De Novo Secondary Structure Motif Discovery Using RNAProfile

Author(s):  
Federico Zambelli ◽  
Giulio Pavesi
2021 ◽  
Vol 30 (5) ◽  
pp. 982-989
Author(s):  
Jonas Gregor Wiese ◽  
Sooruban Shanmugaratnam ◽  
Birte Höcker

Langmuir ◽  
2010 ◽  
Vol 26 (9) ◽  
pp. 6437-6448 ◽  
Author(s):  
Patrik Nygren ◽  
Martin Lundqvist ◽  
Bo Liedberg ◽  
Bengt-Harald Jonsson ◽  
Thomas Ederth

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Louis T. Dang ◽  
Markus Tondl ◽  
Man Ho H. Chiu ◽  
Jerico Revote ◽  
Benedict Paten ◽  
...  

Biotechnology ◽  
2019 ◽  
pp. 1069-1085
Author(s):  
Andrei Lihu ◽  
Ștefan Holban

De novo motif discovery is essential in understanding the cis-regulatory processes that play a role in gene expression. Finding unknown patterns of unknown lengths in massive amounts of data has long been a major challenge in computational biology. Because algorithms for motif prediction have always suffered of low performance issues, there is a constant effort to find better techniques. Evolutionary methods, including swarm intelligence algorithms, have been applied with limited success for motif prediction. However, recently developed methods, such as the Fireworks Algorithm (FWA) which simulates the explosion process of fireworks, may show better prospects. This paper describes a motif finding algorithm based on FWA that maximizes the Kullback-Leibler divergence between candidate solutions and the background noise. Following the terminology of FWA's framework, the candidate motifs are fireworks that generate additional sparks (i.e. derived motifs) in their neighborhood. During the iterations, better sparks can replace the fireworks, as the Fireworks Motif Finder (FW-MF) assumes a one occurrence per sequence mode. The results obtained on a standard benchmark for promoter analysis show that our proof of concept is promising.


2019 ◽  
Vol 32 (7) ◽  
pp. 317-329
Author(s):  
Matthew Gill ◽  
Michelle E McCully

Abstract Designing functional proteins that can withstand extreme heat is beneficial for industrial and protein therapeutic applications. Thus, elucidating the atomic-level determinants of thermostability is a major interest for rational protein design. To that end, we compared the structure and dynamics of a set of previously designed, thermostable proteins based on the activation domain of human procarboxypeptidase A2 (AYEwt). The mutations in these designed proteins were intended to increase hydrophobic core packing and inter-secondary-structure interactions. To evaluate whether these design strategies were successfully deployed, we performed all-atom, explicit-solvent molecular dynamics (MD) simulations of AYEwt and three designed variants at both 25 and 100°C. Our MD simulations agreed with the relative experimental stabilities of the designs based on their secondary structure content, Cα root-mean-square deviation/fluctuation, and buried-residue solvent accessible surface area. Using a contact analysis, we found that the designs stabilize inter-secondary structure interactions and buried hydrophobic surface area, as intended. Based on our analysis, we designed three additional variants to test the role of helix stabilization, core packing, and a Phe → Met mutation on thermostability. We performed the additional MD simulations and analysis on these variants, and these data supported our predictions.


Sign in / Sign up

Export Citation Format

Share Document