Biology subjects

Tejera Nevado, P.

Publications and source records attributed to Tejera Nevado, P..

3 recordsLinked to original sources

bioRxiv2025

Sharing Patterns Between Proteins and Exploring Possible Drug Interactions

Due to the nature of amino acids that make up proteins, it is possible to detect small patterns within their sequence. Some of these sequences have been identified as motifs, characterized by defined signatures that are shared across different proteins. In this study, patterns are defined as short amino acid sequences that are commonly found among proteins involved in various types of cancer. The focus of this research is on the localization of such patterns in ALDH2, which is used as a reference protein because it is known to be inhibited by disulfiram. Disulfiram, originally used to treat alcohol use disorder (AUD), has been repurposed for the treatment of lung cancer. Analyzing shared patterns can help identify common protein structures and may assist in locating potential binding cavities and interaction sites. Moreover, it can improve our understanding of how closely related or structurally similar proteins might also be affected by certain drugs. This approach could offer a new perspective on drug specificity and targeting strategies in the context of disease.

bioinformatics↗

bioRxiv2025

Exploring Protein Patterns, Cavity Interactions, and Therapeutic Insights in Cancer

Protein sequence alignments are essential for identifying proteins shared structural and functional features. Detecting short amino acid sequences, termed patterns, across lung cancer and other related datasets facilitates the identification of relevant features. This study builds on previous findings by exploring proteins that share common patterns already identified. Using sequence matching at 5% and 10% occurrence thresholds, we identified 2,368 and 47 patterns, respectively. To reduce complexity and refine the dataset, shorter patterns from the 10% occurrence streamlined the analysis by isolating highly relevant patterns while reducing redundancy among proteins sharing sequence segments. Subsequent analyses integrated structural predictions for protein folding comparison, enabling the detection of patterns in different proteins and the identification of potential key residues. During cavity detection prediction, some amino acids were inspected in detail to assess their impact on protein function and their relevance in drug-target interactions. These insights were considered during docking studies, focusing on proteins used in treatments with pre-described ligands. By connecting raw sequence data to folding structures and functional features, we identified critical protein cavities that underscore the role of mutations in altering protein behavior and influencing drug-target interactions. These findings highlight protein activitys structural foundations and their importance in understanding cancer biology. By uncovering conserved sequence patterns and their structural implications, this study provides insights into potential biomarkers and therapeutic targets, that could aid in developing more effective cancer treatments.

bioinformatics↗

bioRxiv2025

Benchmarking Docking Tools on Experimental and Artificial Intelligence-Predicted Protein Structures

In silico analysis provides valuable insights into studying macromolecules, particularly proteins. Protein structure prediction models, like AlphaFold (AF), offer a cost-effective and time-efficient alternative to traditional methods like X-ray crystallography, NMR spectroscopy, and cryo-EM for determining protein structures. These models are increasingly used in protein-ligand interaction studies, a key aspect of drug discovery. Docking and molecular dynamics simulations facilitate this process, and researchers are continuously developing open-access tools for cavity detection and docking to accelerate protein-ligand interaction studies. However, while many of these tools perform well in specific cases, their strengths and weaknesses in analyzing predicted protein structures remain largely unknown. Therefore, it is crucial to compare docking analyses using experimentally determined protein structures and deep learning-based models. In this study, two well-characterized proteins, dopamine D3 receptor with its ligand ETQ and neprilysin with its ligand sacubitrilat, are used to evaluate docking predictions. The docking tools CB-Dock 2 and COACH-D are applied to both X-ray crystallography-derived structures and five different AF-generated models. The objective is to assess the accuracy of these docking approaches and determine whether this strategy can effectively simulate macromolecular behavior in their microenvironment. By doing so, this study aims to generate new insights and contribute to accelerating research in protein-ligand interactions.

bioinformatics↗