化学修饰寡核苷酸在核酸配基扩增技术中的应用

核酸酸基扩增技术（SELEX)可从极大容量的随机寡核苷酸文库中筛选得到与靶分子高特异性和高亲和力结合的核酸配基。对寡核苷酸进行化学修饰,可以提高核酸配基的稳定性,增加其功能多样性及生物利用度。SELEX在基础研究、诊断和治疗试剂的研制及药物筛选等领域有广泛用途。     

基于核酸适配体的DNA纳米结构在肿瘤研究中的应用进展

DNA纳米结构具有强大的分子载带量、良好的稳定性、可编辑性和生物相容性等特点,是纳米材料领域的研究热点.核酸适配体是一段短的寡核苷酸序列(RNA或ssDNA),能够折叠成特定的三维结构与靶标高特异性、高亲和力的结合.将核酸适配体的分子识别特性和DNA纳米结构相结合,可将靶向识别、生物成像及药物递送等特点集于一体,在生命...     

核酸熔解温度的研究进展与寡核苷酸设计平台的研制

核酸熔解温度的预测在PCR等分子生物学实验中具有重要意义,近年来出现了一些基于最邻近热力学的计算方法,并不断得到优化。介绍两个重要的算法,并应用于寡核苷酸设计平台的研制中。基于较新的Tm值算法,提出了一套简并核酸热力学参数,以满足简并寡核苷酸设计的要求。     

RNA大分子的二极结构预测

本文提出能预测单链核酸分子的具有最小自由能的二级结构的计算方法。方法的基础是拓扑平面图的最大C—匹配原理和现有的单链核酸分子折叠构象的热力学数据资料。为了说明算法的能力,对免疫球蛋白r1重链的mRNA片段序列(459个核苷酸残基)大肠杆菌16s rRNA片段序列(567一883)以及脊髓灰白质炎病毒RNA片段序列(1—74O)的二级结构进行了计算机预测并同现有的结构模型进行了比较和讨论。由计算机预测的大肠杆菌16s rRNA中心域的二级结构与Noller和Woese提出的结构模型基本一致。     

BioSun--辅助分子生物学实验设计的生物信息学软件系统

经过两年多的努力 ,军事医学科学院基础医学研究所计算生物学中心成功研发了辅助分子生物学实验设计的软件系统BioSun ,该系统主要功能有 :可视化的序列编辑器、完善的序列数据库管理系统、多种方式的序列比较、蛋白质基本性质分析 (MW、PI、… )、蛋白质功能位点及模式特异性分析、多种方式的抗原表位预测、基于随机肽库实验数据的抗原识别、蛋白质二级结构预测、基于一定字长的蛋白质与DNA组成分析、DNA模式分析、PCR实验辅助设计、转录因子结合位点预测、酶切位点分析及酶切图谱制作、基于多种算法的RNA二级结构预测、原核系统外…     

基于最小自由能和协变信息预测带伪结RNA二级结构的迭代化方法

多数RNA分子的结构在进化中是高度保守的, 其中很多包含伪结。而RNA伪结的预测一直是一个棘手问题, 很多RNA 二级结构预测算法都不能预测伪结。文章提出一种基于迭代法预测带伪结RNA 二级结构的新方法。该方法在给潜在碱基对打分时综合了热力学和协变信息, 通过基于最小自由能RNA折叠算法的多次迭代选出所有的碱基对。测试结果表明: 此方法几乎能预测到所有的伪结。与其他方法相比, 敏感度接近最优, 而特异性达到最优。     

运用原子力显微镜研究核糖体RNA的二级结构

通过反复冻融的方法使核糖体在低温下瓦解,制备了rRNA,不经抽提、变性和染色处理就能使rRNA在云母表面上较好地分散.用原子力显微镜对其进行观察,发现rRNA分子呈多分支的棒状结构,且有很好的规律性.根据RNAs的大小和形状可将其分为三种,它们分别与计算机所预测的28S-5.8S、18S、5S rRNA的二级结构相似.我们得到的28S-5.8S、18S、5.8S、5S rRNAs的结构信息,支持基于热力学考虑推测的rRNAs的二级结构.     

CXCL12-α基因编码区的克隆与生物信息学分析

目的:克隆人源CXCL12-α的成熟肽编码序列后进行生物信息学分析及其蛋白质结构与功能预测。方法:采用RTPCR方法从人骨髓组织中克隆CXCL12-α基因序列,并将编码其成熟蛋白的核酸片段插入原核表达载体pET-30a(+)中,转化Escherichia coli后进行酶切鉴定和DNA序列测定。利用在线网络生物信息学相关数据库和分析软件对测序结果进行分析,并对重组蛋白的一、二、三级结构及功能等进行验证和预测。结果:获得了基因序列为263 bp的人源CXCL12-α基因,与GenBank中公布序列一致。双酶切鉴定及DNA测序验证结果正确。生物信息学检索该基因编码蛋白的氨基酸序列与理化参数显示,蛋白无跨膜区,在第29位有一个Ser为蛋白激酶磷酸化位点,第1~21位可能为信号肽区域。ɑ-螺旋,无规则卷曲,延伸链和β-转角数量分别占总二级结构的44.94%、22.47%、21.35%、11.24%。同源建模预测信息可信度为0.68,该蛋白G-factor结构计分总平均为0.33,结构检验表明该蛋白属于正常范围内。二级结构和拓扑结构信息、蛋白质结合位点三维图和预测蛋白可能催化口袋及结合位点、三维结构模拟的可视化结构显示其空间结构稳定。结论:人源CXCL12-α分子克隆成功,网络数据库等生物信息学分析及结合蛋白质结构与功能预测可为深入研究CXCL12-α提供理论指导。     

SELEX技术与适体

SELEX技术是从随机寡核苷酸评议库中筛选与靶分子特异结合序列的组合化学技术。随机寡核苷酸的多种空间结构是筛选基础。该技术对靶分子无特殊要求,筛选出的寡核苷酸被称为适体,适体最突出的特征是与靶分子的特异性高亲和力。     

血红素对大肠杆菌铁蛋白稳定性及自组装的影响

大肠杆菌铁蛋白(bacterioferritin,BFR)是由同源亚基24聚体组成的高度对称的八面体壳状结构,其中处在C2对称轴的每两个亚基之间即存在一个血红素分子。为了探讨血红素对细菌铁蛋白结构稳定性及在蛋白质自组装过程中的影响,通过定点突变将第52位蛋氨酸突变为丙氨酸以去除血红素分子,并应用体积排阻色谱、透射电镜、紫外-可见光吸收光谱及圆二色谱等手段对突变蛋白M52A的聚合态、二级结构及热力学性质等进行分析。结果表明:去除血红素虽然对铁蛋白的自组装没有明显影响,但是对其热稳定性产生较大影响,突变后蛋白质的T_m值为54.3℃,远小于野生型铁蛋白T_m值69.9℃,且经过氯化血红素与同浓度下该突变体M52A化学诱导结合后,发现其T_m值重新恢复至67.7℃。由此可知,血红素对大肠杆菌铁蛋白稳定性影响很大,且具有调控铁蛋白稳定性的作用。     

Parsing nucleic acid pseudoknotted secondary structure: algorithm and applications.

Accurate prediction of pseudoknotted nucleic acid secondary structure is an important computational challenge. Prediction algorithms based on dynamic programming aim to find a structure with minimum free energy according to some thermodynamic ("sum of loop energies") model that is implicit in the recurrences of the algorithm. However, a clear definition of what exactly are the loops in pseudoknotted structures, and their associated energies, has been lacking. In this work, we present a complete classification of loops in pseudoknotted nucleic secondary structures, and describe the Rivas and Eddy and other energy models as sum-of-loops energy models. We give a linear time algorithm for parsing a pseudoknotted secondary structure into its component loops. We give two applications of our parsing algorithm. The first is a linear time algorithm to calculate the free energy of a pseudoknotted secondary structure. This is useful for heuristic prediction algorithms, which are widely used since (pseudoknotted) RNA secondary structure prediction is NP-hard. The second application is a linear time algorithm to test the generality of the dynamic programming algorithm of Akutsu for secondary structure prediction.Together with previous work, we use this algorithm to compare the generality of state-of-the-art algorithms on real biological structures.     

Correlation of RNA Secondary Structure Statistics with Thermodynamic Stability and Applications to Folding

The accurate prediction of the secondary and tertiary structure of an RNA with different folding algorithms is dependent on several factors, including the energy functions. However, an RNA higher-order structure cannot be predicted accurately from its sequence based on a limited set of energy parameters. The inter- and intramolecular forces between this RNA and other small molecules and macromolecules, in addition to other factors in the cell such as pH, ionic strength, and temperature, influence the complex dynamics associated with transition of a single stranded RNA to its secondary and tertiary structure. Since all of the factors that affect the formation of an RNAs 3D structure cannot be determined experimentally, statistically derived potential energy has been used in the prediction of protein structure. In the current work, we evaluate the statistical free energy of various secondary structure motifs, including base-pair stacks, hairpin loops, and internal loops, using their statistical frequency obtained from the comparative analysis of more than 50,000 RNA sequences stored in the RNA Comparative Analysis Database (rCAD) at the Comparative RNA Web (CRW) Site. Statistical energy was computed from the structural statistics for several datasets. While the statistical energy for a base-pair stack correlates with experimentally derived free energy values, suggesting a Boltzmann-like distribution, variation is observed between different molecules and their location on the phylogenetic tree of life. Our statistical energy values calculated for several structural elements were utilized in the Mfold RNA-folding algorithm. The combined statistical energy values for base-pair stacks, hairpins and internal loop flanks result in a significant improvement in the accuracy of secondary structure prediction; the hairpin flanks contribute the most.     

Thermodynamic characterization of RNA 2 × 3 nucleotide internal loops

To better elucidate RNA structure-function relationships and to improve the design of pharmaceutical agents that target specific RNA motifs, an understanding of RNA primary, secondary, and tertiary structure is necessary. The prediction of RNA secondary structure from sequence is an intermediate step in predicting RNA three-dimensional structure. RNA secondary structure is typically predicted using a nearest neighbor model based on free energy parameters. The current free energy parameters for 2 × 3 nucleotide loops are based on a 23-member data set of 2 × 3 loops and internal loops of other sizes. A database of representative RNA secondary structures was searched to identify 2 × 3 nucleotide loops that occur in nature. Seventeen of the most frequent 2 × 3 nucleotide loops in this database were studied by optical melting experiments. Fifteen of these loops melted in a two-state manner, and the associated experimental ΔG°(37,2×3) values are, on average, 0.6 and 0.7 kcal/mol different from the values predicted for these internal loops using the predictive models proposed by Lu, Turner, and Mathews [Lu, Z. J., Turner, D. H., and Mathews, D. H. (2006) Nucleic Acids Res. 34, 4912-4924] and Chen and Turner [Chen, G., and Turner, D. H. (2006) Biochemistry 45, 4025-4043], respectively. These new ΔG°(37,2×3) values can be used to update the current algorithms that predict secondary structure from sequence. To improve free energy calculations for duplexes containing 2 × 3 nucleotide loops that still do not have experimentally determined free energy contributions, an updated predictive model was derived. This new model resulted from a linear regression analysis of the data reported here combined with 31 previously studied 2 × 3 nucleotide internal loops. Most of the values for the parameters in this new predictive model are within experimental error of those of the previous models, suggesting that approximations and assumptions associated with the derivation of the previous nearest neighbor parameters were valid. The updated predictive model predicts free energies of 2 × 3 nucleotide internal loops within 0.4 kcal/mol, on average, of the experimental free energy values. Both the experimental values and the updated predictive model can be used to improve secondary structure prediction from sequence.     

Thermodynamic stabilities of internal loops with GU closing pairs in RNA

Many internal loops that form tertiary contacts in natural RNAs have GU closing pairs; examples include the tetraloop receptor and P1 helix docking site in group I introns. Thus, thermodynamic parameters of internal loops with GU closing pairs can contribute to the prediction of both secondary and tertiary structure. Oligoribonucleotide duplexes containing small internal loops with GU closing pairs were studied by optical melting, one-dimensional imino proton NMR, and one-dimensional phosphorus NMR. The thermodynamic stabilities of asymmetric internal loops with GU closing pairs relative to those of loops with GC closing pairs may be explained by hydrogen bonds. In contrast, the free energy increments for symmetric internal loops of two noncanonical pairs with GU closing pairs relative to loops with GC closing pairs show much more sequence dependence. Imino proton and phosphorus NMR spectra suggest that some GA pairs adjacent to GU closing pairs may form an overall thermodynamically stable but non-A-form conformation.     

CONTRAfold: RNA secondary structure prediction without physics-based models

MOTIVATION: For several decades, free energy minimization methods have been the dominant strategy for single sequence RNA secondary structure prediction. More recently, stochastic context-free grammars (SCFGs) have emerged as an alternative probabilistic methodology for modeling RNA structure. Unlike physics-based methods, which rely on thousands of experimentally-measured thermodynamic parameters, SCFGs use fully-automated statistical learning algorithms to derive model parameters. Despite this advantage, however, probabilistic methods have not replaced free energy minimization methods as the tool of choice for secondary structure prediction, as the accuracies of the best current SCFGs have yet to match those of the best physics-based models. RESULTS: In this paper, we present CONTRAfold, a novel secondary structure prediction method based on conditional log-linear models (CLLMs), a flexible class of probabilistic models which generalize upon SCFGs by using discriminative training and feature-rich scoring. In a series of cross-validation experiments, we show that grammar-based secondary structure prediction methods formulated as CLLMs consistently outperform their SCFG analogs. Furthermore, CONTRAfold, a CLLM incorporating most of the features found in typical thermodynamic models, achieves the highest single sequence prediction accuracies to date, outperforming currently available probabilistic and physics-based techniques. Our result thus closes the gap between probabilistic and thermodynamic models, demonstrating that statistical learning procedures provide an effective alternative to empirical measurement of thermodynamic parameters for RNA secondary structure prediction. AVAILABILITY: Source code for CONTRAfold is available at http://contra.stanford.edu/contrafold/.     

Thermodynamic characterization of RNA duplexes containing naturally occurring 1 x 2 nucleotide internal loops

Thermodynamic data for RNA 1 x 2 nucleotide internal loops are lacking. Thermodynamic data that are available for 1 x 2 loops, however, are for loops that rarely occur in nature. In order to identify the most frequently occurring 1 x 2 nucleotide internal loops, a database of 955 RNA secondary structures was compiled and searched. Twenty-four RNA duplexes containing the most common 1 x 2 nucleotide loops were optically melted, and the thermodynamic parameters DeltaH degrees , DeltaS degrees , DeltaG degrees 37, and TM for each duplex were determined. This data set more than doubles the number of 1 x 2 nucleotide loops previously studied. A table of experimental free energy contributions for frequently occurring 1 x 2 nucleotide loops (as opposed to a predictive model) is likely to result in better prediction of RNA secondary structure from sequence. In order to improve free energy calculations for duplexes containing 1 x 2 nucleotide loops that do not have experimental free energy contributions, the data collected here were combined with data from 21 previously studied 1 x 2 loops. Using linear regression, the entire dataset was used to derive nearest neighbor parameters that can be used to predict the thermodynamics of previously unmeasured 1 x 2 nucleotide loops. The DeltaG degrees 37,loop and DeltaH degrees loop nearest neighbor parameters derived here were compared to values that were published previously for 1 x 2 nucleotide loops but were derived from either a significantly smaller dataset of 1 x 2 nucleotide loops or from internal loops of various sizes [Lu, Z. J., Turner, D. H., and Mathews, D. H. (2006) Nucleic Acids Res. 34, 4912-4924]. Most of these values were found to be within experimental error, suggesting that previous approximations and assumptions associated with the derivation of those nearest neighbor parameters were valid. DeltaS degrees loop nearest neighbor parameters are also reported for 1 x 2 nucleotide loops. Both the experimental thermodynamics and the nearest neighbor parameters reported here can be used to improve secondary structure prediction from sequence.     

Thermodynamic characterization of the complete set of sequence symmetric tandem mismatches in RNA and an improved model for predicting the free energy contribution of sequence asymmetric tandem mismatches

Because of the availability of an abundance of RNA sequence information, the ability to rapidly and accurately predict the secondary structure of RNA from sequence is becoming increasingly important. A common method for predicting RNA secondary structure from sequence is free energy minimization. Therefore, accurate free energy contributions for every RNA secondary structure motif are necessary for accurate secondary structure predictions. Tandem mismatches are prevalent in naturally occurring sequences and are biologically important. A common method for predicting the stability of a sequence asymmetric tandem mismatch relies on the stabilities of the two corresponding sequence symmetric tandem mismatches [Mathews, D. H., Sabina, J., Zuker, M., and Turner, D. H. (1999) J. Mol. Biol. 288, 911-940]. To improve the prediction of sequence asymmetric tandem mismatches, the experimental thermodynamic parameters for the 22 previously unmeasured sequence symmetric tandem mismatches are reported. These new data, however, do not improve prediction of the free energy contributions of sequence asymmetric tandem mismatches. Therefore, a new model, independent of sequence symmetric tandem mismatch free energies, is proposed. This model consists of two penalties to account for destabilizing tandem mismatches, two bonuses to account for stabilizing tandem mismatches, and two penalties to account for A-U and G-U adjacent base pairs. This model improves the prediction of asymmetric tandem mismatch free energy contributions and is likely to improve the prediction of RNA secondary structure from sequence.     

Thermodynamic study of internal loops in oligoribonucleotides: symmetric loops are more stable than asymmetric loops.

Thermodynamic parameters for internal loops of unpaired adenosines in oligoribonucleotides have been measured by optical melting studies. Comparisons are made between helices containing symmetric and asymmetric loops. Asymmetric loops destabilize a helix more than symmetric loops. The differences in free energy between symmetric and asymmetric loops are roughly half the magnitude suggested from a study of parameters required to give accurate predictions of RNA secondary structure [Papanicolaou, C., Gouy, M., & Ninio, J. (1984) Nucleic Acids Res. 12, 31-44]. Circular dichroism spectra indicate no major structural difference between helices containing symmetric and asymmetric loops. The measured sequence dependence of internal loop stability is not consistent with approximations used in current algorithms for predicting RNA secondary structure.     

GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops

ABSTRACT: BACKGROUND: Accurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction programs achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage of today's computing technology. FINDINGS: We present here the first implementation of RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers. We show that GTfold predicts secondary structure in less time than UNAfold and RNAfold, without sacrificing accuracy, on machines with four or more cores. CONCLUSIONS: GTfold supports advances in RNA structural biology by reducing the timescales for secondary structure prediction. The difference will be particularly valuable to researchers working with lengthy RNA sequences, such as RNA viral genomes.     

Review of machine learning methods for RNA secondary structure prediction

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.