Recent Progress in Protein 3D Structure Comparison

Air pollution is becoming an increasingly important global issue. Toxic gases such as ammonia, nitrogen dioxide, and volatile organic compounds (VOCs) like phenol are very common air pollutants. To date, various sensing methods have been proposed to detect these toxic gases. Researchers are trying their best to build sensors with the lowest detection limit, the highest sensitivity, and the best selectivity. As a 2D material, graphene is very sensitive to many gases and so can be used for gas sensors. Recent studies have shown that graphene with a 3D structure can increase the gas sensitivity of the sensors. The limit of detection (LOD) of the sensors can be upgraded from ppm level to several ppb level. In this review, the recent progress of the gas sensors based on 3D graphene frameworks in the detection of harmful gases is summarized and discussed.

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SASA-Net: A spatial-aware self-attention mechanism for building protein 3D structure directly from inter-residue distances

10.1109/bibm52615.2021.9669346 ◽

2021 ◽

Author(s):

Tiansu Gong ◽

Fusong Ju ◽

Shiwei Sun ◽

Dongbo Bu

Keyword(s):

3D Structure ◽

Attention Mechanism ◽

Protein 3D Structure

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RNA and protein 3D structure modeling: similarities and differences

Journal of Molecular Modeling ◽

10.1007/s00894-010-0951-x ◽

2011 ◽

Vol 17 (9) ◽

pp. 2325-2336 ◽

Cited By ~ 58

Author(s):

Kristian Rother ◽

Magdalena Rother ◽

Michał Boniecki ◽

Tomasz Puton ◽

Janusz M. Bujnicki

Keyword(s):

3D Structure ◽

Structure Modeling ◽

Protein 3D Structure ◽

3D Structure Modeling ◽

Similarities And Differences

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A Method of Structure Comparison Using Spatial Topological Patterns

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.277-279.272 ◽

2005 ◽

Vol 277-279 ◽

pp. 272-277

Author(s):

Sung Hee Park ◽

Keun Ho Ryu

Keyword(s):

Spatial Data ◽

Similarity Search ◽

3D Structure ◽

Structural Similarity ◽

Query Protein ◽

Data Types ◽

Structure Comparison ◽

3D Space ◽

Structure Databases ◽

Computationally Expensive

The problem of comparison of structural similarity has been complex and computationally expensive. The first step to solve comparison of structural similarity in 3D structure databases is to develop fast methods for structural similarity. Therefore, we propose a new method of comparing structural similarity in protein structure databases by using topological patterns of proteins. In our approach, the geometry of secondary structure elements in 3D space is represented by spatial data types and is indexed using Rtrees. Topological patterns are discovered by spatial topology relations based on the Rtree index join. An algorithm for a similarity search compares topological patterns of a query protein with those of proteins in structure databases by the intersection frequency of SSEs. Our experimental results show that the execution time of our method is three times faster than the generally known method DALITE. Our method can generate small candidate sets for more accurate alignment tools such as DALI and SSAP.

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