蛋白质-配体柔性对接综述

蛋白质与配体的相互作用在药物发现与筛选、功能食品开发等方面,都具有重要的科学意义。研究蛋白质-配体对接的目的在于通过已知配体和目标蛋白质的三维结构,运用计算手段来预测和评估蛋白质-配体复合物的三维构象,从而更好地理解蛋白质-配体之间的相互作用。随着已解析蛋白质单体结构的数量和计算能力的不断增加,蛋白质-配体对接也越来越现实并可靠。作者对蛋白质-配体柔性对接过程中所涉及的蛋白质的柔性、配体的准备、构象空间的采样方法、打分函数及其后期处理等方面进行了综述。     

蛋白质折叠、动力学以及蛋白质-配体结合的物理化学基础

蛋白质是生物体的重要组成部分并参与细胞内几乎所有的生物学过程.随着越来越多物种基因组序列的测定,准确理解基因产物的功能并探索蛋白质功能多样性的原因,已经成为当前的研究热点.为了研究蛋白质的功能,已有大量蛋白质的静态三维结构被测定.但是,蛋白功能最终受其动力学行为所控制,这包括折叠过程、构象波动、分子运动以及蛋白质-配体相互作用等.基于自由能图谱理论,本文深入讨论了蛋白质动力学的底层物理化学机制,并回答了以下问题:蛋白质为什么能够折叠、以及如何折叠成其天然三维结构?为什么蛋白质的动力学特征是固有的?其动力学行为如何控制蛋白质的功能?讨论结果将有助于后基因组时代生命科学研究中蛋白质结构-功能关系的理解.     

基于功能域组分的蛋白质折叠类型识别

蛋白质空间结构研究是分子生物学、细胞生物学、生物化学以及药物设计等领域的重要课题.折叠类型反映了蛋白质核心结构的拓扑模式,对折叠类型的识别是蛋白质序列与结构关系研究的重要内容.选取LIFCA数据库中样本量较大的53种折叠类型,应用功能域组分方法进行折叠识别.将Astral 1.65中序列一致性小于95%的样本作为检验集,全库检验结果中平均敏感性为96.42%,特异性为99.91%,马修相关系数(MCC)为0.91,各项统计结果表明:功能域组分方法可以很好地应用在蛋白质折叠识别中,LIFCA相对简单的分类规则可以很好地集中蛋白质的大部分功能特性,反映了结构与功能的对应关系.     

蛋白质- 核酸对接方法研究进展

分子对接技术作为预测蛋白质-核酸复合物结构的有效方法,为研究在生物学过程中蛋白质-核酸的相互作用提供了重要的工具。本文首先分析了当前蛋白质-核酸对接研究中的主要困难,例如构象变化和核糖磷酸骨架的带电性问题。然后从构象搜索、打分函数、柔性策略三个方面比较和总结了蛋白质-核酸对接中主要的计算方法。最后回顾了蛋白质-核酸对接计算模型的应用,并对未来的工作进行了展望。     

无序蛋白质的判定及其结构、功能和进化特征

固有无序蛋白质(intrinsically disordered proteins,IDPs)是天然条件下自身不能折叠为明确唯一的空间结构,却具有生物学功能的一类新发现的蛋白质.这类蛋白质的发现是对传统的"结构-功能"关系认识模式的挑战.本文首先总结了无序蛋白质的实验鉴定手段、预测方法、数据库;并介绍了无序蛋白质结构(包括一级结构、二级结构、结构域无序性及变构效应)和功能特征;然后重点总结了无序蛋白质在进化角度研究的进展,包括无序区域产生的进化机制、进化速率,蛋白无序性的进化在蛋白质功能进化及生物学复杂性增加等方面的重要作用;最后展望了无序蛋白质在医药方面的应用前景.本文对于深入认识无序蛋白质的形成机制、结构和功能特征及其潜在的临床应用前景具有重要意义.     

蛋白质结构与功能中的结构域

结构域是蛋白质亚基结构中的紧密球状区域.结构域作为蛋白质结构中介于二级与三级结构之间的又一结构层次,在蛋白质中起着独立的结构单位、功能单位与折叠单位的作用.在复杂蛋白质中,结构域具有结构与功能组件与遗传单位的作用.结构域层次的研究将会促进蛋白质结构与功能关系、蛋白质折叠机制以及蛋白质设计的研究.     

DRSP:蛋白质单残基替换结构数据库（英文）

蛋白质残基替换是基因突变的产物之一,它可能改变蛋白质三维结构,对其生物学功能产生重大影响,因此研究蛋白质残基替换与结构改变的关系具有重要意义.随着实验解析蛋白质结构的数量迅猛增长,越来越多的野生型-突变体被应用于结构生物学的比较研究中.本研究从蛋白质三维结构数据库(PDB)出发,收集和计算了大量结构特征数据,构建了一个目前已知最大的野生型-突变体(单残基差异)的结构对数据库DRSP,展示出氨基酸类型和主链偏好性对结构保守性的相关性.DRSP的开放使用可为高精度的蛋白质结构分析预测提供有用信息,它的数据库网址是http://www.labshare.cn/drsp/index.php.     

DRSP：蛋白质单残基替换结构数据库

蛋白质残基替换是基因突变的产物之一,它可能改变蛋白质三维结构,对其生物学功能产生重大影响,因此研究蛋白质残基替换与结构改变的关系具有重要意义．随着实验解析蛋白质结构的数量迅猛增长,越来越多的野生型-突变体被应用于结构生物学的比较研究中．本研究从蛋白质三维结构数据库(PDB)出发,收集和计算了大量结构特征数据,构建了一个目前已知最大的野生型-突变体(单残基差异)的结构对数据库DRSP,展示出氨基酸类型和主链偏好性对结构保守性的相关性．DRSP的开放使用可为高精度的蛋白质结构分析预测提供有用信息,它的数据库网址是http://www.labshare.cn/drsp/index.php．     

燐光探针技术--蛋白质研究的新方法

为了研究蛋白质复杂的结构和特殊的生物学功能,人们采用了各种各样的技术:X-射线衍射技术、圆二色谱技术、超离心技术、各种层析、电泳技术及荧光、烧光光谱技术.这些技术相互补充,相互印证,是研究蛋白质结构、功能和动力学的得力工具.尤其是近年来,由于燐光探针技术在研究水溶液中蛋白质分子疏水微区内不同基团间距离、局部结构柔性、动力学性质及构象变化等独特的优势而引起人们的广泛关注.     

蛋白质-蛋白质分子对接方法中分子柔性处理与近天然结构筛选的研究

蛋白质-蛋白质分子对接方法是研究蛋白质分子间相互作用与识别的重要理论方法。该方法主要涉及复合物结合模式的构象搜索和近天然结构的筛选两个问题。在构象搜索中,分子柔性的处理是重点也是难点,围绕这一问题,近年来提出了许多新的方法。针对近天然结构的筛选问题,目前主要采用三种解决策略:结合位点信息的利用、相似结构的聚类和打分函数对结构的评价。本文围绕以上问题,就国内外研究进展和本研究小组的工作作详细的综述,并对进一步的研究方向进行了展望。     

Protein Interactions and Fluctuations in a Proteomic Network using an Elastic Network Model

Abstract

A set of protein conformations are analyzed by normal mode analysis. An elastic network model is used to obtain fluctuation and cooperativity of residues with low amplitude fluctuations across different species. Slow modes that are associated with the function of proteins have common features among different protein structures. We show that the degree of flexibility of the protein is important for proteins to interact with other proteins and as the species gets more complex its proteins become more flexible. In the complex organism, higher cooperativity arises due to protein structure and connectivity.     

类肌钙蛋白（Calponin）亚型和生物学功能

Calponin(类肌钙蛋白)是一个肌动蛋白细肌丝相关的调节蛋白,表达在平滑肌细胞和许多类型的非肌细胞中。哺乳动物有Calponin 1、Calponin 2和Calponin 3三个亚型,分别由三个同源基因CNN1、CNN2和CNN3编码,表达于不同的细胞类型,并执行细胞类型特异性的生理功能。除了调节平滑肌收缩功能,Calponin还调节非肌细胞肌动蛋白骨架的功能并参与多种细胞生命活动,如增殖、粘附、迁移、分化、吞噬和细胞融合等。本综述重点讨论Calponin亚型的基因进化、组织和细胞类型特异性表达、结构和功能的关系以及相关的调节机制。     

ProDCoNN: Protein design using a convolutional neural network

Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this problem as predicting the residue type given the 3D structural environment around the C _α atom of a residue, which is repeated for each residue of a protein. We designed a nine-layer 3D deep convolutional neural network (CNN) that takes as input a gridded box with the atomic coordinates and types around a residue. Several CNN layers were designed to capture structure information at different scales, such as bond lengths, bond angles, torsion angles, and secondary structures. Trained on a very large number of protein structures, the method, called ProDCoNN (protein design with CNN), achieved state-of-the-art performance when tested on large numbers of test proteins and benchmark datasets.     

4D prediction of protein <Superscript>1</Superscript>H chemical shifts

A 4D approach for protein ¹H chemical shift prediction was explored. The 4th dimension is the molecular flexibility, mapped using molecular dynamics simulations. The chemical shifts were predicted with a principal component model based on atom coordinates from a database of 40 protein structures. When compared to the corresponding non-dynamic (3D) model, the 4th dimension improved prediction by 6–7%. The prediction method achieved RMS errors of 0.29 and 0.50 ppm for Hα and HN shifts, respectively. However, for individual proteins the RMS errors were 0.17–0.34 and 0.34–0.65 ppm for the Hα and HN shifts, respectively. X-ray structures gave better predictions than the corresponding NMR structures, indicating that chemical shifts contain invaluable information about local structures. The ¹H chemical shift prediction tool 4DSPOT is available from .     

Post-Translational Modifications in sperm Proteome: The Chemistry of Proteome diversifications in the Pathophysiology of male factor infertility

BackgroundThe spermatozoa undergo a series of changes in the epididymis to mature after their release from the testis and subsequently in the female reproductive tract after ejaculation to get capacitated and achieve fertilization potential. Despite having a silenced protein synthesis machinery, the dynamic change in protein profile of the spermatozoa is attributed either to acquisition of new proteins via vescicular transport or to several post-translational modifications (PTMs) occurring on the already expressed protein complement.Scope of reviewIn this review emphasis is given on the PTMs already reported on the human sperm proteins under normal and pathologic conditions with particular reference to sperm function such as motility and fertilization. An attempt has been made to summarize different protocols and methods used for analysis of PTMs on sperm proteins and the newer trends those were emerging.Major conclusionsDeciphering the differential occurrence of PTM on protein at ultrastructural level would give us a better insight of structure-function relationship of the particular protein. Protein with multiple PTMs could be used to generate the complex interaction network involved in a physiological function of a sperm. It can be speculated that crosstalk between different PTMs occurring either on same/ other proteins actually regulate the protein stability and activity both in physiological and pathological states.General significanceThe analytical prospective of various PTMs reported in human spermatozoa and their relevance to sperm function particularly in various pathophysiological states, would pave way for development of biomarkers for diagnosis, prognosis and therapeutic intervention of male infertility.     

基于弹性网络模型的蛋白质变构路径与关键残基识别研究

目的 变构效应在蛋白质生物学功能执行过程中发挥着重要的调控作用，如何基于蛋白质空间结构，有效识别变构信号的传播路径和关键的残基位点是蛋白质结构-功能关系研究领域的热点科学问题。方法 本研究利用基于弹性网络模型（elastic network model，ENM）的力分布计算方法，通过分析蛋白质对外力的响应过程，来识别体系的变构路径以及变构过程中的关键残基。在该方法中，对蛋白质的关键变构位点施加外力，通过对体系形变以及内力分布情况的分析，有效识别与外力承载区域形变相耦合的关键残基，从而得到力信号在蛋白质结构内的传播路径。结果 利用该方法研究了人类磷酸甘油酸激酶（human phosphoglycerate kinase，hPGK）和蛋白质酪氨酸磷酸酶（protein tyrosine phosphatase，PTP）PDZ2结构域的变构调控路径和关键残基。对于hPGK，识别出从底物结合位点到铰链区的两条变构信号传导路径。对于PTP PDZ2，也成功识别出从配体结合位点传递到蛋白质远端的两条长程变构调控路径。计算结果与实验和分子动力学（molecular dynamics，MD）模拟得到的结果一致。结论 本研究为蛋白质体系关键残基识别及变构路径研究提供了有效的分析方法。     

FLEXc: protein flexibility prediction using context-based statistics,predicted structural features,and sequence information

Background

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.

Results

In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.

Conclusions

Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc.

     

Accuracy of protein flexibility predictions

Protein structural flexibility is important for catalysis, binding, and allostery. Flexibility has been predicted from amino acid sequence with a sliding window averaging technique and applied primarily to epitope search. New prediction parameters were derived from 92 refined protein structures in an unbiased selection of the Protein Data Bank by developing further the method of Karplus and Schulz (Naturwissenschaften 72:212–213, 1985). The accuracy of four flexibility prediction techniques was studied by comparing atomic temperature factors of known three-dimensional protein structures to predictions by using correlation coefficients. The size of the prediction window was optimized for each method. Predictions made with our new parameters, using an optimized window size of 9 residues in the prediction window, were giving the best results. The difference from another previously used technique was small, whereas two other methods were much poorer. Applicability of the predictions was also tested by searching for known epitopes from amino acid sequences. The best techniques predicted correctly 20 of 31 continuous epitopes in seven proteins. Flexibility parameters have previously been used for calculating protein average flexibility indices which are inversely correlated to protein stability. Indices with the new parameters showed better correlation to protein stability than those used previously; furthermore they had relationship even when the old parameters failed. © 1994 Wiley-Liss, Inc.     

Could CCI or FBCI Fully Eliminate the Impact of Curve Flexibility When Evaluating the Surgery Outcome for Thoracic Curve Idiopathic Scoliosis Patient? A Retrospective Study

PurposeTo clarify if CCI or FBCI could fully eliminate the influence of curve flexibility on the coronal correction rate.MethodsWe reviewed medical record of all thoracic curve AIS cases undergoing posterior spinal fusion with all pedicle screw systems from June 2011 to July 2013. Radiographical data was collected and calculated. Student t test, Pearson correlation analysis and linear regression analysis were used to analyze the data.Results60 were included in this study. The mean age was 14.7y (10-18y) with 10 males (17%) and 50 females (83%). The average Risser sign was 2.7. The mean thoracic Cobb angle before operation was 51.9°. The mean bending Cobb angle was 27.6° and the mean fulcrum bending Cobb angle was 17.4°. The mean Cobb angle at 2 week after surgery was 16.3°. The Pearson correlation coefficient r between CCI and BFR was -0.856(P<0.001), and between FBCI and FFR was -0.728 (P<0.001). A modified FBCI (M-FBCI) = (CR-0.513)/BFR or a modified CCI (M-CCI) = (CR-0.279)/FFR was generated by curve estimation has no significant correlation with FFR (r=-0.08, p=0.950) or with BFR (r=0.123, p=0.349).ConclusionsFulcrum-bending radiographs may better predict the outcome of AIS coronal correction than bending radiographs in thoracic curveAIS patients. Neither CCI nor FBCI can fully eliminate the impact of curve flexibility on the outcome of correction. A modified CCI or FBCI can better evaluating the corrective effects of different surgical techniques or instruments.     

VITAL NMR: using chemical shift derived secondary structure information for a limited set of amino acids to assess homology model accuracy

Homology modeling is a powerful tool for predicting protein structures, whose success depends on obtaining a reasonable alignment between a given structural template and the protein sequence being analyzed. In order to leverage greater predictive power for proteins with few structural templates, we have developed a method to rank homology models based upon their compliance to secondary structure derived from experimental solid-state NMR (SSNMR) data. Such data is obtainable in a rapid manner by simple SSNMR experiments (e.g., ¹³C–¹³C 2D correlation spectra). To test our homology model scoring procedure for various amino acid labeling schemes, we generated a library of 7,474 homology models for 22 protein targets culled from the TALOS+/SPARTA+ training set of protein structures. Using subsets of amino acids that are plausibly assigned by SSNMR, we discovered that pairs of the residues Val, Ile, Thr, Ala and Leu (VITAL) emulate an ideal dataset where all residues are site specifically assigned. Scoring the models with a predicted VITAL site-specific dataset and calculating secondary structure with the Chemical Shift Index resulted in a Pearson correlation coefficient (−0.75) commensurate to the control (−0.77), where secondary structure was scored site specifically for all amino acids (ALL 20) using STRIDE. This method promises to accelerate structure procurement by SSNMR for proteins with unknown folds through guiding the selection of remotely homologous protein templates and assessing model quality.