BetaSuperposer: superposition of protein surfaces using beta-shapes

The comparison between two protein structures is important for understanding a molecular function. In particular, the comparison of protein surfaces to measure their similarity provides another challenge useful for studying molecular evolution, docking, and drug design. This paper presents an algorithm, called the BetaSuperposer, which evaluates the similarity between the surfaces of two structures using the beta-shape which is a geometric structure derived from the Voronoi diagram of molecule. The algorithm performs iterations of mix-and-match between the beta-shapes of two structures for the optimal superposition from which a similarity measure is computed, where each mix-and-match step attempts to solve an NP-hard problem. The devised heuristic algorithm based on the assignment problem formulation quickly produces a good superposition and an assessment of similarity. The BetaSuperposer was fully implemented and benchmarked against popular programs, the Dali and the Click, using the SCOP models. The BetaSuperposer is freely available to the public from the Voronoi Diagram Research Center (http://voronoi.hanyang.ac.kr).     

BetaSuperposer: superposition of protein surfaces using beta-shapes

The comparison between two protein structures is important for understanding a molecular function. In particular, the comparison of protein surfaces to measure their similarity provides another challenge useful for studying molecular evolution, docking, and drug design. This paper presents an algorithm, called the BetaSuperposer, which evaluates the similarity between the surfaces of two structures using the beta-shape which is a geometric structure derived from the Voronoi diagram of molecule. The algorithm performs iterations of mix-and-match between the beta-shapes of two structures for the optimal superposition from which a similarity measure is computed, where each mix-and-match step attempts to solve an NP-hard problem. The devised heuristic algorithm based on the assignment problem formulation quickly produces a good superposition and an assessment of similarity. The BetaSuperposer was fully implemented and benchmarked against popular programs, the Dali and the Click, using the SCOP models. The BetaSuperposer is freely available to the public from the Voronoi Diagram Research Center ( http://voronoi.hanyang.ac.kr ).     

Analytical shape computation of macromolecules: I. molecular area and volume through alpha shape

The size and shape of macromolecules such as proteins and nucleic acids play an important role in their functions. Prior efforts to quantify these properties have been based on various discretization or tessellation procedures involving analytical or numerical computations. In this article, we present an analytically exact method for computing the metric properties of macromolecules based on the alpha shape theory. This method uses the duality between alpha complex and the weighted Voronoi decomposition of a molecule. We describe the intuitive ideas and concepts behind the alpha shape theory and the algorithm for computing areas and volumes of macromolecules. We apply our method to compute areas and volumes of a number of protein systems. We also discuss several difficulties commonly encountered in molecular shape computations and outline methods to overcome these problems. Proteins 33:1–17, 1998. © 1998 Wiley-Liss, Inc.     

Stepwise Paring down Variation for Identifying Influential Multi-factor Interactions Related to a Continuous Response Variable

Although several model-based methods are promising for the identification of influential single factors and multi-factor interactions, few are widely used in real applications for most of the model-selection procedures are complex and/or infeasible in computation for high-dimensional data. In particular, the ability of the methods to reveal more true factors and fewer false ones often relies heavily on the selection of appropriate values of tuning parameters, which is still a difficult task to practical analysts. This article provides a simple algorithm modified from stepwise forward regression for the identification of influential factors. Instead of keeping the identified factors in the next models for adjustment in stepwise regression, we propose to subtract the effects of identified factors in each run and always fit a single-term model to the effect-subtracted responses. The computation is lighter as the proposed method only involves calculations of a simple test statistic; and therefore it could be applied to screen ultrahigh-dimensional data for important single factors and multi-factor interactions. Most importantly, we have proposed a novel stopping rule of using a constant threshold for the simple test statistic, which is different from the conventional stepwise regression with AIC or BIC criterion. The performance of the new algorithm has been confirmed competitive by extensive simulation studies compared to several methods available in R packages, including the popular group lasso, surely independence screening, Bayesian quantitative trait locus mapping methods and others. Findings from two real data examples, including a genome-wide association study, demonstrate additional useful information of high-order interactions that can be gained from implementing the proposed algorithm.     

A computational approach to motion perception

In this paper it is shown that the computation of the optical flow from a sequence of timevarying images is not, in general, an underconstrained problem. A local algorithm for the computation of the optical flow which uses second order derivatives of the image brightness pattern, and that avoids the aperture problem, is presented. The obtained optical flow is very similar to the true motion field — which is the vector field associated with moving features on the image plane — and can be used to recover 3D motion information. Experimental results on sequences of real images, together with estimates of relevant motion parameters, like time-to-crash for translation and angular velocity for rotation, are presented and discussed. Due to the remarkable accuracy which can be achieved in estimating motion parameters, the proposed method is likely to be very useful in a number of computer vision applications.     

Computational identification of post‐translational modification‐based nuclear import regulations by characterizing nuclear localization signal‐import receptor interaction

The binding affinity between a nuclear localization signal (NLS) and its import receptor is closely related to corresponding nuclear import activity. PTM‐based modulation of the NLS binding affinity to the import receptor is one of the most understood mechanisms to regulate nuclear import of proteins. However, identification of such regulation mechanisms is challenging due to the difficulty of assessing the impact of PTM on corresponding nuclear import activities. In this study we proposed NIpredict, an effective algorithm to predict nuclear import activity given its NLS, in which molecular interaction energy components (MIECs) were used to characterize the NLS‐import receptor interaction, and the support vector regression machine (SVR) was used to learn the relationship between the characterized NLS‐import receptor interaction and the corresponding nuclear import activity. Our experiments showed that nuclear import activity change due to NLS change could be accurately predicted by the NIpredict algorithm. Based on NIpredict, we developed a systematic framework to identify potential PTM‐based nuclear import regulations for human and yeast nuclear proteins. Application of this approach has identified the potential nuclear import regulation mechanisms by phosphorylation of two nuclear proteins including SF1 and ORC6. Proteins 2014; 82:2783–2796. © 2014 Wiley Periodicals, Inc.     

purpl: program for marker correction on multicross designs made of Pure Lines

In plants, molecular analyses carried out on breeding populations are becoming increasingly popular. Here we introduce a new software tool, purpl , which was designed to follow and correct the inheritance of codominant molecular markers in such populations in which the level of difficulties in marker notation and the uncertainty of pedigrees are generally high. Based on a five‐step algorithm and on the computation of intuitive probability scores, purpl corrects many different possible sources of bias by extracting the more likely information in the parent and progeny files (initially linked by putative pedigrees), and rebuilds possible missing information. PurPL and the user's guide are available at http://www.clermont.inra.fr/umr‐asp/purpl/     

Molecular determinants of the ATP hydrolysis asymmetry of the CCT chaperonin complex

The eukaryotic cytosolic chaperonin CCT is a molecular machine involved in assisting the folding of proteins involved in important cellular processes. Like other chaperonins, CCT is formed by a double‐ring structure but, unlike all of them, each ring is composed of eight different, albeit homologous subunits. This complexity has probably to do with the specificity in substrate interaction and with the mechanism of protein folding that takes place during the chaperonin functional cycle, but its detailed molecular basis remains unknown. We have analyzed the known proteomes in search of residues that are differentially conserved in the eight subunits, as predictors of functional specificity (specificity‐determining positions; SDPs). We have found that most of these SDPs are located near the ATP binding site, and that they define four CCT clusters, corresponding to subunits CCT3, CCT6, CCT8 and CCT1/2/4/5/7. Our results point to a spatial organisation of the CCT subunits in two opposite areas of the ring and provide a molecular explanation for the previously described asymmetry in the hydrolysis of ATP. Proteins 2014; 82:703–707. © 2014 Wiley Periodicals, Inc.     

BetaDock: Shape-Priority Docking Method Based on Beta-Complex

Abstract

This paper presents an approach and a software, BetaDock, to the docking problem by putting the priority on shape complementarity between a receptor and a ligand. The approach is based on the theory of the β-complex. Given the Voronoi diagram of the receptor whose topology is stored in the quasi-triangulation, the β-complex corresponding to water molecule is computed. Then, the boundary of the β-complex defines the β-shape which has the complete proximity information among all atoms on the receptor boundary. From the β-shape, we first compute pockets where the ligand may bind. Then, we quickly place the ligand within each pocket by solving the singular value decomposition problem and the assignment problem. Using the conformations of the ligands within the pockets as the initial solutions, we run the genetic algorithm to find the optimal solution for the docking problem. The performance of the proposed algorithm was verified through a benchmark test and showed that BetaDock is superior to a popular docking software AutoDock 4.     

The bioactivity and fractionation of peptide hydrolysates in cultures of CHO cells

Peptide hydrolysate supplements in mammalian cell cultures provide enhanced growth and productivity. The objective of this study was to compare the bioactivity of ten different commercially available hydrolysates from plant, microbial, and animal sources. The peptide hydrolysates were tested as supplements to cultures of Chinese hamster ovary (CHO) cells that produce human beta interferon (β‐IFN). A soy hydrolysate was shown to support high cell growth but not protein productivity compared to an animal component hydrolysate (Primatone RL). On the other hand, a yeast hydrolysate showed lower cell growth, but comparable productivity of the recombinant protein. Glycosylation analysis showed that the glycan profile of β‐IFN produced in yeast hydrolysate supplemented cultures was equivalent to that from Primatone RL‐supplemented cultures. Fractionation of the yeast hydrolysate and Primatone RL produced a similar protein‐assayed pattern except for one extra peak at around 1 kDa in the Primatone RL profile. A fraction taken at a molecular weight range of 1.5–1.7 kDa showed the highest growth promoting activity in both samples. However, four other fractions in yeast hydrolysate and two in Primatone RL at lower molecular weights showed some growth promoting activity. In conclusion, the yeast hydrolysates provided a good alternative to the animal sourced Primatone RL for high productivity of β‐IFN from CHO cells with equivalent glycosylation. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:584–593, 2014     

Application of Artificial Bee Colony Algorithm to Maximum Likelihood DOA Estimation

Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation, but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony (ABC) algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the behavior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the conventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio (SNR), and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investigated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.     

PRIMO/PRIMONA: A coarse‐grained model for proteins and nucleic acids that preserves near‐atomistic accuracy

The new coarse graining model PRIMO/PRIMONA for proteins and nucleic acids is proposed. This model combines one to several heavy atoms into coarse‐grained sites that are chosen to allow an analytical, high‐resolution reconstruction of all‐atom models based on molecular bonding geometry constraints. The accuracy of proposed reconstruction method in terms of structure and energetics is tested and compared with other popular reconstruction methods for a variety of protein and nucleic acid test sets. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

城镇化过程中福建省山区县农村聚落景观格局变化特征

农村聚落是农村居民与其周围环境相互作用的现象与过程,是城乡二元关系发展的重要一环,科学评估山地型农村聚落景观时空变化及影响因素对山地城镇化发展具有重要意义。选取了城镇化迅速发展的福建省上杭县作为案例,使用2004年与2014年高分辨率遥感影像、DEM等数据,运用RS、GIS及Fragstats技术,定量分析了上杭县农村聚落景观变化过程、格局趋势。研究表明:(1)2004—2014年上杭县农村聚落数量出现大幅下降,空间分布态势并没有发生太大变化,主要集中在西部汀江水系与东北部,并有进一步加强的趋势,Voronoi模型Cv值结果表现出这一趋势,但最近邻分析结果却显示出2014年相对2004年略微分散。(2)Ripley's K函数显示山地型农村聚落集聚与分散存在尺度效应,65km以内都表现出集聚趋势,2004年在35km处出现最高集聚状态,2014年则在15km集聚达到最高之后出现缓慢下降,并且在30km尺度范围内集聚程度大于2004年。(3)坡度、水系与道路对山地型农村聚落有显著影响,坡度5°是最为关键的因素,10°是一个阈值,80%的农村聚落都分布在10°以内;67%以上的农村聚落分布在距河流湖泊2km范围内;交通对农村聚落影响愈发显著,98.28%聚落分布在交通道路500m范围,未来推进山地城镇化应更加重视交通的作用。(4)乡镇政府所在地中心村镇面积扩大了4.59倍,远高于一般聚落,是山地城镇化过程中景观变化最剧烈的地区,也是为来应重点发展的地区。研究可为山地型农村聚落建设、山地型城镇化村镇规划理论与决策提供一定借鉴。     

MOLE: a Voronoi diagram-based explorer of molecular channels, pores, and tunnels

We have developed an algorithm, "MOLE," for the rapid, fully automated location and characterization of molecular channels, tunnels, and pores. This algorithm has been made freely available on the Internet (http://mole.chemi.muni.cz/) and overcomes many of the shortcomings and limitations of the recently developed CAVER software. The core of our MOLE algorithm is a Dijkstra's path search algorithm, which is applied to a Voronoi mesh. Tests on a wide variety of biomolecular systems including gramicidine, acetylcholinesterase, cytochromes P450, potassium channels, DNA quadruplexes, ribozymes, and the large ribosomal subunit have demonstrated that the MOLE algorithm performs well. MOLE is thus a powerful tool for exploring large molecular channels, complex networks of channels, and molecular dynamics trajectories in which analysis of a large number of snapshots is required.     

A quantile‐slicing approach for sufficient dimension reduction with censored responses

Sufficient dimension reduction (SDR) that effectively reduces the predictor dimension in regression has been popular in high‐dimensional data analysis. Under the presence of censoring, however, most existing SDR methods suffer. In this article, we propose a new algorithm to perform SDR with censored responses based on the quantile‐slicing scheme recently proposed by Kim et al. First, we estimate the conditional quantile function of the true survival time via the censored kernel quantile regression (Shin et al.) and then slice the data based on the estimated censored regression quantiles instead of the responses. Both simulated and real data analysis demonstrate promising performance of the proposed method.     

Neuromolecular computing: a new approach to human brain evolution

Evolutionary approaches in human cognitive neurobiology traditionally emphasize macroscopic structures. It may soon be possible to supplement these studies with models of human information-processing of the molecular level. Thin-film, simulation, fluorescence microscopy, and high-resolution X-ray crystallographic studies provide evidence for transiently organized neural membrane molecular systems with possible computational properties. This review article examines evidence for hydrophobic-mismatch molecular interactions within phospholipid microdomains of a neural membrane bilayer. It is proposed that these interactions are a massively parallel algorithm which can rapidly compute near-optimal solutions to complex cognitive and physiological problems. Coupling of microdomain activity to permenant ion movements at ligand-gated and voltage-gated channels permits the conversion of molecular computations into neuron frequency codes. Evidence for microdomain transport of proteins to specific locations within the bilayer suggests that neuromolecular computation may be under some genetic control and thus modifiable by natural selection. A possible experimental approach for examining evolutionary changes in neuromolecular computation is briefly discussed. Received: 2 October 1998 / Accepted in revised form: 19 March 1999     

An iterative method for faster sum-of-pairs multiple sequence alignment

MOTIVATION: Multiple sequence alignment is an important tool in computational biology. In order to solve the task of computing multiple alignments in affordable time, the most commonly used multiple alignment methods have to use heuristics. Nevertheless, the computation of optimal multiple alignments is important in its own right, and it provides a means of evaluating heuristic approaches or serves as a subprocedure of heuristic alignment methods. RESULTS: We present an algorithm that uses the divide-and-conquer alignment approach together with recent results on search space reduction to speed up the computation of multiple sequence alignments. The method is adaptive in that depending on the time one wants to spend on the alignment, a better, up to optimal alignment can be obtained. To speed up the computation in the optimal alignment step, we apply the alpha(*) algorithm which leads to a procedure provably more efficient than previous exact algorithms. We also describe our implementation of the algorithm and present results showing the effectiveness and limitations of the procedure.