Benchmarks

Rosetta benchmarks

Existing approaches

Sequence only

intogen

mCSM: predicting the effects of mutations in proteins using graph-based signatures.

  • http://www.ncbi.nlm.nih.gov/pubmed/24281696
  • “To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein-protein and protein-nucleic acid interactions”.
  • cite{pires_mcsm_2014}

Sequence and structure

Predicting Binding Free Energy Change Caused by Point Mutations with Knowledge-Modified MM/PBSA Method

MAESTRO cite{laimer_maestro_2015}

  • https://biwww.che.sbg.ac.at/?page_id=477
  • MAESTRO implements a multi-agent machine learning system.
  • Structure based tools AUTO-MUTE [7], CUPSAT [8], Dmutant [9], FoldX [10], Eris [11], PoPMuSiC [12], SDM [13] or mCSM [14] usually perform better than the sequence based counterparts. Recently, SDM and mCSM have been integrated into a new method called DUET [15].

INPS: predicting the impact of non-synonymous variations on protein stability from sequence

FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants

  • http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004556
  • Predict the structural effect of multiple mutations.
  • “Stability effects of all possible single-point mutations were estimated using the <BuildModel> module of FoldX”.
  • We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased.
  • cite{bednar_fireprot_2015}