Background: Lysine succinylation is one of the reversible protein post-translational modifications (PTMs), which regulate the structure and function of proteins. It plays a significant role in various cellular physiologies including some diseases of human as well as many other organisms. The accurate identification of succinylation site is essential to understand the various biological functions and drug development. In this study, we developed an improved method to predict lysine succinylation sites mapping on Homo sapiens by the fusion of three encoding schemes such as binary, composition of k-spaced amino acid pairs (CKSAAP) and Amino acid composition (AAC) with the random forest (RF) classifier. The prediction performance of the proposed random forest (RF) based fusion model in a comparison of the other candidates was investigated by using 20-fold cross-validation (CV) and two independent test datasets that were collected from two different sources. The CV results showed that the proposed predictor achieves the highest scores of sensitivity (SN) as 0.800, specificity (SP) as 0.902, accuracy (ACC) as 0.919, Mathew correlation coefficient (MCC) as 0.766 and partial AUC (pAUC) as 0.163 at false positive rate (FPR) = 0.10 and area under the ROC curve (AUC) as 0.958. It achieved the highest performance scores of SN as 0.811, SP as 0.902, ACC as 0.891, MCC as 0.629 and pAUC as 0.139 and AUC as 0.921 for the independent test protein set-1 and SN as 0.772, SP as 0.901, ACC as 0.836, MCC as 0.677 and pAUC as 0.141 at FPR = 0.10 and AUC as 0.923 for the independent test protein set-2. It also outperformed all the other existing prediction models. Conclusion: The prediction performances as discussed in this article recommend that the proposed method might be a useful and encouraging computational resource for lysine succinylation site prediction in the case of human population.

A fine-tuned RNA interference (RNAi) pathway has been developed by plants to restrain distinct biological processes in various life stages including stress responses, development and maintenance of genome integrity. The Dicer-Like (DCL) proteins starts the RNAi process by producing complementary double-stranded RNAs (dsRNAs) into small RNA duplexes (21-24 nucleotides) trigger the RNAi process. Nevertheless, these members of RNAi pathway have not been deciphered in one of the most economically important plant coffee (Coffea arabica). Therefore, it is of interest to report the identification and phylogenetic analysis of the DCL genes in C. arabica. We report 5 DCL genes and categorized them into three significant groups to interpret the evolutionary relationship with DCLs of the model plant Arabidopsis thaliana. Moreover, the subcellular location of the reported DCL proteins and the associated cis-acting regulatory elements were also identified and discussed in this report. The cis-regulatory elements indicated the biological and molecular functional diversity of the identified DCL genes related with plant growth and development. The present findings will provide a better basis for further experimental research on RNAi pathway genes in C. arabica.

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