@article{5995,
  abstract     = {Motivation
Computational prediction of the effect of mutations on protein stability is used by researchers in many fields. The utility of the prediction methods is affected by their accuracy and bias. Bias, a systematic shift of the predicted change of stability, has been noted as an issue for several methods, but has not been investigated systematically. Presence of the bias may lead to misleading results especially when exploring the effects of combination of different mutations.

Results
Here we use a protocol to measure the bias as a function of the number of introduced mutations. It is based on a self-consistency test of the reciprocity the effect of a mutation. An advantage of the used approach is that it relies solely on crystal structures without experimentally measured stability values. We applied the protocol to four popular algorithms predicting change of protein stability upon mutation, FoldX, Eris, Rosetta and I-Mutant, and found an inherent bias. For one program, FoldX, we manage to substantially reduce the bias using additional relaxation by Modeller. Authors using algorithms for predicting effects of mutations should be aware of the bias described here.},
  author       = {Usmanova, Dinara R and Bogatyreva, Natalya S and Ariño Bernad, Joan and Eremina, Aleksandra A and Gorshkova, Anastasiya A and Kanevskiy, German M and Lonishin, Lyubov R and Meister, Alexander V and Yakupova, Alisa G and Kondrashov, Fyodor and Ivankov, Dmitry},
  issn         = {1367-4803},
  journal      = {Bioinformatics},
  number       = {21},
  pages        = {3653--3658},
  publisher    = {Oxford University Press },
  title        = {{Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation}},
  doi          = {10.1093/bioinformatics/bty340},
  volume       = {34},
  year         = {2018},
}

@article{8459,
  abstract     = {Nuclear magnetic resonance (NMR) is a powerful tool for observing the motion of biomolecules at the atomic level. One technique, the analysis of relaxation dispersion phenomenon, is highly suited for studying the kinetics and thermodynamics of biological processes. Built on top of the relax computational environment for NMR dynamics is a new dispersion analysis designed to be comprehensive, accurate and easy-to-use. The software supports more models, both numeric and analytic, than current solutions. An automated protocol, available for scripting and driving the graphical user interface (GUI), is designed to simplify the analysis of dispersion data for NMR spectroscopists. Decreases in optimization time are granted by parallelization for running on computer clusters and by skipping an initial grid search by using parameters from one solution as the starting point for another —using analytic model results for the numeric models, taking advantage of model nesting, and using averaged non-clustered results for the clustered analysis.},
  author       = {Morin, Sébastien and Linnet, Troels E and Lescanne, Mathilde and Schanda, Paul and Thompson, Gary S and Tollinger, Martin and Teilum, Kaare and Gagné, Stéphane and Marion, Dominique and Griesinger, Christian and Blackledge, Martin and d’Auvergne, Edward J},
  issn         = {1367-4803},
  journal      = {Bioinformatics},
  keywords     = {Statistics and Probability, Computational Theory and Mathematics, Biochemistry, Molecular Biology, Computational Mathematics, Computer Science Applications},
  number       = {15},
  pages        = {2219--2220},
  publisher    = {Oxford University Press},
  title        = {{Relax: The analysis of biomolecular kinetics and thermodynamics using NMR relaxation dispersion data}},
  doi          = {10.1093/bioinformatics/btu166},
  volume       = {30},
  year         = {2014},
}