Predicting Protein-Protein Interface using Desolvation Energy Similarity Matching

2006 ◽  
Author(s):  
Yasir Arafat ◽  
Gour C. Karmakar ◽  
Joarder Kamruzzaman ◽  
Juan Fernandez-Recio
Keyword(s):  
Protein Interface ◽  
Similarity Matching ◽  
Desolvation Energy ◽  
Energy Similarity

scholarly journals A Densely Connected GRU Neural Network Based on Coattention Mechanism for Chinese Rice-Related Question Similarity Matching

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1307
Author(s):  
Haoriqin Wang ◽  
Huaji Zhu ◽  
Huarui Wu ◽  
Xiaomin Wang ◽  
Xiao Han ◽  
...  
Keyword(s):  
Related Question ◽  
Similarity Matching ◽  
Frequency Method ◽  
Text Data ◽  
Information Platform ◽  
Document Frequency ◽  
Proposed Model ◽  
Related Text ◽  
Text Features ◽  
Matching Models

In the question-and-answer (Q&A) communities of the “China Agricultural Technology Extension Information Platform”, thousands of rice-related Chinese questions are newly added every day. The rapid detection of the same semantic question is the key to the success of a rice-related intelligent Q&A system. To allow the fast and automatic detection of the same semantic rice-related questions, we propose a new method based on the Coattention-DenseGRU (Gated Recurrent Unit). According to the rice-related question characteristics, we applied word2vec with the TF-IDF (Term Frequency–Inverse Document Frequency) method to process and analyze the text data and compare it with the Word2vec, GloVe, and TF-IDF methods. Combined with the agricultural word segmentation dictionary, we applied Word2vec with the TF-IDF method, effectively solving the problem of high dimension and sparse data in the rice-related text. Each network layer employed the connection information of features and all previous recursive layers’ hidden features. To alleviate the problem of feature vector size increasing due to dense splicing, an autoencoder was used after dense concatenation. The experimental results show that rice-related question similarity matching based on Coattention-DenseGRU can improve the utilization of text features, reduce the loss of features, and achieve fast and accurate similarity matching of the rice-related question dataset. The precision and F1 values of the proposed model were 96.3% and 96.9%, respectively. Compared with seven other kinds of question similarity matching models, we present a new state-of-the-art method with our rice-related question dataset.

Structural response to mutation at a protein-protein interface 1 1Edited by R. Huber

1999 ◽  
Vol 286 (5) ◽  
pp. 1487-1506 ◽  
Author(s):  
Cara K Vaughan ◽  
Ashley M Buckle ◽  
Alan R Fersht
Keyword(s):  
Structural Response ◽  
Protein Interface

An ultra‐high affinity protein–protein interface displaying sequence‐robustness

2021 ◽  
Author(s):  
Marie Sofie Møller ◽  
Sita Vaag Olesen ◽  
Ingemar André
Keyword(s):  
Protein Interface ◽  
High Affinity

scholarly journals PIER: Protein interface recognition for structural proteomics

2007 ◽  
Vol 67 (2) ◽  
pp. 400-417 ◽  
Author(s):  
Irina Kufareva ◽  
Levon Budagyan ◽  
Eugene Raush ◽  
Maxim Totrov ◽  
Ruben Abagyan
Keyword(s):  
Structural Proteomics ◽  
Protein Interface
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