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.

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.

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/.

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 Adaptive Residue-wise Profile Fusion for Low Homologous Protein Secondary Structure Prediction Using External Knowledge

2021 ◽  
Author(s):  
Qin Wang ◽  
Jun Wei ◽  
Boyuan Wang ◽  
Zhen Li ◽  
Sheng Wang ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Protein Function ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Function Analysis ◽  
Protein Secondary Structure ◽  
Protein Secondary Structure Prediction ◽  
Model Learning ◽  
Homologous Proteins ◽  
The Arts

Protein secondary structure prediction (PSSP) is essential for protein function analysis. However, for low homologous proteins, the PSSP suffers from insufficient input features. In this paper, we explicitly import external self-supervised knowledge for low homologous PSSP under the guidance of residue-wise (amino acid wise) profile fusion. In practice, we firstly demonstrate the superiority of profile over Position-Specific Scoring Matrix (PSSM) for low homologous PSSP. Based on this observation, we introduce the novel self-supervised BERT features as the pseudo profile, which implicitly involves the residue distribution in all native discovered sequences as the complementary features. Furthermore, a novel residue-wise attention is specially designed to adaptively fuse different features (i.e., original low-quality profile, BERT based pseudo profile), which not only takes full advantage of each feature but also avoids noise disturbance. Besides, the feature consistency loss is proposed to accelerate the model learning from multiple semantic levels. Extensive experiments confirm that our method outperforms state-of-the-arts (i.e., 4.7% for extremely low homologous cases on BC40 dataset).

scholarly journals Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies

Molecules ◽  
2021 ◽  
Vol 26 (22) ◽  
pp. 7049
Author(s):  
Maytha Alshammari ◽  
Jing He
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Dimensional Space ◽  
Protein A ◽  
Three Dimensional ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Sources Of Information ◽  
Density Map

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set of sequence segments in 1D, a set of amino acid contact pairs in 2D, and a set of traces in 3D at the secondary structure level. A test of fourteen cases shows that the accuracy of predicted secondary structures is critical for deriving topologies. The use of significant long-range contact pairs is most effective at enriching the rank of the maximum-match topology for proteins with a large number of secondary structures, if the secondary structure prediction is fairly accurate. It was observed that the enrichment depends on the quality of initial topology candidates in this approach. We provide detailed analysis in various cases to show the potential and challenge when combining three sources of information.

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