Surveillance Epidemiology, and End Results Program.

Sathishkumar, K., Chaturvedi, M., Das, P., Stephen, S., & Mathur, P. (2022). Cancer incidence estimates for 2022 & projection for 2025: Result from National Cancer Registry Programme, India. Indian Journal of Medical Research, 156(4), 598–607. https://doi.org/10.4103/ijmr.ijmr_1821_22

Article  PubMed  PubMed Central  Google Scholar 

Schafer, E. J. (2025). Mar., Recent Patterns and Trends in Global Prostate Cancer Incidence and Mortality: An Update. Eur. Urol., 87(3), 302–313. https://doi.org/10.1016/j.eururo.2024.11.013

Article  PubMed  Google Scholar 

Einstein, D. J. (2021). Metastatic Castration-Resistant Prostate Cancer Remains Dependent on Oncogenic Drivers Found in Primary Tumors, https://doi.org/10.1200/PO.21

Tan, M. E., Li, J., Xu, H. E., Melcher, K., & Yong, E. L. (2015). Androgen receptor: Structure, role in prostate cancer and drug discovery. Nature Publishing Group. https://doi.org/10.1038/aps.2014.18

Article  Google Scholar 

Li, X. Androgen receptor cofactors: A potential role in understanding prostate cancer. Apr 01 2024 Elsevier Masson s r l https://doi.org/10.1016/j.biopha.2024.116338

Haelens, A., Verrijdt, G., Callewaert, L., Peeters, B., Rombauts, W., & Claessens, F. (2001). Androgen-receptor-specific DNA binding to an element in the first exon of the human secretory component gene.

Nazareth, L. V. (1999). A C619Y Mutation in the Human Androgen Receptor Causes Inactivation and Mislocalization of the Receptor with Concomitant Sequestration of SRC-1 (Steroid Receptor Coactivator 1), [Online]. Available: https://www.mcgill.ca/androgendb

Kumar, S., & Tyagi, R. K. (2012). Androgen receptor association with mitotic chromatin - Analysis with introduced deletions and disease-inflicting mutations. FEBS Journal, 279(24), 4598–4614, https://doi.org/10.1111/febs.12046

Article  CAS  PubMed  Google Scholar 

Cao, H., Wang, J., He, L., Qi, Y., & Zhang, J. Z. (2019). DeepDDG: Predicting the Stability Change of Protein Point Mutations Using Neural Networks. Journal Of Chemical Information And Modeling, 59(4), 1508–1514. https://doi.org/10.1021/acs.jcim.8b00697

Article  CAS  PubMed  Google Scholar 

Abramson, J., et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630(8016), 493–500. https://doi.org/10.1038/s41586-024-07487-w

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wu, R., et al. (2022). High-resolution de novo structure prediction from primary sequence. Jul 22. https://doi.org/10.1101/2022.07.21.500999

Article  Google Scholar 

Heo, L., Park, H., & Seok, C. (2013). GalaxyRefine: Protein structure refinement driven by side-chain repacking., Nucleic Acids Res., vol. 41, no. Web Server issue. https://doi.org/10.1093/nar/gkt458

Krüger, D. M., Ahmed, A., & Gohlke, H. (2012). NMSim web server: Integrated approach for normal mode-based geometric simulations of biologically relevant conformational transitions in proteins, Nucleic Acids Res., 40, W1, https://doi.org/10.1093/nar/gks478

Chen, J., Wang, J., Yang, W., Zhao, L., & Su, J. (2025). Activity Regulation and Conformation Response of Janus Kinase 3 Mediated by Phosphorylation: Exploration from Correlation Network Analysis and Markov Model. Journal Of Chemical Information And Modeling, 65(8), 4189–4205. https://doi.org/10.1021/acs.jcim.5c00096

Article  CAS  PubMed  Google Scholar 

Chen, J., Wang, J., Wang, W., Yang, W., Zhao, L., & Su, J. (2025). Conformational Transition and Recognition Mechanism of Eukaryotic Riboswitches Powered by Thiamine Pyrophosphate Analogues: An Elucidation through Multiple Short Molecular Dynamics Simulations and Markov Model. Journal of Physical Chemistry B, 129(40), 10256–10271. https://doi.org/10.1021/acs.jpcb.5c04050

Article  CAS  PubMed  Google Scholar 

Amani, K., Shivnauth, V., & Castroverde, C. D. M. (Jul. 2023). CBP60-DB: An AlphaFold-predicted plant kingdom-wide database of the CALMODULIN-BINDING PROTEIN 60 protein family with a novel structural clustering algorithm. Plant Direct, 7(7). https://doi.org/10.1002/pld3.509

Grant, B. J., Skjærven, L., & Yao, X. Q. (2021). The Bio3D packages for structural bioinformatics. Protein Science, 30(1), 20–30. https://doi.org/10.1002/pro.3923

Article  CAS  PubMed  Google Scholar 

Leon Sulfierry (2024). sulfierry/free_energy_landscape: v1.0.3 (v1.0.3). Zenodo. https://doi.org/10.5281/zenodo.10850229

Mikulska-Ruminska, K., Krieger, J. M., Banerjee, A.,Cao, X., Wu, G., Bogetti, A. T., Zhang, F., Simmerling, C., Coutsias, E. A., & Bahar, I. (2025). InSty: AProDy Module for Evaluating Protein Interactions and Stability. Journal of molecular biology,437(15), 169009. https://doi.org/10.1016/j.jmb.2025.169009

Kabsch, W., & Sander, C. (1983). Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22(12), 2577–2637. https://doi.org/10.1002/bip.360221211

Article  CAS  PubMed  Google Scholar 

Lobanov, M. Y., Bogatyreva, N. S., & Galzitskaya, O. V. (2008). Radius of gyration as an indicator of protein structure compactness. Molecular Biology, 42(4), 623–628. https://doi.org/10.1134/S0026893308040195

Article  CAS  Google Scholar 

Mitternacht, S. (2016). FreeSASA: An open source C library for solvent accessible surface area calculations. F1000Res, 5. https://doi.org/10.12688/f1000research.7931.1

Huang, S. Y., & Zou, X. (2010). MDockPP: A hierarchical approach for protein-protein docking and its application to CAPRI rounds 15–19, Proteins: Structure. Function and Bioinformatics, 78(15), 3096–3103, https://doi.org/10.1002/prot.22797

Article  CAS  Google Scholar 

Yan, Y., Zhang, D., Zhou, P., Li, B., & Huang, S. Y. (Jul. 2017). HDOCK: A web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy. Nucleic Acids Research, 45, W365–W373. https://doi.org/10.1093/nar/gkx407. no. W1.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jiménez-Garciá, B., Roel-Touris, J., & Barradas-Bautista, D. (Jul. 2023). The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions. Nucleic Acids Research, 51, W298–W304. https://doi.org/10.1093/nar/gkad327

Article  CAS  PubMed  PubMed Central  Google Scholar 

Harini, K., Kihara, D., & Gromiha, M. M. (May 2023). PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features. Methods, 213, 10–17. https://doi.org/10.1016/j.ymeth.2023.03.002

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li, G., Panday, S. K., Peng, Y., & Alexov, E. (2021). SAMPDI-3D: predicting the effects of protein and DNA mutations on protein-DNA interactions. Bioinformatics, 37(21), 3760–3765. https://doi.org/10.1093/bioinformatics/btab567

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ashkenazy, H., & Aug. (2016). ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Research, 44, W344–W350. https://doi.org/10.1093/NAR/GKW408

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hwang, S., Guo, Z., & Kuznetsov, I. B. (2007). DP-Bind: A web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins. Bioinformatics, 23(5), 634–636. https://doi.org/10.1093/bioinformatics/btl672

Article  CAS  PubMed  Google Scholar 

Mitra, R., Cohen, A. S., Sagendorf, J. M., Berman, H. M., & Rohs, R. (Jan. 2025). DNAproDB: an updated database for the automated and interactive analysis of protein–DNA complexes. Nucleic Acids Research, 53, D396–D402. https://doi.org/10.1093/nar/gkae970

Article  CAS  PubMed  PubMed Central  Google Scholar 

Badaczewska-Dawid, A. E., Nithin, C., Wroblewski, K., Kurcinski, M., & Kmiecik, S. (2022). MAPIYA contact map server for identification and visualization of molecular interactions in proteins and biological complexes. Nucleic Acids Research, 50, W474–W482. https://doi.org/10.1093/nar/gkac307

Article  CAS  PubMed  PubMed Central  Google Scholar 

Colovos, C., & Yeates, T. O. (1993). Verification of protein structures: patterns of nonbonded atomic interactions. Protein science: a publication of the Protein Society, 2(9), 1511–1519. https://doi.org/10.1002/pro.5560020916

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wiederstein, M., & Sippl, M. J. (2007). ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins, Nucleic Acids Res., 35, SUPPL.2, https://doi.org/10.1093/nar/gkm290

Wallner, B., & Elofsson, A. (May 2003). Can correct protein models be identified? Protein Science, 12(5), 1073–1086. https://doi.org/10.1110/ps.0236803

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ramachandran, G. N., Ramakrishnan, C., & Sasisekharan, V. (1963). Stereochemistry of polypeptide chain configurations, https://doi.org/10.1016/S0022-2836(63)80023-6

Hubbard, R. E., & Haider, M. K. (2010). Hydrogen Bonds in Proteins: Role and Strength. in Encyclopedia of Life Sciences. Wiley. https://doi.org/10.1002/9780470015902.a0003011.pub2

Mieres-Perez, J., Almeida-Hernandez, Y., Sander, W., & Sanchez-Garcia, E. (2025). A Computational Perspective to Intermolecular Interactions and the Role of the Solvent on Regulating Protein Properties, Aug. 13, American Chemical Society. https://doi.org/10.1021/acs.chemrev.4c00807

Bao, H., Wang, W., Sun, H., & Chen, J. (2023). Probing mutation-induced conformational transformation of the GTP/M-RAS complex through Gaussian accelerated molecular dynamics simulations. Journal Of Enzyme Inhibition And Medicinal Chemistry, 38(1). https://doi.org/10.1080/14756366.2023.2195995

David, C. C., & Jacobs, D. J. (2014). P