Biology subjects

Alam, U.

Publications and source records attributed to Alam, U..

6 recordsLinked to original sources

bioRxiv2025

Morphological Identification of Ticks in district Swabi, Mardan, and Charsadda

Ticks are ectoparasitic arthropods that play a significant role in transmitting pathogens to humans and animals. This study aimed to address the research gap in understanding the morphological diversity and distribution of tick species in Swabi, Mardan, and Charsadda districts of Pakistan, where tick-borne diseases are prevalent but poorly documented. The primary objectives were to identify tick species, document their morphological characteristics, and map their distribution patterns across different habitats. Field surveys were conducted over nine months, collecting tick specimens from forests, grasslands, and residential areas. Specimens were preserved and analyzed using stereo-zoom microscopy and taxonomic keys for accurate classification. Key findings revealed a diverse tick fauna, including hard ticks (Ixodidae), with variations in body size, colour patterns, capitulum shape, and scutum features. Some species were widely distributed across all three districts, while others exhibited localized patterns. The study provides the first comprehensive morphological identification of tick species in these regions, highlighting their potential role in disease transmission. The implications of this research are significant for public health and disease control. The data can inform targeted interventions, enhance disease surveillance programs, and support the development of preventive measures to mitigate tick-borne diseases. By contributing to tick taxonomy and epidemiology, this study lays the groundwork for protecting public health in the study regions and beyond. This study addresses the lack of data on tick diversity in high-prevalence regions of Pakistan, providing critical insights for disease control and public health strategies. Limited information exists on the morphological diversity and distribution of tick species in Swabi, Mardan, and Charsadda districts. To identify tick species, document morphological characteristics, and map their distribution patterns in the study areas. Field surveys, specimen collection, stereo-zoom microscopy, and taxonomic classification were employed over nine months. Diverse tick fauna was identified, including hard ticks, with variations in morphology and distribution. Some species were widespread, while others were localized, highlighting potential disease transmission risks. The findings support targeted interventions, disease surveillance, and preventive measures, contributing to public health protection and tick-borne disease control in the region.

zoology↗

bioRxiv2025

PEPlife2: A Updated Repository of the Half-life of Peptides

This manuscript presents an updated version of PEPlife, a manually curated database that provides comprehensive information on peptide half-life. The updated version, PEPlife2, includes 4,412 entries, compared to 2,229 entries in the previous version. Each entry contains detailed information that include experimental methods used to determine half-life, chemical modifications, biological activity, routes of administration, and other relevant data. The database encompasses 402 proteins and 1,781 peptides, derived from 105 unique assays. In addition to natural peptide sequences, PEPlife2 includes cyclic peptides and chemically modified peptides, such as those with N- and C-terminal modifications. To provide structural insights, peptide and protein structures are sourced from the Protein Data Bank (PDB) or predicted using PEPstrMOD. To support the scientific community, we have developed a user-friendly interface and integrated advanced analytical tools, including BLAST, Smith-Waterman, GGSEARCH, CLUSTALW, and MUSTANG. The updated database is accessible at: https://webs.iiitd.edu.in/raghava/peplife2/. Authors BiographyO_LIUrooj Alam is currently pursuing a Masters degree in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIKunal Chaudhary is working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LINishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIRitu Tomer is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LISumeet Patiyal is currently working as a postdoctoral visiting fellow Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗

bioRxiv2025

The sensitising effect of IgG in fibromyalgia syndrome is mediated by Mrgprb2 in mast cells

Fibromyalgia syndrome (FMS) is characterized by elevated levels of immunoglobulin G (IgG), altered bowel habits, and increased pain sensitivity, suggesting immune dysregulation, but the exact mechanism remains unclear. Here, we found that FMS-IgG binds to mast cells in a MRGPRX2/b2-dependent manner, leading to mast cell recruitment and IL-6 secretion. Transferring serum-IgG from FMS patients to mice induced FMS-like symptoms and increased skin mast cells, indicating that FMS-IgG acts through mast cell activation. The ablation of mice Mrgprb2 mast cells or deleting Mrgprb2 receptors prevented IgG-induced heightened sensitivity to mechanical and cold stimuli. Stimulating human LAD2 cells with FMS IgG elicited MRGPRX2-dependent IL-6 production. Consistent with mice findings, mast cell density and tryptase levels increased in human FMS skin samples compared to healthy controls. Taken together our results suggests that FMS IgG mediates hypersensitivity via activation of mast cells bearing the MRGPRX2 receptor and that these cells are a potential therapeutic target.

immunology↗

bioRxiv2023

PandoraRLO: DQN and Graph convolution based method for optimized ligand pose

Predicting how proteins interact with small molecules is a complex and challenging task in the field of drug discovery. Two important aspects in this are shape complementarity and inter molecular interactions which are highly driven by the binding site and the ultimate pose of the ligand in which it interacts with the protein. Various state of the art methods exist which provide a range of ligand poses that are potentially a good fit for a given specific receptor, these are usually compute intensive and expensive. In this study, we have designed a method that provides a single optimized ligand pose for a specific receptor. The method is based on reinforcement learning where when exposed to a diverse protein ligand data set the agent is able to learn the underlying complex biochemistry of the protein ligand pair and provide an optimized pair. As a first study on usage of reinforcement learning for optimized ligand pose, the PandoraRLO model is able to predict pose within a range of 0.5[A] to 4[A] for a large number of test complexes. This indicates the potential of reinforcement learning in uncovering the inherent patterns of protein-ligand pair in 3D space.

bioinformatics↗

bioRxiv2022

PandoraRL: DQN and Graph Convolution based ligand pose learning for SARS-COV1 Mprotease

The ability to predict the correct ligand binding pose for proteinligand complex is vital for drug design. Recently several machine learning methods have suggested knowledge based scoring functions for binding energy prediction. In this study, we propose a reinforcement learning (RL) based model, PandoraRL, where the RL agent helps the ligand traverse to the optimal binding pose. The underlying representation of molecules utilizes generalized graph convolution to represent the protein ligand complex with various atomic and spatial features. The representation consists of edges formed on the basis of inter molecular interactions such as hydrogen bonds, hydrophobic interactions, etc, and nodes representing atomic features. This study presents our initial model which can train on a protein-ligand pair and predict optimal binding pose for a different ligand with the same protein. To the best of our knowledge, this is the first time an RL based approach has been put forward for predicting optimized ligand pose. CCS CONCEPTSO_LIComputing methodologies [->] Reinforcement learning. C_LI

bioinformatics↗

bioRxiv2021

Reinforcement Learning Based Approach for Ligand Pose Prediction

Identification of the potential binding site and the correct ligand pose are two crucial steps among the various steps in protein ligand interaction for a novel or known target. Currently most of the deep learning methods work on protein ligand pocket datasets for various predictions. In this study, we propose a reinforcement learning (RL) based method for predicting the optimized ligand pose where the RL agent also identifies the binding site based on its training. In order to apply various reinforcement learning techniques, we suggest a novel approach to represent the protein ligand complex using graph CNN which would help utilize both atomic and spatial features. To the best of our knowledge, this is the first time an RL based approach has been put forward for predicting optimized ligand pose.

bioinformatics↗