Structure Prediction of Partial-Length Protein Sequences

Protein structure information is essential to understand protein function. Computational methods to accurately predict protein structure from the sequence have primarily been evaluated on protein sequences representing full-length native proteins. Here, we demonstrate that top-performing structure prediction methods can accurately predict the partial structures of proteins encoded by sequences that contain approximately 50% or more of the full-length protein sequence. We hypothesize that structure prediction may be useful for predicting functions of proteins whose corresponding genes are mapped expressed sequence tags (ESTs) that encode partial-length amino acid sequences. Additionally, we identify a confidence score representing the quality of a predicted structure as a useful means of predicting the likelihood that an arbitrary polypeptide sequence represents a portion of a foldable protein sequence (“foldability”). This work has ramifications for the prediction of protein structure with limited or noisy sequence information, as well as genome annotation.     

豆壳过氧化物酶的序列联配、二级结构预测及疏水性分析

利用生物大分子数据库和软件技术对豆壳过氧化物酶 (Soybeanhullperoxidase ,SHP ,EC 1 .1 1 .1 .7)进行了序列联配、二级结构预测及疏水性分析 ,通过与其它来源过氧化物酶的比较研究 ,分析了结构保守性与功能域的关系 ,从分子水平探讨了过氧化物酶活性位点结构特征。     

A Statistical Analysis of the Sequence and Structure of Thermophilic and Non-Thermophilic Proteins

Thermophilic proteins have various practical applications in theoretical research and in industry. In recent years, the demand for thermophilic proteins on an industrial scale has been increasing; therefore, the engineering of thermophilic proteins has become a hot direction in the field of protein engineering. However, the exact mechanism of thermostability of proteins is not yet known, for engineering thermophilic proteins knowing the basis of thermostability is necessary. In order to understand the basis of the thermostability in proteins, we have made a statistical analysis of the sequences, secondary structures, hydrogen bonds, salt bridges, DHA (Donor–Hydrogen–Accepter) angles, and bond lengths of ten pairs of thermophilic proteins and their non-thermophilic orthologous. Our findings suggest that polar amino acids contribute to thermostability in proteins by forming hydrogen bonds and salt bridges which provide resistance against protein denaturation. Short bond length and a wider DHA angle provide greater bond stability in thermophilic proteins. Moreover, the increased frequency of aromatic amino acids in thermophilic proteins contributes to thermal stability by forming more aromatic interactions. Additionally, the coil, helix, and loop in the secondary structure also contribute to thermostability.     

Correlation between Protein Sequence Similarity and Crystallization Reagents in the Biological Macromolecule Crystallization Database

The protein structural entries grew far slower than the sequence entries. This is partly due to the bottleneck in obtaining diffraction quality protein crystals for structural determination using X-ray crystallography. The first step to achieve protein crystallization is to find out suitable chemical reagents. However, it is not an easy task. Exhausting trial and error tests of numerous combinations of different reagents mixed with the protein solution are usually necessary to screen out the pursuing crystallization conditions. Therefore, any attempts to help find suitable reagents for protein crystallization are helpful. In this paper, an analysis of the relationship between the protein sequence similarity and the crystallization reagents according to the information from the existing databases is presented. We extracted information of reagents and sequences from the Biological Macromolecule Crystallization Database (BMCD) and the Protein Data Bank (PDB) database, classified the proteins into different clusters according to the sequence similarity, and statistically analyzed the relationship between the sequence similarity and the crystallization reagents. The results showed that there is a pronounced positive correlation between them. Therefore, according to the correlation, prediction of feasible chemical reagents that are suitable to be used in crystallization screens for a specific protein is possible.     

蛋白质折叠及二级结构预测

蛋白质二级结构预测是研究蛋白质折叠的主要内容之一,也是获得新氨基酸序列结构信息的一般方法。从热力学和动力学两方面对蛋白质折叠机理进行分析,对蛋白质二级结构预测的常用方法进行分析和评价,并提出蛋白质空间结构预测的途径。     

Mass Spectrometry Coupled Experiments and Protein Structure Modeling Methods

With the accumulation of next generation sequencing data, there is increasing interest in the study of intra-species difference in molecular biology, especially in relation to disease analysis. Furthermore, the dynamics of the protein is being identified as a critical factor in its function. Although accuracy of protein structure prediction methods is high, provided there are structural templates, most methods are still insensitive to amino-acid differences at critical points that may change the overall structure. Also, predicted structures are inherently static and do not provide information about structural change over time. It is challenging to address the sensitivity and the dynamics by computational structure predictions alone. However, with the fast development of diverse mass spectrometry coupled experiments, low-resolution but fast and sensitive structural information can be obtained. This information can then be integrated into the structure prediction process to further improve the sensitivity and address the dynamics of the protein structures. For this purpose, this article focuses on reviewing two aspects: the types of mass spectrometry coupled experiments and structural data that are obtainable through those experiments; and the structure prediction methods that can utilize these data as constraints. Also, short review of current efforts in integrating experimental data in the structural modeling is provided.     

分级结构纳米材料研究进展

将纳米级材料通过模板法或自组装方法进行组装可以获得有序复杂的分级结构微纳米材料,它能展现出优于单一纳米材料的特殊性能,因此是科学界研究的热点。文章主要针对几种常见的复杂结构纳米材料的制备及形成机理进行了简要的探讨。     

含氢聚硅氧烷序列结构的控制研究

为方便研究含氢聚硅氧烷的硅氢加成反应及其构效关系,需知道含氢聚硅氧烷链节的微观排布状况,本研究通过对聚合物链节序列长度的计算公式进行修改和补充,定义分散度(η)的概念并分析调聚过程中聚合物链节的排布。使用调聚法合成低含氢聚硅氧烷,研究了催化剂用量、反应温度和和搅拌转速等条件对调聚过程中聚合物含量和分散度的影响。结果表明:调聚过程包含聚合平衡和-Si(CH_3)H-O-(D_H)链节分散平衡两个过程,聚合平衡远远快于D_H分散平衡。温度为65℃时,聚合反应在2~3 h内达到平衡,D_H分散平衡则需要8 h甚至更长。增加催化剂用量、升高温度、增大搅拌转速均有利于两个平衡过程的进行。催化剂用量为5%,搅拌转速为400 r/min,温度65℃时,分散度最高为0.805。     

Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction

Protein secondary structures are important in many biological processes and applications. Due to advances in sequencing methods, there are many proteins sequenced, but fewer proteins with secondary structures defined by laboratory methods. With the development of computer technology, computational methods have (started to) become the most important methodologies for predicting secondary structures. We evaluated two different approaches to this problem—driven by the recent results obtained by computational methods in this task—(i) template-free classifiers, based on machine learning techniques; and (ii) template-based classifiers, based on searching tools. Both approaches are formed by different sub-classifiers—six for template-free and two for template-based, each with a specific view of the protein. Our results show that these ensembles improve the results of each approach individually.     

A Perspective on the (Rise and Fall of) Protein β-Turns

The β-turn is the third defined secondary structure after the α-helix and the β-sheet. The β-turns were described more than 50 years ago and account for more than 20% of protein residues. Nonetheless, they are often overlooked or even misunderstood. This poor knowledge of these local protein conformations is due to various factors, causes that I discuss here. For example, confusion still exists about the assignment of these local protein structures, their overlaps with other structures, the potential absence of a stabilizing hydrogen bond, the numerous types of β-turns and the software’s difficulty in assigning or visualizing them. I also propose some ideas to potentially/partially remedy this and present why β-turns can still be helpful, even in the AlphaFold 2 era.     

低熔点共聚酯序列结构分析

采用PTA路线合成一系列低熔点共聚酯,在不改变其它条件的基础下,通过改变单体的配比,将所获得的聚合物的熔点控制在110℃左右。利用核磁共振对聚合物的序列结构进行表征,发现所制得的聚合物的无规度均接近1,表明所得共聚酯均接近无规共聚物。     

牛奶蛋白纤维的结构性能及其应用

论述了牛奶蛋白纤维的发展历史。在形态结构中,通过扫描电子显微镜观察到纤维横截面呈扁平状,纵向有不规则的沟槽和海岛状的凹凸,其结晶结构存在明显的结晶和无定形的两相结构,并且有明显的结晶峰。分析了牛奶蛋白纤维的优良性能,探讨了其在纺织面料上的广泛应用。     

Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins’ 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug–target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.     

PITHIA: Protein Interaction Site Prediction Using Multiple Sequence Alignments and Attention

Cellular functions are governed by proteins, and, while some proteins work independently, most work by interacting with other proteins. As a result it is crucially important to know the interaction sites that facilitate the interactions between the proteins. Since the experimental methods are costly and time consuming, it is essential to develop effective computational methods. We present PITHIA, a sequence-based deep learning model for protein interaction site prediction that exploits the combination of multiple sequence alignments and learning attention. We demonstrate that our new model clearly outperforms the state-of-the-art models on a wide range of metrics. In order to provide meaningful comparison, we update existing test datasets with new information regarding interaction site, as well as introduce an additional new testing dataset which resolves the shortcomings of the existing ones.     

PET/PTT共聚酯的序列结构

采用TPA生产路线合成一系列PET/PTT共聚物,发现在不改变其他条件的情况下,改变单体配比,所获得的共聚物特性黏度值都在0.6dL/g以上。对共聚物进行核磁共振测试来表征其序列结构,发现1H-NMR和13C-NMR所测试的结论一致,所得共聚酯无规度值均接近1,表明所得共聚酯均接近无规共聚物。     

化学专业《生物化学》课程中蛋白质结构与功能教学探讨

在化学专业、制药专业的蛋白质结构和功能的教学中,选择最基础的、具有代表性的内容和具体事例,展现化学的理论和方法在蛋白质结构与功能关系研究中的重要性,彰显生命科学广阔的发展前景。采用教师主讲、教师指导学生主讲、讨论等方法,可以更好地帮助学生构建生物化学理论体系,提高思维能力和创新能力,为今后从事生命科学领域的研究、生产、销售打下良好的基础。     

cpxDeepMSA: A Deep Cascade Algorithm for Constructing Multiple Sequence Alignments of Protein–Protein Interactions

Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant progress in the protein monomer sequence analysis. However, no standard or efficient pipelines are available for the sensitive protein complex MSA (cpxMSA) collection. How to generate cpxMSA is one of the most challenging problems of sequence coevolution analysis. Although several methods have been developed to address this problem, no standalone program exists. Furthermore, the number of built-in properties is limited; hence, it is often difficult for users to analyze sequence coevolution according to their desired cpxMSA. In this article, we developed a novel cpxMSA approach (cpxDeepMSA. We used different protein monomer databases and incorporated the three strategies (genomic distance, phylogeny information, and STRING interaction network) used to join the monomer MSA results of protein complexes, which can prevent using a single method fail to the joint two-monomer MSA causing the cpxMSA construction failure. We anticipate that the cpxDeepMSA algorithm will become a useful high-throughput tool in protein complex structure predictions, inter-protein residue-residue contacts, and the biological sequence coevolution analysis.     

Crystal Structure of Okadaic Acid Binding Protein 2.1: A Sponge Protein Implicated in Cytotoxin Accumulation

Okadaic acid (OA) is a marine polyether cytotoxin that was first isolated from the marine sponge Halichondria okadai. OA is a potent inhibitor of protein serine/threonine phosphatases (PP) 1 and 2A, and the structural basis of phosphatase inhibition has been well investigated. However, the role and mechanism of OA retention in the marine sponge have remained elusive. We have solved the crystal structure of okadaic acid binding protein 2.1 (OABP2.1) isolated from H. okadai; it has strong affinity for OA and limited sequence homology to other proteins. The structure revealed that OABP2.1 consists of two α‐helical domains, with the OA molecule deeply buried inside the protein. In addition, the global fold of OABP2.1 was unexpectedly similar to that of aequorin, a jellyfish photoprotein. The presence of structural homologues suggested that, by using similar protein scaffolds, marine invertebrates have developed diverse survival systems adapted to their living environments.     

基于自由能的生物大分子结构研究

生物大分子结构研究是生物学的一个重要内容,基于自由能的生物大分子结构研究在理论和应用上都具有重要意义。综述了自由能这个经典判据在划分蛋白质结构域、预测RNA二级结构及蛋白质工程等中的应用。