Results 101 to 110 of about 8,194,450 (255)

Ligand‐dependent transcriptional heterogeneity in cell cycle gene expression delays G1/S entry

FEBS Letters, EarlyView.
EGF and HRG induce distinct G1/S progression programs in ErbB2‐amplified BT474 breast cancer cells. Despite activating the potent ErbB2–ErbB3 heterodimer, HRG does not accelerate cell‐cycle entry. Instead, EGF promotes earlier restriction‐point passage via ERK–FOS signaling, whereas HRG activates the AKT–MYC axis, driving transcriptional heterogeneity ...
Ririn Rahmala Febri, Ayaka Nagasato‐Ichikawa, Keita Iida, Shuhei Kimura, Akira Imamoto, Mariko Okada   +5 more
wiley   +1 more source

Synergistic perspectives—How single‐molecule biophysics complement biochemical understanding

FEBS Letters, EarlyView.
In this review, we discuss how ensemble biochemistry and single‐molecule approaches are complementary, outline commonly used single‐molecule techniques, and illustrate their relevance through two representative case studies: chromatin organization by SMC complexes and pathway choice during DNA double‐strand break repair.
Sara De Bragança, Eline L. Jansen, Jorine M. Eeftens   +2 more
wiley   +1 more source

Drug Target Interaction Prediction Using Machine Learning Techniques – A Review.

International Journal of Interactive Multimedia and Artificial Intelligence
Drug discovery is a key process, given the rising and ubiquitous demand for medication to stay in good shape right through the course of one’s life. Drugs are small molecules that inhibit or activate the function of a protein, offering patients a host ...
A. Suruliandi, T. Idhaya, S. P. Raja
doaj   +1 more source

Translophagy—A potential link between autophagy impairment and translational errors

FEBS Letters, EarlyView.
Neurodegenerative diseases are characterised by the accumulation of abnormal proteins and protein aggregates, but their origin often remains unknown. We propose that selective autophagy removes damaged protein‐making machinery, preventing errors during protein synthesis.
Mykola V. Korolchuk, Gailing Ma, Robin Antrobus, Diego Manni, Marco Trevisan‐Herraz, Mustafa Khwaja, Max Brown, Gavin D. Richardson, Masaaki Komatsu, Daniel Erskine, Viktor I. Korolchuk, Tetsushi Kataura   +11 more
wiley   +1 more source

Biases of drug-target interaction network data

, 2020
. Network based prediction of interaction between drug compounds and target proteins is a core step in the drug discovery process. The availability of drug-target interaction data has boosted the development of machine learning methods for the in silico ...
Twan Van Laarhoven, Elena Marchiori
core  

Drug target identification in protozoan parasites.

, 2016
INTRODUCTION Despite the fact that diseases caused by protozoan parasites represent serious challenges for public health, animal production and welfare, only a limited panel of drugs has been marketed for clinical applications.
Joachim Müller, Andrew Hemphill, Müller, Joachim, Hemphill, Andrew   +3 more
core   +1 more source

ISAAC: Prior-aligned structural sensitivity auditing for drug–target interaction models

Artificial Intelligence in the Life Sciences
Deep learning models for drug–target interaction (DTI) prediction often achieve strong benchmark performance while relying on input patterns that are not captured by standard accuracy-based evaluation.
Barbara Tarantino, Sun Kim, Yijingxiu Lu, Paolo Giudici   +3 more
doaj   +1 more source

Structural and biochemical analysis of a B12 superbinder

FEBS Letters, EarlyView.
BtuG proteins are vitamin B12 scavengers in Bacteroides thetaiotaomicron, a dominant human gut bacterium. We present crystal structures of three BtuG homologs bound to cobalamin and its precursor cobinamide, revealing picomolar binding affinities, among the highest known for any natural protein.
Jose M. Martinez Felices, Jacob J. Whittaker, Dirk J. Slotboom, Albert Guskov   +3 more
wiley   +1 more source

Deep-Learning-Based Drug–Target Interaction Prediction

, 2017
Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug–target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs ...
Haozhi Sha (3832690), Yonghuan Yun (3832693), Ruihan Yang (3832696), Ming Wen (1717642), Shaoyu Niu (3689209), Zhimin Zhang (408803), Hongmei Lu (2630605)   +6 more
core   +1 more source

Structures of mycobacterial 3‐methylcrotonyl‐CoA carboxylase reveal carrier‐domain translocation between catalytic sites

FEBS Letters, EarlyView.
Mycobacterial 3‐methylcrotonyl‐CoA carboxylase uses a mobile biotin‐carrying domain to shuttle a carboxyl group between two catalytic sites, enabling carboxylation of 3‐methylcrotonyl‐CoA during leucine breakdown. Cryo‐electron microscopy captures the carrier at both sites and reveals an inward loop movement that may prevent futile rebinding to the ...
Ajit Yadav, Bogdan I. Florea, Sebastian Geibel   +2 more
wiley   +1 more source