Prediction of Protein Secondary-Structure by Monte Carlo Simulation

2008 ◽  
Vol 59 (2) ◽  
pp. 199-204
Author(s):  
Alexandru Woinaroschy
Keyword(s):  
Amino Acids ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Practical Importance ◽  
Protein Secondary Structure ◽  
Large Data ◽  
Cancer Disease

Proteins have four structural categories. The primary structure is the amino-acid sequence of the polypeptide chain. The secondary structure is the conformation, representing of the backbone (a-helix or b-sheet). The knowledge of protein structure has a paramount theoretical and practical importance (e.g. cancer disease) and a huge effort of research was devoted to this subject. Despite the fact that several methods were developed for protein secondary-structure prediction, there are no consensuses of their results. In this paper was proposed an new, original, method to investigate the influence of the number of amino acids and the percentage contents in the twenty amino acids for the prediction of protein secondary-structure, respectively Monte Carlo simulation using a multilayer neural networks. The method is very promising in connection with the use of large data bases.

scholarly journals Protein Secondary Structure Prediction

2019 ◽  
Vol 8 (9) ◽  
pp. 1370-1373
Keyword(s):  
Amino Acids ◽  
Comparative Analysis ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Two Dimensional ◽  
Ionic Bond

Proteins are made up of basic units called amino acids which are held together by bonds namely hydrogen and ionic bond. The way in which the amino acids are sequenced has been categorized into two dimensional and three dimensional structures. The main advantage of predicting secondary structure is to produce tertiary structure likelihoods that are in great demand for continuous detection of proteins. This paper reviews the different methods adopted for predicting the protein secondary structure and provides a comparative analysis of accuracies obtained from various input datasets [1].

Multi-Output Interval Type-2 Fuzzy Logic System for Protein Secondary Structure Prediction

2015 ◽  
Vol 23 (05) ◽  
pp. 735-760 ◽  
Author(s):  
Thanh Nguyen ◽  
Abbas Khosravi ◽  
Douglas Creighton ◽  
Saeid Nahavandi
Keyword(s):  
Amino Acids ◽  
Fuzzy Logic ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Fuzzy Logic System ◽  
Interval Type ◽  
Logic System

A new multi-output interval type-2 fuzzy logic system (MOIT2FLS) is introduced for protein secondary structure prediction in this paper. Three outputs of the MOIT2FLS correspond to three structure classes including helix, strand (sheet) and coil. Quantitative properties of amino acids are employed to characterize twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Three clustering tasks are performed using the adaptive vector quantization method to construct an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS. The genetic fitness function is designed based on the Q3 measure. Experimental results demonstrate the dominance of the proposed approach against the traditional methods that are Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

2019 ◽  
Vol 16 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Elaheh Kashani-Amin ◽  
Ozra Tabatabaei-Malazy ◽  
Amirhossein Sakhteman ◽  
Bagher Larijani ◽  
Azadeh Ebrahim-Habibi
Keyword(s):  
Systematic Review ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Web Of Science ◽  
Secondary Structure Prediction ◽  
Structural Information ◽  
Protein Secondary Structure ◽  
Structural Features ◽  
Prediction Tools ◽  
Insight Into

Background: Prediction of proteins’ secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts. Objective: A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Methods: Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data. Results: Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. Conclusion: This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

In Silico Study of Secondary Structure of Hemoglobin Protein

2021 ◽  
pp. 6245-6249
Author(s):  
Roma Chandra
Keyword(s):  
Secondary Structure ◽  
Protein Sequence ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Alpha Helix ◽  
Prediction Methods ◽  
Protein Secondary Structures

Protein structure prediction is one of the important goals in the area of bioinformatics and biotechnology. Prediction methods include structure prediction of both secondary and tertiary structures of protein. Protein secondary structure prediction infers knowledge related to presence of helixes, sheets and coils in a polypeptide chain whereas protein tertiary structure prediction infers knowledge related to three dimensional structures of proteins. Protein secondary structures represent the possible motifs or regular expressions represented as patterns that are predicted from primary protein sequence in the form of alpha helix, betastr and and coils. The secondary structure prediction is useful as it infers information related to the structure and function of unknown protein sequence. There are various secondary structure prediction methods used to predict about helixes, sheets and coils. Based on these methods there are various prediction tools under study. This study includes prediction of hemoglobin using various tools. The results produced inferred knowledge with reference to percentage of amino acids participating to produce helices, sheets and coils. PHD and DSC produced the best of the results out of all the tools used.

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