Current Chinese Science - Volume 1, Issue 1, 2021

Background: Predicting the three-dimensional structure of globular proteins from their amino acid sequence has reached a fair accuracy, but predicting the structure of membrane proteins, especially loop regions, is still a difficult task in structural bioinformatics. The difficulty in predicting membrane loops is due to various factors like length variation, position, flexibility, and they are easily prone to mutation. Objective: In the present work, we address the problem of identifying and ranking near-native loops from a set of decoys generated by Monte-Carlo simulations. Methods: We systematically analyzed native and generated non-native decoys to develop a scoring function. The scoring function uses four important stabilizing energy terms from three popular force fields, such as FOLDX, OPLS, and AMBER, to identify and rank near-native membrane loops. Results: The results reveal better discrimination of native and non-natives and perform poor prediction in binary classifying native and near-native defined based on Root Mean Square Deviation (RMSD), Global Distance Test (GDT), and Template Modeling (TM) score, respectively. Conclusion: From our observations, we conclude that the important energy features described here may help to improve the loop prediction when the membrane protein database size increases.

Two-dimensional (2D) nanomaterials with unique anisotropy and electronic properties are deemed as an ideal platform for establishing clear relationships between structure and catalytic reactivity. Knowledge of their structures is essential for understanding the catalytic behavior, which further facilitates the development of high-performance catalysts. In this review, we focus on the recent progress of synchrotron radiation X-ray absorption spectrum (XAS) techniques in exploring the structure-function relationship of two-dimensional electrocatalysts. Also, we summarize the application of XAS technique in disclosing key factors that affect the catalytic activity, including identification of local atomic structure, electronic structure and defect structure. Through the characterization of the catalytic process with XAS technique, we further highlight the atomic-level correlation between structure and function in the field of oxygen evolution, oxygen reduction, hydrogen evolution and CO2 reduction. Finally, we propose the major challenges and prospects of XAS technique in advancing the development of two-dimensional electrocatalysts. We anticipate that this review provides critical insights into the application of the XAS technique in electrocatalysis, thereby promoting the development of advanced characterization techniques and the design of high-active catalysts.

Background: The use of an amine solution to capture CO2 from flue gases is one of the methods applied commercially to clean up the exhaust gas stream of a power plant. One of the issues in this process is foaming which should be known in order to select a suitable amine for design. Objectives: In this work, all possible types of amines used for CO2 capture, namely, alkanolamines, sterically hindered alkanolamines, multi-alkylamines and cyclic amines, were investigated to elucidate their chemical structure–foaming relationships. Methods: Foam volume produced by each type of 2M amine solution with its equilibrium CO2 loading was measured at 40°C using 94 mL/min of N2 flow. Results: Amines with a higher number or a longer chain of the alkyl group exhibited higher foam volume because of alkyl group’s ability to decrease the surface tension while increasing the viscosity of the solution. An increase in the number of hydroxyl or amino groups in the amine led to the reduction of foam formation due to the increase in surface tension and a decrease in viscosity of the solution. The predictive foam models for non-cyclic and cyclic-amines developed based on the structural variations, surface tension and viscosity of 29 amines predicted the foam volume very well with average absolute deviations (AAD) of 12.7 and 0.001%, respectively. The model accurately predicted the foam volume of BDEA, which was not used in model development with 13.3 %AD. Conclusion: This foam model is, therefore, indispensable in selecting a suitable amine for an amine-based CO2 capture plant design and operation.

Background: Obesity is an emerging pandemic considered to be an outcome of change in lifestyle owing to more processed food and the use of mechanical locomotives. Obesity has not only appeared as a problem in the esthetic appearance of an individual rather is a serious health issue due to its associations with various chronic diseases such as coronary and cardiovascular problems, hypertension, osteoarthritis, type-II diabetes mellitus, hyperlipidemia, and certain cancers. It is estimated that 30 percent of the world’s population, i.e. approximately 2.1 billion people, are victims of obesity. In addition to environmental causes, various genes and a group of genes are reported to be increasing the suceptibility of obesity. Objective: Pakistan is a heterogeneous population, an amalgam of various races, therefore, narrowing down the list of obesity-associated genes and their functional variance could help molecular biologists to select potential SNPs in the Pakistani population for molecular diagnosis and treatment. Method: The extraction of a set of obesity-associated genes has been performed by using Polysearch2. SNPs for each gene are retrieved from dbSNP. RegulomeDB and SNPinfo tools have been used for the functional analysis of SNPs retrieved against the Pakistani population. For the prediction of potential deleterious SNPs, SIFT, Polyphen-2, MUTTASTER, MUTASSESSOR, and LRT (likelihood ratio test) are utilized. Functional analysis of potential deleterious SNPs has been performed by studying protein stability and mapping of identified SNPs to protein structure. For the protein stability analysis, I-Mutant and SNPs3D have been used. Results: Four genes FTO, POMC, LEPR, and MC4R and further analysis revealed 3 deleterious SNPs in FTO, 4 in POMC, 1 in LEPR, and 1 in MC4R. Conclusion: This research was designed to identify obesity-associated genes and the most impactful deleterious SNPs in these genes. These findings will be helpful for the molecular biologists and pharmacists to design better and focused diagnosis and treatment strategies.

Background: CRISPR-Cas9 is a powerful technology that allows us to modify DNA sequences in a specific manner across a variety of organisms. Due to its high efficiency and specificity, and ease of use, it becomes a commonly used method for gene editing. Although many structural and biochemical studies have been carried out to understand the fundamental mechanism of CRISPR/Cas9, our understanding of CRISPR/Cas9 caused off-target effects is still lacking. Methods: The enhanced in vitro cleavage activity of Cas9 protein from Streptococcus pyogenes (SpCas9) was evaluated by both synthetic crRNA-tracrRNA duplexes and in vitro transcribed single guide RNAs. Results: Here, we report an unexpected finding that mismatches between the guide RNA and target DNA significantly enhanced the in vitro cleavage activity of SpCas9 by more than 2 folds. Conclusion: Our observation that mismatches between the guide RNA and target DNA can dramatically increase the in vitro cleavage of Cas9 suggests the potential sequence preference for the CRSIPR/Cas9 system.

Mangroves forests inhabiting the south coast of China are crucial habitats for the functioning of the coastal zone. This role has not been carefully considered in China and compared to their functional role worldwide. China’s mangroves currently occupy 20,303 ha. Average forest biomass is equivalent to the global average, but the mean ratio of below-ground to above-ground biomass is high (46%), reflecting the young age of most forests. Rates of annual litterfall, net primary productivity, and root production are above the global average, indicating that China’s mangrove forests are highly productive within their latitudinal band. High productivity may reflect high rates of organic inputs, young forest ages, and high rates of precipitation. As China’s mangrove forests have short canopies, these data imply that the rates of forest turnover are more rapid than in most other mangroves. Mangrove organic carbon (Corg) stock averaged 190.96 Mg Corg ha^-1, mostly (58%) in soils, less than the global average, reflecting young forest age and frequent soil disturbance. Total Corg stored in China’s mangroves is 3.9 Tg, only 0.03% of the global total, but the total Corg sequestration rate is 139.4 Gg Corg a^-1 while the average Corg sequestration rate is 6.87 Mg Corg ha^-1 a^-1, which is greater than the global average. Corg losses via microbial mineralization are large as total Corg export from mangroves accounts for 44% of the total Corg exported to the South China Sea. Nutrients are efficiently retained suggesting their use and proper management as aquaculture filters. Mangroves have close links to other coastal food webs.

Background: The basic principle of genome selection (GS) is to establish a model of genome estimated breeding value (GEBV) by using single-nucleotide polymorphisms (SNPs) covering the entire genome. Despite the decreasing cost of high-throughput genotyping, the GS strategy remains expensive due to the need for phenotyping and genotyping for a large number of samples. Simulation analysis of genome selection is a popular, lower-cost method to determine an optimal breeding program of GS. Objective: To evaluate the utility of simulation data to study the influence of different factors on algorithms. This could be helpful for developing genome selection breeding strategies, especially for stress and resistance traits of fish. Methods: Real data of orange-spotted grouper (Epinephelus coioides) were obtained from a previous genome-wide association study. Ammonia tolerance, different population sizes, SNP density, QTL number, kinship (base mutation rate), and heritability were considered. All of the phenotypes and genotypes were generated by AlphaSimR simulation software. Four genome selection algorithms (gBLUP, rrBLUP, BayesA, and BayesC) were tested to derive GEBV, and their accuracies (area under the curve, AUC) were compared. Results: In different scenarios, the AUC ranges from 0.4237 to 0.6895 for BayesA, 0.4282 to 0.6878 for BayesC, 0.4278 to 0.6798 for gBLUP, and 0.4346 to 0.6834 for rrBLUP. The mean AUC of these four algorithms was not significantly different (0.547–0.548). The accuracies of the four genome selection algorithms were similar but had different predictive performances in specific scenarios. The gBLUP was most stable, and the rrBLUP was slightly better at predicting low heritability traits. When the number of individuals was small, the BayesA and BayesC algorithms were more robust. Conclusion: A practical GS scheme should be optimized in accordance with marker density, heritability, and reference population size. Adequate preliminary research is necessary. The results provide a framework for the design of genomic selection schemes in E. coioides breeding.