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Supercharged Phosphotriesterase for improved Paraoxon activity 增压磷酸酯酶可提高副氧自由基的活性
Pub Date : 2024-09-18 DOI: 10.1093/protein/gzae015
Jacob Kronenberg, Dustin Britton, Leif Halvorsen, Stanley Chu, Maria Jinu Kulapurathazhe, Jason Chen, Ashwitha Lakshmi, P Douglas Renfrew, Richard Bonneau, Jin Kim Montclare
Phosphotriesterases (PTEs) represent a class of enzymes capable of efficient neutralization of organophosphates (OPs), a dangerous class of neurotoxic chemicals. PTEs suffer from low catalytic activity, particularly at higher temperatures, due to low thermostability and low solubility. Supercharging, a protein engineering approach via selective mutation of surface residues to charged residues, has been successfully employed to generate proteins with increased solubility and thermostability by promoting charge–charge repulsion between proteins. We set out to overcome the challenges in improving PTE activity against OPs by employing a computational protein supercharging algorithm in Rosetta. Here, we discover two supercharged PTE variants, one negatively supercharged (with −14 net charge) and one positively supercharged (with +12 net charge) and characterize them for their thermostability and catalytic activity. We find that positively supercharged PTE possesses slight but significant losses in thermostability, which correlates to losses in catalytic efficiency at all temperatures, whereas negatively supercharged PTE possesses increased catalytic activity across 25°C – 55°C while offering similar thermostability characteristic to the parent PTE. The impact of supercharging on catalytic efficiency will inform the design of shelf-stable PTE and criteria for enzyme engineering.
磷酸三酯酶(PTEs)是一类能够有效中和有机磷酸酯(OPs)的酶,OPs 是一类危险的神经毒性化学品。由于热稳定性低和溶解度低,PTEs 的催化活性较低,尤其是在较高温度下。超充电(Supercharging)是一种蛋白质工程方法,通过选择性地将表面残基突变为带电残基,促进蛋白质之间的电荷排斥,从而成功地生成具有更高溶解度和耐热性的蛋白质。我们在 Rosetta 中采用了一种计算蛋白质增重算法,以克服在提高 PTE 对 OPs 的活性方面所面临的挑战。在这里,我们发现了两种超电荷 PTE 变体,一种是负超电荷(净电荷为 -14),另一种是正超电荷(净电荷为 +12),并对它们的耐热性和催化活性进行了表征。我们发现,正向增压的 PTE 在热稳定性方面有轻微但显著的损失,这与在所有温度下催化效率的损失有关,而负向增压的 PTE 在 25°C - 55°C 范围内的催化活性有所提高,同时具有与母体 PTE 相似的热稳定性特征。增压对催化效率的影响将为设计货架稳定的 PTE 和酶工程标准提供参考。
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引用次数: 0
Engineered FHA domains can bind to a variety of Phosphothreonine-containing peptides 设计的 FHA 结构域可与多种含磷苏氨酸的肽结合
Pub Date : 2024-09-13 DOI: 10.1093/protein/gzae014
Srinivas S Thota, Grace L Allen, Ashley K Grahn, Brian K Kay
Antibodies play a crucial role in monitoring post-translational modifications, like phosphorylation, which regulates protein activity and location; however, commercial polyclonal and monoclonal antibodies have limitations in renewability and engineering compared to recombinant affinity reagents. A scaffold based on the Forkhead-associated domain (FHA) has potential as a selective affinity reagent for this post-translational modification. Engineered FHA domains, termed phosphothreonine-binding domains (pTBDs), with limited cross-reactivity were isolated from an M13 bacteriophage display library by affinity selection with phosphopeptides corresponding to human mTOR, Chk2, 53BP1, and Akt1 proteins. To determine the specificity of the representative pTBDs, we focused on binders to the pT543 phosphopeptide (536-IDEDGENpTQIEDTEP-551) of the DNA repair protein 53BP1. ELISA and western blot experiments have demonstrated the pTBDs are specific to phosphothreonine, demonstrating the potential utility of pTBDs for monitoring the phosphorylation of specific threonine residues in clinically relevant human proteins.
抗体在监测翻译后修饰(如磷酸化)方面发挥着至关重要的作用,磷酸化可调节蛋白质的活性和位置;然而,与重组亲和试剂相比,商业多克隆和单克隆抗体在可再生性和工程方面存在局限性。基于叉头相关结构域(FHA)的支架有可能成为这种翻译后修饰的选择性亲和试剂。通过与对应于人类 mTOR、Chk2、53BP1 和 Akt1 蛋白的磷酸肽进行亲和选择,从 M13 噬菌体展示文库中分离出了具有有限交叉反应性的工程化 FHA 结构域,称为磷酸苏氨酸结合结构域(phosphothreonine-binding domains,pTBDs)。为了确定代表性 pTBD 的特异性,我们重点研究了与 DNA 修复蛋白 53BP1 的 pT543 磷酸肽(536-IDEDGENpTQIEDTEP-551)的结合者。ELISA 和 Western 印迹实验表明 pTBDs 对磷酸苏氨酸具有特异性,这证明了 pTBDs 在监测临床相关人类蛋白质中特定苏氨酸残基磷酸化方面的潜在用途。
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引用次数: 0
Modular and integrative activity reporters enhance biochemical studies in the yeast ER 模块化和综合活动报告器加强了酵母ER的生化研究
Pub Date : 2024-05-02 DOI: 10.1093/protein/gzae008
Samantha G Martinusen, Ethan W Slaton, Sage E Nelson, Marian A Pulgar, Julia T Besu, Cassidy F Simas, Carl A Denard
The yeast endoplasmic reticulum sequestration and screening (YESS) system is a generalizable platform that has become highly useful to investigate post-translational modification enzymes (PTM-enzymes). This system enables researchers to profile and engineer the activity and substrate specificity of PTM-enzymes and to discover inhibitor-resistant enzyme mutants. In this study, we expand the capabilities of YESS by transferring its functional components to integrative plasmids. The YESS integrative system yields uniform protein expression and protease activities in various configurations, allows one to integrate activity reporters at two independent loci and to split the system between integrative and centromeric plasmids. We characterize these integrative reporters with two viral proteases, Tobacco etch virus (TEVp) and 3-chymotrypsin like protease (3CLpro), in terms of coefficient of variance, signal-to-noise ratio and fold-activation. Overall, we provide a framework for chromosomal-based studies that is modular, enabling rigorous high-throughput assays of PTM-enzymes in yeast.
酵母内质网封存和筛选(YESS)系统是一个通用平台,在研究翻译后修饰酶(PTM-酶)方面非常有用。研究人员可以利用该系统分析和设计 PTM 酶的活性和底物特异性,并发现抗抑制剂的酶突变体。在这项研究中,我们将 YESS 的功能元件转移到整合质粒上,从而扩展了 YESS 的功能。YESS 整合系统能以各种配置产生统一的蛋白质表达和蛋白酶活性,允许在两个独立位点整合活性报告物,并能在整合质粒和中心质粒之间拆分该系统。我们从方差系数、信噪比和折叠活化等方面描述了这些整合报告器与两种病毒蛋白酶--烟草蚀刻病毒(TEVp)和3-糜蛋白酶样蛋白酶(3CLpro)--的特性。总之,我们为基于染色体的研究提供了一个模块化框架,可以对酵母中的 PTM 酶进行严格的高通量检测。
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引用次数: 0
Protein sequence design on given backbones with deep learning 利用深度学习在给定骨架上设计蛋白质序列
Pub Date : 2023-12-29 DOI: 10.1093/protein/gzad024
Yufeng Liu, Haiyan Liu
Deep learning methods for protein sequence design focus on modeling and sampling the many- dimensional distribution of amino acid sequences conditioned on the backbone structure. To produce physically foldable sequences, inter-residue couplings need to be considered properly. These couplings are treated explicitly in iterative methods or autoregressive methods. Non-autoregressive models treating these couplings implicitly are computationally more efficient, but still await tests by wet experiment. Currently, sequence design methods are evaluated mainly using native sequence recovery rate and native sequence perplexity. These metrics can be complemented by sequence-structure compatibility metrics obtained from energy calculation or structure prediction. However, existing computational metrics have important limitations that may render the generalization of computational test results to performance in real applications unwarranted. Validation of design methods by wet experiments should be encouraged.
用于蛋白质序列设计的深度学习方法侧重于以骨架结构为条件,对氨基酸序列的多维分布进行建模和采样。为了生成物理上可折叠的序列,需要适当考虑残基间的耦合。这些耦合在迭代法或自回归法中得到了明确的处理。隐含处理这些耦合的非自回归模型计算效率更高,但仍有待湿实验的检验。目前,序列设计方法主要使用原生序列恢复率和原生序列复杂度进行评估。通过能量计算或结构预测获得的序列-结构兼容性指标可以对这些指标进行补充。然而,现有的计算指标有很大的局限性,可能导致无法将计算测试结果推广到实际应用中。应鼓励通过湿实验验证设计方法。
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引用次数: 0
Growing ecosystem of deep learning methods for modeling protein–protein interactions 用于蛋白质-蛋白质相互作用建模的深度学习方法生态系统日益壮大
Pub Date : 2023-12-15 DOI: 10.1093/protein/gzad023
Julia R Rogers, Gergö Nikolényi, Mohammed AlQuraishi
Numerous cellular functions rely on protein–protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically-informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.
许多细胞功能都依赖于蛋白质之间的相互作用。然而,由于蛋白质组中采用的分子识别机制多种多样,全面描述它们的工作仍然面临挑战。深度学习通过利用蛋白质相互作用的实验数据和基本生物物理知识,已成为解决这一问题的一种有前途的方法。在此,我们回顾了用于蛋白质相互作用建模的深度学习方法日益增长的生态系统,强调了这些生物物理知识模型的多样性及其各自的权衡。我们讨论了最近在利用表征学习捕捉与预测蛋白质相互作用和相互作用位点相关的复杂特征、利用几何深度学习推理蛋白质结构和预测复杂结构以及利用生成模型设计全新蛋白质组装方面取得的成功。我们还概述了一些突出的挑战和有希望的新方向。发现新的相互作用、阐明其物理机制、利用深度学习设计粘合剂以调节其功能,以及最终揭示蛋白质相互作用如何协调复杂的细胞行为的机会比比皆是。
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引用次数: 0
Cyan fluorescent proteins derived from mNeonGreen. 源自 mNeonGreen 的青色荧光蛋白。
Pub Date : 2022-02-17 DOI: 10.1093/protein/gzac004
Landon Zarowny, Damien Clavel, Ryan Johannson, Kévin Duarte, Hadrien Depernet, Jérôme Dupuy, Heather Baker, Alex Brown, Antoine Royant, Robert E Campbell

mNeonGreen, an engineered green fluorescent protein (GFP) derived from lancelet, is one of the most brightly fluorescent homologs of Aequorea victoria jellyfish GFP (avGFP) yet reported. In this work, we investigated whether this bright fluorescence might be retained in homologs of mNeonGreen with modified chromophore structures and altered fluorescent hues. We found mNeonGreen to be generally less tolerant than avGFP to chromophore modification by substitution of the key chromophore-forming tyrosine residue with other aromatic amino acids. However, we were ultimately successful in creating a variant, designated as NeonCyan1, with a tryptophan-derived cyan fluorescent protein (CFP)-type chromophore, and two additional mutants with distinct spectral hues. Structural, computational, and photophysical characterization of NeonCyan1 and its variants provided insight into the factors that control the fluorescence emission color. Though not recommended as replacements for contemporary CFP variants, we demonstrate that NeonCyan1 variants are potentially suitable for live cell imaging applications.

mNeonGreen 是一种源自长尾鳕的工程化绿色荧光蛋白(GFP),是目前报道的维多利亚水母 GFP(avGFP)最明亮的荧光同源物之一。在这项工作中,我们研究了 mNeonGreen 的同源物在改变了发色团结构和荧光色调后是否还能保持这种明亮的荧光。我们发现,通过用其他芳香族氨基酸取代形成发色团的关键酪氨酸残基,mNeonGreen 对发色团修饰的耐受性普遍低于 avGFP。不过,我们最终还是成功地创造出了一种变体,命名为 NeonCyan1,它具有源自色氨酸的青色荧光蛋白(CFP)型发色团,以及另外两种具有不同光谱色调的突变体。通过对 NeonCyan1 及其变体进行结构、计算和光物理表征,我们深入了解了控制荧光发射颜色的因素。虽然我们不建议用 NeonCyan1 变体来替代现有的 CFP 变体,但我们证明了 NeonCyan1 变体有可能适用于活细胞成像应用。
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引用次数: 0
Free-energy landscape of molecular interactions between endothelin 1 and human endothelin type B receptor: fly-casting mechanism. 内皮素1与人内皮素B型受体分子相互作用的自由能格局:飞投机制。
Pub Date : 2019-12-31 DOI: 10.1093/protein/gzz029
Junichi Higo, Kota Kasahara, Mitsuhito Wada, Bhaskar Dasgupta, Narutoshi Kamiya, Tomonori Hayami, Ikuo Fukuda, Yoshifumi Fukunishi, Haruki Nakamura

The free-energy landscape of interaction between a medium-sized peptide, endothelin 1 (ET1), and its receptor, human endothelin type B receptor (hETB), was computed using multidimensional virtual-system coupled molecular dynamics, which controls the system's motions by introducing multiple reaction coordinates. The hETB embedded in lipid bilayer was immersed in explicit solvent. All molecules were expressed as all-atom models. The resultant free-energy landscape had five ranges with decreasing ET1-hETB distance: completely dissociative, outside-gate, gate, binding pocket, and genuine-bound ranges. In the completely dissociative range, no ET1-hETB interaction appeared. In the outside-gate range, an ET1-hETB attractive interaction was the fly-casting mechanism. In the gate range, the ET1 orientational variety decreased rapidly. In the binding pocket range, ET1 was in a narrow pathway with a steep free-energy slope. In the genuine-bound range, ET1 was in a stable free-energy basin. A G-protein-coupled receptor (GPCR) might capture its ligand from a distant place.

利用多维虚拟系统耦合分子动力学计算了中等大小肽内皮素1 (ET1)与其受体人内皮素B型受体(hETB)之间相互作用的自由能格局,该动力学通过引入多个反应坐标来控制系统的运动。将嵌入脂质双分子层的hETB浸入外显溶剂中。所有分子都表示为全原子模型。随着ET1-hETB距离的减小,得到的自由能景观有5个范围:完全解离、外门、门、结合口袋和真正结合范围。在完全解离范围内,未出现ET1-hETB相互作用。在外-门范围内,ET1-hETB吸引相互作用是飞铸机制。在栅极范围内,ET1取向变化迅速减小。在结合口袋范围内,ET1路径狭窄,自由能斜率陡。在真实限定范围内,ET1处于稳定的自由能盆地。g蛋白偶联受体(GPCR)可能从远处捕获其配体。
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引用次数: 11
Synaptic vesicle mimics affect the aggregation of wild-type and A53T α-synuclein variants differently albeit similar membrane affinity. 突触小泡模拟物对野生型和 A53T α-突触核蛋白变体的聚集有不同的影响,尽管它们的膜亲和力相似。
Pub Date : 2019-12-13 DOI: 10.1093/protein/gzz021
Sandra Rocha, Ranjeet Kumar, Istvan Horvath, Pernilla Wittung-Stafshede

α-Synuclein misfolding results in the accumulation of amyloid fibrils in Parkinson's disease. Missense protein mutations (e.g. A53T) have been linked to early onset disease. Although α-synuclein interacts with synaptic vesicles in the brain, it is not clear what role they play in the protein aggregation process. Here, we compare the effect of small unilamellar vesicles (lipid composition similar to synaptic vesicles) on wild-type (WT) and A53T α-synuclein aggregation. Using biophysical techniques, we reveal that binding affinity to the vesicles is similar for the two proteins, and both interact with the helix long axis parallel to the membrane surface. Still, the vesicles affect the aggregation of the variants differently: effects on secondary processes such as fragmentation dominate for WT, whereas for A53T, fibril elongation is mostly affected. We speculate that vesicle interactions with aggregate intermediate species, in addition to monomer binding, vary between WT and A53T, resulting in different consequences for amyloid formation.

在帕金森病中,α-突触核蛋白的错误折叠会导致淀粉样纤维的堆积。蛋白质的错义突变(如 A53T)与早发疾病有关。虽然α-突触核蛋白与大脑中的突触小泡相互作用,但目前还不清楚它们在蛋白质聚集过程中发挥了什么作用。在这里,我们比较了小的单拉米尔囊泡(脂质成分类似于突触囊泡)对野生型(WT)和 A53T α-突触核蛋白聚集的影响。通过生物物理技术,我们发现这两种蛋白质与囊泡的结合亲和力相似,并且都与平行于膜表面的螺旋长轴相互作用。不过,囊泡对变体聚集的影响不同:WT 主要影响碎裂等次生过程,而 A53T 则主要影响纤维的伸长。我们推测,除了单体结合外,囊泡与聚集体中间物质的相互作用在 WT 和 A53T 之间也存在差异,从而导致淀粉样蛋白形成的后果不同。
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引用次数: 0
Quantifying the nativeness of antibody sequences using long short-term memory networks 利用长短期记忆网络定量抗体序列的原生性
Pub Date : 2019-07-01 DOI: 10.1093/protein/gzz031
A. Wollacott, Chonghua Xue, Qiuyuan Qin, June Hua, T. Bohnuud, Karthik Viswanathan, V. Kolachalama
Abstract Antibodies often undergo substantial engineering en route to the generation of a therapeutic candidate with good developability properties. Characterization of antibody libraries has shown that retaining native-like sequence improves the overall quality of the library. Motivated by recent advances in deep learning, we developed a bi-directional long short-term memory (LSTM) network model to make use of the large amount of available antibody sequence information, and use this model to quantify the nativeness of antibody sequences. The model scores sequences for their similarity to naturally occurring antibodies, which can be used as a consideration during design and engineering of libraries. We demonstrate the performance of this approach by training a model on human antibody sequences and show that our method outperforms other approaches at distinguishing human antibodies from those of other species. We show the applicability of this method for the evaluation of synthesized antibody libraries and humanization of mouse antibodies.
在产生具有良好可发展性的候选治疗药物的过程中,抗体经常经历大量的工程设计。抗体文库的表征表明,保留天然样序列提高了文库的整体质量。受深度学习最新进展的启发,我们开发了一个双向长短期记忆(LSTM)网络模型来利用大量可用的抗体序列信息,并使用该模型来量化抗体序列的本地性。该模型对序列与天然抗体的相似性进行评分,这可以作为文库设计和工程的考虑因素。我们通过训练人类抗体序列的模型来证明这种方法的性能,并表明我们的方法在区分人类抗体和其他物种的抗体方面优于其他方法。我们证明了该方法在评价合成抗体库和小鼠抗体人源化方面的适用性。
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引用次数: 15
Information theoretic measures for quantifying sequence–ensemble relationships of intrinsically disordered proteins 定量内在无序蛋白质序列集合关系的信息论方法
Pub Date : 2019-04-01 DOI: 10.1093/protein/gzz014
Megan C. Cohan, Kiersten M. Ruff, R. Pappu
Abstract Intrinsically disordered proteins (IDPs) contribute to a multitude of functions. De novo design of IDPs should open the door to modulating functions and phenotypes controlled by these systems. Recent design efforts have focused on compositional biases and specific sequence patterns as the design features. Analysis of the impact of these designs on sequence-function relationships indicates that individual sequence/compositional parameters are insufficient for describing sequence-function relationships in IDPs. To remedy this problem, we have developed information theoretic measures for sequence–ensemble relationships (SERs) of IDPs. These measures rely on prior availability of statistically robust conformational ensembles derived from all atom simulations. We show that the measures we have developed are useful for comparing sequence-ensemble relationships even when sequence is poorly conserved. Based on our results, we propose that de novo designs of IDPs, guided by knowledge of their SERs, should provide improved insights into their sequence–ensemble–function relationships.
内在无序蛋白(IDPs)具有多种功能。IDPs的从头设计应该打开由这些系统控制的调节功能和表型的大门。最近的设计工作集中在组合偏差和特定序列模式作为设计特征。分析这些设计对序列-函数关系的影响表明,单个序列/组成参数不足以描述国内流离失所者的序列-函数关系。为了解决这一问题,我们开发了IDPs序列集成关系(SERs)的信息理论度量。这些措施依赖于从所有原子模拟中获得的统计上可靠的构象集合的先验可用性。我们表明,我们已经开发的措施是有用的比较序列-集成关系,甚至当序列保守性差。基于我们的研究结果,我们建议在SERs知识的指导下重新设计IDPs,应该提供对其序列-集成-函数关系的更好见解。
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引用次数: 18
期刊
Protein Engineering, Design and Selection
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