RT Journal Article
T1 TorsiFlex: an automatic generator of torsional conformers. Application to the twenty proteinogenic amino acids
A1 Ferro Costas, David
A1 Mosquera Lois, Irea
A1 Fernández Ramos, Antonio
K1 Conformations
K1 Flexible molecules
K1 Torsions
K1 Preconditioned search
K1 Stochastic search
K1 Validation tests
K1 Amino acids
AB In this work, we introduce TorsiFlex, a userfriendly software written in Python 3 and designed to fnd all the torsional conformers of fexible acyclic molecules in an automatic fashion. For the mapping of the torsional potential energy surface, the algorithm implemented in TorsiFlex combines two searching strategies: preconditioned and stochastic. The former is a type of systematic search based on chemical knowledge and should be carried out before the stochastic (random) search. The algorithm applies several validation tests to accelerate the exploration of the torsional space. For instance, the optimized structures are stored and this information is used to prevent revisiting these points and their surroundings in future iterations. TorsiFlex operates with a duallevel strategy by which the initial search is carried out at an inexpensive electronic structure level of theory and the located conformers are reoptimized at a higher level. Additionally, the program takes advantage of conformational enantiomerism, when possible. As a case study, and in order to exemplify the efectiveness and capabilities of this program, we have employed TorsiFlex to locate the conformers of the twenty proteinogenic amino acids in their neutral canonical form. TorsiFlex has produced a number of conformers that roughly doubles the amount of the most complete work to date.
PB BioMed Central
SN 1758-2946
YR 2021
FD 2021
LK https://hdl.handle.net/10347/38267
UL https://hdl.handle.net/10347/38267
LA eng
NO Ferro‑Costas, David ,  Mosquera‑Lois, Irea and Fernández‑Ramos, Antonio(2021).TorsiFlex: an automatic generator of torsional conformers. Application to the twenty proteinogenic amino acids ." Journal of Cheminformatics" vol. 13, 100
NO This work was partially supported by the Ministerio de Ciencia e Innovación (Grant # PID2019107307RBI00), the Consellería de Cultura, Educación e Ordenación Universitaria (Centro singular de investigación de Galicia acredi‑ tación 20192022, ED431G 2019/03), and the European Regional Develop‑ ment Fund (ERDF).
DS Minerva
RD 28 abr 2026