Partial Consensus Design and Enhancement of Protein Function by Secondary-Structure-Guided Consensus Mutations

Biochemistry ◽  
2021 ◽  
Author(s):  
Kohei Kozuka ◽  
Shogo Nakano ◽  
Yasuhisa Asano ◽  
Sohei Ito
Keyword(s):  
Secondary Structure ◽  
Protein Function ◽  
Partial Consensus

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.

scholarly journals ArabidopsismRNA secondary structure correlates with protein function and domains

2013 ◽  
Vol 8 (6) ◽  
pp. e24301 ◽  
Author(s):  
Lee E. Vandivier ◽  
Fan Li ◽  
Qi Zheng ◽  
Matthew R. Willmann ◽  
Ying Chen ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Protein Function

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).

Predict Interactions between Protein Residues for Developing Antibody Therapeutics

2011 ◽  
Vol 383-390 ◽  
pp. 4003-4006
Author(s):  
Wen Lung Shu ◽  
Chia Hsuan Lee
Keyword(s):  
Mathematical Model ◽  
Secondary Structure ◽  
Protein Sequence ◽  
Protein Function ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Residues ◽  
Antibody Therapeutics ◽  
Predict Protein Function ◽  
Data Files

Recently, protein-antibody therapeutics becomes a hot search topic. In this paper, all protein interaction data files are collected from INTERPARE. Protein sequence and its secondary structure both are used to build HMM mathematical model. We randomly take 80% data to train positive and negative HMM models and 20% data to test. The accuracy of this approach can reach to 79.80%. This model can further be used to predict protein function sites and predict if a protein interacts with other proteins.

scholarly journals Isoenergetic penta- and hexanucleotide microarray probing and chemical mapping provide a secondary structure model for an RNA element orchestrating R2 retrotransposon protein function

2008 ◽  
Vol 36 (6) ◽  
pp. 1770-1782 ◽  
Author(s):  
Elzbieta Kierzek ◽  
Ryszard Kierzek ◽  
Walter N. Moss ◽  
Shawn M. Christensen ◽  
Thomas H. Eickbush ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Protein Function ◽  
Structure Model ◽  
Chemical Mapping ◽  
Secondary Structure Model

Protein Structure Prediction Based on Profile HMM and QPSO

2014 ◽  
Vol 1004-1005 ◽  
pp. 853-856
Author(s):  
Hai Xia Long ◽  
Shu Lei Wu ◽  
Yan Lv
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Protein Structure Prediction ◽  
Protein Function ◽  
Structure Prediction ◽  
Secondary Structure Information ◽  
Structure Information ◽  
Significant Process ◽  
Profile Hmm ◽  
Made In

Protein structure prediction is a challenging field strongly associated with protein function and evolution determination, which is crucial for biologists. Despite significant process made in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. In this study, we have developed a method for protein structure prediction based on 7-state HMM which can reduce the number of states using secondary structure information about proteins for each fold. The QPSO is an efficient optimization algorithm which is used to train profile HMM. Experiment results show that the proposed method is reasonable.

Quantification of protein secondary structure content by multivariate analysis of deep-ultraviolet resonance Raman and circular dichroism spectroscopies

2014 ◽  
Vol 6 (6) ◽  
pp. 1691-1699 ◽  
Author(s):  
Olayinka O. Oshokoya ◽  
Carol A. Roach ◽  
Renee D. JiJi
Keyword(s):  
Multivariate Analysis ◽  
Secondary Structure ◽  
Protein Function ◽  
Resonance Raman ◽  
Protein Secondary Structure ◽  
Secondary Structure Content ◽  
Deep Ultraviolet ◽  
Ultraviolet Resonance Raman ◽  
Β Sheet

Determination of protein secondary structure (α-helical, β-sheet, and disordered motifs) has become an area of great importance in biochemistry and biophysics as protein secondary structure is directly related to protein function and protein related diseases.

Towards a Better Understanding of Computational Models for Predicting DNA Methylation Effects at the Molecular Level

2020 ◽  
Vol 20 (10) ◽  
pp. 901-909
Author(s):  
Nathanael K. Proctor ◽  
Tugba Ertan-Bolelli ◽  
Kayhan Bolelli ◽  
Ethan W. Taylor ◽  
Norman H.L. Chiu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Secondary Structure ◽  
Protein Function ◽  
Computational Models ◽  
Double Helix ◽  
Experimental Studies ◽  
Anomeric Effect ◽  
Dynamic Simulations ◽  
The Right ◽  
Dna Double Helix

Human DNA is a very sensitive macromolecule and slight changes in the structure of DNA can have disastrous effects on the organism. When nucleotides are modified, or changed, the resulting DNA sequence can lose its information, if it is part of a gene, or it can become a problem for replication and repair. Human cells can regulate themselves by using a process known as DNA methylation. This methylation is vitally important in cell differentiation and expression of genes. When the methylation is uncontrolled, however, or does not occur in the right place, serious pathophysiological consequences may result. Excess methylation causes changes in the conformation of the DNA double helix. The secondary structure of DNA is highly dependent upon the sequence. Therefore, if the sequence changes slightly the secondary structure can change as well. These slight changes will then cause the doublestranded DNA to be more open and available in some places where large adductions can come in and react with the DNA base pairs. Computer models have been used to simulate a variety of biological processes including protein function and binding, and there is a growing body of evidence that in silico methods can shed light on DNA methylation. Understanding the anomeric effect that contributes to the structural and conformational flexibility of furanose rings through a combination of quantum mechanical and experimental studies is critical for successful molecular dynamic simulations.

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