质谱在肽和蛋白质序列分析中的应用

了解肽和蛋白质的序列对理解其功能具有重要意义，测定其序列也是当前生命 科学研究中的重要内容之一，质谱作为高灵敏度的测定分子结构的仪器，其高灵敏 度、广泛的适用性及快速性等特性使它具有很大潜力发展成为辅助传统测序方法的 新方法，并得到了广泛的关注。从离子活化方法（包括碰撞诱导解离CID、源后裂 解PSD、源内裂解ISD等）、衍生化作用以及氨基酸残基消除方式（高能活化产生亚 稳离子、化学降解、酶降解）等多个角度介绍了利用质谱分析多肽和蛋白质序列的 方法，并对其发展前景作出展望。     

血红素蛋白质微小仿生模型的设计

金属蛋白种类繁多，功能各异，血红素蛋白质是其中非常重要的一类。为了研究其结构。功能之间的关系，人们采用不同的方法设计了多种血红素蛋白质的微小仿生模型。本文介绍了螺旋束血红素蛋白质，螺旋—血红素—螺旋夹心蛋白质的设计和表征。     

高通量蛋白质结晶及其在药物设计中的应用

针对靶标蛋白质的小分子药物研究中,蛋白质的三维结构起到了非常重要的作用.最近高通量蛋白质结晶的快速发展为蛋白质结构的快速确定和药物先导的发现提供了许多新的机会.本文综述了高通量蛋白质结晶及其在新药设计中应用的最新进展。     

一种3D打印蛋白质模型的方法及其在教学中的应用

本文报道了一种3D打印蛋白质模型的方法,得到了一系列不同的蛋白质模型。这些模型可作为可视化教具,帮助学生理解蛋白质多级结构、蛋白质折叠、蛋白质-蛋白质相互作用等相关知识点,同时也可作为艺术展示教具应用于科学交流与科普宣传。     

基于深度学习的3D分子生成模型研究进展

分子生成作为药物设计领域的一个基本问题,旨在以低成本和高效率的方式设计出具有理想生物活性和药代动力学属性的新颖分子.近年来深度生成模型在药物设计中得到了广泛应用,大量的模型结构和优化策略得到探索,其中大多是生成一维或二维的分子结构.随着深度学习在处理几何图形数据上的快速发展,面向3D分子的生成模型被提出,因其在直接生成3D分子构象和基于结构的药物设计上的优势和潜力而越来越受到关注.本文对近年来国内外学者在3D分子生成上取得的成果进行了系统的总结和分析,从3D分子生成算法输入的角度将其分为基于隐变量的生成、基于2D分子图的生成和基于3D分子构象的生成;接着从3D分子生成算法输出的角度将其分类为定向生成和非定向生成;随后总结了相关生成模型在主要的公开数据集上的性能,以探究各种生成模型的优缺点;最后对未来可能的研究方向进行了展望.     

非线性和时变参数时间序列模型及其在水质分析中的应用

时间序列分析是根据观测值建立数学模型，研究数据的内在规律，现有文献主要是介绍线性时间序列模型。研究表明化学分析数据很多涉及非线性时间序列，且具有时变参数特性。本文在研究线性、非线性时间序列模型基础上，提出一种具有时变参数特性的非线性时间序列模型。该模型用于加酸调ｐＨ的循环冷却水系统进行预报，可使ｐＨ值极差降低３～６倍，对保证水质稳定具有重要意义。     

深度学习在蛋白质结构预测中的应用及启示

蛋白质结构预测通常指借助计算机计算模拟方法从氨基酸序列推断其三维空间结构.而空间结构决定其生理功能,故结构预测问题尤为重要.基于单纯物理学的预测仅能应对较短蛋白质且精度不高.而基于数据驱动和生物信息学的方法近十多年备受重视.本文主要回顾近十多年来深度学习在蛋白质预测领域的应用,重点介绍Deepmind团队的AlphaFold方法,此方法预测在单域蛋白质达到了中低分辨率实验精度,一定程度上解决了困扰人们五十多年的蛋白质结构预测难题.     

表面等离子体共振技术在蛋白质-蛋白质相互作用研究中的应用

表面等离子共振(SPR)近年来迅速发展为用于分析生物分子相互作用的一项技术.该技术无需标记、特异性强、灵敏度高、样品用量小,可实现在线连续实时检测.目前SPR已被广泛应用于免疫学、蛋白质组学、药物筛选、细胞信号转导、受体/配体垂钓等领域.该文阐述了基于表面等离子体共振技术生物传感器的基本原理和技术流程,综述了SPR在蛋白质-蛋白质相互作用动力学研究、蛋白质结构及功能研究、蛋白质突变和碎片分析、信号转导中的应用以及SPR在蛋白质-蛋白质相互作用研究中的多项关键技术.指出SPR通过与光谱、电化学等多技术联用后,可以获得更加详实的信息.     

亲和层析在蛋白质研究中的应用进展

文中介绍了生物、免疫、固定化金属离子拟生物几类常规亲和层析的原理及其在蛋白质分离纯化以及分析鉴定方面的应用（引用文献25篇）。     

纳米材料在蛋白质分离中的应用

蛋白质的分离技术不仅在药物检测和制药工程中具有重要意义,而且还是生化工程和蛋白质分析的一个研究热点.纳米材料具有许多与众不同的特性,广泛应用于化工、生物、医药、航天等多个领域,被认为是21世纪最有前途的材料.本文作者从非金属氧化物纳米材料、非金属单质纳米材料、金属氧化物纳米材料、金属单质纳米材料、纳米聚合物、纳米复合材料等方面综述了纳米材料在蛋白质分离方面的应用现状,总结了其在蛋白质分离中的优缺点,并就其在蛋白质固定和分离领域的应用前景进行了展望.     

Generative Adversarial Learning of Protein Tertiary Structures

Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions. Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures. The analysis presented here shows that several GAN models fail to capture complex, distal structural patterns present in protein tertiary structures. The study additionally reveals that mechanisms touted as effective in stabilizing the training of a GAN model are not all effective, and that performance based on loss alone may be orthogonal to performance based on the quality of generated datasets. A novel contribution in this study is the demonstration that Wasserstein GAN strikes a good balance and manages to capture both local and distal patterns, thus presenting a first step towards more powerful deep generative models for exploring a possibly very diverse set of structures supporting diverse activities of a protein molecule in the cell.     

基于模型与建模的化学学习过程设计

在阐释模型分类、模型功能和建模学习的意义的基础上,以“浓度对化学反应速率的影响”之学习过程设计为例,说明在化学教学中通过确立建模目标、模型参数选择、模型建构及表征、分析、评价、调用、重构等活动,可有效提升学生心智模型与表达模型的内在一致性和有效性,促进学生对化学知识的有效探究和深度理解。     

Approach for the Design of Covalent Protein Kinase Inhibitors via Focused Deep Generative Modeling

Deep machine learning is expanding the conceptual framework and capacity of computational compound design, enabling new applications through generative modeling. We have explored the systematic design of covalent protein kinase inhibitors by learning from kinome-relevant chemical space, followed by focusing on an exemplary kinase of interest. Covalent inhibitors experience a renaissance in drug discovery, especially for targeting protein kinases. However, computational design of this class of inhibitors has thus far only been little investigated. To this end, we have devised a computational approach combining fragment-based design and deep generative modeling augmented by three-dimensional pharmacophore screening. This approach is thought to be particularly relevant for medicinal chemistry applications because it combines knowledge-based elements with deep learning and is chemically intuitive. As an exemplary application, we report for Bruton’s tyrosine kinase (BTK), a major drug target for the treatment of inflammatory diseases and leukemia, the generation of novel candidate inhibitors with a specific chemically reactive group for covalent modification, requiring only little target-specific compound information to guide the design efforts. Newly generated compounds include known inhibitors and characteristic substructures and many novel candidates, thus lending credence to the computational approach, which is readily applicable to other targets.     

Evolutionary Approach in Copolymer Sequence Design

Summary : In this work, we discuss a simple evolutionary algorithm that introduces a “selection pressure” under which two-letter (AB) copolymer sequences can mutate and transform into the sequences tuned to microphase separation transition (MIST). In particular, we are interested in determining how a sequence of A and B units should be organized in order to reach maximum length scale for MIST at a given AB composition. It is found that such sequences are similar to those known for tapered or gradient copolymers exhibiting strong composition inhomogeneity along their chain. The problems of the evolution of copolymer sequences are considered from the viewpoint of emerging of information complexity in the sequences in the course of this evolution.     

Thermodynamic Insight into Protein Aggregation Using a Kinetic Ising Model

In this work, we present a kinetic analysis for protein aggregation using the kinetic Ising model, which serves as a new application of a previously proposed model [Liang et al., J. Chin. Chem. Soc.­ 2003 , 50, 335]. Considering protein as a single spin unit, we map two states of a unit to the aggregation‐prone (AP) and the fibril (F) states. This work shows that the model can successfully capture the nucleation‐growth features of protein aggregation from experiments, which offers thermodynamic interpretations of aggregation properties, such as lag‐time and fibril stability.     

Protein secondary structure prediction using neural networks and deep learning: A review

Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico. A large part of this collection is comprised of artificial neural network-based approaches, a field of artificial intelligence and machine learning that is gaining increasing popularity in various application areas. The primary objective of this paper is to put together the summary of works that are important but sparse in time, to help new researchers have a clear view of the domain in a single place. An informative introduction to protein secondary structure and artificial neural networks is also included for context. This review will be valuable in designing future methods to improve protein secondary structure prediction accuracy. The various neural network methods found in this problem domain employ varying architectures and feature spaces, and a handful stand out due to significant improvements in prediction. Neural networks with larger feature scope and higher architecture complexity have been found to produce better protein secondary structure prediction. The current prediction accuracy lies around the 84% marks, leaving much room for further improvement in the prediction of secondary structures in silico. It was found that the estimated limit of 88% prediction accuracy has not been reached yet, hence further research is a timely demand.     

磺基异硫氰酸苯酯化学辅助方法对新蛋白质进行从头测序

利用基质辅助激光解吸电离-串联飞行时间(MALDI-TOF-TOF)质谱结合磺基异硫氰酸苯酯化学辅助的方法对一种从拟青霉(Paecilomyces bainier)分离纯化到的新人参皂苷Rb1水解酶的部分肽段进行了从头测序. 共获得了这个新蛋白质8条肽段的序列, 一些磺化后信噪比非常低的肽段也获得了比较完整的序列. 同时通过从头测序分析确定了一对甲硫氨酸非氧化和氧化肽段的序列. 结果表明, 磺化后的肽段离子化效率大大增强, 在PSD(源后裂解)过程中只有肽键断裂产生的C端的碎片离子系列(y离子系列)出现在质谱图中, 图谱背景清晰, 信噪比高, 单纯的y离子系列使得图谱解析变得非常容易. 将这8条序列在NCBI(美国国立生物技术信息中心)数据库中进行BLAST(蛋白质序列比对工具)检索印证这种β-葡萄糖甘酶是一个新蛋白质, 发现的两条相对保守的序列为进一步研究奠定了基础.     

Machine Learning Models to Predict Protein–Protein Interaction Inhibitors

Protein–protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.     

Conformation‐Dependent Sequence Design of HP Copolymers: An Algorithm Based on Sequential Modifications of Monomer Units

We present a new modification of the so‐called conformation‐dependent sequence design scheme for HP copolymers which was proposed several years ago (H and P refer to the hydrophobic and polar monomer units, respectively). New method models the real chemical experiments more realistically. We performed Monte Carlo computer simulations using the bond‐fluctuation model for protein‐like copolymers obtained by means of the new “iterative” method and compared the results with those obtained for originally proposed “instantaneous coloring” procedure. Copolymers designed by the “iterative” method are shown to have better‐optimized functional properties. The investigation of the influence of sequence preparation conditions has revealed that the statistical properties of designed HP sequences depend rather strongly on the density of the parent homopolymer globule but not on the composition of H and P units.