Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

BACKGROUND: The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. RESULTS: We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. CONCLUSION: As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential

SVRMHC prediction server for MHC-binding peptides

BACKGROUND: The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. RESULTS: Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. CONCLUSION: SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers

Meta-prediction of protein subcellular localization with reduced voting

Meta-prediction seeks to harness the combined strengths of multiple predicting programs with the hope of achieving predicting performance surpassing that of all existing predictors in a defined problem domain. We investigated meta-prediction for the four-compartment eukaryotic subcellular localization problem. We compiled an unbiased subcellular localization dataset of 1693 nuclear, cytoplasmic, mitochondrial and extracellular animal proteins from Swiss-Prot 50.2. Using this dataset, we assessed the predicting performance of 12 predictors from eight independent subcellular localization predicting programs: ELSPred, LOCtree, PLOC, Proteome Analyst, PSORT, PSORT II, SubLoc and WoLF PSORT. Gorodkin correlation coefficient (GCC) was one of the performance measures. Proteome Analyst is the best individual subcellular localization predictor tested in this four-compartment prediction problem, with GCC = 0.811. A reduced voting strategy eliminating six of the 12 predictors yields a meta-predictor (RAW-RAG-6) with GCC = 0.856, substantially better than all tested individual subcellular localization predictors (P = 8.2 × 10−6, Fisher's Z-transformation test). The improvement in performance persists when the meta-predictor is tested with data not used in its development. This and similar voting strategies, when properly applied, are expected to produce meta-predictors with outstanding performance in other life sciences problem domains

Ultrahigh-content nitrogen-decorated nanoporous carbon derived from metal organic frameworks and its application in supercapacitors

Single electric double-layer capacitors cannot meet the growing demand for energy due to their insufficient energy density. Generally speaking, the supercapacitors introduced with pseudo-capacitance by doping heteroatoms (N, O) in porous carbon materials can obtain much higher capacitance than electric double-layer capacitors. In view of above merits, in this study, nanoporous carbon materials with ultrahigh N enrichment (14.23 wt%) and high specific surface area (942 m2 g−1) by in situ introduction of N-doped MOF (ZTIF-1, Organic ligands 5-methyltetrazole/C2H4N4) were produced. It was found that as supercapacitors' electrode materials, these nanoporous carbons exhibit a capacitance as high as 272 F g-1 at 0.1 A g−1, and an excellent cycle life (almost no attenuation after 10,000 cycles.). Moreover, the symmetric supercapacitors were assembled to further investigate the actual capacitive performance, and the capacitance shows up to 154 F g-1 at 0.1 A g−1. Such excellent properties may be attributed to a combination of a high specific surface area, ultrahigh nitrogen content and hierarchically porous structure. The results shown in this study fully demonstrate that the nanoporous carbon materials containing ultrahigh nitrogen content can be used as a potential electrode material in supercapacitors

Research status and development trend of compressed air energy storage in abandoned coal mines

Compressed air energy storage (CAES) has the advantages of low construction cost, small equipment footprint, long storage cycle and environmental protection. Exploring the development of CAES technology in underground space is one of the innovative approaches to achieve China’s “dual-carbon” goal. Underground energy storage reservoirs can be classified into salt caverns, aquifers, depleted oil and gas fields, abandoned coal mines, and caverns. With the increasing number of abandoned coal mines in China, the direct closure of resource-depleted coal mines not only cause a significant waste of underground space resources, but also induce a series of safety, environmental and other issues. Therefore, utilizing the underground space of abandoned coal mines as CAES reservoirs holds great application prospects. The analysis shows that, ① There is a large amount of usable space in abandoned coal mines, and eight reuse modes of underground space in abandoned coal mines have been summarized: agricultural and forestry land, construction land, site greening, watershed utilization, water-heat combination, wetland park, mine park, and space reuse. ② The research on CAES in abandoned coal mines in China started late, the basic theoretical research is weak, the key technologies is immature, and geological conditions in coal mines are complex, the relevant applications of basic research is insufficient, and the commercialization, large-scale promotion and application have not yet been achieved. ③ Three key technologies are summarized and proposed to cope with the CAES in abandoned coal mines, i.e., the evaluation method of site selection for the construction of abandoned coal mine energy storage reservoirs, the key technology for the sealing of abandoned coal mine energy storage reservoirs, and the stability and safety evaluation of abandoned coal mine energy storage reservoirs. A flowchart for siting the construction of CAES reservoirs in abandoned coal mines has been established

Facile synthesis of ultrahigh-surface-area hollow carbon nanospheres and their application in lithium-sulfur batteries

Hollow carbon nanospheres (HCNs) with specific surface areas up to 2949 m2 g−1 and pore volume up to 2.9 cm3 g−1 were successfully synthesized from polyaniline‐co‐polypyrrole hollow nanospheres by carbonization and CO2 activation. The cavity diameter and wall thickness of HCNs can be easily controlled by activation time. Owing to their large inner cavity and enclosed structure, HCNs are desirable carriers for encapsulating sulfur. To better understand the effects of pore characteristics and sulfur contents on the performances of lithium‐sulfur batteries, three composites of HCNs and sulfur are prepared and studied in detail. The composites of HCNs with moderate specific surface areas and suitable sulfur content present a better performance. The first discharge capacity of this composite reaches 1401 mAh g−1 at 0.2 C. Even after 200 cycles, the discharge capacity remains at 626 mAh g−1

CoSe2/Co nanoheteroparticles embedded in Co, Nco-doped carbon nanopolyhedra/nanotubes as anefficient oxygen bifunctional electrocatalyst for Zn–air batteries

Transition metal selenide-based materials have been demonstrated as promising electrocatalysts for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), yet the actual design of a highly efficient and stable electro-catalyst based on these materials still remains a long and arduous challenge. Herein, a predesigned hybrid Zn/Co zeolitic imidazole framework was used to fabricate CoSe2/Co nanoheteroparticles embedded within hierarchically porous Co, N co-doped carbonnanopolyhedra/nanotubes (CoSe2/Co@NC-CNTs) through a facile approach involving controlled carbonization and selenization procedures. As expected, the optimized CoSe2/Co@NC-CNT-1 displayed outstanding electrocatalytic performance for the ORR and OER, with an onset potential of 0.95 V vs. RHE, a half-wave potential of 0.84 V vs. RHE for ORR, and a potential of 1.69 V vs. RHE for OER at 10 mA cm−2. It also exhibited excellent long-term stability and methanol resistance ability, which were superior to commercial IrO2 and the commercial 20 wt% Pt/C catalyst. Notably, the assembled Zn–air battery with CoSe2/Co@NC-CNT-1 showed a low charge–discharge voltage gap (0.696 V at 10 mA cm−2) and a high peak power density (100.28 mW cm−2) with long-term cycling stability. These superior performances can be ascribed to the synergistic effects of the highly active CoSe2/Co nanoheterostructure, hierarchically porous structure with a large surface area, high electrical conductivity and uniform doping of the Co and