Supplementary data are available at Bioinformatics online.
Biochar typically consists of both carbon and mineral fractions, and the carbon fraction has been generally considered to determine its properties and applications. Recently, an increasing body of research has demonstrated that mineral components inherent in biochar, such as alkali or alkaline earth metals in the form of carbonates, phosphates, or oxides, could also influence the properties and thus the applications. The review articles published thus far have mainly focused on multiple environmental and agronomic applications of biochar, including carbon sequestration, soil improvement, environmental remediation, etc. This review aims to highlight the indispensable role of the mineral fraction of biochar in these different applications, especially in environmental applications. Specifically, it provides a critical review of current research findings related to the mineral composition of biochar and the effect of the mineral fraction on the physicochemical properties, contaminant sorption, carbon retention and stability, and nutrient bioavailability of biochar. Furthermore, the role of minerals in the emerging applications of biochar, as a precursor for fuel cells, supercapacitors, and photoactive components, is also summarized. Overall, inherent minerals should be fully considered while determining the most appropriate application for any given biochar. A thorough understanding of the role of biochar-bound minerals in different applications will also allow the design or selection of the most suitable biochar for specific applications based on the consideration of feedstock composition, production parameters, and post-treatment.
Hormone-binding protein (HBP) is a kind of soluble carrier protein and can selectively and non-covalently interact with hormone. HBP plays an important role in life growth, but its function is still unclear. Correct recognition of HBPs is the first step to further study their function and understand their biological process. However, it is difficult to correctly recognize HBPs from more and more proteins through traditional biochemical experiments because of high experimental cost and long experimental period. To overcome these disadvantages, we designed a computational method for identifying HBPs accurately in the study. At first, we collected HBP data from UniProt to establish a high-quality benchmark dataset. Based on the dataset, the dipeptide composition was extracted from HBP residue sequences. In order to find out the optimal features to provide key clues for HBP identification, the analysis of various (ANOVA) was performed for feature ranking. The optimal features were selected through the incremental feature selection strategy. Subsequently, the features were inputted into support vector machine (SVM) for prediction model construction. Jackknife cross-validation results showed that 88.6% HBPs and 81.3% non-HBPs were correctly recognized, suggesting that our proposed model was powerful. This study provides a new strategy to identify HBPs. Moreover, based on the proposed model, we established a webserver called HBPred, which could be freely accessed at http://lin-group.cn/server/HBPred.
Most natural protein sequences have resulted from millions or even billions of years of evolution. How they differ from random sequences is not fully understood. Previous computational and experimental studies of random proteins generated from noncoding regions yielded inclusive results due to species-dependent codon biases and GC contents. Here, we approach this problem by investigating 10,000 sequences randomized at the amino acid level. Using well-established predictors for protein intrinsic disorder, we found that natural sequences have more long disordered regions than random sequences, even when random and natural sequences have the same overall composition of amino acid residues. We also showed that random sequences are as structured as natural sequences according to contents and length distributions of predicted secondary structure, although the structures from random sequences may be in a molten globular-like state, according to molecular dynamics simulations. The bias of natural sequences toward more intrinsic disorder suggests that natural sequences are created and evolved to avoid protein aggregation and increase functional diversity.
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