scholarly journals A Peptides Prediction Methodology for Tertiary Structure Based on Simulated Annealing

2021 ◽  
Vol 26 (2) ◽  
pp. 39
Author(s):  
Juan P. Sánchez-Hernández ◽  
Juan Frausto-Solís ◽  
Juan J. González-Barbosa ◽  
Diego A. Soto-Monterrubio ◽  
Fanny G. Maldonado-Nava ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Ab Initio ◽  
Primary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Golden Ratio ◽  
Small Peptides ◽  
Hybrid Simulated Annealing ◽  
Ab Initio Models

The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.

scholarly journals DNSS2: improved ab initio protein secondary structure prediction using advanced deep learning architectures

2019 ◽  
Author(s):  
Jie Hou ◽  
Zhiye Guo ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Ab Initio ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Alpha Helix ◽  
Protein Secondary Structure Prediction ◽  
Learning Architectures

AbstractMotivationAccurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction. In a previous study, we developed a deep belief network-based protein secondary structure method (DNSS1) and successfully advanced the prediction accuracy beyond 80%. In this work, we developed multiple advanced deep learning architectures (DNSS2) to further improve secondary structure prediction.ResultsThe major improvements over the DNSS1 method include (i) designing and integrating six advanced one-dimensional deep convolutional/recurrent/residual/memory/fractal/inception networks to predict secondary structure, and (ii) using more sensitive profile features inferred from Hidden Markov model (HMM) and multiple sequence alignment (MSA). Most of the deep learning architectures are novel for protein secondary structure prediction. DNSS2 was systematically benchmarked on two independent test datasets with eight state-of-art tools and consistently ranked as one of the best methods. Particularly, DNSS2 was tested on the 82 protein targets of 2018 CASP13 experiment and achieved the best Q3 score of 83.74% and SOV score of 72.46%. DNSS2 is freely available at: https://github.com/multicom-toolbox/DNSS2.

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.

Structural modeling identified the tRNA-binding domain of Utp8p, an essential nucleolar component of the nuclear tRNA export machinery of Saccharomyces cerevisiae

2009 ◽  
Vol 87 (2) ◽  
pp. 431-443 ◽  
Author(s):  
Andrew T. McGuire ◽  
Robert A.B. Keates ◽  
Stephanie Cook ◽  
Dev Mangroo
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Binding Site ◽  
Structure Prediction ◽  
Positive Charge ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Fold Recognition ◽  
Terminal Domain ◽  
Fold Type ◽  
Trna Binding

Utp8p is an essential 80 kDa intranuclear tRNA chaperone that transports tRNAs from the nucleolus to the nuclear tRNA export receptors in Saccharomyces cerevisiae . To help understand the mechanism of Utp8p function, predictive tools were used to derive a partial model of the tertiary structure of Utp8p. Secondary structure prediction, supported by circular dichroism measurements, indicated that Utp8p is divided into 2 domains: the N-terminal beta sheet and the C-terminal alpha helical domain. Tertiary structure prediction was more challenging, because the amino acid sequence of Utp8p is not directly homologous to any known protein structure. The tertiary structures predicted by threading and fold recognition had generally modest scores, but for the C-terminal domain, threading and fold recognition consistently pointed to an alpha–alpha superhelix. Because of the sequence diversity of this fold type, no single structural template was an ideal fit to the Utp8p sequence. Instead, a composite template was constructed from 3 different alpha–alpha superhelix structures that gave the best matches to different portions of the C-terminal domain sequence. In the resulting model, the most conserved sequences grouped in a tight cluster of positive charges on a protein that is otherwise predominantly negative, suggesting that the positive-charge cleft may be the tRNA-binding site. Mutations of conserved positive residues in the proposed binding site resulted in a reduction in the affinity of Utp8p for tRNA both in vivo and in vitro. Models were also derived for the 10 fungal homologues of Utp8p, and the localization of the positive charges on the conserved surface was found in all cases. Taken together, these data suggest that the positive-charge cleft of the C-terminal domain of Utp8p is involved in tRNA-binding.

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 .

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