scholarly journals Protein Structure Prediction: Assembly of Secondary Structure Elements by Basin-Hopping

ChemPhysChem ◽  
2014 ◽  
Vol 15 (15) ◽  
pp. 3378-3390 ◽  
Author(s):  
Falk Hoffmann ◽  
Ioan Vancea ◽  
Sanjay G. Kamat ◽  
Birgit Strodel
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Basin Hopping

scholarly journals Protein structure prediction using basin-hopping

2008 ◽  
Vol 128 (22) ◽  
pp. 225106 ◽  
Author(s):  
Michael C. Prentiss ◽  
David J. Wales ◽  
Peter G. Wolynes
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Basin Hopping

scholarly journals Improving protein structure prediction by deep learning and computational optimization

2019 ◽  
Author(s):  
◽  
Jie Hou
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Secondary Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Structures ◽  
Protein Secondary Structure ◽  
Scattering Data ◽  
Protein Secondary Structure Prediction

Protein structure prediction is one of the most important scientific problems in the field of bioinformatics and computational biology. The availability of protein three-dimensional (3D) structure is crucial for studying biological and cellular functions of proteins. The importance of four major sub-problems in protein structure prediction have been clearly recognized. Those include, first, protein secondary structure prediction, second, protein fold recognition, third, protein quality assessment, and fourth, multi-domain assembly. In recent years, deep learning techniques have proved to be a highly effective machine learning method, which has brought revolutionary advances in computer vision, speech recognition and bioinformatics. In this dissertation, five contributions are described. First, DNSS2, a method for protein secondary structure prediction using one-dimensional deep convolution network. Second, DeepSF, a method of applying deep convolutional network to classify protein sequence into one of thousands known folds. Third, CNNQA and DeepRank, two deep neural network approaches to systematically evaluate the quality of predicted protein structures and select the most accurate model as the final protein structure prediction. Fourth, MULTICOM, a protein structure prediction system empowered by deep learning and protein contact prediction. Finally, SAXSDOM, a data-assisted method for protein domain assembly using small-angle X-ray scattering data. All the methods are available as software tools or web servers which are freely available to the scientific community.

scholarly journals Construct a variable-length fragment library for de novo protein structure prediction

2022 ◽  
Author(s):  
Qiongqiong Feng ◽  
Minghua Hou ◽  
Jun Liu ◽  
Kailong Zhao ◽  
Guijun Zhang
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Query Sequence ◽  
Variable Length ◽  
Experimental Results ◽  
Structure Database ◽  
Fragment Library

Although remarkable achievements, such as AlphaFold2, have been made in end-to-end structure prediction, fragment libraries remain essential for de novo protein structure prediction, which can help explore and understand the protein-folding mechanism. In this work, we developed a variable-length fragment library (VFlib). In VFlib, a master structure database was first constructed from the Protein Data Bank through sequence clustering. The Hidden Markov Model (HMM) profile of each protein in the master structure database was generated by HHsuite, and the secondary structure of each protein was calculated by DSSP. For the query sequence, the HMM-profile was first constructed. Then, variable-length fragments were retrieved from the master structure database through dynamically variable-length profile-profile comparison. A complete method for chopping the query HMM-profile during this process was proposed to obtain fragments with increased diversity. Finally, secondary structure information was used to further screen the retrieved fragments to generate the final fragment library of specific query sequence. The experimental results obtained with a set of 120 nonredundant proteins showed that the global precision and coverage of the fragment library generated by VFlib were 55.04% and 94.95% at the RMSD cutoff of 1.5 Å, respectively. Compared to the benchmark method of NNMake, the global precision of our fragment library had increased by 62.89% with equivalent coverage. Furthermore, the fragments generated by VFlib and NNMake were used to predict structure models through fragment assembly. Controlled experimental results demonstrated that the average TM-score of VFlib was 16.00% higher than that of NNMake.

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.

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