Assessment of template based protein structure predictions in CASP10

By Yuanpeng Janet Huang, Binchen Mao, J. Aramini, Gaetano T. Montelione. 2014

101 citations recorded by the source corpus.

Template‐based modeling (TBM) is a major component of the critical assessment of protein structure prediction (CASP). In CASP10, some 41,740 predicted models submitted by 150 predictor groups were assessed as TBM predictions. The accuracy of protein structure prediction was assessed by geometric comparison with experimental X‐ray crystal and NMR structures using a composite score that included both global alignment metrics and distance‐matrix–based metrics. These included GDT‐HA and GDC‐all global alignment scores, and the superimposition‐independent LDDT distance‐matrix–based score. In addition, a superimposition‐independent RPF metric, similar to that described previously for comparing protein models against experimental NMR data, was used for comparing predicted protein structure models against experimental protein structures. To score well on all four of these metrics, models must feature accurate predictions of both backbone and side‐chain conformations. Performance rankings were determined independently for server and the combined server plus human‐curated predictor groups. Final rankings were made using paired head‐to‐head Student's t‐test analysis of raw metric scores among the top 25 performing groups in each category. Proteins 2014; 82(Suppl 2):43–56. © 2013 Wiley Periodicals, Inc.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Critical assessment of methods of protein structure prediction (CASP) — round x (2013, background): “There is a paper from the assessment team for that class of models.27 The second category is free modeling (FM), where there are either no usefully related structures, or the relationship is so distant that it cannot be detected.”
CASP10 results compared to those of previous CASP experiments (2013, methodology): “…accuracy, together with the fraction of residues that can be aligned to a single template, dominate model quality. performed at the domain level.18 ,20,21 We adopt the same practice here, but use a somewhat different domain separation procedure from that used by the assessors.22 We required that…”
CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides (2015, methodology): “Thus, the total entries in CPPsite 2.0 are 1855, which provide information of 1699 experimentally validated unique CPPs. CPPsite 2.0 is a unique collection of diverse CPPs that includes linear (1753), cyclic (102), CPPs with L-amino acids (1564), Damino acids (63) and having both L-and D-residues (3”
Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling (2015, methodology): “Even so, great progress has been made in the last 10 to 15 years in template-based protein structure prediction, fuelled by advances in techniques for remote homology detection and alignment [6] and techniques for model quality assessment [3].”
Evaluation of the template-based modeling in CASP12 (2017, methodology): “14 The GDT_HA measure and local measures have been already used in previous TBM assessments 15,16 and have proved useful.”
Computational protein structure refinement: Almost there, yet still so far to go (2017, background): “64,188 Closer inspection of Figure 2 reveals a general trend that re ﬁ nement is most successful in regions where only minor rearrangements are necessary.”
Protein Structure and Function Prediction Using I-TASSER (2015, background): “One noticeable advance, for instance, is that automated computer servers can now generate models with accuracy comparable to the best human-expert modeling that combines a variety of manual inspections and structural and functional analyses (Battey et al., 2007; Huang et al., 2014).”
CancerPPD: a database of anticancer peptides and proteins (2014, methodology): “A total of 9 sequences with lengths of >40 residues each, were treated as proteins and their structure was predicted using I-TASSER (23,24) server, which was among the best template-based protein structure prediction servers in CASP10 (25) assessment.”