两种特殊类型蛋白质功能残基的预测与生物序列比对
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摘要
生物信息学对制约生物学发展的诸多问题的探索性研究和得出的具有指导意义的研究成果,受到了生物学、信息学及其相关学科的高度关注,同时也促进了生物信息学自身的快速发展。目前,生物信息学的基本框架已经构建,其着力解决的科学问题也更加清晰、明确。但从学科自身发展的逻辑严密和求解复杂问题的实际效果思考,生物信息学尚有许多工作要做,有些方法需要进一步完善、有些方法则需要设计、开发和创建。
本文的主要结果由以下三部分组成:
第一部分:推广了SPA算法,使之适用于一般罚分(或得分)矩阵,并做出了数学证明。推广后的SPA算法有了更广的应用范围,可以根据实际问题的需要来调整打分矩阵,进而得到需要的比对结果。这就缓解了使用Hamming矩阵时经常出现的最优比对不唯一的问题,为设计基于SPA的多重序列比对算法打下了基础。
第二部分:设计了基于序列的蛋白质功能残基的预测方法,实现了蛋白质功能残基的大规模快速预测,为蛋白质功能实现的机理研究提供参考与指导。预测的结果可用于蛋白质功能残基实验测定的初步筛选,大大地节省了时间、人力和物力,提高了工作效率。使用特征选择算法对设计的特征进行筛选,减少了输入的特征维数,提高了预测的速度;还可以从选中的特征中总结出一些有意义的生物结论。
(1)开发了基于序列的预测酶催化残基的方法CRpred。CRpred的预测精度超过了现有的基于序列的预测方法,且与当前的基于结构的预测方法的预测精度相当。对选中特征的分析表明:一些氨基酸(His,Cys,Asp,Arg,Glu,Tyr)有着较高的催化倾向性;而另外一些氨基酸(Val,Ala,Ile,Pro,Leu,Met)则不容易成为催化残基;甘氨酸(Gly)能够为催化部位提供柔韧性;残基的保守性之于预测催化残基至关重要,催化残基通常比一般的残基更保守,对催化倾向性较高的氨基酸保守的残基更有可能成为催化残基;催化残基与特定的序列模式,如CysXXCys,AspXLysXXAsn等相关联;虽然催化残基偏爱一个相对疏水的大环境,但在局部范围内,它们通常被一些亲水残基包围。
(2)采用基于原子间距离的定义方式定义RNA绑定残基,设计了基于序列的预测蛋白质的RNA绑定残基的方法RBRpred。相对已有的基于序列的预测方法,RBRpred的预测精度有了提升。通过特征选择得到以下结论:侧链带正电的精氨酸(Arg)和赖氨酸(Lys),容易与带负电的RNA的磷酸基团相互吸引,结合形成稳定结构;甘氨酸(Gly)相对其他的氨基酸体积较小,可以增加RNA绑定部位的柔韧性:侧链带负电的谷氨酸(Glu),以及疏水的亮氨酸(Leu)、缬氨酸(Val)、丙氨酸(Ala)和苯丙氨酸(Phe)不容易出现在RNA绑定部位。序列保守性对蛋白质的RNA绑定残基的预测非常重要。在三种二级结构中,Coil结构的残基,尤其是在较长Coil片段中的残基有着更好的柔韧性,容易与RNA分子发生相互作用:而Helix结构则相反,结构比较稳固,位于其中的残基较难成为RNA绑定残基。相对溶剂可及面积较大的残基更有可能成为RNA绑定残基。
第三部分:将广义纠错码应用于DNA计算。针对DNA计算中可能出现的突变误差问题,设计了一个可自动纠错的DNA操作系统。提出了解决DNA计算中突变误差纠正问题的一种方案。
Bioinformatics concentrates on solving the problems constraining the development of biology. The progress made so far has been encouraging, and a number of results of great interest to those in biology, informatics and other subjects have been made, in addition to facilitating the rapid development of bioinformatics itself. At present, the basic framework of bioinformatics has been established, and the main problems to be solved are better-defined. However, considering that the internal logic of bioinformatics must be rigorously maintained as the subject evolves, and keeping in mind the goal of actual solutions to complex problems, it is clear that much still remains to be done. New methods of solving synchronous practical problems are desired, and the existing methods must be further improved. The focus is on problems related to biological sequence alignment, and protein functional residue prediction.
This thesis contains three main results:
First, the SPA algorithm is extended to be applicable to a general penalty/scoring matrix and the mathematical proof is given. This extended SPA algorithm has a wider range of uses. By adjusting the penalty/scoring matrix in terms of practical demands, the algorithm can provide an appropriate alignment. It also reduces the number of cases in which the optimal alignment may not be exclusive when the Hamming matrix is used, and therefore may be used in the design of SPA-based multiple sequence alignment algorithms.
Second, sequence-based methods for the large-scale fast prediction of protein functional residues are designed. This work enhances the fundamental understanding of how proteins perform their functions, and can be used to screen possible functional residues for experimental determination. Since experiments can be costly and time consuming, screening will save time, labor and material resources. Feature selection is performed, not only reducing the dimensionality of the input and decreasing the computational time, but also revealing the biological meaning inside features.
(1) A sequence-based catalytic residue predictor called "CRpred" is proposed, with predictions of quality comparable to modern structure-based methods, and exceeding the quality of state-of-the-art sequence-based methods. This analysis, performed on selected features, indicates the following four characteristics: a) Amino acids are characterized by varied propensities to become catalytic residues, from high (His, Cys, Asp, Arg, Glu and Tyr) to low (Val, Ala, Ile, Pro, Leu and Met), with glycine (Gly) providing flexibility for catalytic sites; b) The most important factor contributing towards accurate predictions is residue conservation. Catalytic residues, irrespective of type, tend to be more conserved compared to the general population of residues. Highly conserved amino acids, characterized by high catalytic propensity, are likely to form catalytic sites; c) Certain sequence motifs such as CysXXCys, AspXLysXXAsn, which are associated with catalytic reactions, are found to contribute to the prediction; and d) Although catalytic residues prefer a relatively more hydrophobic neighborhood, they are likely to be surrounded locally (with respect to the sequence) by hydrophilic residues.
(2) RNA-binding residues are identified according to a distance-based cutoff definition, and a new predictor "RBRpred" is designed which predicts RNA-binding residues from protein sequence, improving quality with respect to the current sequence-based methods. The four findings through feature selection are as follows: a) The positively charged amino acids Arg and Lys show higher propensity to form RNA-binding sites, due to their ability to participate in interactions with the negatively charged phosphate backbone of RNA; the small size of Gly provides flexibility for protein-RNA interactions; and Asp (with its negatively charged side chain) together with several hydrophobic residues (such as Leu, Val, Ala and Phe) are not preferred in RNA-binding sites; b) Sequence conservation plays a fundamental role in predicting RNA-binding residues; c) Coil residues, especially those in long coil segments, are more flexible and can easily interact with RNA; helices, however, are more rigid, and consequently residues in helices have less chance to bind with RNA; and d) Residues with higher relative solvent accessibility are more likely to be in RNA-binding sites.
Third, generalized error correcting code is applied to DNA computing, with focus on mutation errors in DNA computing, and design of a DNA operating system with error correction in order to solve the Hamiltonian circuit problem.
本文的主要结果由以下三部分组成:
第一部分:推广了SPA算法,使之适用于一般罚分(或得分)矩阵,并做出了数学证明。推广后的SPA算法有了更广的应用范围,可以根据实际问题的需要来调整打分矩阵,进而得到需要的比对结果。这就缓解了使用Hamming矩阵时经常出现的最优比对不唯一的问题,为设计基于SPA的多重序列比对算法打下了基础。
第二部分:设计了基于序列的蛋白质功能残基的预测方法,实现了蛋白质功能残基的大规模快速预测,为蛋白质功能实现的机理研究提供参考与指导。预测的结果可用于蛋白质功能残基实验测定的初步筛选,大大地节省了时间、人力和物力,提高了工作效率。使用特征选择算法对设计的特征进行筛选,减少了输入的特征维数,提高了预测的速度;还可以从选中的特征中总结出一些有意义的生物结论。
(1)开发了基于序列的预测酶催化残基的方法CRpred。CRpred的预测精度超过了现有的基于序列的预测方法,且与当前的基于结构的预测方法的预测精度相当。对选中特征的分析表明:一些氨基酸(His,Cys,Asp,Arg,Glu,Tyr)有着较高的催化倾向性;而另外一些氨基酸(Val,Ala,Ile,Pro,Leu,Met)则不容易成为催化残基;甘氨酸(Gly)能够为催化部位提供柔韧性;残基的保守性之于预测催化残基至关重要,催化残基通常比一般的残基更保守,对催化倾向性较高的氨基酸保守的残基更有可能成为催化残基;催化残基与特定的序列模式,如CysXXCys,AspXLysXXAsn等相关联;虽然催化残基偏爱一个相对疏水的大环境,但在局部范围内,它们通常被一些亲水残基包围。
(2)采用基于原子间距离的定义方式定义RNA绑定残基,设计了基于序列的预测蛋白质的RNA绑定残基的方法RBRpred。相对已有的基于序列的预测方法,RBRpred的预测精度有了提升。通过特征选择得到以下结论:侧链带正电的精氨酸(Arg)和赖氨酸(Lys),容易与带负电的RNA的磷酸基团相互吸引,结合形成稳定结构;甘氨酸(Gly)相对其他的氨基酸体积较小,可以增加RNA绑定部位的柔韧性:侧链带负电的谷氨酸(Glu),以及疏水的亮氨酸(Leu)、缬氨酸(Val)、丙氨酸(Ala)和苯丙氨酸(Phe)不容易出现在RNA绑定部位。序列保守性对蛋白质的RNA绑定残基的预测非常重要。在三种二级结构中,Coil结构的残基,尤其是在较长Coil片段中的残基有着更好的柔韧性,容易与RNA分子发生相互作用:而Helix结构则相反,结构比较稳固,位于其中的残基较难成为RNA绑定残基。相对溶剂可及面积较大的残基更有可能成为RNA绑定残基。
第三部分:将广义纠错码应用于DNA计算。针对DNA计算中可能出现的突变误差问题,设计了一个可自动纠错的DNA操作系统。提出了解决DNA计算中突变误差纠正问题的一种方案。
Bioinformatics concentrates on solving the problems constraining the development of biology. The progress made so far has been encouraging, and a number of results of great interest to those in biology, informatics and other subjects have been made, in addition to facilitating the rapid development of bioinformatics itself. At present, the basic framework of bioinformatics has been established, and the main problems to be solved are better-defined. However, considering that the internal logic of bioinformatics must be rigorously maintained as the subject evolves, and keeping in mind the goal of actual solutions to complex problems, it is clear that much still remains to be done. New methods of solving synchronous practical problems are desired, and the existing methods must be further improved. The focus is on problems related to biological sequence alignment, and protein functional residue prediction.
This thesis contains three main results:
First, the SPA algorithm is extended to be applicable to a general penalty/scoring matrix and the mathematical proof is given. This extended SPA algorithm has a wider range of uses. By adjusting the penalty/scoring matrix in terms of practical demands, the algorithm can provide an appropriate alignment. It also reduces the number of cases in which the optimal alignment may not be exclusive when the Hamming matrix is used, and therefore may be used in the design of SPA-based multiple sequence alignment algorithms.
Second, sequence-based methods for the large-scale fast prediction of protein functional residues are designed. This work enhances the fundamental understanding of how proteins perform their functions, and can be used to screen possible functional residues for experimental determination. Since experiments can be costly and time consuming, screening will save time, labor and material resources. Feature selection is performed, not only reducing the dimensionality of the input and decreasing the computational time, but also revealing the biological meaning inside features.
(1) A sequence-based catalytic residue predictor called "CRpred" is proposed, with predictions of quality comparable to modern structure-based methods, and exceeding the quality of state-of-the-art sequence-based methods. This analysis, performed on selected features, indicates the following four characteristics: a) Amino acids are characterized by varied propensities to become catalytic residues, from high (His, Cys, Asp, Arg, Glu and Tyr) to low (Val, Ala, Ile, Pro, Leu and Met), with glycine (Gly) providing flexibility for catalytic sites; b) The most important factor contributing towards accurate predictions is residue conservation. Catalytic residues, irrespective of type, tend to be more conserved compared to the general population of residues. Highly conserved amino acids, characterized by high catalytic propensity, are likely to form catalytic sites; c) Certain sequence motifs such as CysXXCys, AspXLysXXAsn, which are associated with catalytic reactions, are found to contribute to the prediction; and d) Although catalytic residues prefer a relatively more hydrophobic neighborhood, they are likely to be surrounded locally (with respect to the sequence) by hydrophilic residues.
(2) RNA-binding residues are identified according to a distance-based cutoff definition, and a new predictor "RBRpred" is designed which predicts RNA-binding residues from protein sequence, improving quality with respect to the current sequence-based methods. The four findings through feature selection are as follows: a) The positively charged amino acids Arg and Lys show higher propensity to form RNA-binding sites, due to their ability to participate in interactions with the negatively charged phosphate backbone of RNA; the small size of Gly provides flexibility for protein-RNA interactions; and Asp (with its negatively charged side chain) together with several hydrophobic residues (such as Leu, Val, Ala and Phe) are not preferred in RNA-binding sites; b) Sequence conservation plays a fundamental role in predicting RNA-binding residues; c) Coil residues, especially those in long coil segments, are more flexible and can easily interact with RNA; helices, however, are more rigid, and consequently residues in helices have less chance to bind with RNA; and d) Residues with higher relative solvent accessibility are more likely to be in RNA-binding sites.
Third, generalized error correcting code is applied to DNA computing, with focus on mutation errors in DNA computing, and design of a DNA operating system with error correction in order to solve the Hamiltonian circuit problem.
引文
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