四种结构类型的蛋白质设计方法

给出了以疏水一亲水模型为基础的蛋白质设计方法,该方法以物理学原理为基础,以相对熵作为优化的目标函数。对四种不同结构类型的天然结构的真实蛋白质进行了检测,分析了影响检测成功率的主要因素,结果表明,该方法是普适的,可用于对不同结构类型的蛋白质设计序列。     

单形格子和单形重心设计统计模型的优化分析方法

单形格子和单形重心设计是两种非常实用的配方试验设计方法,但其统计模型的优化分析却很困难．本文通过对单形格子和单形重心设计基本原理的分析,根据数学规划理论,构建了专门对这两种试验设计的统计模型进行优化分析的方法,同时给出了应用实例．     

基于HNP三态模型及相对熵方法的蛋白质折叠研究

把20种氨基酸简化为3类:疏水氨基酸(hydrophobic,H)、亲水氨基酸(hydrophilic,P)及中性氨基酸(neutral,N),每个氨基酸简化为一个点,用其C!原子来代替.采用非格点模型,以相对熵作为优化函数,进行蛋白质三维结构预测.为了与基于相对熵方法的蛋白质设计工作进行统一,采用了新的接触强度函数.选用蛋白质数据库中的天然蛋白质作为测试靶蛋白,结果表明,采用该模型和方法取得了较好的结果,预测结构相对于天然结构的均方根偏差在0.30～0.70nm之间.该工作为基于相对熵及HNP模型的蛋白质设计研究打下了基础.     

食物链种群模型的格子Boltzmann方法模拟

用格子Boltzmann方法求解用反应一扩散方程组描述的食物链种群模型．我们用一维和二维方程组进行数值实验,模拟结果与现有的数值实验结果很好地吻合,反映了格子Boltzmann方法的高效性和稳定性,并就二维格子、Boltzmann格式,通过其等价的差分格式,由极值原理证明了该格式的稳定性．     

基于HNP模型及相对熵的蛋白质设计方法在不同蛋白质体系中的应用

详细考察了基于HNP(H:hydtophobic,N:neutral,P:hydrophilic)模型及相对熵的蛋白质设计方法对于不同结构类型蛋白质的适用性,并与基于HP模型的结果进行了比较.通过对190个4种不同结构类型的蛋白质进行预测,结果表明,基于HNP模型及相对熵的设计方法对于不同结构类型的蛋白质具有普适性.进一步的研究发现,对于α螺旋、β折叠等规则的二级结构,该方法的预测成功率高于无规卷曲结构预测成功率.另外,还比较了对不同氨基酸的预测差异,结果显示亲水残基的预测成功率较高.此外,研究表明该方法对于蛋白质保守残基的预测成功率高于非保守残基.在以上分析的基础上,进一步讨论了导致这些差异的原因.这些研究为基于相对熵的蛋白质设计方法的实际应用和进一步的发展打下了良好基础.     

蛋白质二级结构预测: 基于词条的最大熵马尔科夫方法

提出了一种新的蛋白质二级结构预测方法. 该方法从氨基酸序列中提取出和自然语言中的“词”类似的与物种相关的蛋白质二级结构词条, 这些词条形成了蛋白质二级结构词典, 该词典描述了氨基酸序列和蛋白质二级结构之间的关系. 预测蛋白质二级结构的过程和自然语言中的分词和词性标注一体化的过程类似. 该方法把词条序列看成是马尔科夫链, 通过Viterbi算法搜索每个词条被标注为某种二级结构类型的最大概率, 其中使用词网格描述分词的结果, 使用最大熵马尔科夫模型计算词条的二级结构概率. 蛋白质二级结构预测的结果是最优的分词所对应的二级结构类型. 在4个物种的蛋白质序列上对这种方法进行测试, 并和PHD方法进行比较. 试验结果显示, 这种方法的Q3准确率比PHD方法高3.9%, SOV准确率比PHD方法高4.6%. 结合BLAST搜索的局部相似的序列可以进一步提高预测的准确率. 在50个CASP5目标蛋白质序列上进行测试的结果是: Q3准确率为78.9%, SOV准确率为77.1%. 基于这种方法建立了一个蛋白质二级结构预测的服务器, 可以通过http://www.insun.hit.edu.cn:81/demos/biology/index.html来访问.     

微孔板蛋白质芯片技术应用于单克隆抗体分型研究

设计与构建了可用于单克隆抗体分型鉴定的微孔板蛋白质芯片,利用该芯片进行了12株单克隆抗体和2种多克隆抗体的分型鉴定,并与ELISA方法进行了对比。结果表明,蛋白质芯片方法对单克隆抗体和多克隆抗体进行鉴定的结果,与ELISA方法进行鉴定的结果一致;与ELISA方法相比,蛋白质芯片的方法降低了试剂与样品的用量,缩短了工作时间,提高了工作效率。对于高通量的单克隆抗体制备体系,单克隆抗体分型蛋白质芯片是一种敏感、快捷的分型鉴定工具。     

基于HP模型的蛋白质折叠问题的研究

基于蛋白质二维HP模型提出改进的遗传算法对真实蛋白质进行计算机折叠模拟。结果显示疏水能量函数最小值的蛋白质构象对应含疏水核心的稳定结构,疏水作用在蛋白质折叠中起主要作用。研究表明二维HP模型在蛋白质折叠研究中是可行的和有效的并为进一步揭示蛋白质折叠机理提供重要参考信息。     

蛋白质结构预测研究进展

蛋白质结构预测是生物信息学当前的主要挑战之一.按照蛋白质结构预测对PDB数据 库信息的依赖程度,可以将其划分成两类:模板依赖模型和从头预测方法.其中模板依赖模 型又可以分为同源模型与穿线法.本文介绍了各种预测方法主要步骤,分析了制约各种方法 的瓶颈,及其研究进展.同源模型所取得的结构精度较高,但其对模板依赖性强;用于低同 源性的穿线法是模板依赖的模型重要的研究方向;从头预测法中统计学函数与物理函数的综 合使用取得了很好的效果,但是对于超过150个残基的片段,依然是巨大的挑战.     

PRP8蛋白质反式剪接系统的建立

真菌病原体Cryptococcus neoformansAD血清型剪接体蛋白PRP8蛋白质内含子是目前 发现的第2个存在于真核生物体核基因组中的蛋白质内含子.它的宿主基因prp8编码的PRP 8蛋白作为剪接体的1个组分,是1个高度保守的mRNA剪接蛋白.将组氨酸标签插入克隆自真菌病原体Cryptococcus neoformans AD血清型的PRP8蛋白质内含子中,并将该蛋白质内含子进行人工断裂,获得断裂蛋白质内含子,在大肠杆菌中鉴定其剪接活性.研究结果表明：所获得的改造型蛋白质内含子均表现出高效的剪接活性.利用此Cryptococcus neoformansAD血清型PRP8 断裂蛋白质内含子,成功构建了蛋白质反式剪接系统.这一反式剪接系统可用于其他蛋白质的连接与合成,有望成为蛋白质工程中的一种有用工具.     

Fitting conditional survival models to meta-analytic data by using a transformation toward mixed-effects models

Summary .   Frailty models are widely used to model clustered survival data. Classical ways to fit frailty models are likelihood-based. We propose an alternative approach in which the original problem of "fitting a frailty model" is reformulated into the problem of "fitting a linear mixed model" using model transformation. We show that the transformation idea also works for multivariate proportional odds models and for multivariate additive risks models. It therefore bridges segregated methodologies as it provides a general way to fit conditional models for multivariate survival data by using mixed models methodology. To study the specific features of the proposed method we focus on frailty models. Based on a simulation study, we show that the proposed method provides a good and simple alternative for fitting frailty models for data sets with a sufficiently large number of clusters and moderate to large sample sizes within covariate-level subgroups in the clusters. The proposed method is applied to data from 27 randomized trials in advanced colorectal cancer, which are available through the Meta-Analysis Group in Cancer.     

Evaluating the absolute quality of a single protein model using structural features and support vector machines

Knowing the quality of a protein structure model is important for its appropriate usage. We developed a model evaluation method to assess the absolute quality of a single protein model using only structural features with support vector machine regression. The method assigns an absolute quantitative score (i.e. GDT‐TS) to a model by comparing its secondary structure, relative solvent accessibility, contact map, and beta sheet structure with their counterparts predicted from its primary sequence. We trained and tested the method on the CASP6 dataset using cross‐validation. The correlation between predicted and true scores is 0.82. On the independent CASP7 dataset, the correlation averaged over 95 protein targets is 0.76; the average correlation for template‐based and ab initio targets is 0.82 and 0.50, respectively. Furthermore, the predicted absolute quality scores can be used to rank models effectively. The average difference (or loss) between the scores of the top‐ranked models and the best models is 5.70 on the CASP7 targets. This method performs favorably when compared with the other methods used on the same dataset. Moreover, the predicted absolute quality scores are comparable across models for different proteins. These features make the method a valuable tool for model quality assurance and ranking. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

An iterative self-refining and self-evaluating approach for protein model quality estimation

Evaluating or predicting the quality of protein models (i.e., predicted protein tertiary structures) without knowing their native structures is important for selecting and appropriately using protein models. We describe an iterative approach that improves the performances of protein Model Quality Assurance Programs (MQAPs). Given the initial quality scores of a list of models assigned by a MQAP, the method iteratively refines the scores until the ranking of the models does not change. We applied the method to the model quality assessment data generated by 30 MQAPs during the Eighth Critical Assessment of Techniques for Protein Structure Prediction. To various degrees, our method increased the average correlation between predicted and real quality scores of 25 out of 30 MQAPs and reduced the average loss (i.e., the difference between the top ranked model and the best model) for 28 MQAPs. Particularly, for MQAPs with low average correlations (<0.4), the correlation can be increased by several times. Similar experiments conducted on the CASP9 MQAPs also demonstrated the effectiveness of the method. Our method is a hybrid method that combines the original method of a MQAP and the pair-wise comparison clustering method. It can achieve a high accuracy similar to a full pair-wise clustering method, but with much less computation time when evaluating hundreds of models. Furthermore, without knowing native structures, the iterative refining method can evaluate the performance of a MQAP by analyzing its model quality predictions.     

Comparative modeling and protein-like features of hydrophobic-polar models on a two-dimensional lattice

Lattice models of proteins have been extensively used to study protein thermodynamics, folding dynamics, and evolution. Our study considers two different hydrophobic-polar (HP) models on the 2D square lattice: the purely HP model and a model where a compactness-favoring term is added. We exhaustively enumerate all the possible structures in our models and perform the study of their corresponding folds, HP arrangements in space and shapes. The two models considered differ greatly in their numbers of structures, folds, arrangements, and shapes. Despite their differences, both lattice models have distinctive protein-like features: (1) Shapes are compact in both models, especially when a compactness-favoring energy term is added. (2) The residue composition is independent of the chain length and is very close to 50% hydrophobic in both models, as we observe in real proteins. (3) Comparative modeling works well in both models, particularly in the more compact one. The fact that our models show protein-like features suggests that lattice models incorporate the fundamental physical principles of proteins. Our study supports the use of lattice models to study questions about proteins that require exactness and extensive calculations, such as protein design and evolution, which are often too complex and computationally demanding to be addressed with more detailed models.     

Partial unfolding and refolding for structure refinement: A unified approach of geometric simulations and molecular dynamics

The most successful protein structure prediction methods to date have been template‐based modeling (TBM) or homology modeling, which predicts protein structure based on experimental structures. These high accuracy predictions sometimes retain structural errors due to incorrect templates or a lack of accurate templates in the case of low sequence similarity, making these structures inadequate in drug‐design studies or molecular dynamics simulations. We have developed a new physics based approach to the protein refinement problem by mimicking the mechanism of chaperons that rehabilitate misfolded proteins. The template structure is unfolded by selectively (targeted) pulling on different portions of the protein using the geometric based technique FRODA, and then refolded using hierarchically restrained replica exchange molecular dynamics simulations (hr‐REMD). FRODA unfolding is used to create a diverse set of topologies for surveying near native‐like structures from a template and to provide a set of persistent contacts to be employed during re‐folding. We have tested our approach on 13 previous CASP targets and observed that this method of folding an ensemble of partially unfolded structures, through the hierarchical addition of contact restraints (that is, first local and then nonlocal interactions), leads to a refolding of the structure along with refinement in most cases (12/13). Although this approach yields refined models through advancement in sampling, the task of blind selection of the best refined models still needs to be solved. Overall, the method can be useful for improved sampling for low resolution models where certain of the portions of the structure are incorrectly modeled. Proteins 2015; 83:2279–2292. © 2015 Wiley Periodicals, Inc.     

Assessment of protein model structure accuracy estimation in CASP13: Challenges in the era of deep learning

Scoring model structure is an essential component of protein structure prediction that can affect the prediction accuracy tremendously. Users of protein structure prediction results also need to score models to select the best models for their application studies. In Critical Assessment of techniques for protein Structure Prediction (CASP), model accuracy estimation methods have been tested in a blind fashion by providing models submitted by the tertiary structure prediction servers for scoring. In CASP13, model accuracy estimation results were evaluated in terms of both global and local structure accuracy. Global structure accuracy estimation was evaluated by the quality of the models selected by the global structure scores and by the absolute estimates of the global scores. Residue-wise, local structure accuracy estimations were evaluated by three different measures. A new measure introduced in CASP13 evaluates the ability to predict inaccurately modeled regions that may be improved by refinement. An intensive comparative analysis on CASP13 and the previous CASPs revealed that the tertiary structure models generated by the CASP13 servers show very distinct features. Higher consensus toward models of higher global accuracy appeared even for free modeling targets, and many models of high global accuracy were not well optimized at the atomic level. This is related to the new technology in CASP13, deep learning for tertiary contact prediction. The tertiary model structures generated by deep learning pose a new challenge for EMA (estimation of model accuracy) method developers. Model accuracy estimation itself is also an area where deep learning can potentially have an impact, although current EMA methods have not fully explored that direction.     

The Rubinstein–Taybi syndrome: modeling mental impairment in the mouse

Mental impairment syndromes are diagnosed based on below-average general intellectual function originated during developmental periods. Intellectual abilities rely on the capability of our brain to obtain, process, store and retrieve information. Advances in the past decade on the molecular basis of memory have led to a better understanding of how a normal brain works but also have shed new light on our understanding of many pathologies of the nervous system, including diverse syndromes involving mental impairment. The recent multidisciplinary analysis of various mouse models for Rubinstein–Taybi syndrome has shown the power of animal models to produce an important leap forward in our understanding of a complex mental disease while simultaneously opening new avenues for its treatment. These studies also suggest that some of the cognitive and physiological deficits observed in mental impairment syndromes may not simply be caused by defects originated during development but may result from the continued requirement of specific enzymatic activities throughout life.     

The conductor-like screening model for polymers and surfaces

The DMol³ COSMO method is revisited and generalized for infinite polymer and surface models with periodic boundary conditions. The procedure works also for three dimensionally periodic solid models with internal surfaces. A new solvent accessible surface grid construction is presented, where the grid points and weights are a continuous function for all atomic geometries. The calculated solvation energy is also continuous by consequence, which is useful for all calculations which involve geometry changes of the atomic framework. The new method is tested with a few examples.     

Learning generative models for protein fold families

We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position-specific conservation statistics and the correlated mutation statistics between sequential and long-range pairs of residues. Existing techniques for learning graphical models from MSA either make strong, and often inappropriate assumptions about the conditional independencies within the MSA (e.g., Hidden Markov Models), or else use suboptimal algorithms to learn the parameters of the model. In contrast, GREMLIN makes no a priori assumptions about the conditional independencies within the MSA. We formulate and solve a convex optimization problem, thus guaranteeing that we find a globally optimal model at convergence. The resulting model is also generative, allowing for the design of new protein sequences that have the same statistical properties as those in the MSA. We perform a detailed analysis of covariation statistics on the extensively studied WW and PDZ domains and show that our method out-performs an existing algorithm for learning undirected probabilistic graphical models from MSA. We then apply our approach to 71 additional families from the PFAM database and demonstrate that the resulting models significantly out-perform Hidden Markov Models in terms of predictive accuracy.     

冬小麦高产优质高效栽培决策模拟研究

在产量和品质形成规律及投入产出效益规律基础上,应用模糊集方法,构建了一个综合的冬小麦高产优质高效栽培优化决策模拟模型.并以Window XP为平台,采用VB(Visual Basic)语言编程,建立了相应的可视化决策支持系统.实现了在不同时空、自然、社会、经济、技术条件下,进行多目标、可综合、可选择、可调控的冬小麦田间水肥管理决策的目标.通过检验证明模拟模型是可行的.