Vibrational entropy differences between mesophile and thermophile proteins and their use in protein engineering

We recently introduced ENCoM, an elastic network atomic contact model, as the first coarse-grained normal mode analysis method that accounts for the nature of amino acids and can predict the effect of mutations on thermostability based on changes vibrational entropy. In this proof-of-concept article, we use pairs of mesophile and thermophile homolog proteins with identical structures to determine if a measure of vibrational entropy based on normal mode analysis can discriminate thermophile from mesophile proteins. We observe that in around 60% of cases, thermophile proteins are more rigid at equivalent temperatures than their mesophile counterpart and this difference can guide the design of proteins to increase their thermostability through series of mutations. We observe that mutations separating thermophile proteins from their mesophile orthologs contribute independently to a decrease in vibrational entropy and discuss the application and implications of this methodology to protein engineering.     

Survey of the year 2009: applications of isothermal titration calorimetry

Isothermal titration calorimetry (ITC) is now an established and invaluable method for determining the thermodynamic constants, association constant and stoichiometry of molecular interactions in aqueous solutions. The technique has become widely used by biochemists to study protein interaction with other proteins, small molecules, metal ions, lipids, nucleic acids and carbohydrates; and nucleic acid interaction with small molecules. The drug discovery industry has utilized this approach to measure protein (or nucleic acid) interaction with drug candidates. ITC has been used to screen candidates, guide the design of potential drugs and validate the modelling used in structure-based drug design. Emerging disciplines including nanotechnology and drug delivery could benefit greatly from ITC in enhancing their understanding and control of nano-particle assembly, and drug binding and controlled release.     

Virtual screening using molecular simulations

Effective virtual screening relies on our ability to make accurate prediction of protein-ligand binding, which remains a great challenge. In this work, utilizing the molecular-mechanics Poisson-Boltzmann (or Generalized Born) surface area approach, we have evaluated the binding affinity of a set of 156 ligands to seven families of proteins, trypsin β, thrombin α, cyclin-dependent kinase (CDK), cAMP-dependent kinase (PKA), urokinase-type plasminogen activator, β-glucosidase A, and coagulation factor Xa. The effect of protein dielectric constant in the implicit-solvent model on the binding free energy calculation is shown to be important. The statistical correlations between the binding energy calculated from the implicit-solvent approach and experimental free energy are in the range of 0.56-0.79 across all the families. This performance is better than that of typical docking programs especially given that the latter is directly trained using known binding data whereas the molecular mechanics is based on general physical parameters. Estimation of entropic contribution remains the barrier to accurate free energy calculation. We show that the traditional rigid rotor harmonic oscillator approximation is unable to improve the binding free energy prediction. Inclusion of conformational restriction seems to be promising but requires further investigation. On the other hand, our preliminary study suggests that implicit-solvent based alchemical perturbation, which offers explicit sampling of configuration entropy, can be a viable approach to significantly improve the prediction of binding free energy. Overall, the molecular mechanics approach has the potential for medium to high-throughput computational drug discovery.     

Finessing atlas data for species distribution models

Aim The spatial resolution of species atlases and therefore resulting model predictions are often too coarse for local applications. Collecting distribution data at a finer resolution for large numbers of species requires a comprehensive sampling effort, making it impractical and expensive. This study outlines the incorporation of existing knowledge into a conventional approach to predict the distribution of Bonelli’s eagle (Aquila fasciata) at a resolution 100 times finer than available atlas data. Location Malaga province, Andalusia, southern Spain. Methods A Bayesian expert system was proposed to utilize the knowledge from distribution models to yield the probability of a species being recorded at a finer resolution (1 × 1 km) than the original atlas data (10 × 10 km). The recorded probability was then used as a weight vector to generate a sampling scheme from the species atlas to enhance the accuracy of the modelling procedure. The maximum entropy for species distribution modelling (MaxEnt) was used as the species distribution model. A comparison was made between the results of the MaxEnt using the enhanced and, the random sampling scheme, based on four groups of environmental variables: topographic, climatic, biological and anthropogenic. Results The models with the sampling scheme enhanced by an expert system had a higher discriminative capacity than the baseline models. The downscaled (i.e. finer scale) species distribution maps using a hybrid MaxEnt/expert system approach were more specific to the nest locations and were more contrasted than those of the baseline model. Main conclusions The proposed method is a feasible substitute for comprehensive field work. The approach developed in this study is applicable for predicting the distribution of Bonelli’s eagle at a local scale from a national‐level occurrence data set; however, the usefulness of this approach may be limited to well‐known species.     

利用序列保守模体和局部构象信息预测转录因子结合位点

转录因子结合位点的计算预测是研究基因转录调控的重要环节,但常用的位置特异得分矩阵方法预测特异性偏低.通过深入分析结合位点的生物特征,提出了一种综合利用序列保守模体和局部构象信息的结合位点预测方法,以极大相关得分矩阵作为保守模体的描述模型,并根据二苷参数模型计算位点序列的局部构象,将两类信息得分组合为多维特征向量,在二次判别分析的框架下进行训练和滑动预测.预测过程中还引入了位置信息量以优化似然得分和过滤备选结果.针对大肠杆菌CRP和Fis结合位点数据的留一法测试结果表明,描述模型的改进和多种信息的融合能有效地改善预测方法的性能,大幅度提高特异性.     

老爷岭南部狍冬季移动、卧息生境选择及其适宜性评价

在动物生境研究中,移动生境和卧息生境是生境研究的焦点。开展移动生境和卧息生境选择,并在此基础上进行生境评价,有利于深入了解动物对移动和卧息生境条件的需求,制定科学合理的栖息地保护计划。以东北虎（Panthera tigris altaica）的主要猎物物种之一-狍（Capreolus pygargus）为研究对象,于2017-2019年冬季积雪覆盖期在老爷岭南部通过随机布设28个大样方和84条用于足迹链跟踪的样线收集狍的移动点和卧息点信息,再结合近年来收集的东北虎出现点,利用广义可加模型（GAM）和最大熵模型（MaxEnt）进行狍移动、卧息生境选择及评价研究。移动生境选择研究表明,狍在移动的过程中偏好选择坡度小、距农田距离>500 m、远离道路、居民点和低海拔或较高海拔的区域;移动生境评价分析表明,移动适宜和次适宜生境面积之和为1318.16 km²,占研究区域面积的51.28%,当加入虎活动点影响因子后,狍移动适宜和次适宜生境面积之和为901.52 km²,适宜和次适宜生境面积之和减少了31.61%。狍卧息生境选择研究表明,水源、农田、道路和雪深是影响狍卧息的关键因素,其中雪深对狍卧息生境选择的贡献率达到70.13%;卧息生境评价表明,卧息适宜和次适宜生境面积之和为1243.77 km²,占研究区域面积的48.39%,当加入虎出现点因子后,适宜生境和次适宜生境面积之和减少了61.00%,仅为485.02 km²。研究认为,虎的出现对狍移动和卧息生境选择均产生影响,虎的活动及捕食行为可能会减少狍的活动范围和频次,狍远离虎活动区域卧息休息,压缩了狍适宜卧息的空间。     

中国三种常见蒿属植物潜在地理分布及其主导气候因子

裂叶蒿(Artemisia tanacetifolia)、大籽蒿(Artemisia sieversiana)和艾(Artemisia argyi)是我国常见的蒿属(Artemisia)植物,其分布区域遍布全国.本文利用MaxEnt模型预测3种蒿属植物在当前气候条件以及未来两种气候情景下的潜在分布区.采用受试者工作特征...     

Variation, natural selection, and information content--a simulation

In Neo-Darwinism, variation and natural selection are the two evolutionary mechanisms that propel biological evolution. Variation implies changes in the gene pool of a population, enlarging the genetic variability from which natural selection can choose. But in the absence of natural selection, variation causes dissipation and randomization. Natural selection, in contrast, constrains this variability by decreasing the survival and fertility of the less-adapted organisms. The objective of this study is to propose a highly simplified simulation of variation and natural selection, and to relate the observed evolutionary changes in a population to its information content. The model involves an imaginary population of individuals. A quantifiable character allows the individuals to be categorized into bins. The distribution of bins (a histogram) was assumed to be Gaussian. The content of each bin was calculated after one to twelve cycles, each cycle spanning N generations (N being undefined). In a first study, selection was simulated in the absence of variation. This was modeled by assuming a differential fertility factor F that increased linearly from the lower bins (F<1.00) to the higher bins (F>1.00). The fertility factor was applied as a multiplication factor during each cycle. Several ranges of fertility were investigated. The resulting histograms became skewed to the right. In a second study, variation was simulated in the absence of selection. This was modeled by assuming that during each cycle each bin lost a fixed percentage of its content (variation factor Y) to its two adjacent bins. The resulting histograms became broader and flatter, while retaining their bilateral symmetry. Different values of Y were monitored. In a third study, various values of F and Y were combined. Our model allows the straightforward application of Shannon's equation and the calculation of a Shannon-entropy (SE) values for each histogram. Natural selection was, thus, shown to result in a progressive decrease in SE as a function of F. In other words, natural selection, when acting alone, progressively increased the information content of the population. In contrast, variation resulted in a progressive increase in SE as a function of Y. In other words, variation acting alone progressively decreased the information content of a population. When both factors, F and Y, were applied simultaneously, their relative weight determined the progressive change in SE.