Protein Structural Ensembles Visualized by Solvent Paramagnetic Relaxation Enhancement

A protein can be in different conformations when fulfilling its function. Yet depiction of protein structural ensembles remains difficult. Here we show that the accurate measurement of solvent paramagnetic relaxation enhancement (sPRE) in the presence of an inert paramagnetic cosolute allows the assessment of protein dynamics. Demonstrated with two multi‐domain proteins, we present a method to characterize protein microsecond–millisecond dynamics based on the analysis of the sPRE. Provided with the known structures of a protein, our method uncovers an ensemble of structures that fully accounts for the observed sPRE. In conjunction with molecular dynamics simulations, our method can identify protein alternative conformation that has only been theorized before. Together, our method expands the application of sPRE beyond structural characterization of rigid proteins and complements the established PRE NMR technique.     

Modifying the Thioester Linkage Affects the Structure of the Acyl Carrier Protein

At the center of many complex biosynthetic pathways, the acyl carrier protein (ACP) shuttles substrates to appropriate enzymatic partners to produce fatty acids and polyketides. Carrier proteins covalently tether their cargo via a thioester linkage to a phosphopantetheine cofactor. Due to the labile nature of this linkage, chemoenzymatic methods have been developed that involve replacement of the thioester with a more stable amide or ester bond. We explored the importance of the thioester bond to the structure of the carrier protein by using solution NMR spectroscopy and molecular dynamics simulations. Remarkably, the replacement of sulfur with other heteroatoms results in significant structural changes, thus suggesting more rigorous selections of isosteric substitutes is needed.     

低同源性蛋白质结构预测

基因组计划在实施产生了大量的DNA序列信息,如何有效地利用这些信息来研究基因的产物－蛋白质的结构与功能成为引入注目的研究领域,同源蛋白质结构预测及蛋白质折工识别是在基因组水平上进行蛋白质结构预测的有效方法,酵母基因组中约有50％的基因可以通过这类方法来确定其表面产物蛋白质的结构[1],但是目前所采用的方法在低同源性蛋白质的结构预测方面尚存在较大困难。     

Real‐Time Analysis of Folding upon Binding of a Disordered Protein by Using Dissolution DNP NMR Spectroscopy

The kinase inhibitory domain of the cell cycle regulatory protein p27^Kip1 (p27) was nuclear spin hyperpolarized using dissolution dynamic nuclear polarization (D‐DNP). While intrinsically disordered in isolation, p27 adopts secondary structural motifs, including an α‐helical structure, upon binding to cyclin‐dependent kinase 2 (Cdk2)/cyclin A. The sensitivity gains obtained with hyperpolarization enable the real‐time observation of ¹³C NMR signals during p27 folding upon binding to Cdk2/cyclin A on a time scale of several seconds. Time‐dependent intensity changes are dependent on the extent of folding and binding, as manifested in differential spin relaxation. The analysis of signal decay rates suggests the existence of a partially folded p27 intermediate during the timescale of the D‐DNP NMR experiment.     

蛋白质表面模块划分及其在结合位点预测中的应用

蛋白质-蛋白质复合物的结合位点预测是计算分子生物学的一个难题. 本文对蛋白质-蛋白质复合物数据集Benchmark 3.0 中的双链蛋白质复合物进行了研究, 计算了单体的残基溶剂可接近表面积和残基间的接触面积, 并据此提出了蛋白质表面模块划分方法. 发现模块的溶剂可接近表面积与其内部接触面积的乘积(PSAIA)值能够提供结合位点的信息. 在78 个双链蛋白质复合物中, 有74 个体系其受体或配体上具有最大或次大PSAIA值的模块是界面模块. 将该方法获得的结合位点信息应用在CAPRI竞赛Target 39 的复合物结构预测中取得了较好的结果. 本文提出的基于模块的蛋白质结合位点预测方法不同于以残基为基础且仅考虑表面残基的传统预测方法, 为蛋白质-蛋白质复合物结合位点预测提供了新思路.     

应用连续小波变换预测蛋白质的二级结构

将代码为lgca蛋白质的氨基酸序列映射为疏水值序列，在合适的尺度下，通过 连续小波变换法分别对其α螺旋,α螺旋和β折叠之间的连接多肽(即部分规则和无 规则二级结构)进行预测，准确率分别为76．5％和85．7％．从PDBsum数据库中随 机抽取100个蛋白质作为测试对象，其中全α螺旋、全β折叠、α／β以及α+β蛋 白质各25个．在100个蛋白质中共有1618个连接多肽和747个α螺旋.本法预测到的 连接多肽共有1536个，其中1308个与实际结构一致，平均预测准确率为85.2％；预 测到的α螺旋有770个，其中581个与实际结构一致，平均预测准确率为75．5％． 结果表明：该法可较好地预测蛋白质的α螺旋、连接多肽，具有极大的发展前景．     

Protein Secondary Structure Prediction with Partially Recurrent Neural Networks

Abstract

Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins. The state of some activations in the network is available after a pattern presentation via feedback connections as additional input during the processing of the next pattern in a sequence. A reference data set containing 91 proteins in the training set and 15 non-homologous proteins in the test set is used for training and testing a network with a modified, hierarchical Elman architecture. The network predicts the secondary structures α-helix, β-sheet, and “coil” for each amino acid. The percentage of correctly classified amino acids is 67.83% on the training set and 63.98% on the test set. The best performance of a three-layer feedforward network is 62.7% on the same test set. A cascaded network, where the outputs of the recurrent network are processed by a second net with 13 × 3 inputs, four hidden and three output units has a predictive performance of 64.49%. The best corresponding feedforward net has a performance of 64.3%.     

基于疏水性小波分析的膜蛋白结构预测

膜蛋白在细胞膜上具有重要的生理功能,大部分膜蛋白在药物设计、转运蛋白和免疫识别等方面起着关键的作用,从分子水平上预测这类蛋白质的结构具有非常重要的意义。本文提出一种基于氨基酸疏水性小波变换技术预测膜蛋白跨膜区段数目和位置的新方法。以代码为upkb_bovin的膜蛋白为例,对跨膜螺旋区数目和位置的预测分析进行了描述。从膜蛋白数据库中随机抽取36个蛋白质(含跨膜螺旋区232)作为测试集检验小波分析的预测方法,其中226个跨膜螺旋区能被准确预测,准确率为96．8％。结果表明,这种预测方法具有较高的准确性。     

新型室温熔盐二(三氟甲基磺酸酰)亚胺锂-乙酰胺体系的谱学研究

从分析二(三氟甲基磺酸酰)亚胺锂(LiTFSI)与乙酰胺形成熔盐的作用机制出发,通过红外和拉曼光谱的谱学分析并应用非局部密度泛函方法进行量化计算来对二者的相互作用进行了讨论.发现乙酰胺通过Li—O键与LiTFSI中Li+配位而破坏了LiTFSI的离子键,形成很大的配位阳离子,且正电荷被屏蔽在乙酰胺分子中;而TFSI-离子中电荷的部分离域导致电荷被终端—CF3基团屏蔽在整个分子中,这样两个大的阴阳离子间的库伦作用很弱;同时Li—O配位也导致乙酰胺分子间的氢键断裂,因而室温下体系以液体状态稳定存在.     

Revealing Higher Order Protein Structure Using Mass Spectrometry

The development of rapid, sensitive, and accurate mass spectrometric methods for measuring peptides, proteins, and even intact protein assemblies has made mass spectrometry (MS) an extraordinarily enabling tool for structural biology. Here, we provide a personal perspective of the increasingly useful role that mass spectrometric techniques are exerting during the elucidation of higher order protein structures. Areas covered in this brief perspective include MS as an enabling tool for the high resolution structural biologist, for compositional analysis of endogenous protein complexes, for stoichiometry determination, as well as for integrated approaches for the structural elucidation of protein complexes. We conclude with a vision for the future role of MS-based techniques in the development of a multi-scale molecular microscope.

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Metal‐Chelated Polymer Nanodiscs for NMR Studies

Paramagnetic relaxation enhancement (PRE) is commonly used to speed up spin lattice relaxation time (T₁) for rapid data acquisition in NMR structural studies. Consequently, there is significant interest in novel paramagnetic labels for enhanced NMR studies on biomolecules. Herein, we report the synthesis and characterization of a modified poly(styrene‐co‐maleic acid) polymer which forms nanodiscs while showing the ability to chelate metal ions. Cu²⁺‐chelated nanodiscs are demonstrated to reduce the T₁ of protons for both polymer and lipid‐nanodisc components. The chelated nanodiscs also decrease the proton T₁ values for a water‐soluble DNA G‐quadruplex. These results suggest that polymer nanodiscs functionalized with paramagnetic tags can be used to speed‐up data acquisition from lipid bilayer samples and also to provide structural information from water‐soluble biomolecules.     

Assessment of AI-Based Protein Structure Prediction for the NLRP3 Target

The recent successes of AlphaFold and RoseTTAFold have demonstrated the value of AI methods in highly accurate protein structure prediction. Despite these advances, the role of these methods in the context of small-molecule drug discovery still needs to be thoroughly explored. In this study, we evaluated whether the AI-based models can reliably reproduce the three-dimensional structures of protein–ligand complexes. The structure we chose was NLRP3, a challenging protein target in terms of obtaining a three-dimensional model both experimentally and computationally. The conformation of the binding pockets generated by the AI models was carefully characterized and compared with experimental structures. Further molecular docking results indicated that AI-predicted protein structures combined with molecular dynamics simulations offers a promising approach in small-molecule drug discovery.     

SPOT-Fold: Fragment-Free Protein Structure Prediction Guided by Predicted Backbone Structure and Contact Map

Protein structure determination has long been one of the most challenging problems in molecular biology for the past 60 years. Here we present an ab initio protein tertiary-structure prediction method assisted by predicted contact maps from SPOT-Contact and predicted dihedral angles from SPIDER 3. These predicted properties were then fed to the crystallography and NMR system (CNS) for restrained structure modeling. The resulted structures are first evaluated by the potential energy calculated by CNS, followed by dDFIRE energy function for model selections. The method called SPOT-Fold has been tested on 241 CASP targets between 67 and 670 amino acid residues, 60 randomly selected globular proteins under 100 amino acids. The method has a comparable accuracy to other contact-map-based modeling techniques. © 2019 Wiley Periodicals, Inc.