Recent computational advances in the identification of cryptic binding sites for drug discovery. [PDF]
Gašparíková D +3 more
europepmc +1 more source
Screening and epitope characterization of Nidogen‐2‐specific nanobodies
Camel immunization and phage display were employed to generate high‐affinity VHH nanobodies against Nidogen‐2. After library construction, biopanning, ELISA screening, sequencing, and recombinant expression, selected nanobodies were purified and characterized, leading to the preliminary exploration of a nanobody‐based sandwich ELISA for specific ...
Jianchuan Wen +9 more
wiley +1 more source
Development and Application of Radioactive Ligands Targeting Fibroblasts with Albumin-Binding Sites. [PDF]
Wu T +9 more
europepmc +1 more source
Above-Filter Digestion Proteomics Reveals Drug Targets and Localizes Ligand Binding Site
Bohdana Sokolova +7 more
openalex +1 more source
Matrix metalloproteinase‐9 (MMP9) drives ovarian cancer progression. Using MMP9‐null cells (M9‐KO) created from ovarian cancer cells, we found MMP9 loss did not block Epidermal Growth Factor (EGF)‐driven E‐cadherin dissolution or EMT but delayed and reduced EGF‐driven membrane protrusions. Transient MMP9 re‐expression drove membrane protrusion.
Claire Strauel +8 more
wiley +1 more source
Simultaneous ligand binding to intact and partially formed ATP-binding sites in the hexameric termination factor Rho. [PDF]
Billings TD +6 more
europepmc +1 more source
MiR‐513a promotes human erythroid differentiation by modulating c‐Jun
During early human erythropoiesis, miR‐513a promoted erythroid differentiation in primary human CD34+ hematopoietic stem‐progenitor cells and human TF‐1 erythroleukemic cells by indirectly decreasing c‐Jun and phospho‐c‐Jun expression, which are associated with increased GATA1 expression.
MinJung Kim +11 more
wiley +1 more source
Identifying 14-3-3 interactome binding sites with deep learning.
van Weesep L +6 more
europepmc +1 more source
Accurate prediction of protein-ATP binding sites based on a protein pretrained large language model and a fractional-order convolutional neural network. [PDF]
Guo M, Tu Y, Yu J, Wang Y.
europepmc +1 more source

