制蛋白p53的已知变构效应子的示例。 MD模拟和MD-Markov 状态模型将识别所需的活性构象取代物，矢量张量将指出作为药物的力恢复了功能构象的突变体。第三，基于α螺旋肽的药物将与MD部门的变构控制位点结合以挽救癌性热点突变。向量会通过修改α螺旋变构的侧链组成，确定需要更改哪些相互作用效应子将蛋白质引导到所需的活性构象中，如Molecular Dynamics-Markov所确定的状态模型。使用化学原理，肽的功能组侧链将进行迭代精制将能量向量后的蛋白质引导到所需的活性取代中，从而选择功能。这项工作将通过创建一个新的变构药物的开发，从而大大推动通过迭代细化，将非功能突变蛋白转化为活性构象的算法该药物的功能性R组。这种新药设计采用了捕获免费的创新策略能量景观本身在矢量张量的领域中直接指出每种蛋白质残基感觉的力阐明所需的修改以将突变体引导到主动构象中。这将使开发所有变构蛋白的变构治疗剂，从而允许开发药物尚未吸毒的目标。这将导致治愈癌症等疾病。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Variable Regions of p53 Isoforms Allosterically Hard Code DNA Interaction.

DOI：
10.1021/acs.jpcb.2c06229
发表时间：
2022-10-27
期刊：
JOURNAL OF PHYSICAL CHEMISTRY B
影响因子：
3.3
作者：
Armour-Garb, Isabel;Han, In Sub Mark;Cowan, Benjamin S.;Thayer, Kelly M.
通讯作者：
Thayer, Kelly M.

The CAR-mRNA Interaction Surface Is a Zipper Extension of the Ribosome A Site.

DOI：
10.3390/ijms23031417
发表时间：
2022-01-26
期刊：
International journal of molecular sciences
影响因子：
5.6
作者：
Dalgarno C;Scopino K;Raval M;Nachmanoff C;Sakkas ED;Krizanc D;Thayer KM;Weir MP
通讯作者：
Weir MP

GCN sensitive protein translation in yeast.

DOI：
10.1371/journal.pone.0233197
发表时间：
2020
期刊：
PloS one
影响因子：
3.7
作者：
Barr WA;Sheth RB;Kwon J;Cho J;Glickman JW;Hart F;Chatterji OK;Scopino K;Voelkel-Meiman K;Krizanc D;Thayer KM;Weir MP
通讯作者：
Weir MP

Toward a Universal Structural and Energetic Model for Prokaryotic Promoters.

走向原核启动子的通用结构和能量模型。

DOI：
10.1016/j.bpj.2018.08.002
发表时间：
2018
期刊：
Biophysical journal
影响因子：
3.4
作者：
Mishra,Akhilesh;Siwach,Priyanka;Misra,Pallavi;Jayaram,Bhyravabhotla;Bansal,Manju;Olson,WilmaK;Thayer,KellyM;Beveridge,DavidL
通讯作者：
Beveridge,DavidL

Allosteric Signaling in PDZ Energetic Networks: Embedding Error Analysis.