Mishra, G. | bioRxiv Science

bioRxiv2019

SAMbinder: A web server for predicting SAM binding residues of a protein from its amino acid sequence

MotivationS-adenosyl-L-methionine (SAM) is one of the important cofactor present in the biological system and play a key role in many diseases. There is a need to develop a method for predicting SAM binding sites in a protein for designing drugs against SAM associated disease. Best of our knowledge, there is no method that can predict the binding site of SAM in a given protein sequence.\n\nResultThis manuscript describes a method SAMbinder, developed for predicting SAM binding sites in a protein from its primary sequence. All models were trained, tested and evaluated on 145 SAM binding protein chains where no two chains have more than 40% sequence similarity. Firstly, models were developed using different machine learning techniques on a balanced dataset contain 2188 SAM interacting and an equal number of non-interacting residues. Our Random Forest based model developed using binary profile feature got maximum MCC 0.42 with AUROC 0.79 on the validation dataset. The performance of our models improved significantly from MCC 0.42 to 0.61, when evolutionary information in the form of PSSM profile is used as a feature. We also developed models on realistic dataset contains 2188 SAM interacting and 40029 non-interacting residues and got maximum MCC 0.61 with AUROC of 0.89. In order to evaluate the performance of our models, we used internal as well as external cross-validation technique.\n\nAvailability and implementationhttps://webs.iiitd.edu.in/raghava/sambinder/.

bioinformatics↗

bioRxiv2019

Computing wide range of protein/peptide features from their sequence and structure

MotivationIn last three decades, a wide range of protein descriptors/features have been discovered to annotate a protein with high precision. A wide range of features have been integrated in numerous software packages (e.g., PROFEAT, PyBioMed, iFeature, protr, Rcpi, propy) to predict function of a protein. These features are not suitable to predict function of a protein at residue level such as prediction of ligand binding residues, DNA interacting residues, post translational modification etc.\n\nResultsIn order to facilitate scientific community, we have developed a software package that computes more than 50,000 features, important for predicting function of a protein and its residues. It has five major modules for computing; composition-based features, binary profiles, evolutionary information, structure-based features and patterns. The composition-based module allows user to compute; i) simple compositions like amino acid, dipeptide, tripeptide; ii) Properties based compositions; iii) Repeats and distribution of amino acids; iv) Shannon entropy to measure the low complexity regions; iv) Miscellaneous compositions like pseudo amino acid, autocorrelation, conjoint triad, quasi-sequence order. Binary profile of amino acid sequences provides complete information including order of residues or type of residues; specifically, suitable to predict function of a protein at residue level. Pfeature allows one to compute evolutionary information-based features in form of PSSM profile generated using PSIBLAST. Structure based module allows computing structure-based features, specifically suitable to annotate chemically modified peptides/proteins. Pfeature also allows generating overlapping patterns and feature from whole protein or its parts (e.g., N-terminal, C-terminal). In summary, Pfeature comprises of almost all features used till now, for predicting function of a protein/peptide including its residues.\n\nAvailabilityIt is available in form of a web server, named as Pfeature (https://webs.iiitd.edu.in/raghava/pfeature/), as well as python library and standalone package (https://github.com/raghavagps/Pfeature) suitable for Windows, Ubuntu, Fedora, MacOS and Centos based operating system.

bioinformatics↗