IMPROVING SEARCH FOR LOW ENERGY PROTEIN STRUCTURES WITH AN ITERATIVE NICHE GENETIC ALGORITHM

2016 ◽  
Vol 85 ◽  
pp. 99-108 ◽  
Author(s):  
CĂRUŢAŞIU Mihail–Bogdan ◽  
IONESCU Constantin ◽  
NECULA Horia

2021 ◽  
Vol 118 (51) ◽  
pp. e2112651118
Author(s):  
Hannah Ochner ◽  
Sven Szilagyi ◽  
Sabine Abb ◽  
Joseph Gault ◽  
Carol V. Robinson ◽  
...  

Imaging of proteins at the single-molecule level can reveal conformational variability, which is essential for the understanding of biomolecules. To this end, a biologically relevant state of the sample must be retained during both sample preparation and imaging. Native electrospray ionization (ESI) can transfer even the largest protein complexes into the gas phase while preserving their stoichiometry and overall shape. High-resolution imaging of protein structures following native ESI is thus of fundamental interest for establishing the relation between gas phase and solution structure. Taking advantage of low-energy electron holography’s (LEEH) unique capability of imaging individual proteins with subnanometer resolution, we investigate the conformational flexibility of Herceptin, a monoclonal IgG antibody, deposited by native electrospray mass-selected ion beam deposition (ES-IBD) on graphene. Images reconstructed from holograms reveal a large variety of conformers. Some of these conformations can be mapped to the crystallographic structure of IgG, while others suggest that a compact, gas-phase–related conformation, adopted by the molecules during ES-IBD, is retained. We can steer the ratio of those two types of conformations by changing the landing energy of the protein on the single-layer graphene surface. Overall, we show that LEEH can elucidate the conformational heterogeneity of inherently flexible proteins, exemplified here by IgG antibodies, and thereby distinguish gas-phase collapse from rearrangement on surfaces.


2016 ◽  
Vol 18 (34) ◽  
pp. 23916-23922 ◽  
Author(s):  
P. Wu ◽  
S. Q. Wu ◽  
X. Lv ◽  
X. Zhao ◽  
Z. Ye ◽  
...  

Using a combination of adaptive genetic algorithm search, motif-network search scheme and first-principles calculations, we have systematically studied the low-energy crystal structures of Na2FeSiO4.


Nano LIFE ◽  
2012 ◽  
Vol 02 (02) ◽  
pp. 1240006
Author(s):  
Y. X. YAO ◽  
C. RARESHIDE ◽  
T. L. CHAN ◽  
C. Z. WANG ◽  
K. M. HO

We report a collection of lowest-energy structures of hydrocarbon molecules C n H m (n = 6-18, m = 0 - 2n + 2) within the wide hydrogen chemical potential range. The genetic algorithm combined with Brenner's empirical potential is applied for the search. The resultant low-energy structures are further examined by ab initio quantum chemical calculations. The lowest-energy molecules with several additional low-energy structures are classified to four groups according to their structural motifs and the phase diagram with respect to carbon atom number and hydrogen chemical potential is presented. The results provide useful information for identifying the hydrocarbon molecules in the interstellar medium as well as addressing the hydrocarbon-related nanofragment growth in experiments.


2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Min Tian ◽  
Jie Zhou ◽  
Xin Lv

Large-scale wireless sensor networks consist of a large number of tiny sensors that have sensing, computation, wireless communication, and free-infrastructure abilities. The low-energy clustering scheme is usually designed for large-scale wireless sensor networks to improve the communication energy efficiency. However, the low-energy clustering problem can be formulated as a nonlinear mixed integer combinatorial optimization problem. In this paper, we propose a low-energy clustering approach based on improved niche chaotic genetic algorithm (INCGA) for minimizing the communication energy consumption. We formulate our objective function to minimize the communication energy consumption under multiple constraints. Although suboptimal for LSWSN systems, simulation results show that the proposed INCGA algorithm allows to reduce the communication energy consumption with lower complexity compared to the QEA (quantum evolutionary algorithm) and PSO (particle swarm optimization) approaches.


Author(s):  
Jiaxi Liu ◽  

The prediction of protein three-dimensional structure from amino acid sequence has been a challenge problem in bioinformatics, owing to the many potential applications for robust protein structure prediction methods. Protein structure prediction is essential to bioscience, and its research results are important for other research areas. Methods for the prediction an才d design of protein structures have advanced dramatically. The prediction of protein structure based on average hydrophobic values is discussed and an improved genetic algorithm is proposed to solve the optimization problem of hydrophobic protein structure prediction. An adjustment operator is designed with the average hydrophobic value to prevent the overlapping of amino acid positions. Finally, some numerical experiments are conducted to verify the feasibility and effectiveness of the proposed algorithm by comparing with the traditional HNN algorithm.


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