Prediction for understanding the effectiveness of antiviral peptides

The low efficacy of current antivirals in conjunction with the resistance of viruses against existing antiviral drugs has resulted in the demand for the development of novel antiviral agents. Antiviral peptides (AVPs) are those bioactive peptides having virucidal activity and they can be developed into promising antiviral drugs. They are shorter length peptides having the ability to cease the progression of viral infections. The use of antiviral peptides in therapeutics has recently attracted the attention of the research community. The development and identification of AVPs is imperative for the discovery of novel therapeutics for viral infections. In the present work, a meta classifier (stacking) based approach is implemented for the prediction of IC₅₀ (half maximal inhibitory concentration) and pIC₅₀ (negative log of half maximal inhibitory concentration) values. The best prediction model with evolutionary information and local alignment scores as features achieved a correlation coefficient values of 0.670 and 0.753 on the training and testing sets respectively for IC₅₀. Further, the prediction of pIC₅₀ reached a correlation coefficient value of 0.797 and 0.789 for training and testing sets respectively. For the development of machine learning models involved in the prediction of IC₅₀, the use of pIC₅₀ over IC₅₀ is recommended as the target variable. Further on a systematic comparison of AVPs with high IC₅₀ values and Low IC₅₀ values, it is revealed that higher mean charge and tiny amino acids are preferred and higher length and consecutive hydrophilic amino acids are avoided in the former.     

Who Binds Better? Let Alphafold2 Decide!

Deep learning is revolutionizing structural biology to an unprecedented extent. Spearheaded by DeepMind's Alphafold2, structural models of high quality can be generated, and are now available for most known proteins and many protein interactions. The next challenge will be to leverage this rich structural corpus to learn about binding: which protein can contact which partner(s), and at what affinity? In a recent study, Chang and Perez have presented an elegant approach towards this challenging goal for interactions that involve a short peptide binding to its receptor. The basic idea is straightforward: given a receptor that binds to two peptides, if the receptor sequence is presented with both peptides together at the same time, AlphaFold2 should model the tighter binding peptide into the binding site, while excluding the second. A simple idea that works!     

Prediction of Nucleophilicity and Electrophilicity Based on a Machine-Learning Approach

Nucleophilicity and electrophilicity dictate the reactivity of polar organic reactions. In the past decades, Mayr et al. established a quantitative scale for nucleophilicity (N) and electrophilicity (E), which proved to be a useful tool for the rationalization of chemical reactivity. In this study, a holistic prediction model was developed through a machine-learning approach. rSPOC, an ensemble molecular representation with structural, physicochemical and solvent features, was developed for this purpose. With 1115 nucleophiles, 285 electrophiles, and 22 solvents, the dataset is currently the largest one for reactivity prediction. The rSPOC model trained with the Extra Trees algorithm showed high accuracy in predicting Mayr's N and E parameters with R² of 0.92 and 0.93, MAE of 1.45 and 1.45, respectively. Furthermore, the practical applications of the model, for instance, nucleophilicity prediction of NADH, NADPH and a series of enamines showed potential in predicting molecules with unknown reactivity within seconds. An online prediction platform (http://isyn.luoszgroup.com/) was constructed based on the current model, which is available free to the scientific community.     

Void fraction for random loose packing of the cylindrical particles considering filling rate,material and shape

Particle packing is widely applied in organic pollutant adsorption, catalytic reaction, biomass combustion, nuclear cooling, and other scenarios. Due to the complexity of the shape, the studies on the void fraction of the cylindrical particles are not as thorough as the spherical particles. This study investigated the influence of the filling rate, material properties and sphericity on the void fraction of cylinders through experiments and simulation. DEM (discrete element method) was validated by the internal structures of the packing obtained by CT (computed tomography). Based on the logarithmic correlation between the void fraction and filling rate, an ingenious framework for predicting the void fraction of cylindrical particles was presented with two intermediate coefficients. By correlating the coefficients with the material property and sphericity, a novel void-fraction prediction model was established with R-squared of 0.996. The mechanism of void fraction under random loose packing for cylinders was eventually found in this study.     

Physico-chemical properties and phase behaviour of piperidinium-based ionic liquids

The sufficient review of the existing literature of the 1-alkyl-1-methylppiperidinium-based ionic liquids has been presented. The phase diagrams for the binary systems of {1-butyl-1-methylpiperidinium thiocyanate [BMPIP][SCN] + an alcohol (1-hexanol, 1-heptanol, 1-octanol, 1-nonanol, 1-decanol, 1-dodecanol), or + water, or + aliphatic hydrocarbons (n-hexane, n-heptane, n-octane), or + cyclohexane, or, + cycloheptane, or + aromatic hydrocarbons (benzene, toluene, ethylbenzene)} and for the binary systems of {1-ethyl-1-methylpiperidinium bis{(trifluoromethyl)sulfonyl}imide [EMPIP][NTf₂] + an alcohol (ethanol, 1-propanol, 1-butanol, 1-hexanol, 1-heptanol, 1-octanol, 1-nonanol), or + water} have been determined at atmospheric pressure using a dynamic method. The influence of an alcohol chain length was discussed for these ionic liquids. A systematic decrease in the solubility was observed with an increase of the alkyl chain length of an alcohol. (Solid + liquid) phase equilibria with complete miscibility in the liquid phase region were observed for the systems involving water and the alcohols for the thiocyanate-based ionic liquid. Opposite, the bis{(trifluoromethyl)sulfonyl}imide-based ionic liquid reveal the immiscibility gap in the liquid phase. The correlation of the experimental data has been carried out using the NRTL equation. The phase diagrams reported here have been compared to the systems published earlier with the 1-alkyl-1-methylpiperidinium-based ionic liquids. The influence of the cation and anion on the phase behaviour has been discussed. The basic thermal properties of pure ILs, i.e. melting temperature and the enthalpy of fusion, the solid-solid phase transition temperature and enthalpy have been measured using a differential scanning microcalorimetry technique.     

Isothermal vapour-liquid equilibria in the binary and ternary systems composed of 2,2,4-trimethylpentane, 2-methyl-1-propanol, and 4-methyl-2-pentanone

Vapour-liquid equilibrium data in the three binary 2,2,4-trimethylpentane + 2-methyl-1-propanol, 2-methyl-1-propanol + 4-methyl-2-pentanone, 2,2,4-trimethylpentane + 4-methyl-2-pentanone systems, and in the ternary 2,2,4-trimethylpentane + 2-methyl-1-propanol + 4-methyl-2-pentanone system are reported. The data were measured isothermally at 333.15, 348.15 and 364.15 K covering the pressure range 12-100 kPa. The binary vapour-liquid equilibrium data were correlated using the Wilson and NRTL equations by means of a robust algorithm for processing all isotherms together; resulting parameters were then used for calculation of phase behaviour in the ternary system and for subsequent comparison with experimental data.     

海绵中提取的异臭椿萜类化合物作用靶标的识别

采取了包括化学结构相似性学习、靶标聚类分析以及反向对接筛选等多种方法在内的综合性策略, 尝试对中国南海海绵中提取得到的异臭椿萜类化合物进行生物学活性和作用靶标的预测. 结果表明: 这类化合物具有治疗心肌缺血和抗肿瘤的潜在生物学活性; 表皮细胞生长因子受体(EGFR), 焦点(局部)粘着斑激酶(FAK), 胰岛素样生长因子1受体 (IGF1-R), c-Src激酶以及血管表皮生长因子受体2 (VEGF-R2)是这类化合物可能的作用靶标. IC₅₀值从0.41 g·m^-3 (0.41 μg·mL^-1)到9.8 g·m^-3 (9.8 μg·mL^-1)不等. 活性数据显示这些海绵提取的海洋天然产物可作为先导化合物, 通过进一步的优化获得新的药物. 同时还讨论了化合物与预测靶标的结合模式, 结果显示四个化合物都与相应的受体有较好的结合.     

基于Adaptive Elastic Net方法的近红外光谱建模技术

当近红外光谱信息远远大于样本量时,对光谱信息进行自动变量选择进而建立光谱与微量成分含量之间的稀疏线性模型重要且具有挑战性。针对聚苯醚生产过程中微量成分邻甲酚难以测量的问题,将变量选择方法Adaptive Elastic Net用于建立近红外光谱与邻甲酚含量之间的定量校正模型,并将其模型性能与ElasticNet方法进行对比。在变量数目远远大于样本量的情形下,ElasticNet方法虽可以实现变量选择,但由于其系数估计不具备Oracle性质,使得模型的可解释性和预测精度受到影响,而Adaptive Elastic Net方法通过对L1惩罚项施加自适应权重从而很好的解决了上述问题并提高了模型性能。为了验证Adaptive Elastic Net方法的模型性能指标,用最终被选中的自变量数目来评价模型复杂度;利用复相关系数R2来评价模型的可解释性,利用平均相对预测误差MRPE(mean relative prediction error)和预测相关系数R_p来评价模型的预测精度。Elastic Net方法建立的模型性能指标为：NSIV=529,R2=0.96, MRPE=3.22％, R_p=0.97; Adaptive Elastic Net方法的性能指标为：NSIV=139, R2=0.99, MRPE=2.00％, R_p=0.99。结果表明：Adaptive Elastic Net所建立模型的性能指标优于Elastic Net方法,可以得到更加简单且具有较强可解释性和较高预测精度的稀疏线性模型。     

In silico epitope identification of unique multidrug resistance proteins from Salmonella Typhi for vaccine development

Typhoid fever is a multisystemic illness caused by Salmonella enterica serovars Typhi and is resistant to most antibiotics and drugs. The resistance is conferred through multidrug resistance (MDR) proteins, which efflux most antibiotics and other drugs. We predicted potential candidate B-cell and T-cell epitopes using bio- and immune-informatics tools in the 11 MDR proteins - EmrA, EmrB, EmrD, MdtA, MdtB, MdtC, MdtG, MdtH, MdtK, MdtL and TolC. The antigenic potential of the MDR proteins was calculated using VaxiJen server. The B-cell and T-cell epitopes of the MDR proteins were predicted using BCPred and ProPredI and ProPred respectively. The binding affinities of the predicted T-cell epitopes were estimated using T-epitope designer and MHCPred tools. 10, 7, 5, 12, 14, 21, 26, 3, 3 and 3 B-cell epitopes were identified in EmrA, EmrB, EmrD, TolC, MdtA, MdtB, MdtC, MdtG, MdtH and MdtL respectively. We predicted 9 T-cell epitopes - YVSRRAVQP (EmrA), FGVANAISI (EmrB), MVNSQVKQA and YQGGMVNSQ (TolC), WDRTNSHKL (MdtA), FLRNIPTAI (MdtB), YVEQLGVTG (MdtG), VKWMYAIEA (MdtH) and LAHTNTVTL (MdtL) capable of eliciting both humoral and adaptive immune responses. These T-cell epitopes specifically bind to HLA alleles - DRB1*0101 and DRB1*0401. This is the first report of epitope prediction in the MDR proteins of S. Typhi. Taken together, these results indicate the MDR proteins – EmrA, MdtA and TolC are the most suitable vaccine candidates for S. Typhi. The findings of our study on the MDR proteins prove to be useful in the development of peptide-based vaccine for the prevention and/or treatment of typhoid fever.