蛋白质折叠与力学传感的单分子磁镊操纵研究进展

蛋白质的折叠与相互作用是物理、生物等多学科交叉领域关注的基本问题.力生物学的前沿研究表明，一系列对力敏感的蛋白质在机械力作用下的去折叠与复折叠动力学及相互作用调控，是实现其力感知的物理机制.前沿单分子操纵技术的发展使得在单分子水平定量探究蛋白质的折叠与细胞力学传感的分子机制成为可能.本文重点介绍近年来蛋白质折叠与力学传感的单分子磁镊操纵研究进展，包括单结构域蛋白质的折叠-去折叠动力学、自由能曲面的构造，及细胞黏着斑与胞间连接的力敏感蛋白行使生物功能的分子机制.     

小分子物质与蛋白质相互作用研究方法的现状与进展

小分子与蛋白质相互作用的研究是化学生物学的重要研究内容.蛋白质能与许多内源性和外源性小分子物质结合形成超分子复合物.通过实验方法可以获得蛋白质与小分子配体作用的结合常数、结合位点数、结合位置、作用力类型以及在客体小分子作用下蛋白质结构与功能的变化等信息.简述紫外可见光谱、荧光光谱法、傅立叶变换红外光谱和分子模拟法及其他分析方法在小分子与蛋白质相互作用方面的应用和研究进展.     

BIA技术及其在蛋白质科学研究中的应用

生物大分子相互作用分析(BIA)技术可以实时观察分子问相互作用．本文简要阐述了该技术的基本原理及系统组成，以及在蛋白质科学研究中的应用．包括配体垂钓，蛋白质相互作用，蛋白质功能鉴定等．     

核磁共振波谱研究蛋白质三维结构及功能

文中总结了中国科学技术大学生命科学学院核磁共振波谱实验室十多年来的工作.我们的研究主要集中于研究人和其他真核生物基因表达调控相关蛋白质以及细胞连接处相关蛋白质.在这两个体系中许多蛋白质与人类健康及疾病相关,有的可能是潜在的药物作用靶标.我们主要关注用核磁共振波谱方法研究蛋白质-蛋白质相互作用的结构基础.核磁共振适合研究在接近生理条件下的分子相互作用,特别是适合研究低亲和力的瞬态的复合物.它可以提供蛋白质相互作用界面,复合物结构,以及蛋白质相互识别过程动力学的信息.文中给出了一些例子.我们也研究蛋白质内部动力学,包括皮秒-纳秒时间尺度,与毫秒-微秒时间尺度的动力学.与圆二色谱及荧光光谱结合,核磁共振可以详细表征蛋白质的折叠与去折叠.文中给出的核磁共振应用的最后一个例子是用计算机虚拟筛选,核磁筛选,我们发现了一个人的双功能的磷酸酶的一种新类型的抑制剂,并研究了该抑制剂对细胞功能的影响.这一策略有可能用于早期药物的发现.     

蛋白质与蛋白质相互作用预测模型综述

蛋白质作为生命活动的物质基础,通过彼此之间的相互作用来参与生物信号传递、能量和物质代谢及细胞周期调控等,因此,预测蛋白质与蛋白质相互作用(PPIs)有助于从系统角度理解生命过程,同时为细胞机制的研究奠定重要基础。目前,高通量测序技术的进步使研究人员获取了海量的蛋白质相互作用数据。面对大量具有潜在可用性的PPIs数据,国内外研究人员提出了多种PPIs预测模型。文章综述了现有的基于计算的PPIs预测方法,并进行分类,讨论了其各自优缺点。最后对PPIs预测方法进行总结和展望,提出了未来可以深入研究的方向。     

我国科学家研制出蛋白质芯片生物传感器系统

在中国科学院知识创新工程和国家自然科学基金的资助下,中科院力学所国家微重力实验室靳刚课题组经过多年努力,成功研制出“蛋白质芯片生物传感器系统”及其实用化样机,并于7月5日通过成果鉴定。该研究将多种蛋白质活性微列阵、生物分子特异结合性,与高分辨率椭偏光学成像技术相结合,提供了一种新型无标记蛋白质分析技术。蛋白质芯片生物传感器系统的特点在于,它使用单一非标记试剂检测靶分子,能够更好地保持生物分子的活性,减少非特异信号的影响,提高蛋白质分子相互作用检测的灵敏度,实时、直观地显示检测结果,并具有鉴别伪信号的功能。它…     

一种融合多组学数据的关键蛋白质预测算法

关键蛋白质在维持生物体的生理活动中发挥着重要的作用，预测关键蛋白质有助于设计药物分子靶标.随着高通量技术的发展，基于蛋白质相互作用关系数据采用计算方法识别关键蛋白质成为当前的热门研究.研究表明，将蛋白质相互作用网络与其他生物学信息结合起来能够更有效地识别关键蛋白质.因此，本研究提出一种整合蛋白质相互作用数据、基因本体注释信息、蛋白质亚细胞定位信息及蛋白质结构域信息的识别关键蛋白质的新方法TGSD.为了评估新算法的有效性，选取4组常用的酵母测试数据集进行仿真实验，详细比较TGSD方法与其他7种经典方法的识别效果.数值结果显示，TGSD在预测正确关键蛋白质数目和准确率等统计指标上明显优于其他算法.     

一种改良的蛋白质复合物识别方法

高通量的方法产生了大量蛋白质相互作用数据.然而研究表明,对于已过滤的双杂交酵母数据集,假阳性相互作用的比例达到50%左右.这些假阳性这对于进一步的PPI网络研究带来了负面影响.因此,消除假阳性或者降低假阳性带来的负面影响变得非常重要.根据蛋白质相互作用网络的拓扑特性,每一组相互作用被赋予一个存在概率,概率值表示相互作用的可靠程度,概率值为0的相互作用被标识为假阳性予以消除.提出了一种改良的蛋白质复合物识别方法,该方法基于核-附件的思想,识别的复合物具有更强的生物统计特性.改良的新方法及其他经典的识别方法在酵母数据集上得到运行,实验结果表明,改良的方法性能优于现有的经典方法.     

融合互作网络和多模态信息的化合物-蛋白质相互作用预测模型(INMI)

化合物-蛋白质相互作用(Compound-protein interaction, CPI)预测是药物研发领域的一个重大课题.随着生物科学的飞速发展，各种科学实验产生了大量的生物数据，通过计算方法能够快速有效地提取和利用这些信息.已有方法未能将相互作用网络中的信息显式地进行提取并加以利用，且多模态信息的融合方式未能抓住蛋白质和化合物之间的联系.为了解决上述问题，本文提出了一个二分类深度学习模型.该模型使用交叉注意力模块整合分子图和蛋白质序列信息，并从相互作用网络中显式提取节点的中心性和相关性信息，作为模型编码.实验表明，本文所提出的模型可以准确预测蛋白质和化合物之间的相互作用，而且节点中心性编码能够大大提高模型性能.     

蛋白质相互作用的研究方法及进展

介绍了蛋白质与蛋白质相互作用的研究方法及进展,包括已经应用的标准技术、物理学方法、最新进展及其他方法.利用蛋白质间相互作用为工具,通过合成可调控转录系统来调整生物系统,可实现对基因功能的微调.     

蛋白质相互作用的研究方法及进展

介绍了蛋白质与蛋白质相互作用的研究方法及进展,包括已经应用的标准技术、物理学方法、最新进展及其他方法。利用蛋白质间相互作用为工具,通过合成可调控转录系统来调整生物系统,可实现对基因功能的微调。     

Reaching for high-hanging fruit in drug discovery at protein-protein interfaces

Targeting the interfaces between proteins has huge therapeutic potential, but discovering small-molecule drugs that disrupt protein-protein interactions is an enormous challenge. Several recent success stories, however, indicate that protein-protein interfaces might be more tractable than has been thought. These studies discovered small molecules that bind with drug-like potencies to 'hotspots' on the contact surfaces involved in protein-protein interactions. Remarkably, these small molecules bind deeper within the contact surface of the target protein, and bind with much higher efficiencies, than do the contact atoms of the natural protein partner. Some of these small molecules are now making their way through clinical trials, so this high-hanging fruit might not be far out of reach.     

Prediction of the structure of a receptor-protein complex using a binary docking method.

To validate procedures of rational drug design, it is important to develop computational methods that predict binding sites between a protein and a ligand molecule. Many small molecules have been tested using such programs, but examination of protein-protein and peptide-protein interactions has been sparse. We were able to test such applications once the structures of both the maltose-binding protein (MBP) and the ligand-binding domain of the aspartate receptor, which binds MBP, became available. Here we predict the binding site of MBP to its receptor using a 'binary docking' technique in which two MBP octapeptide sequences containing mutations that eliminate maltose chemotaxis are independently docked to the receptor. The peptides in the docked solutions superimpose on their original positions in the structure of MBP and allow the formation of an MBP-receptor complex. The consistency of the computational and biological results supports this approach for predicting protein-protein and peptide-protein interactions.     

The protein-protein interaction map of Helicobacter pylori

With the availability of complete DNA sequences for many prokaryotic and eukaryotic genomes, and soon for the human genome itself, it is important to develop reliable proteome-wide approaches for a better understanding of protein function. As elementary constituents of cellular protein complexes and pathways, protein-protein interactions are key determinants of protein function. Here we have built a large-scale protein-protein interaction map of the human gastric pathogen Helicobacter pylori. We have used a high-throughput strategy of the yeast two-hybrid assay to screen 261 H. pylori proteins against a highly complex library of genome-encoded polypeptides. Over 1,200 interactions were identified between H. pylori proteins, connecting 46.6% of the proteome. The determination of a reliability score for every single protein-protein interaction and the identification of the actual interacting domains permitted the assignment of unannotated proteins to biological pathways.     

Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

Identification of protein-protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein-protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein-protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.     

Prediction of Protein-Protein Interactions by a Novel Model Based on Domain Information

Domain-based protein-protein interactions( PPIs) is a problem that has drawn the attentions of many researchers in recent years and it has been studied using lots of computational approaches from many different perspectives. Existing domain-based methods to predict PPIs typically infer domain interactions from known interacting sets of proteins. However,these methods are costly and complex to implement. In this paper, a simple and effective prediction model is proposed. In this model,an improved multiinstance learning( MIL) algorithm( MilCaA) is designed that doesn't need to take the domain interactions into consideration to construct MIL bags. Then, the pseudo-amino acid composition( PseAAC) transformation method is used to encode the instances in a multi-instance bag and the principal components analysis( PCA) is also used to reduce the feature dimension. Finally, several traditional machine learning and MIL methods are used to verify the proposed model. Experimental results demonstrate that MilCaA performs better than state-of-the-art techniques including the traditional machine learning methods which are widely used in PPIs prediction.     

酵母双杂交系统的研究进展

酵母双杂交系统自创建以来,已成为研究蛋白质相互作用的重要手段,揭示了大量未知蛋白质之间的相互作用。随着该系统的广泛应用,近年来又发展了三杂交系统、单杂交系统、逆向双杂交系统、SOS富集系统等。酵母双杂交及其衍生系统已经成功地运用于蛋白质之间,蛋白质与DNA、RNA、配体之间相互作用的研究。     

An improved method of potential of mean force for protein-protein interactions

In this work, the traditional method of potential of mean force （PMF） is improved for describing the protein-protein interactions. This method is developed at atomic level and is distance-dependent. Compared with the traditional method, our model can reasonably consider the effects of the environmental factors. With this modification, we can obtain more reasonable and accurate pair potentials, which are the pre-requisite for precisely describing the protein-protein interactions and can help us to recognize the interaction rules of residues in protein systems. Our method can also be applied to other fields of protein science, e.g., protein fold recognition, structure prediction and prediction of thermostability.