Elucidating the Potential Compounds Targeting OPNL1W Through Virtual Screening for the Treatment of Color Blindness
DOI:
https://doi.org/10.55627/mmc.004.001.0462Keywords:
OPN1LW, Color Blindness, CADD, Bioinformatics, Molecular ModelingAbstract
Partial blindness occurs and a patient is unable to differentiate colors in a typical manner. It is otherwise called variety lack generally acquired, however, can likewise be brought about by eye sicknesses, drugs, and aging. Color visual impairment frequently happens when somebody cannot recognize specific colors and at least one of the variety cone cells is missing, not working, or there is an unexpected variety in comparison to the typical and normal. Partial blindness is more normal in males and influences around 8% of males and 0.5% of females around the world. In current efforts, the molecular docking approach was applied to screen the novel compounds from the ZINC compound database that may show effectiveness against color blindness. Various computational strategies including homology modeling, threading, and ab initio methods were applied for the 3D structure prediction of the target protein OPN1LW. The 3D structures of OPN1LW were additionally evaluated and their efficacy was calculated. It was observed that the overall quality of the predicted structure was 95.88%. Ramachandran plot showed reliable results as the conserved residues were falling in the most favored region. After structure evaluation the energy of the top-picked structures was minimized and molecular docking analyses were performed against the FDA database from the ZINC compounds database. The suitable docked compounds were analyzed and ADMET analyses were performed. After analyzing the molecular docking and ADMET results concluded that the screened compound (ZINC000000005560) with interacting residues Thr-118, Glu-122, Ile-189, Tyr-191, Met-207, Phe-212, Trp-265, Tyr-268, Ala-269, Ala-272, can be used against color blindness and may prove reliable results.
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Copyright (c) 2024 Hassan Bin Waseem, Kashif Shahid, Azhar Iqbal, Ayesha Zafar, Sidrah Hafeez, Ayesha Afzal, Muhammad Jahangir Shafi, Ayman Rashid, Keziah Shaheen, Rabbia Allah Rakha, Noel Shamaun, Muhammad Mumtaz Tahir
This work is licensed under a Creative Commons Attribution 4.0 International License.