Generation of deviation parameters for amino acid singlets, doublets and triplets from three-dimensional structures of proteins and its implications for secondary structure prediction from amino acid sequences

2000 ◽  
Vol 25 (1) ◽  
pp. 81-90 ◽  
Author(s):  
S. A. Mugilan ◽  
K. Veluraja
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Amino Acid Sequences

scholarly journals Proteins sequence analysis of Contagious Caprine Pleuropneumonia

2017 ◽  
Vol 33 (3) ◽  
pp. 309-319
Author(s):  
Ayuba Dauda ◽  
Abdulmojeed Yakubu ◽  
Ihe Dim ◽  
Deeve Gwaza
Keyword(s):  
Amino Acid ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Chemical Properties ◽  
Alpha Helix ◽  
Amino Acid Sequences ◽  
Contagious Bovine Pleuropneumonia ◽  
Isoleucine Leucine ◽  
Grand Average

A total of twenty (20) contagious bovine pleuropneumonia (CCPP) proteins were retrieved from the GenBank (www.ncbi.nlm.nih.gov). The proteins sequences were used to investigate the molecular identity of various CCPP proteins. The physico-chemical properties of CCPP proteins were performed using protparam tool. Isoelectric point (pI), molecular weight (MW), extinction coefficient (EC); instability index (II), aliphatic index (AI) and grand average of hydropathicity (GRAVY) were computed. The study revealed that the pI of CCPP proteins were acidic and basic in nature. The EC and II of CCPP proteins indicate better stability which is an indication of resistant to mutation and thermally stable. The GRAVY of CCPP proteins revealed some are positive while some are negative. The positive value indicates solubility (hydrophilic) in water while negative is not soluble (hydrophobic) in water. The amino acid composition of CCPP proteins indicates that they are rich in isoleucine, leucine and lysine. The three dimensional structures (3D) of the CCPP proteins were determine using Phyre2 server. The amino acid sequences of CCPP proteins were subjected to secondary structure prediction using ExPASy?s SOPMA tool. The proteins are more of alpha helix structure. The genetic information eminating from this study may bring insight into mutagenesis and pharmacogenetic. <br><br><font color="red"><b> This article has been retracted. Link to the retraction <u><a href="http://dx.doi.org/10.2298/BAH1803369E">10.2298/BAH1803369E</a><u></b></font>

scholarly journals Localnet: A Simple Recurrent Neural Network Model for Protein Secondary Structure Prediction Using Local Amino Acid Sequences Only

2021 ◽  
Author(s):  
Shutong Yang ◽  
Yuhong Wang ◽  
Kennie Cruz-Gutierrez ◽  
Fangling Wu ◽  
Chuan-Fan Ding
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Amino Acid Sequences ◽  
Evolutionary Information ◽  
Structure Modeling ◽  
Protein Structure Modeling

Abstract BackgroundProtein secondary structure prediction (PSSP) is important for protein structure modeling and design. Over the past a few years, deep learning models have shown promising results for PSSP. However, the current good performers for PSSP often require evolutionary information such as multiple sequence alignments and even real protein structures (templates), entire protein sequences, and amino acid property profiles. ResultsIn this study, we used a fixed-size window of adjacent residues and only amino acid sequences, without any evolutionary information, as inputs, and developed a very simple, yet accurate RNN model: LocalNet. The accuracy for three states of secondary structures is as high as 85.15%, indicating that the local amino acid sequence itself contains enough information for PSSP, a well-known classical view. By comparing to other predictors, we also achieve an state-of-art accuracy on dataset of CASP11, CASP12 and CASP13.ConclusionThe well-trained models are expected to have good applications in protein structure modeling and protein design. This model can be downloaded from https://github.com/lake-chao/protein-secondary-structure-prediction.

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.

Backbone dihedral angles prediction servers for protein early-stage structure prediction

2019 ◽  
Vol 15 (4) ◽  
Author(s):  
Tomasz Smolarczyk ◽  
Katarzyna Stapor ◽  
Irena Roterman-Konieczna
Keyword(s):  
Protein Folding ◽  
Secondary Structure ◽  
Protein Function ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Early Stage ◽  
Three Dimensional ◽  
Stage Structure ◽  
Dihedral Angles ◽  
Starting Point

AbstractThree-dimensional protein structure prediction is an important task in science at the intersection of biology, chemistry, and informatics, and it is crucial for determining the protein function. In the two-stage protein folding model, based on an early- and late-stage intermediates, we propose to use state-of-the-art secondary structure prediction servers for backbone dihedral angles prediction and devise an early-stage structure. Early-stage structures are used as a starting point for protein folding simulations, and any errors in this stage affect the final predictions. We have shown that modern secondary structure prediction servers could increase the accuracy of early-stage predictions compared to previously reported models.

CSSP(Consensus Secondary Structure Prediction): a web-based server for structural biologists

2009 ◽  
Vol 42 (2) ◽  
pp. 336-338 ◽  
Author(s):  
Ankit Gupta ◽  
Avnish Deshpande ◽  
Janardhan Kumar Amburi ◽  
Radhakrishnan Sabarinathan ◽  
Ramaswamy Senthilkumar ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Three Dimensional ◽  
Web Server ◽  
Dimensional Structure ◽  
Structural Elements ◽  
Web Based ◽  
Consensus Secondary Structure ◽  
Helical Wheel

Sequence–structure correlation studies are important in deciphering the relationships between various structural aspects, which may shed light on the protein-folding problem. The first step of this process is the prediction of secondary structure for a protein sequence of unknown three-dimensional structure. To this end, a web server has been created to predict the consensus secondary structure using well known algorithms from the literature. Furthermore, the server allows users to see the occurrence of predicted secondary structural elements in other structure and sequence databases and to visualize predicted helices as a helical wheel plot. The web server is accessible at http://bioserver1.physics.iisc.ernet.in/cssp/.

CLUSTERING OF AMINO ACIDS FOR PROTEIN SECONDARY STRUCTURE PREDICTION

2004 ◽  
Vol 02 (02) ◽  
pp. 333-342 ◽  
Author(s):  
WEI-MOU ZHENG
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Markov Models ◽  
Secondary Structure Prediction ◽  
Length Distribution ◽  
Protein Secondary Structure ◽  
Coarse Graining ◽  
High Prediction ◽  
Duration Effect

Simple hidden Markov models are proposed for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of length distribution, and focus on inclusion of short range correlations of residues and of conformation states in the models. Conformation-independent and -dependent amino acid coarse-graining schemes are designed for the models by means of proper mutual information. We compare models of different level of complexity, and establish a practical model with a high prediction accuracy.

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].

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