English Journal
- Bayesian inference of protein structure from chemical shift data.
- Bratholm LA1, Christensen AS1, Hamelryck T2, Jensen JH1.
- PeerJ.PeerJ.2015 Mar 24;3:e861. doi: 10.7717/peerj.861. eCollection 2015.
- Protein chemical shifts are routinely used to augment molecular mechanics force fields in protein structure simulations, with weights of the chemical shift restraints determined empirically. These weights, however, might not be an optimal descriptor of a given protein structure and predictive model,
- PMID 25825683
- Application of the maximum entropy principle to determine ensembles of intrinsically disordered proteins from residual dipolar couplings.
- Sanchez-Martinez M1, Crehuet R.
- Physical chemistry chemical physics : PCCP.Phys Chem Chem Phys.2014 Dec 21;16(47):26030-9. doi: 10.1039/c4cp03114h. Epub 2014 Oct 31.
- We present a method based on the maximum entropy principle that can re-weight an ensemble of protein structures based on data from residual dipolar couplings (RDCs). The RDCs of intrinsically disordered proteins (IDPs) provide information on the secondary structure elements present in an ensemble; h
- PMID 25358803
- PHAISTOS: a framework for Markov chain Monte Carlo simulation and inference of protein structure.
- Boomsma W1, Frellsen J, Harder T, Bottaro S, Johansson KE, Tian P, Stovgaard K, Andreetta C, Olsson S, Valentin JB, Antonov LD, Christensen AS, Borg M, Jensen JH, Lindorff-Larsen K, Ferkinghoff-Borg J, Hamelryck T.
- Journal of computational chemistry.J Comput Chem.2013 Jul 15;34(19):1697-705. doi: 10.1002/jcc.23292. Epub 2013 Apr 26.
- We present a new software framework for Markov chain Monte Carlo sampling for simulation, prediction, and inference of protein structure. The software package contains implementations of recent advances in Monte Carlo methodology, such as efficient local updates and sampling from probabilistic model
- PMID 23619610
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