Wajid Arshad Abbasi

Alzheimer’s disease (AD) is a chronic, advanced brain sickness disease that slowly destroys memory and thinking skills and, in the end, the ability to perform routine tasks. This disease is caused by the abnormal clumping of proteins such as amyloids around the brain cells. The identification of proteins involved in Alzheimer’s is essential to understand the disease and to discover and design the drugs. Experimental processes involving in‐vitro or in‐vivo experiments for this purpose are very time‐consuming, laborious, and highly costly. However, costly and tedious experimental procedures can be performed efficiently by targeting the most probable proteins involved in Alzheimer’s predicted and ranked through a computational method with better generalization accuracy. In this study, we have proposed a machine learning (ML)–based predictive model to identify proteins potentially involved in Alzheimer’s. Through a series of simulation studies, we have shown that our proposed model by using protein sequence information only gives state‐of‐the‐art generalization performance with an area under the precision‐recall curve of 0.93 verified through various ML‐centric and biologically relevant techniques and metrics. Through data mining in this study, we have also performed feature analysis to identify the role of individual amino acids in such proteins. Python code for feature extraction, training, and evaluating our proposed models together with the dataset is available at the URL: https://sourceforge.net/projects/alzheimer-associated-proteins/files/.

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. Additionally, Bayesian phylogenetic inference based on five mitochondrial and nuclear molecular markers was performed. Results provided a phylogenetic placement of the genus Perelia within Lumbricidae for the first time, indicating a close relationship with Eophila. This approach should be implemented to Perelia arnoldiana and further representatives of the genus in order to understand their biogeography, diversity and evolutionary history.

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. However, costly and tedious experimental procedures can be assisted by ranking the most probable glycoproteins through computational methods with improved accuracy. In this study, we have proposed a novel machine learning-based predictive model for glycoproteins identification. Our proposed model is based on sequence-derived structural descriptors (SDSD) that fill the gap of unavailability of protein 3D structures and lack of accuracy in sequence information alone. Through a series of simulation studies, we have shown that our proposed model gives state-of-the-art generalization performance verified through various machine learning-centric and biologically relevant techniques and metrics. Through data mining in this study, we have also identified the role of descriptors in determining glycoproteins. Python-based standalone code together with a webserver implementation of our proposed model (COYOTE: identifiCation Of glYcoprOteins Through sEquences) is available at the URL: https://sites.google.com/view/wajidarshad/software .

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. The plant growth promoting traits such as siderophore, phytohormone, and hydrolytic enzymes production, and phosphate solubiliation were assessed. Eleven vermi-bacteria i.e. Bacillus mycoides, B. aryabhattai, B. megaterium, Staphylococcus hominis, B. subtilis, B. spizizenii, B. licheniformis, B. mojavensis, B. toyonensis, B. anthracis, B. cereus, B. thuringiensis, and B. paranthracis were isolated and identified based on microscopic studies, biochemical tests, ribotyping, and agricultural traits. All vermi-bacteria are Gram-positive rods except Staphylococcus hominis and produce different compounds such as siderophore, indole acetic acid, catalase, oxidase, proteases, amylases, and lipases. All vermi-bacteria also act as phosphate solubilizers. Therefore, all isolated vermi-bacteria could be used as potential microbial biofertilizers to enhance crops production in Pakistan.

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. The taxonomy of earthworms cannot reliably be established through morphological characteristics because the small and simple body plan of the earthworm does not have anatomical complex and highly specialized structures. Recently, molecular techniques have been adopted to accurately classify the earthworm species but these techniques are time-consuming and costly. To combat this issue, in this study, we propose a machine learning-based earthworm species identification model that uses digital images of earthworms. We performed a stringent performance evaluation not only through 10-fold cross-validation and on an external validation dataset but also in real settings by involving an experienced taxonomist. In all the evaluation settings, our proposed model has given state-of-the-art performance and justified its use to aid earthworm taxonomy studies. We made this model openly accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/ESIDE.

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. In this work, we explore sequence-based protein binding affinity prediction using machine learning.Method: We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity.Results: We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available athttps://sites.google.com/view/wajidarshad/software.Conclusion: This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.