A simple microcomputer program for predicting the secondary structure of proteins

An Applesoft BASIC program is presented which applies the Chou-Fasman and Garnier-Osguthorpe-Robson rules for predicting the secondary structure of proteins. This program plots the result of predictions and shows the charge distribution and the consensus prediction. The program is run on an Apple IIe and the results are printed by an Imagewriter printer.     

HYBP_PSSP: a hybrid back propagation method for predicting protein secondary structure

The prediction of secondary structure is an important topic in the field of bioinformatics, even if the methods have matured, and development of the algorithms is a far less active area than a decade ago. Accurate prediction is very useful to biologists in its own right, but it is worth pointing out that it is also an essential component of tertiary structure prediction, which in contrast is far from solved and continues to be a highly active area of research. In addition, sequence comparison methods have more recently incorporated local structure tracks. The extra information utilized by the new methods has led to considerable improvements in fold recognition and alignment accuracy. In this paper, a novel method for protein secondary structure prediction is presented. Using evolutionary information contained in amino acid’s physicochemical properties, position-specific scoring matrix generated by PSI-BLAST and HMMER3 profiles as input to hybrid back propagation system, secondary structure can be predicted at significantly increased accuracy. Based on knowledge discovery theory based on inner cognitive mechanism (KDTICM) theory, we have constructed a compound pyramid model approach, which is composed of four layers of the intelligent interface and integrated in several ways, such as hybrid back propagation method (HBP), modified knowledge discovery in databases (KDD*), hybrid SVM method (HSVM) and so on. Experiments on three standard datasets (RS126, CB513 and CASP8) show that CPM is capable of producing the higher Q ₃ and SOV scores than that achieved by existing widely used schemes such as PSIPRED, PHD, Predator, as well as previously developed prediction methods. On the RS126 and CB513 datasets, it achieves a Q ₃ and SOV99 score are considerably higher than the best reported scores, respectively. It is also tested on target proteins of critical assessment of protein structure prediction experiment (CASP8) and achieves better results than the traditional methods, including the popular PSIPRED method over overall prediction accuracy. Available: .     

基于免疫粒子群集成的RNA二级结构预测算法

RNA二级结构预测在计算生物学中具有重要意义,针对RNA二级结构预测,提出了一种新的免疫粒子群集成算法,根据个体的浓度和适应值概率,利用免疫机制,在粒子群优化算法中设计了免疫替换算子,有效防止了粒子群优化算法易陷入局部最优的缺陷;通过集成技术,充分发挥各种粒子群优化算法的优点,实现协同演化,提高了算法的全局搜索能力。最后用免疫粒子群集成算法去预测RNA二级结构,实验证明了算法的有效性。     

A program for predicting significant RNA secondary structures

We describe a program for the analysis of RNA secondary structure. There are two new features in this program. (i) To get vector speeds on a vector pipeline machine (such as Cray X-MP/24) we have vectorized the secondary structure dynamic algorithm. (ii) The statistical significance of a locally 'optimal' secondary structure is assessed by a Monte Carlo method. The results can be depicted graphically including profiles of the stability of local secondary structures and the distribution of the potentially significant secondary structures in the RNA molecules. Interesting regions where both the potentially significant secondary structures and 'open' structures (single-stranded coils) occur can be identified by the plots mentioned above. Furthermore, the speed of the vectorized code allows repeated Monte Carlo simulations with different overlapping window sizes. Thus, the optimal size of the significant secondary structure occurring in the interesting region can be assessed by repeating the Monte Carlo simulation. The power of the program is demonstrated in the analysis of local secondary structures of human T-cell lymphotrophic virus type III (HIV).     

A simple method for constructing heptadecagon

It is proved that among the regular polygons with prime edges, only the regular polygons with Fermat prime edges are constructable with compass and straightedge. As 17 is a Fermat prime number, the construction of heptadecagon has been discussing all the time. Many different construction methods are proposed although they are based on the same theory. Simplification of the construction is still a sensible problem. Here, we propose a simple method for constructing regular heptadecagon with the fewest steps. The accumulation of construction errors is also avoided. This method is more applicable than the previous construction.     

A method for predicting the probability of business network profitability

In the design phase of business collaboration, it is desirable to be able to predict the profitability of the business-to-be. Therefore, techniques to assess qualities such as costs, revenues, risks, and profitability have been previously proposed. However, they do not allow the modeler to properly manage uncertainty with respect to the design of the considered business collaboration. In many real collaboration projects today, uncertainty regarding the business’ present or future characteristics is so significant that ignoring it becomes problematic. In this paper, we propose an approach based on the predictive, probabilistic architecture modeling framework (P²AMF), capable of advanced and probabilistically sound reasoning about profitability risks. The P²AMF-based approach for profitability risk prediction is also based on the e³-value modeling language and on the object constraint language. The paper introduces the prediction and modeling approach, and a supporting software tool. The use of the approach is illustrated by means of a case study originated from the Stockholm Royal Seaport smart city project.     

A statistical method for predicting automobile driving posture

A new model for predicting automobile driving posture is presented. The model, based on data from a study of 68 men and women in 18 vehicle package and seat conditions, is designed for use in posturing the human figure models that are increasingly used for vehicle interior design. The model uses a series of independent regression models, coupled with data-guided inverse kinematics, to fit a whole-body linkage. An important characteristic of the new model is that it places greatest importance on prediction accuracy for the body locations that are most important for vehicle interior design: eye location and hip location. The model predictions were compared with the driving postures of 120 men and women in five vehicles. Errors in mean eye location predictions in the vehicles were typically less than 10 mm. Prediction errors were largely independent of anthropometric variables and vehicle layout. Although the average posture of a group of people can be predicted accurately, individuals' postures cannot be predicted precisely because of interindividual posture variance that is unrelated to key anthropometric variables. The posture prediction models developed in this research can be applied to posturing computer-rendered human models to improve the accuracy of ergonomic assessments of vehicle interiors.     

A simple method for hardware and software evaluation

Subjective data are often the only data available for making decisions and choices in both hardware and software development. These data are therefore both important and valuable. Their importance is such that it is necessary to elicit subjective data from a variety of sources and in a manner that is relatively unbiased. A method which requires participants to compare all criteria and all proposed concepts by pairs provides a non-statistical mechanism for collecting data from a broad base of expertise, for giving appropriate weight to all input, and for reducing personal bias. These characteristics make a comparison of all pairs a useful tool for hardware and software evaluation when decisions and choices must be made. The efficacy of the method was recently demonstrated in the selection of a restraint-attachment device for Space Station Freedom.     

A simple method for protein structural classification

Since the concept of structural classes of proteins was proposed, the problem of protein classification has been tackled by many groups. Most of their classification criteria are based only on the helix/strand contents of proteins. In this paper, we proposed a method for protein structural classification based on their secondary structure sequences. It is a classification scheme that can confirm existing classifications. Here a mathematical model is constructed to describe protein secondary structure sequences, in which each protein secondary structure sequence corresponds to a transition probability matrix that characterizes and differentiates protein structure numerically. Its application to a set of real data has indicated that our method can classify protein structures correctly. The final classification result is shown schematically. So it is visual to observe the structural classifications, which is different from traditional methods.     

A simple method for fitting sphere-like surfaces

In this paper we present a new and simple method for fitting a sphere-like surface to a set of data points in 3-space. In comparison to the standard method that involves using spherical harmonics, this method is conceptually simpler and computationally less complex and intensive, especially when used to address problems in which data is sparse or nonuniform.     

A simple method for detecting salient regions

A simple method for detecting salient regions in images is proposed. It requires only edge detection, threshold decomposition, the distance transform, and thresholding. Moreover, it avoids the need for setting any parameter values. Experiments show that the resulting regions are relatively coarse, but overall the method is surprisingly effective, and has the benefit of easy implementation. Quantitative tests were carried out on Liu et al.'s dataset of 5000 images. Although the ratings of our simple method were not as good as their approach which involved an extensive training stage, they were comparable to several other popular methods from the literature. Further tests on Kootstra and Schomaker's dataset of 99 images also showed promising results.     

A secondary and tertiary structure editor for nucleic acids

A major difficulty in the evaluation of secondary and tertiary structures of nucleic acids is the lack of convenient methods for their construction and representation. As a first step in a study of the symbolic representation of biopolymers, we report the development of a structure editor written in Pascal, permitting model construction on the screen of a personal computer. The program calculates energies for helical regions, allows user-defined helices and displays the secondary structure of a nucleic acid based on a user-selected set of helices. Screen and printer outputs can be in the form of a backbone or the letters of the primary sequence. The molecule can then be displayed in a format which simulates its three-dimensional structure. Using appropriate glasses, the molecule can be viewed on the screen in three dimensions. Other options include the manipulation of helices and single-stranded regions which results in changes in the spatial relationship between different regions of the molecule. The editor requires an IBM or compatible PC, 640 kbyte memory and a medium or high resolution graphics card.     

A method for improving the accuracy of data mining classification algorithms

In this paper we introduce a method called CL.E.D.M. (CLassification through ELECTRE and Data Mining), that employs aspects of the methodological framework of the ELECTRE I outranking method, and aims at increasing the accuracy of existing data mining classification algorithms. In particular, the method chooses the best decision rules extracted from the training process of the data mining classification algorithms, and then it assigns the classes that correspond to these rules, to the objects that must be classified. Three well known data mining classification algorithms are tested in five different widely used databases to verify the robustness of the proposed method.     

A BASIC microcomputer program to calculate the secondary structure of proteins from their circular dichroism spectrum

A BASIC program (CDPROT) has been developed to calculate the secondary structure of proteins from their far UV circular dichroism spectrum. This implementation can use different reference spectra, calculated either from model polypeptides or proteins of known tertiary structure. Apart from obtaining the alpha-helical, beta-structure, beta-turns or random percentages which would generate the spectrum of best fit with respect to the experimental measures, CDPROT represents on screen both theoretical and experimental spectra indicating the root-mean-square error. The provision of additional reference spectra by the user is also considered, and another program (STOREREF) performs the editing in an adequate format for CDPROT.