A complete thermodynamic description of protein-ligand binding includes parameters related to pressure and temperature. The changes in the protein volume and compressibility upon binding a ligand are pressure-related parameters that are often neglected due to the lack of routine methods for their determination. Fluorescent pressure shift assay (FPSA) is based on pressure-induced protein unfolding and its stabilization by a ligand and offers a universal approach to determine protein-ligand binding volumes. Extremely high pressures are required to unfold most proteins and protein-ligand complexes. Thus, guanidinium hydrochloride (GdmHCl) is used as a protein-destabilizing agent. We determined that GdmHCl unfolds carbonic anhydrase isoforms in a different pathway, but the destabilization effect is linear in a particular concentration range. We developed a concept for the FPSA experiment, where both - the ligand and GdmHCl - concentrations are varied. This approach enabled us to determine protein-ligand binding volumes that otherwise would be impossible due to the equipment-unreachable pressures of protein unfolding.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1039/d2cp01046a | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Synthesis, antimicrobial evaluation, ADMET prediction, molecular docking and dynamics studies of pyridine and thiophene moiety-containing chalcones.
R Soc Open Sci
January 2025
Department of Chemistry, Jagannath University, Dhaka 1100, Bangladesh.
In this study, three pyridine- and four thiophene-containing chalcone derivatives were synthesized via Claisen-Schmidt condensation reaction, where five derivatives were new. Different spectral analyses (IR, H NMR, HRMS) clarified the structures and these proposed compounds were screened for antimicrobial activity by the agar disc diffusion technique. Compound was conspicuously active against most of the bacterial and fungal strains.
View Article and Find Full Text PDFElucidating the potential of EGFR mutated NSCLC and identifying its multitargeted inhibitors.
Sci Rep
January 2025
Amrita School of Artificial Intelligences, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India.
Lung cancer is the leading cause of cancer-related fatalities globally, accounting for the highest mortality rate among both men and women. Mutations in the epidermal growth factor receptor (EGFR) gene are frequently found in non-small cell lung cancer (NSCLC). Since curcumin and CB[2]UN support various medicinal applications in drug delivery and design, we investigated the effect of curcumin and CB[2]UN-based drugs in controlling EGFR-mutant NSCLC through a dodecagonal computational approach.
View Article and Find Full Text PDFDesign, Synthesis, Biocompatibility, molecular docking and molecular dynamics studies of novel Benzo[b]thiophene-2-carbaldehyde derivatives targeting human IgM Fc Domains.
Bioorg Chem
January 2025
Department of Chemistry, St Berchmans College (Autonomous), Changanassery, Kerala 686101, India; Centre for Theoretical and Computational Chemistry, St Berchmans College (Autonomous), Changanassery, Kerala 686101, India. Electronic address:
In this study, three novel derivatives of benzo[b]thiophene-2-carbaldehyde (BTAP1, BTAP2, and BTAP3) were successfully synthesized and comprehensively characterized using spectroscopic techniques including FTIR, UV-VIS, HNMR, and CNMR. Thermal analysis through TGA and DTA demonstrated remarkable thermal stability with a maximum threshold at 270 °C. Spectroscopic investigations revealed π → π* transitions in all compounds, attributed to the conjugated system comprising benzothiophene rings connected to bromophenyl/ aminophenyl/phenol rings via α, β-unsaturated ketone bridges.
View Article and Find Full Text PDFPowerful generative AI models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically relevant drug targets, hindering their widespread adoption in drug discovery efforts. In this work, we propose FlowDock, the first deep geometric generative model based on conditional flow matching that learns to directly map unbound (apo) structures to their bound (holo) counterparts for an arbitrary number of binding ligands.
View Article and Find Full Text PDFTransformer Decoder Learns from a Pretrained Protein Language Model to Generate Ligands with High Affinity.
J Chem Inf Model
January 2025
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Consiglio Nazionale delle Ricerche, Via G. Amendola, 122/d, Bari 70126, Italy.
The drug discovery process can be significantly accelerated by using deep learning methods to suggest molecules with druglike features and, more importantly, that are good candidates to bind specific proteins of interest. We present a novel deep learning generative model, Prot2Drug, that learns to generate ligands binding specific targets leveraging (i) the information carried by a pretrained protein language model and (ii) the ability of transformers to capitalize the knowledge gathered from thousands of protein-ligand interactions. The embedding unveils the receipt to follow for designing molecules binding a given protein, and Prot2Drug translates such instructions by using the syntax of the molecular language generating novel compounds which are predicted to have favorable physicochemical properties and high affinity toward specific targets.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!