The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are known as membrane proteins and are considered to play a significant role. These membrane proteins exhibit their effect in cellular activities inside and outside of the cell. According to the scientists in pharmaceutical organizations, these membrane proteins perform key task in drug interactions. In this study, a technique is presented that is based on various computationally intelligent methods used for the prediction of membrane protein without the experimental use of mass spectrometry. Statistical moments were used to extract features and furthermore a Multilayer Neural Network was trained using backpropagation for the prediction of membrane proteins. Results show that the proposed technique performs better than existing methodologies.

Cancerlectins are significantly important group of lectins that have an inhibitory effect on cancer cells with respect to their growth. They have a vital role in various tumor cell interactions like adhesion, growth, metastasis, differentiation and mainly in cellular infection. The investigations associated with cancerlectins are applicable to relevant studies in laboratories, diagnostics and therapy in clinical applications, and drug discoveries in targeting cancers. Prediction of cancerlectins is considered a helpful task due to the fact that they are specifically useful in dissecting cancers. Although, several Bioinformatics tools have been developed to predict cancerlectins, however, the need for improvement in the quality of its prediction model requires enhancements in the annotation and determination process of cancerlectins. In this study, a new model is proposed that builds on statistical moments based features to distinguish cancerlectins from non-cancerlectins. The currently proposed model achieved an accuracy of 88.36% using jackknife test which is better than current state-of-the-art models. These outcomes suggest that the use of statistical moments could bear more effective and efficient results. For the accessibility of the scientific community, a user-friendly web server has been developed which will associate the researchers in medical science. Web server is freely accessible at https://www.biopred.org/canlect.

Sumoylation is the post-translational modification that is involved in the adaption of the cells and the functional properties of a large number of proteins. Sumoylation has key importance in subcellular concentration, transcriptional synchronization, chromatin remodeling, response to stress, and regulation of mitosis. Sumoylation is associated with developmental defects in many human diseases such as cancer, Huntington’s, Alzheimer’s, Parkinson’s, Spin cerebellar ataxia 1, and amyotrophic lateral sclerosis. The covalent bonding of Sumoylation is essential to inheriting part of the operative characteristics of some other proteins. For that reason, the prediction of the Sumoylation site has significance in the scientific community. A novel and efficient technique is proposed to predict the Sumoylation sites in proteins by incorporating Chou’s Pseudo Amino Acid Composition (PseAAC) with statistical moments-based features. The outcomes from the proposed system using 10 fold cross-validation testing are 94.51%, 94.24%, 94.79% and 0.8903% accuracy, sensitivity, specificity and MCC, respectively. The performance of the proposed system is so far the best in comparison to the other state-of-the-art methods. The codes for the current study are available on the GitHub repository using the link: https://github.com/csbioinfopk/iSumoK-PseAAC.