New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers.

Earthworm diversity and ecology in Pakistan is poorly known, especially in the region of Azad Jammu & Kashmir. An earthworm community survey assisted by genetic barcoding detected an unidentified species which could constitute a new record for Pakistan. Morphological study revealed its identity as Perelia kaznakovi.

COYOTE: Sequence-derived structural descriptors-based computational identification of glycoproteins.

Glycoproteins play an important and ubiquitous role in many biological processes such as protein folding, cell-to-cell signaling, invading microorganism infection, tumor metastasis, and leukocyte trafficking. The key mechanism of glycoproteins must be revealed to model and refine glycosylated protein recognition, which will eventually assist in the design and discovery of carbohydrate-derived therapeutics. Experimental procedures involving wet-lab experiments to reveal glycoproteins are very time-consuming, laborious, and highly costly.

Phylogenetic Illustration of Eisenia fetida Associated Vermi-bacteria Involved in Heavy Metals Remediation and Retaining Plant Growth Promoting Traits.

Heavy metals contamination in the soil is a major threat to wildlife, the environment, and human health. Microbial remediation is an emerging and promising technology to reduce heavy metals toxicity. Therefore, the present research aimed to isolate and to identify the heavy metals tolerated bacteria from the Eisenia fetida for the first time, and to screen the bacto-remediation capabilities and plant growth promoting traits of vermi-bacterial isolates.

Molecular characterization of plant growth-promoting vermi-bacteria associated with Eisenia fetida gastrointestinal tract.

Earthworms are highly productive invertebrates and play a vital role in organic farming and improving soil structure and function. The gastrointestinal tract of earthworms possessed agricultural important bacteria. So, the current research aimed was to examine, screen, and identify the plant growth promoting bacteria existing in the digestive tract of Eisenia fetida called plant growth promoting vermi-bacteria.

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.

ESIDE: A computationally intelligent method to identify earthworm species (E. fetida) from digital images: Application in taxonomy.

Earthworms (Crassiclitellata) being ecosystem engineers significantly affect the physical, chemical, and biological properties of the soil by recycling organic material, increasing nutrient availability, and improving soil structure. The efficiency of earthworms in ecology varies along with species. Therefore, the role of taxonomy in earthworm study is significant.

PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures.

Accurately determining a change in protein binding affinity upon mutations is important to find novel therapeutics and to assist mutagenesis studies. Determination of change in binding affinity upon mutations requires sophisticated, expensive, and time-consuming wet-lab experiments that can be supported with computational methods. Most of the available computational prediction techniques depend upon protein structures that bound their applicability to only protein complexes with recognized 3D structures.

COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology.

Early diagnosis of Coronavirus disease 2019 (COVID-19) is significantly important, especially in the absence or inadequate provision of a specific vaccine, to stop the surge of this lethal infection by advising quarantine. This diagnosis is challenging as most of the patients having COVID-19 infection stay asymptomatic while others showing symptoms are hard to distinguish from patients having different respiratory infections such as severe flu and Pneumonia. Due to cost and time-consuming wet-lab diagnostic tests for COVID-19, there is an utmost requirement for some alternate, non-invasive, rapid, and discounted automatic screening system.

Efficiency of cow dung based vermi-compost on seed germination and plant growth parameters of (Marigold).

Vermi-composting is an environmental friendly and economic process to decompose organic waste. The objective of this study was to produce vermi-compost using and to investigate the impact of vermi-compost (VC) and organic manure (cow dung) on seed germination, seedlings, and growth parameters of . Physio-chemical parameters of vermi-compost and organic manure were recorded.

ISLAND: in-silico proteins binding affinity prediction using sequence information.

Background: Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures.

βC1, pathogenicity determinant encoded by Cotton leaf curl Multan betasatellite, interacts with calmodulin-like protein 11 (Gh-CML11) in Gossypium hirsutum.

Begomoviruses interfere with host plant machinery to evade host defense mechanism by interacting with plant proteins. In the old world, this group of viruses are usually associated with betasatellite that induces severe disease symptoms by encoding a protein, βC1, which is a pathogenicity determinant. Here, we show that βC1 encoded by Cotton leaf curl Multan betasatellite (CLCuMB) requires Gossypium hirsutum calmodulin-like protein 11 (Gh-CML11) to infect cotton.

Learning protein binding affinity using privileged information.

Background: Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data.

Training host-pathogen protein-protein interaction predictors.

Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, pathogenic infections are caused by interactions of host and pathogen proteins. It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases.

CaMELS: In silico prediction of calmodulin binding proteins and their binding sites.

Due to Ca -dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet-lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet-lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone.

Issues in performance evaluation for host-pathogen protein interaction prediction.

The study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein-protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs.