MOTIVATION: Measuring discrepancies between protein models and native structures is at the heart of development of protein structure prediction methods and comparison of their performance. A number of different evaluation methods have been developed; however, their comprehensive and unbiased comparison has not been performed. RESULTS: We carried out a comparative analysis of several popular model assessment methods (RMSD, TM-score, GDT, QCS, CAD-score, LDDT, SphereGrinder and RPF) to reveal their relative strengths and weaknesses. The analysis, performed on a large and diverse model set derived in the course of three latest community-wide CASP experiments (CASP10-12), had two major directions. First, we looked at general differences between the scores by analyzing distribution, correspondence and correlation of their values as well as differences in selecting best models. Second, we examined the score differences taking into account various structural properties of models (stereochemistry, hydrogen bonds, packing of domains and chain fragments, missing residues, protein length and secondary structure). Our results provide a solid basis for an informed selection of the most appropriate score or combination of scores depending on the task at hand. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Measuring discrepancies between protein models and native structures is at the heart of development of protein structure prediction methods and comparison of their performance. A number of different evaluation methods have been developed; however, their comprehensive and unbiased comparison has not been performed. RESULTS: We carried out a comparative analysis of several popular model assessment methods (RMSD, TM-score, GDT, QCS, CAD-score, LDDT, SphereGrinder and RPF) to reveal their relative strengths and weaknesses. The analysis, performed on a large and diverse model set derived in the course of three latest community-wide CASP experiments (CASP10-12), had two major directions. First, we looked at general differences between the scores by analyzing distribution, correspondence and correlation of their values as well as differences in selecting best models. Second, we examined the score differences taking into account various structural properties of models (stereochemistry, hydrogen bonds, packing of domains and chain fragments, missing residues, protein length and secondary structure). Our results provide a solid basis for an informed selection of the most appropriate score or combination of scores depending on the task at hand. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Qian Cong; Lisa N Kinch; Jimin Pei; Shuoyong Shi; Vyacheslav N Grishin; Wenlin Li; Nick V Grishin Journal: Bioinformatics Date: 2011-10-12 Impact factor: 6.937
Authors: J Eduardo Fajardo; Rojan Shrestha; Nelson Gil; Adam Belsom; Silvia N Crivelli; Cezary Czaplewski; Krzysztof Fidelis; Sergei Grudinin; Mikhail Karasikov; Agnieszka S Karczyńska; Andriy Kryshtafovych; Alexander Leitner; Adam Liwo; Emilia A Lubecka; Bohdan Monastyrskyy; Guillaume Pagès; Juri Rappsilber; Adam K Sieradzan; Celina Sikorska; Esben Trabjerg; Andras Fiser Journal: Proteins Date: 2019-10-07
Authors: Davide Sala; Yuanpeng Janet Huang; Casey A Cole; David A Snyder; Gaohua Liu; Yojiro Ishida; G V T Swapna; Kelly P Brock; Chris Sander; Krzysztof Fidelis; Andriy Kryshtafovych; Masayori Inouye; Roberto Tejero; Homayoun Valafar; Antonio Rosato; Gaetano T Montelione Journal: Proteins Date: 2019-12
Authors: Jianlin Cheng; Myong-Ho Choe; Arne Elofsson; Kun-Sop Han; Jie Hou; Ali H A Maghrabi; Liam J McGuffin; David Menéndez-Hurtado; Kliment Olechnovič; Torsten Schwede; Gabriel Studer; Karolis Uziela; Česlovas Venclovas; Björn Wallner Journal: Proteins Date: 2019-07-16
Authors: Greg L Hura; Curtis D Hodge; Daniel Rosenberg; Dmytro Guzenko; Jose M Duarte; Bohdan Monastyrskyy; Sergei Grudinin; Andriy Kryshtafovych; John A Tainer; Krzysztof Fidelis; Susan E Tsutakawa Journal: Proteins Date: 2019-10-16