The structure-function correlation of membrane proteins have been a difficult task, particularly in context to transient protein complexes. The molecular simulation of ternary complex of Rab7::REP1::GGTase-II was carried out to understand the basic structural events occurring during the prenylation event of Rab proteins, using the software YASARA. The study suggested that the C-terminus of Rab7 has to be in completely extended conformation during prenylation to reach the active site of RabGGTase-II. Also, attempt was made to find putative drug binding sites on the ternary complex of Rab7::REP1::GGTase-II using Q-SiteFinder programme. The comprehensive consensus probe generated by the program revealed a total of 10 major pockets as putative drug binding sites on Rab7::REP:: GGTase-II ternary complex. These pockets were found on REP protein and GGTase protein subunits. The Rab7 was found to be devoid of any putative drug binding sites in the ternary complex. The phylogenetic analysis of 60 Rab proteins of human was carried out using PHYLIP and study indicated the close phylogenetic relationship between Rab7 and Rab9 proteins of human and hence with further in silico study, the present observations can be extrapolated to Rab9 proteins. The study paves a good platform for further experimental verifications of the findings and other in silico studies like identifying the potential drug targets by searching the putative drug binding sites, generating pharmacophoric pattern, searching or constructing suitable ligand and docking studies.
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http://dx.doi.org/10.4103/0250-474X.113534 | DOI Listing |
Neural Netw
January 2025
State Key Laboratory of Public Big Data, Guizhou University, 550025, China; Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Guizhou University, 550025, China; College of Computer Science and Technology, Guizhou University, 550025, China. Electronic address:
Relation extraction independently verifies all entity pairs in a sentence to identify predefined relationships between named entities. Because these entity pairs share the same contextual features of a sentence, they lead to a complicated semantic structure. To distinguish semantic expressions between relation instances, manually designed rules or elaborate deep architectures are usually applied to learn task-relevant representations.
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January 2025
Key Laboratory of Cosmetic, China National Light Industry, Beijing Technology and Business University, Beijing, China.
Oily sensitive skin is complex and requires accurate identification and personalized care. However, the current classification method relies on subjective assessment. This study aimed to classify skin type and subtype using objective biophysical parameters to investigate differences in skin characteristics across anatomical and morphological regions.
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January 2025
College of Food Science and Technology, Henan Agricultural University, Zhengzhou 450000, PR China. Electronic address:
This study aims to examine the effect of atmospheric cold plasma (ACP) pretreatment (1-4 min) on the formation, structure, and digestibility of ternary complexes among wheat starch (WS), β-lactoglobulin (βLG) and fatty acids with different chain lengths (octanoic acid (OA), capric acid (CA), lauric acid (LA)). The complexing index results demonstrated that the greatest quantity (72.53 %, 70.
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January 2025
Key Laboratory for the Physics and Chemistry of Nanodevices and Center for Carbon-Based Electronics, School of Electronics, Peking University, Beijing 100871, China.
Multi-valued logics (MVLs) offer higher information density, reduced circuit and interconnect complexity, lower power dissipation, and faster speed over conventional binary logic system. Recent advancement in MVL research, particularly with emerging low-dimensional materials, suggests that breakthroughs may be imminent if multistates transistors can be fabricated controllably for large-scale integration. Here, a concept of source-gating transistors (SGTs) is developed and realized using carbon nanotubes (CNTs).
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January 2025
Department of Materials Science and Engineering, Kyoto University, Sakyo, Kyoto, 606-8501, Japan.
The discovery of novel materials is crucial for developing new functional materials. This study introduces a predictive model designed to forecast complex multi-component oxide compositions, leveraging data derived from simpler pseudo-binary systems. By applying tensor decomposition and machine learning techniques, we transformed pseudo-binary oxide compositions from the Inorganic Crystal Structure Database (ICSD) into tensor representations, capturing key chemical trends such as oxidation states and periodic positions.
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