Results 91 to 100 of about 11,591,695 (294)

Improved Chou-Fasman method for protein secondary structure prediction

BMC Bioinformatics, 2006
Background Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. The prediction technique has been developed for several decades.
Huang Zhengge, Gu Fei, Chen Hang
doaj   +1 more source

Prediction of 8-state protein secondary structures by a novel deep learning architecture

BMC Bioinformatics, 2018
Background Protein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on structure ...
Buzhong Zhang, Jinyan Li, Qiang Lü
doaj   +1 more source

Protein structure prediction [Elektronisk resurs] : Zinc-binding sites, one-dimensional structure and remote homology

, 2010
Predicting the three-dimensional (3D) structure of proteins is a central problem in biology. These computationally predicted 3D protein structures have been successfully applied in many fields of biomedicine, e.g.
Hovmöller, Sven,, Shu, Nanjiang,, Barton, Geoff,, Donald, Bruce,   +3 more
core  

RNA secondary structure prediction using homologous sequences (Problem 13).

, 2013
RNA secondary structure prediction using homologous sequences (Problem 13).
Michiaki Hamada (355897), Kiyoshi Asai (92186), Wataru Iwasaki (235369), Hisanori Kiryu (355898)   +3 more
core   +1 more source

Inhibition of cyclin‐dependent kinases 12/13 using CT7439 as a treatment for colorectal cancer with CDK12 upregulation

Molecular Oncology, EarlyView.
The proposed mechanism of action for the CDK12/13 inhibitor and cyclin K degrader, CT7439. CDK12/13 inhibition interrupts transcription elongation, leading to increased DNA damage that results in cell death. This agent is a potentially novel treatment option for patients with colorectal cancer. Created in BioRender. Cyclin‐dependent kinase (CDK) 12 and
Wylie K. Watlington, Divya L. Dayanidhi, Mohammad Zokaasadi, John B. Mantyh, Pelumi D. Olawuni, Gabrielle Rupprecht, Jeremy M. Force, Shannon McCall, Ashwani K. Bahl, David S. Hsu, Jason A. Somarelli   +10 more
wiley   +1 more source

Protein secondary structure prediction and neural networks

, 1991
A statical algorithm for protein secondary structure prediction, using a specifically adapted neural network, is proposed; the only information needed for prediction is the primary structure of a protein.
RUGGIERO, CARMELINA, SACILE, ROBERTO, GIACOMINI, MAURO   +2 more
core   +1 more source

Prediction of RNA Secondary Structure Using Butterfly Optimization Algorithm

Human-Centric Intelligent Systems
Ribonucleic acid (RNA) structure is vital to its ability to function within the cell. The ability to predict RNA structure is essential to implementing new medications and understanding genetic illnesses.
Sajib Chatterjee, Rameswar Debnath, Sujit Biswas, Anupam Kumar Bairagi   +3 more
doaj   +1 more source

Pairwise covariance adds little to secondary structure prediction but improves the prediction of non-canonical local structure

BMC Bioinformatics, 2008
Background Amino acid sequence probability distributions, or profiles, have been used successfully to predict secondary structure and local structure in proteins.
Webb-Robertson Bobbie-Jo, Bystroff Christopher   +1 more
doaj   +1 more source

Detailed information about template in the secondary structure prediction.

, 2015
Detailed information about template in the secondary structure prediction.
Can Jin (685561), Shimei Zeng (781983), Shaojian Dong (781984), Xiaojun Liu (58952), Jiale Li (103362)   +4 more
core   +1 more source

In vitro and in silico modelling of ROS1‐positive non‐small cell lung cancer reveals fusion‐dependent tyrosine kinase inhibitor responses

Molecular Oncology, EarlyView.
Drug resistance limits treatment success in a subset of lung cancers driven by ROS1 gene alterations. Using patient‐derived cells and computer simulations, we studied three key mutations and how they affect five targeted drugs. The mutations reduced drug effectiveness in different ways by altering protein structure and behavior.
Farhan Ul Haq, Marc Terrones, Felicia Rodrigues Fortes, Anne Schepers, Alessia Denis, Christophe Deben, Ken Op de Beeck, Guy Van Camp, Geert Vandeweyer   +8 more
wiley   +1 more source