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@ARTICLE{Contreras:889057,
      author       = {Contreras, Francisca and Nutschel, Christina and Beust,
                      Laura and Davari, Mehdi D. and Gohlke, Holger and
                      Schwaneberg, Ulrich},
      title        = {{C}an {C}onstraint {N}etwork {A}nalysis guide the
                      identification phase of {K}now{V}olution? {A} case study on
                      improved thermostability of an endo-β-glucanase},
      journal      = {Computational and structural biotechnology journal},
      volume       = {19},
      issn         = {2001-0370},
      address      = {Gotenburg},
      publisher    = {Research Network of Computational and Structural
                      Biotechnology (RNCSB)},
      reportid     = {FZJ-2021-00001},
      pages        = {743-751},
      year         = {2021},
      abstract     = {Cellulases are industrially important enzymes, e.g., in the
                      production of bioethanol, in pulp and paper industry,
                      feedstock, and textile. Thermostability is often a
                      prerequisite for high process stability and improving
                      thermostability without affecting specific activities at
                      lower temperatures is challenging and often time-consuming.
                      Protein engineering strategies that combine experimental and
                      computational are emerging in order to reduce experimental
                      screening efforts and speed up enzyme engineering campaigns.
                      Constraint Network Analysis (CNA) is a promising
                      computational method that identifies beneficial positions in
                      enzymes to improve thermostability. In this study, we
                      compare CNA and directed evolution in the identification of
                      beneficial positions in order to evaluate the potential of
                      CNA in protein engineering campaigns (e.g., in the
                      identification phase of KnowVolution). We engineered the
                      industrially relevant endoglucanase EGLII from Penicillium
                      verruculosum towards increased thermostability. From the CNA
                      approach, six variants were obtained with an up to 2-fold
                      improvement in thermostability. The overall experimental
                      burden was reduced to $40\%$ utilizing the CNA method in
                      comparison to directed evolution. On a variant level, the
                      success rate was similar for both strategies, with $0.27\%$
                      and $0.18\%$ improved variants in the epPCR and CNA-guided
                      library, respectively. In essence, CNA is an effective
                      method for identification of positions that improve
                      thermostability.},
      cin          = {JSC / IBI-7 / NIC},
      ddc          = {570},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IBI-7-20200312 /
                      I:(DE-Juel1)NIC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Forschergruppe
                      Gohlke $(hkf7_20200501)$},
      pid          = {G:(DE-HGF)POF4-5111 / $G:(DE-Juel1)hkf7_20200501$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33552446},
      UT           = {WOS:000684850500008},
      doi          = {10.1016/j.csbj.2020.12.034},
      url          = {https://juser.fz-juelich.de/record/889057},
}