生物序列表征体系构建及结构与功能关系研究
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摘要
生物序列(肽、蛋白质及核酸)结构表征是其结构与功能关系研究中的重要内容及关键前提,序列表征描述子是否能够合理地反映与其功能密切相关的结构信息,决定其结构与功能关系研究的成败。因为决定生物序列功能的结构信息被编码在其一级序列之中,因此,解析其一级序列特征对于生物序列的结构与功能关系研究至关重要。文中综合考察各种生物序列的一级序列特征,构建了两种生物序列结构表征体系,包括:①收集20种天然氨基酸的516种多维性质参数,经因子分析得广义氨基酸信息因子分析标度(FASGAI);②收集5种碱基的1209种多维性质参数,经主成分分析得广义碱基性质得分(SGBP)。研究结果显示,两种表征体系都具有物理化学意义明确,表征能力强,拓展性能好及操作简便等优点。
将FASGAI分别用于苦味二肽、血管紧张素转化酶抑制剂及阳离子抗菌肽的定量构效关系(QSAR)研究,人免疫缺陷病毒蛋白酶(HIV PR)裂解位点预测及特异性分析,HLA-A*0201限制性T细胞表位及人类1型双载蛋白SH3结构域亲和肽的QSAR研究,都取得较好的结果。研究显示,苦味二肽的生物活性与其第1残基的体积性质,第2残基的体积性质与疏水性等性质可能存在较大的正相关关系,而与其第2残基的α-螺旋与转角倾向等性质可能存在较大的负相关关系;血管紧张素转化酶抑制剂活性的第2残基的体积性质与疏水性及第1残基的静电性等性质参数的增大可能有利于其活性的提高,而第2残基的构成特征等性质参数的增大可能易导致其活性的降低;阳离子抗菌肽的第10残基的静电性质,第7残基的体积性质,第12残基的疏水性及第3残基的静电性等性质可能对抗菌活性产生较大的正贡献,而第6残基的疏水性及构成特征,第10残基的疏水性等性质可能对抗菌活性产生较大的负贡献;对HIV PR裂解位点预测及特异性分析知,HIV PR可能识别8肽序列中特定位点的关键特征,第1、2、4、5和6残基的体积性质、二级结构信息、静电性质及疏水性等可能是决定HIV PR是否裂解的重要因素,特别地,体积性质可能是HIV PR被识别的重要特征;HLA-A*0201限制性T细胞表位的第3残基的体积性质与疏水性,第2残基的体积性质及第9残基的疏水性等性质可能对亲和性的正贡献较大,而第4残基的疏水性及第3残基的局部柔性等性质可能对亲和性的负贡献较大;分析影响具有10个残基(P-5P-4P-3P-2P-1P0P1P2P3P4)的人类1型双载蛋白SH3结构域亲和肽亲和性的关键作用力知,第P-3与第P2之间残基(含P-3与P2残基)的相应性质可能对亲和性影响较显著,特别地,第P-3残基的静电性质与疏水性可能对于其亲和性的正、负贡献分别相对最大。
发展了全新的不依赖于序列同源性及结构相似性的蛋白结构与功能预测方法。将FASGAI分别用于碱性螺旋-环-螺旋(bHLH)蛋白,蛋白质的β-转角结构,G蛋白偶联受体家族及高致病性禽流感病毒血凝素蛋白分类或识别研究。结果显示,对bHLH蛋白分类影响较显著的变量大多来自其功能基序(第1到第13残基)的第5、8、9及13等位点,少数来自第4、6、10及12等位点,表明这些相应位点的变量可能是DNA亲和区域的一些重要识别特征,方差分析显示,在第5、8、9及13位点,除了第8残基的局部柔性与第9残基的体积性质外,其它性质都存在不同程度的显著差异,利用这些差异可较好地分类bHLH蛋白;β-转角结构预测结果表明,FASGAI可较好地表征β-转角残基特征,且其能提供β-转角的一些重要特征信息;经FASGAI表征,自交叉协方差(ACC)转换,支持向量机(SVM)建模用于G蛋白偶联受体家族及高致病性禽流感病毒血凝素蛋白识别所得结果显示,FASGAI是一种优良的蛋白序列结构表征方法,同时,FASGAI-ACC-SVM方法为G蛋白偶联受体家族及禽流感病毒血凝素蛋白识别提供了新的研究思路。
将SGBP分别用于大肠杆菌启动子的启动强度及人类基因启动子预测,都取得较好的结果。研究表明,大肠杆菌启动子(-49 bp到+19 bp)的-45,-38,-28,-27,-22,-21,-5,+4,+8,+14及+15等位点碱基的性质可能对启动强度具有较显著的影响,这为启动子的启动强度预测及序列设计提供了可能。以SGBP表征,ACC转换,SVM建模预测人类基因启动子(-250 bp到+50 bp),所得结果不同程度地相当于或优于其它所对比的预测方法。SGBP-ACC-SVM过程建模可以进一步尝试用于其它启动子识别,mRNA转录特性与RNA二级结构预测等。
针对性地比较研究了各种QSAR建模与模式识别方法,特别是偏最小二乘(PLS)、线性判别分析(LDA)及SVM等在生物序列结构与功能关系研究中的应用,其中包含了对变量筛选、参数选择及模型验证等内容的研究和讨论。结果表明,PLS可较好地解决变量数较多且存在多重共线性的情况。LDA用于模式识别所得结果稳健,模型易解释。SVM能较好地解决小样本、非线性、高维数和局部最小等问题,使其在生物序列结构与功能关系研究中具有广阔的应用前景,但其在参数设置等问题上有待进一步研究,文中探索性地将响应面分析法用于SVM的参数设置,结果证明该方法对于其参数设置是较有效的。文中选择性地采用逐步多元回归、遗传算法及逐步方法筛选变量,研究发现,三种方法都可较好地去除原始变量中的噪声信息。文中通过内部和外部双重验证评价模型质量,采用的内部验证方法有自检验、留一法及留组法验证等,在内部验证的基础上,利用预测集样本对模型进行外部预测能力评价,以确保所得模型的有效性。
Representation for biological sequences (peptides, proteins and nucleic acids) is crucial to investigate their structure-activity relationship. The structural descriptors for biological sequences should reflect the structural information tightly related to their activities, which determines the success of study on their structure-activity relationship. The structures related to the activities of biological sequences are determined by the information contained in their primary sequences. Therefore, investigation on characteristics of the primary sequences for biological sequences has great significance in study on their structure-activity relationship. The representation techniques were constructed in this dissertation considering diversified properties and activities of biological sequences, including:①Factor analysis scales of generalized amino acid information (FASGAI) derived from 516 property parameters of 20 coded amino acids;②Scores of generalized base properties (SGBP) derived from principal component analysis of a matrix of 1209 property parameters. Satisfying results demonstrated that both FASGAI and SGBP vectors have many distinct characteristics such as straightforward physicochemical meaning, high characterization competence, convenient expansibility and easy manipulation.
FASGAI vectors were applied to represent the structures of several functional peptides, including bitter tasting thresholds, angiotensin-converting enzyme inhibitors, cationic antimicrobial peptides, octapeptides cleaved by HIV-1 protease (HIV PR), HLA-A*0201 restrictive T-cell epitopes and decapeptides binding to SH3 domain of human protein Amphiphysin-1. Further, favorable quantitative structure-activity relationship (QSAR) models were developed using various modeling techniques and methods. The results showed that the activities of bitter tasting dipeptides may be highly positively correlated to bulky properties of the 1st residue, bulky properties and hydrophobicity of the 2nd residue and so on, and may be highly negatively correlated toα-helix and turn propensities of the 2nd residue and so on. It can be concluded from investigation on the structural information related to the activities of angiotensin-converting enzyme inhibitors that the improvement for bulky properties and hydrophobicity of the 2nd residue, electronic properties of the 1st residue and so on may enhance their activities, and also, the improvement for compositional characteristics of the 2nd residue and so on may restrain their activities. It can be found that electronic properties of the 10th residue, bulky properties of the 7th residue, hydrophobicity of the 12th residue and electronic properties of the 3rd residue and so on may generate high positive effect on the activities of antimicrobial peptides, and also, hydrophobicity and compositional characteristics of the 6th residue and hydrophobicity of the 10th residue and so on may generate high negative effect on antibacterial activities. It can be concluded that HIV PR may recognize diversitied key properties of various sites in the octameric sequences. These diversified properties including bulky properties, secondary conformation characteristics, electronic properties and hydrophobicity of the 1st, 2nd, 4th, 5th and 6th residues and so on may be important factors in determining HIV PR cleavage or not, and particularly, bulky properties of the corresponding sites may be key features recognized by HIV PR. Investigation on properties tightly related to the affinities of HLA-A*0201 restrictive T-cell epitopes demonstrated that bulky properties and hydrophobicity of the 3rd residue, bulky properties of the 2nd residue and hydrophobicity of the 9th residue and so on may positively contribute most to the affinities, and also, hydrophobicity of the 4th residue and local flexibility of the 3rd residue and so on may negatively contribute most to the affinities. Diversified properties of the residues between the P-3 site and the P2 site (including the P-3 site and the P2 site) for the decapeptide (P-5P-4P-3P-2P-1P0P1P2P3P4) may contribute remarkable effect to the interactions between human Amphiphysin-1 SH3 domain and the decapeptide. Particularly, electronic properties of the P-3 residue may provide large positive contribution on the interactions, and hydrophobicity of the P-3 residue may provide large negative contribution on the interactions.
Original prediction techniques independent of sequence homology and structure similarity were developed to predict structure-activity relationship for the proteins. FASGAI vectors were used to identify basic helix-loop-helix (bHLH) proteins,β-turns of proteins, G-protein-coupled receptors (GPCRs) and hemagglutinins of high pathogenic avian influenza virus (HPAIV). It can be concluded that remarkable influence was from the property parameters of the 5th, 8th, 9th and 13th sites in the motif with the 1st 13 residues in bHLH protein sequences, and little remarkable influence was from the property parameters of the 4th, 6th, 10th and 12th sites. This displayed that these properties may be key features recognized for the DNA binding region. Investigation by analysis of variance indicated that there may be significant difference between these property parameters of the 5th, 8th, 9th and 13th sites except local flexibility of the 8th residue and bulky properties of the 9th residue. Therefore, these properties may be utilized to identify bHLH proteins. Satisfying results of prediction forβ-turns showed that characteristics ofβ-turn residues were well represented by FASGAI vectors, meanwhile, some important information related toβ-turn residues was obtained. FASGAI-ACC-SVM methodology involving FASGAI representation, auto cross covariance (ACC) transform and support vector machine (SVM) modeling was utilized to identify GPCRs and hemagglutinins of HPAIV. The results demonstrated that FASGAI vectors are excellent representation technique for protein sequences. FSAGAI-ACC-SVM methodology has thus pointed us further into the direction of identification for GPCRs and hemagglutinins of HPAIV.
SGBP vectors were employed to predict promoter strengths of E.coli promoters and identify human genome promoters. It can be concluded that properties of base position -45, -38, -28, -27, -22, -21, -5, +4, +8, +14 and +15 and so on may yield remarkable influence on promoter strengths of E.coli promoters with 68 base pairs (-49 bp~+19 bp), which has thus pointed us further into the direction of strong promoters. The results for prediction of human genome promoters (-250 bp~+50 bp) revealed that there is a wide prospect for applications of the methodology, i.e., SGBP-ACC-SVM involving SGBP representation, ACC transform and SVM modeling, in prediction of other promoters, transcription properties of mRNA and secondary structure of RNA and so on.
The modeling and the pattern recognition methods, particularly partial least square (PLS), linear discriminant analysis (LDA) and SVM, were investigated. The techniques involving variable selection, parameter determination and model validation were also discussed in this dissertation. The results showed that PLS can well avoid harmful effects in modeling due to multicollinearity, and is particularly fit for regressing when the number observation is less than the number of the variables. Models developed by LDA are robust and interpreted. As a new machine learning arithmetic, SVM can well deal with small dataset, nonlinear optimization, high-dimensional feature space, local minimization and so on. These results showed that there is a wide prospect for the applications of SVM in study on structure-activity relationship for biological sequences. However, there are many issues, i.e., selection of kernel functions and corresponding parameters, leaving to be studied in detailed. Parameters of SVM were tentatively determined by response surface methodology in order to acquire reliable results. The results demonstrated that the methodology is effective for parameter determination of SVM. Besides, stepwise multiple regression, genetic algorithm and a stepwise manner were used to optimize variable subsets. The results indicated that three methods for variable selection can efficiently dismiss noise of original variables. Self-consistency, leave one out and leave group out test were used to carry out internal validations. On the base of internal validations, external validations were performed by using the predictive data set in order to ensure the validity of the models obtained.
将FASGAI分别用于苦味二肽、血管紧张素转化酶抑制剂及阳离子抗菌肽的定量构效关系(QSAR)研究,人免疫缺陷病毒蛋白酶(HIV PR)裂解位点预测及特异性分析,HLA-A*0201限制性T细胞表位及人类1型双载蛋白SH3结构域亲和肽的QSAR研究,都取得较好的结果。研究显示,苦味二肽的生物活性与其第1残基的体积性质,第2残基的体积性质与疏水性等性质可能存在较大的正相关关系,而与其第2残基的α-螺旋与转角倾向等性质可能存在较大的负相关关系;血管紧张素转化酶抑制剂活性的第2残基的体积性质与疏水性及第1残基的静电性等性质参数的增大可能有利于其活性的提高,而第2残基的构成特征等性质参数的增大可能易导致其活性的降低;阳离子抗菌肽的第10残基的静电性质,第7残基的体积性质,第12残基的疏水性及第3残基的静电性等性质可能对抗菌活性产生较大的正贡献,而第6残基的疏水性及构成特征,第10残基的疏水性等性质可能对抗菌活性产生较大的负贡献;对HIV PR裂解位点预测及特异性分析知,HIV PR可能识别8肽序列中特定位点的关键特征,第1、2、4、5和6残基的体积性质、二级结构信息、静电性质及疏水性等可能是决定HIV PR是否裂解的重要因素,特别地,体积性质可能是HIV PR被识别的重要特征;HLA-A*0201限制性T细胞表位的第3残基的体积性质与疏水性,第2残基的体积性质及第9残基的疏水性等性质可能对亲和性的正贡献较大,而第4残基的疏水性及第3残基的局部柔性等性质可能对亲和性的负贡献较大;分析影响具有10个残基(P-5P-4P-3P-2P-1P0P1P2P3P4)的人类1型双载蛋白SH3结构域亲和肽亲和性的关键作用力知,第P-3与第P2之间残基(含P-3与P2残基)的相应性质可能对亲和性影响较显著,特别地,第P-3残基的静电性质与疏水性可能对于其亲和性的正、负贡献分别相对最大。
发展了全新的不依赖于序列同源性及结构相似性的蛋白结构与功能预测方法。将FASGAI分别用于碱性螺旋-环-螺旋(bHLH)蛋白,蛋白质的β-转角结构,G蛋白偶联受体家族及高致病性禽流感病毒血凝素蛋白分类或识别研究。结果显示,对bHLH蛋白分类影响较显著的变量大多来自其功能基序(第1到第13残基)的第5、8、9及13等位点,少数来自第4、6、10及12等位点,表明这些相应位点的变量可能是DNA亲和区域的一些重要识别特征,方差分析显示,在第5、8、9及13位点,除了第8残基的局部柔性与第9残基的体积性质外,其它性质都存在不同程度的显著差异,利用这些差异可较好地分类bHLH蛋白;β-转角结构预测结果表明,FASGAI可较好地表征β-转角残基特征,且其能提供β-转角的一些重要特征信息;经FASGAI表征,自交叉协方差(ACC)转换,支持向量机(SVM)建模用于G蛋白偶联受体家族及高致病性禽流感病毒血凝素蛋白识别所得结果显示,FASGAI是一种优良的蛋白序列结构表征方法,同时,FASGAI-ACC-SVM方法为G蛋白偶联受体家族及禽流感病毒血凝素蛋白识别提供了新的研究思路。
将SGBP分别用于大肠杆菌启动子的启动强度及人类基因启动子预测,都取得较好的结果。研究表明,大肠杆菌启动子(-49 bp到+19 bp)的-45,-38,-28,-27,-22,-21,-5,+4,+8,+14及+15等位点碱基的性质可能对启动强度具有较显著的影响,这为启动子的启动强度预测及序列设计提供了可能。以SGBP表征,ACC转换,SVM建模预测人类基因启动子(-250 bp到+50 bp),所得结果不同程度地相当于或优于其它所对比的预测方法。SGBP-ACC-SVM过程建模可以进一步尝试用于其它启动子识别,mRNA转录特性与RNA二级结构预测等。
针对性地比较研究了各种QSAR建模与模式识别方法,特别是偏最小二乘(PLS)、线性判别分析(LDA)及SVM等在生物序列结构与功能关系研究中的应用,其中包含了对变量筛选、参数选择及模型验证等内容的研究和讨论。结果表明,PLS可较好地解决变量数较多且存在多重共线性的情况。LDA用于模式识别所得结果稳健,模型易解释。SVM能较好地解决小样本、非线性、高维数和局部最小等问题,使其在生物序列结构与功能关系研究中具有广阔的应用前景,但其在参数设置等问题上有待进一步研究,文中探索性地将响应面分析法用于SVM的参数设置,结果证明该方法对于其参数设置是较有效的。文中选择性地采用逐步多元回归、遗传算法及逐步方法筛选变量,研究发现,三种方法都可较好地去除原始变量中的噪声信息。文中通过内部和外部双重验证评价模型质量,采用的内部验证方法有自检验、留一法及留组法验证等,在内部验证的基础上,利用预测集样本对模型进行外部预测能力评价,以确保所得模型的有效性。
Representation for biological sequences (peptides, proteins and nucleic acids) is crucial to investigate their structure-activity relationship. The structural descriptors for biological sequences should reflect the structural information tightly related to their activities, which determines the success of study on their structure-activity relationship. The structures related to the activities of biological sequences are determined by the information contained in their primary sequences. Therefore, investigation on characteristics of the primary sequences for biological sequences has great significance in study on their structure-activity relationship. The representation techniques were constructed in this dissertation considering diversified properties and activities of biological sequences, including:①Factor analysis scales of generalized amino acid information (FASGAI) derived from 516 property parameters of 20 coded amino acids;②Scores of generalized base properties (SGBP) derived from principal component analysis of a matrix of 1209 property parameters. Satisfying results demonstrated that both FASGAI and SGBP vectors have many distinct characteristics such as straightforward physicochemical meaning, high characterization competence, convenient expansibility and easy manipulation.
FASGAI vectors were applied to represent the structures of several functional peptides, including bitter tasting thresholds, angiotensin-converting enzyme inhibitors, cationic antimicrobial peptides, octapeptides cleaved by HIV-1 protease (HIV PR), HLA-A*0201 restrictive T-cell epitopes and decapeptides binding to SH3 domain of human protein Amphiphysin-1. Further, favorable quantitative structure-activity relationship (QSAR) models were developed using various modeling techniques and methods. The results showed that the activities of bitter tasting dipeptides may be highly positively correlated to bulky properties of the 1st residue, bulky properties and hydrophobicity of the 2nd residue and so on, and may be highly negatively correlated toα-helix and turn propensities of the 2nd residue and so on. It can be concluded from investigation on the structural information related to the activities of angiotensin-converting enzyme inhibitors that the improvement for bulky properties and hydrophobicity of the 2nd residue, electronic properties of the 1st residue and so on may enhance their activities, and also, the improvement for compositional characteristics of the 2nd residue and so on may restrain their activities. It can be found that electronic properties of the 10th residue, bulky properties of the 7th residue, hydrophobicity of the 12th residue and electronic properties of the 3rd residue and so on may generate high positive effect on the activities of antimicrobial peptides, and also, hydrophobicity and compositional characteristics of the 6th residue and hydrophobicity of the 10th residue and so on may generate high negative effect on antibacterial activities. It can be concluded that HIV PR may recognize diversitied key properties of various sites in the octameric sequences. These diversified properties including bulky properties, secondary conformation characteristics, electronic properties and hydrophobicity of the 1st, 2nd, 4th, 5th and 6th residues and so on may be important factors in determining HIV PR cleavage or not, and particularly, bulky properties of the corresponding sites may be key features recognized by HIV PR. Investigation on properties tightly related to the affinities of HLA-A*0201 restrictive T-cell epitopes demonstrated that bulky properties and hydrophobicity of the 3rd residue, bulky properties of the 2nd residue and hydrophobicity of the 9th residue and so on may positively contribute most to the affinities, and also, hydrophobicity of the 4th residue and local flexibility of the 3rd residue and so on may negatively contribute most to the affinities. Diversified properties of the residues between the P-3 site and the P2 site (including the P-3 site and the P2 site) for the decapeptide (P-5P-4P-3P-2P-1P0P1P2P3P4) may contribute remarkable effect to the interactions between human Amphiphysin-1 SH3 domain and the decapeptide. Particularly, electronic properties of the P-3 residue may provide large positive contribution on the interactions, and hydrophobicity of the P-3 residue may provide large negative contribution on the interactions.
Original prediction techniques independent of sequence homology and structure similarity were developed to predict structure-activity relationship for the proteins. FASGAI vectors were used to identify basic helix-loop-helix (bHLH) proteins,β-turns of proteins, G-protein-coupled receptors (GPCRs) and hemagglutinins of high pathogenic avian influenza virus (HPAIV). It can be concluded that remarkable influence was from the property parameters of the 5th, 8th, 9th and 13th sites in the motif with the 1st 13 residues in bHLH protein sequences, and little remarkable influence was from the property parameters of the 4th, 6th, 10th and 12th sites. This displayed that these properties may be key features recognized for the DNA binding region. Investigation by analysis of variance indicated that there may be significant difference between these property parameters of the 5th, 8th, 9th and 13th sites except local flexibility of the 8th residue and bulky properties of the 9th residue. Therefore, these properties may be utilized to identify bHLH proteins. Satisfying results of prediction forβ-turns showed that characteristics ofβ-turn residues were well represented by FASGAI vectors, meanwhile, some important information related toβ-turn residues was obtained. FASGAI-ACC-SVM methodology involving FASGAI representation, auto cross covariance (ACC) transform and support vector machine (SVM) modeling was utilized to identify GPCRs and hemagglutinins of HPAIV. The results demonstrated that FASGAI vectors are excellent representation technique for protein sequences. FSAGAI-ACC-SVM methodology has thus pointed us further into the direction of identification for GPCRs and hemagglutinins of HPAIV.
SGBP vectors were employed to predict promoter strengths of E.coli promoters and identify human genome promoters. It can be concluded that properties of base position -45, -38, -28, -27, -22, -21, -5, +4, +8, +14 and +15 and so on may yield remarkable influence on promoter strengths of E.coli promoters with 68 base pairs (-49 bp~+19 bp), which has thus pointed us further into the direction of strong promoters. The results for prediction of human genome promoters (-250 bp~+50 bp) revealed that there is a wide prospect for applications of the methodology, i.e., SGBP-ACC-SVM involving SGBP representation, ACC transform and SVM modeling, in prediction of other promoters, transcription properties of mRNA and secondary structure of RNA and so on.
The modeling and the pattern recognition methods, particularly partial least square (PLS), linear discriminant analysis (LDA) and SVM, were investigated. The techniques involving variable selection, parameter determination and model validation were also discussed in this dissertation. The results showed that PLS can well avoid harmful effects in modeling due to multicollinearity, and is particularly fit for regressing when the number observation is less than the number of the variables. Models developed by LDA are robust and interpreted. As a new machine learning arithmetic, SVM can well deal with small dataset, nonlinear optimization, high-dimensional feature space, local minimization and so on. These results showed that there is a wide prospect for the applications of SVM in study on structure-activity relationship for biological sequences. However, there are many issues, i.e., selection of kernel functions and corresponding parameters, leaving to be studied in detailed. Parameters of SVM were tentatively determined by response surface methodology in order to acquire reliable results. The results demonstrated that the methodology is effective for parameter determination of SVM. Besides, stepwise multiple regression, genetic algorithm and a stepwise manner were used to optimize variable subsets. The results indicated that three methods for variable selection can efficiently dismiss noise of original variables. Self-consistency, leave one out and leave group out test were used to carry out internal validations. On the base of internal validations, external validations were performed by using the predictive data set in order to ensure the validity of the models obtained.
引文
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