Secondary Structure Prediction for the Spectrin 106-Amino Acid Segment, and a Proposed Model for Tertiary Structure

1990 ◽  
Vol 8 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Y. Xu ◽  
M. Prabhakaran ◽  
M. E. Johnson ◽  
L. W.-M. Fung
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Proposed Model ◽  
Amino Acid Segment

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.

PROTEIN SECONDARY STRUCTURE PREDICTION USING NMR CHEMICAL SHIFT DATA

2010 ◽  
Vol 08 (05) ◽  
pp. 867-884 ◽  
Author(s):  
YUZHONG ZHAO ◽  
BABAK ALIPANAHI ◽  
SHUAI CHENG LI ◽  
MING LI
Keyword(s):  
Chemical Shift ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Nearest Neighbor ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Accurate Determination ◽  
Protein Secondary Structure ◽  
Sequence Information ◽  
K Nearest Neighbor

Accurate determination of protein secondary structure from the chemical shift information is a key step for NMR tertiary structure determination. Relatively few work has been done on this subject. There needs to be a systematic investigation of algorithms that are (a) robust for large datasets; (b) easily extendable to (the dynamic) new databases; and (c) approaching to the limit of accuracy. We introduce new approaches using k-nearest neighbor algorithm to do the basic prediction and use the BCJR algorithm to smooth the predictions and combine different predictions from chemical shifts and based on sequence information only. Our new system, SUCCES, improves the accuracy of all existing methods on a large dataset of 805 proteins (at 86% Q3 accuracy and at 92.6% accuracy when the boundary residues are ignored), and it is easily extendable to any new dataset without requiring any new training. The software is publicly available at .

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].

scholarly journals Amino acid side chain contribution to protein FTIR spectra: impact on secondary structure evaluation

2021 ◽  
Author(s):  
Joëlle De Meutter ◽  
Erik Goormaghtigh
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Spectral Region ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Ftir Spectra ◽  
Side Chain ◽  
Amino Acid Side Chain ◽  
Amino Acid Side Chains ◽  
Β Sheet

AbstractPrediction of protein secondary structure from FTIR spectra usually relies on the absorbance in the amide I–amide II region of the spectrum. It assumes that the absorbance in this spectral region, i.e., roughly 1700–1500 cm−1 is solely arising from amide contributions. Yet, it is accepted that, on the average, about 20% of the absorbance is due to amino acid side chains. The present paper evaluates the contribution of amino acid side chains in this spectral region and the potential to improve secondary structure prediction after correcting for their contribution. We show that the β-sheet content prediction is improved upon subtraction of amino acid side chain contributions in the amide I–amide II spectral range. Improvement is relatively important, for instance, the error of prediction of β-sheet content decreases from 5.42 to 4.97% when evaluated by ascending stepwise regression. Other methods tested such as partial least square regression and support vector machine have also improved accuracy for β-sheet content evaluation. The other structures such as α-helix do not significantly benefit from side chain contribution subtraction, in some cases prediction is even degraded. We show that co-linearity between secondary structure content and amino acid composition is not a main limitation for improving secondary structure prediction. We also show that, even though based on different criteria, secondary structures defined by DSSP and XTLSSTR both arrive at the same conclusion: only the β-sheet structure clearly benefits from side chain subtraction. It must be concluded that side chain contribution subtraction benefit for the evaluation of other secondary structure contents is limited by the very rough description of side chain absorbance which does not take into account the variations related to their environment. The study was performed on a large protein set. To deal with the large number of proteins present, we worked on protein microarrays deposited on BaF2 slides and FTIR spectra were acquired with an imaging system.

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