Dataset Information

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

ABSTRACT: We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.

SUBMITTER: Lensink MF

PROVIDER: S-EPMC8616814 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

Lensink Marc F MF Brysbaert Guillaume G Mauri Théo T Nadzirin Nurul N Velankar Sameer S Chaleil Raphael A G RAG Clarence Tereza T Bates Paul A PA Kong Ren R Liu Bin B Yang Guangbo G Liu Ming M Shi Hang H Lu Xufeng X Chang Shan S Roy Raj S RS Quadir Farhan F Liu Jian J Cheng Jianlin J Antoniak Anna A Czaplewski Cezary C Giełdoń Artur A Kogut Mateusz M Lipska Agnieszka G AG Liwo Adam A Lubecka Emilia A EA Maszota-Zieleniak Martyna M Sieradzan Adam K AK Ślusarz Rafał R Wesołowski Patryk A PA Zięba Karolina K Del Carpio Muñoz Carlos A CA Ichiishi Eiichiro E Harmalkar Ameya A Gray Jeffrey J JJ Bonvin Alexandre M J J AMJJ Ambrosetti Francesco F Vargas Honorato Rodrigo R Jandova Zuzana Z Jiménez-García Brian B Koukos Panagiotis I PI Van Keulen Siri S Van Noort Charlotte W CW Réau Manon M Roel-Touris Jorge J Kotelnikov Sergei S Padhorny Dzmitry D Porter Kathryn A KA Alekseenko Andrey A Ignatov Mikhail M Desta Israel I Ashizawa Ryota R Sun Zhuyezi Z Ghani Usman U Hashemi Nasser N Vajda Sandor S Kozakov Dima D Rosell Mireia M Rodríguez-Lumbreras Luis A LA Fernandez-Recio Juan J Karczynska Agnieszka A Grudinin Sergei S Yan Yumeng Y Li Hao H Lin Peicong P Huang Sheng-You SY Christoffer Charles C Terashi Genki G Verburgt Jacob J Sarkar Daipayan D Aderinwale Tunde T Wang Xiao X Kihara Daisuke D Nakamura Tsukasa T Hanazono Yuya Y Gowthaman Ragul R Guest Johnathan D JD Yin Rui R Taherzadeh Ghazaleh G Pierce Brian G BG Barradas-Bautista Didier D Cao Zhen Z Cavallo Luigi L Oliva Romina R Sun Yuanfei Y Zhu Shaowen S Shen Yang Y Park Taeyong T Woo Hyeonuk H Yang Jinsol J Kwon Sohee S Won Jonghun J Seok Chaok C Kiyota Yasuomi Y Kobayashi Shinpei S Harada Yoshiki Y Takeda-Shitaka Mayuko M Kundrotas Petras J PJ Singh Amar A Vakser Ilya A IA Dapkūnas Justas J Olechnovič Kliment K Venclovas Česlovas Č Duan Rui R Qiu Liming L Xu Xianjin X Zhang Shuang S Zou Xiaoqin X Wodak Shoshana J SJ

Proteins 20210913 12

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individ ...[more]

PMID: 34453465

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Project description:Accurate prediction of residue-residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions (i.e. classifying distances between two residues into two categories: in contact (< 8 Angstrom) and not in contact otherwise) and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps. The software package, source code, and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM.

Dataset Information

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

Publications

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

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