SIP: A computational prediction of S-Adenosyl methionine (SAM) interacting proteins and their interaction sites through primary structures.

S-Adenosyl methionine (SAM), a universal methyl group donor, plays a vital role in biosynthesis and acts as an inhibitor to many enzymes. Due to protein interaction-dependent biological role, SAM has become a favorite target in various therapeutical and clinical studies such as treating cancer, Alzheimer's, epilepsy, and neurological disorders. Therefore, the identification of the SAM interacting proteins and their interaction sites is a biologically significant problem. However, wet-lab techniques, though accurate, to identify SAM interactions and interaction sites are tedious and costly. Therefore, efficient and accurate computational methods for this purpose are vital to the design and assist such wet-lab experiments. In this study, we present machine learning-based models to predict SAM interacting proteins and their interaction sites by using only primary structures of proteins. Here we modeled SAM interaction prediction through whole protein sequence features along with different classifiers. Whereas, we modeled SAM interaction site prediction through overlapping sequence windows and ranking with multiple instance learning that allows handling imprecisely annotated SAM interaction sites. Through a series of simulation studies along with biological significant evaluation, we showed that our proposed models give a state-of-the-art performance for both SAM interaction and interaction site prediction. Through data mining in this study, we have also identified various characteristics of amino acid sub-sequences and their relative position to effectively locate interaction sites in a SAM interacting protein. Python code for training and evaluating our proposed models together with a webserver implementation as SIP (Sam Interaction Predictor) is available at the URL: https://sites.google.com/view/wajidarshad/software.

Neonatal calf diarrhea: A potent reservoir of multi-drug resistant bacteria, environmental contamination and public health hazard in Pakistan.

Though emergence of multi-drug resistant bacteria in the environment is a demonstrated worldwide phenomenon, limited research is reported about the prevalence of resistant bacteria in fecal ecology of neonatal calf diarrhea (NCD) animals in Pakistan. The present study aimed to identify and assess the prevalence of bacterial pathogens and their resistance potential in the fecal ecology of NCD diseased animals of Pakistan. The presence of antibiotic resistance genes (blaTEM, blaNDM-1, blaCTX-M, qnrS) was also investigated. A total of 51 bacterial isolates were recovered from feces of young diarrheic animals (n = 11), collected from 7 cities of Pakistan and identified on the basis of 16S rRNA gene sequence and phylogenetic analysis. Selected isolates were subjected to antimicrobial susceptibility by disc diffusion method while polymerase chain reaction (PCR) was used to characterize the blaTEM, blaNDM-1, blaCTX-M, qnrS and mcr-1 antibiotic resistance genes. Based on the 16S rRNA gene sequences (Accession numbers: LC488898 to LC488948), all isolates were identified that belonged to seventeen genera with the highest prevalence rate for phylum Proteobacteria and genus Bacillus (23%). Antibiotic susceptibility explained the prevalence of resistance in isolates ciprofloxacin (100%), ampicillin (100%), sulfamethoxazole-trimethoprim (85%), tetracycline (75%), amoxicillin (55%), ofloxacin (50%), ceftazidime (45%), amoxicillin/clavulanic acid (45%), levofloxacin (30%), cefpodoxime (25%), cefotaxime (25%), cefotaxime/clavulanic acid (20%), and imipenem (10%). MICs demonstrated that almost 90% isolates were multi-drug resistant (against at least three antibiotics), specially against ciprofloxacin, and tetracycline with the highest resistance levels for Shigella sp. (NCCP-421) (MIC-CIP up to 75 µg mL-1) and Escherichia sp. (NCCP-432) (MIC-TET up to 250 µg mL-1). PCR-assisted detection of antibiotic resistance genes showed that 54% isolates were positive for blaTEM gene, 7% isolates were positive for blaCTX-M gene, 23% isolates were positive for each of qnrS and mcr-1 genes, 23% isolates were co-positive in combinations of qnrS and mcr-1 genes and blaTEM and mcr-1 genes, whereas none of the isolate showed presence of blaNDM-1 gene.