TargetATPsite: A template‐free method for ATP‐binding sites prediction with residue evolution image sparse representation and classifier ensemble

Understanding the interactions between proteins and ligands is critical for protein function annotations and drug discovery. We report a new sequence‐based template‐free predictor (TargetATPsite) to identify the Adenosine‐5′‐triphosphate (ATP) binding sites with machine‐learning approaches. Two steps are implemented in TargetATPsite: binding residues and pockets predictions, respectively. To predict the binding residues, a novel image sparse representation technique is proposed to encode residue evolution information treated as the input features. An ensemble classifier constructed based on support vector machines (SVM) from multiple random under‐samplings is used as the prediction model, which is effective for dealing with imbalance phenomenon between the positive and negative training samples. Compared with the existing ATP‐specific sequence‐based predictors, TargetATPsite is featured by the second step of possessing the capability of further identifying the binding pockets from the predicted binding residues through a spatial clustering algorithm. Experimental results on three benchmark datasets demonstrate the efficacy of TargetATPsite. © 2013 Wiley Periodicals, Inc.     

Biosensors based on gold nanoelectrode ensembles and screen printed electrodes

Gold nanowires were synthesized within polycarbonate membranes according to an electroless deposition method, obtaining nanoelectrode ensembles (NEEs) with special electrochemical features. NEEs were coupled with home-produced carbon graphite screen printed electrodes and the electrochemical properties of the original nanoelectrode ensemble on screen printed substrate (NEE/SPS) assembly has been tested for sensors application. Glucose oxidase has been used as model enzyme in order to verify the feasibility of disposable gold NEE/SPS biosensors. Finally, different immobilisation and electrochemical deposition techniques based on either self assembled monolayers of cysteamine (CYS) or amino-propyl-triethoxysilane (APTES) and conductive polyaniline (PANI) molecular wires were used. Spatial patterning of the enzyme on the polycarbonate surface and of PANI wires on gold nanoelectrodes was obtained. Possible direct electron transfer between the enzyme and the PANI modified gold nanoelectrodes has been evaluated.     

Hochschild and cyclic (co)homology of superadditive categories

We define the Hochschild and cyclic (co)homology groups for superadditive categories and show that these (co)homology groups are graded Morita invariants. We also show that the Hochschild and cyclic homology are compatible with the tensor product of superadditive categories.     

Adaptive lambda square dynamics simulation: An efficient conformational sampling method for biomolecules

A novel, efficient sampling method for biomolecules is proposed. The partial multicanonical molecular dynamics (McMD) was recently developed as a method that improved generalized ensemble (GE) methods to focus sampling only on a part of a system (GEPS); however, it was not tested well. We found that partial McMD did not work well for polylysine decapeptide and gave significantly worse sampling efficiency than a conventional GE. Herein, we elucidate the fundamental reason for this and propose a novel GEPS, adaptive lambda square dynamics (ALSD), which can resolve the problem faced when using partial McMD. We demonstrate that ALSD greatly increases the sampling efficiency over a conventional GE. We believe that ALSD is an effective method and is applicable to the conformational sampling of larger and more complicated biomolecule systems. © 2013 Wiley Periodicals, Inc.     

Investigating the Disordered and Membrane-Active Peptide A-Cage-C Using Conformational Ensembles

The driving forces and conformational pathways leading to amphitropic protein-membrane binding and in some cases also to protein misfolding and aggregation is the subject of intensive research. In this study, a chimeric polypeptide, A-Cage-C, derived from α-Lactalbumin is investigated with the aim of elucidating conformational changes promoting interaction with bilayers. From previous studies, it is known that A-Cage-C causes membrane leakages associated with the sporadic formation of amorphous aggregates on solid-supported bilayers. Here we express and purify double-labelled A-Cage-C and prepare partially deuterated bicelles as a membrane mimicking system. We investigate A-Cage-C in the presence and absence of these bicelles at non-binding (pH 7.0) and binding (pH 4.5) conditions. Using in silico analyses, NMR, conformational clustering, and Molecular Dynamics, we provide tentative insights into the conformations of bound and unbound A-Cage-C. The conformation of each state is dynamic and samples a large amount of overlapping conformational space. We identify one of the clusters as likely representing the binding conformation and conclude tentatively that the unfolding around the central W23 segment and its reorientation may be necessary for full intercalation at binding conditions (pH 4.5). We also see evidence for an overall elongation of A-Cage-C in the presence of model bilayers.     

单壁碳纳米管上毒性苯气体净化的分子模拟(英文)

采用巨正则系综蒙特卡罗(GCMC)方法研究了空气中微量苯组分在单臂碳纳米管(SWNTs)上的吸附净化.模拟表明,具有较大孔径的(20,20)纳米管比较适合吸附纯苯蒸汽,而对于移除空气中的毒性苯物质,苯的吸附选择性分别在(12,12)纳米管及4.0 MPa时和(18,18)纳米管及0.1 MPa时出现最小值和最大值.为了解释这一异常行为,我们进一步分析了N2-O2-C6H6混合物的局部密度分布、吸附分子构型和概率密度分布,发现(18,18)纳米管内外完全被苯分子占据,而对于(12,12)纳米管,由于存在更强的吸附质-吸附剂相互作用,空气分子更倾向于吸附在管与管之间的间隙.此外,吸附分子的空间有序参数表明大多数苯分子采取"平躺"在纳米管表面的定位,而线性的N2和O2分子则多数平行于孔轴方向.最后研究了温度和苯分子主体相浓度对分离效果的影响.我们发现较大孔中的选择性随着温度的增加比小孔下降更加明显.与此对比,主体相苯浓度对小孔中的选择性起到更加重要的作用.     

基于二阶段异质随机森林的汽油辛烷值预测

构建了基于二阶段异质随机森林的汽油辛烷值预测模型.首先利用样本-位点信息表知识约简模型,筛选出对汽油辛烷值影响大的位点数据作为第一阶段;然后,利用集成学习思想集成支持向量回归和动态时间序列神经网络,构建异质随机森林预测模型作为第二阶段.利用十折交叉法验证模型精度,结果表明该集成学习算法具有有效性和高精度.