The article introduces the expanded and upgraded PDB2PQR web server, which facilitates the preparation of biomolecular structures for molecular simulations. The server addresses the challenges of converting Protein Data Bank (PDB) structures into formats suitable for computational biology tasks, such as electrostatic field calculations. Key features of the updated PDB2PQR include: 1. **Robust Standalone Command Line Support**: Enhances flexibility and high-throughput usage. 2. **Improved pKₐ Estimation via PROPKA**: Utilizes a fast empirical method to predict pKₐ values. 3. **Ligand Parameterization via PE0E PB Charge Methodology**: Uses the partial equalization of orbital electronegativities (PEOE) procedure to calculate ligand charges. 4. **Expanded Set of Force Fields**: Supports various force fields, including AMBER99, CHARMM27, PARSE, and user-defined parameters. 5. **User-Defined Parameters via XML Input Files**: Allows for customization of atom naming schemes and force field parameters. 6. **Enhanced Atom Addition and Optimization Code**: Improves the process of adding and optimizing missing atoms. The PDB2PQR service is driven by a Python-based modular collection of routines, making it accessible on multiple platforms. The article also outlines the workflow of a PDB2PQR job, from inputting a PDB file to generating a parameterized PQR file and optional APBS input file. Future developments include improvements in ligand parameterization, handling post-translational modifications, and integrating a Poisson–Boltzmann continuum electrostatics-based pKa calculation algorithm.The article introduces the expanded and upgraded PDB2PQR web server, which facilitates the preparation of biomolecular structures for molecular simulations. The server addresses the challenges of converting Protein Data Bank (PDB) structures into formats suitable for computational biology tasks, such as electrostatic field calculations. Key features of the updated PDB2PQR include: 1. **Robust Standalone Command Line Support**: Enhances flexibility and high-throughput usage. 2. **Improved pKₐ Estimation via PROPKA**: Utilizes a fast empirical method to predict pKₐ values. 3. **Ligand Parameterization via PE0E PB Charge Methodology**: Uses the partial equalization of orbital electronegativities (PEOE) procedure to calculate ligand charges. 4. **Expanded Set of Force Fields**: Supports various force fields, including AMBER99, CHARMM27, PARSE, and user-defined parameters. 5. **User-Defined Parameters via XML Input Files**: Allows for customization of atom naming schemes and force field parameters. 6. **Enhanced Atom Addition and Optimization Code**: Improves the process of adding and optimizing missing atoms. The PDB2PQR service is driven by a Python-based modular collection of routines, making it accessible on multiple platforms. The article also outlines the workflow of a PDB2PQR job, from inputting a PDB file to generating a parameterized PQR file and optional APBS input file. Future developments include improvements in ligand parameterization, handling post-translational modifications, and integrating a Poisson–Boltzmann continuum electrostatics-based pKa calculation algorithm.