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1. Blind prediction of homo- and hetero- protein complexes: The CASP13-CAPRI experiment

Author

Lensink, Marc F., Brysbaert, Guillaume, Nadzirin, Nurul, Velankar, Sameer, Chaleil, Raphaël A. G., Gerguri, Tereza, Bates, Paul A., Laine, Elodie, Carbone, Alessandra, Grudinin, Sergei, Kong, Ren, Liu, Ran-Ran, Xu, Xi-Ming, Shi, Hang, Chang, Shan, Eisenstein, Miriam, Karczynska, Agnieszka, Czaplewski, Cezary, Lubecka, Emilia, Lipska, Agnieszka, Krupa, Paweł, Mozolewska, Magdalena, Golon, Łukasz, Samsonov, Sergey, Liwo, Adam, Crivelli, Silvia, Pagès, Guillaume, Karasikov, Mikhail, Kadukova, Maria, Yan, Yumeng, Huang, Sheng-You, Rosell, Mireia, Rodríguez-Lumbreras, Luis A., Romero-Durana, Miguel, Díaz-Bueno, Lucía, Fernandez-Recio, Juan, Christoffer, Charles, Terashi, Genki, Shin, Woong-Hee, Aderinwale, Tunde, Maddhuri Venkata Subraman, Sai Raghavendra, Kihara, Daisuke, Kozakov, Dima, Vajda, Sandor, Porter, Kathryn, Padhorny, Dzmitry, Desta, Israel, Beglov, Dmitri, Ignatov, Mikhail, Kotelnikov, Sergey, Moal, Iain H., Ritchie, David W., Chauvot de Beauchêne, Isaure, Maigret, Bernard, Devignes, Marie-Dominique, Ruiz Echartea, Maria E., Barradas-Bautista, Didier, Cao, Zhen, Cavallo, Luigi, Oliva, Romina, Cao, Yue, Shen, Yang, Baek, Minkyung, Park, Taeyong, Woo, Hyeonuk, Seok, Chaok, Braitbard, Merav, Bitton, Lirane, Scheidman-Duhovny, Dina, Dapkūnas, Justas, Olechnovič, Kliment, Venclovas, Česlovas, Kundrotas, Petras J., Belkin, Saveliy, Chakravarty, Devlina, Badal, Varsha D., Vakser, Ilya A., Vreven, Thom, Vangaveti, Sweta, Borrman, Tyler, Weng, Zhiping, Guest, Johnathan D., Gowthaman, Ragul, Pierce, Brian G., Xu, Xianjin, Duan, Rui, Qiu, Liming, Hou, Jie, Ryan Merideth, Benjamin, Ma, Zhiwei, Cheng, Jianlin, Zou, Xiaoqin, Koukos, Panagiotis I., Roel-Touris, Jorge, Ambrosetti, Francesco, Geng, Cunliang, Schaarschmidt, Jörg, Trellet, Mikael E., Melquiond, Adrien S. J., Xue, Li, Jiménez-García, Brian, van Noort, Charlotte W., Honorato, Rodrigo V., Bonvin, Alexandre M. J. J., Wodak, Shoshana J., 0000-0003-3957-9470, 0000-0003-2759-2088, 0000-0003-0621-0925, 0000-0003-2098-5743, 0000-0001-7169-9398, 0000-0002-0294-3403, 0000-0002-4209-4565, 0000-0002-7787-8896, 0000-0002-3986-7686, 0000-0002-6163-6323, 0000-0003-4091-6614, 0000-0003-0464-4500, 0000-0002-4960-5487, 0000-0002-7035-3042, 0000-0002-1703-7796, 0000-0002-1419-9888, 0000-0002-0496-6107, 0000-0002-4215-0213, 0000-0002-5743-2934, 0000-0003-3413-806X, 0000-0002-3032-7966, 0000-0002-1409-8358, 0000-0001-7369-1322, 0000-0002-0701-6545, Unité de Glycobiologie Structurale et Fonctionnelle UMR 8576 (UGSF), Institut National de la Recherche Agronomique (INRA)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, The Francis Crick Institute [London], School of Geographical Sciences [Bristol], University of Bristol [Bristol], Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Algorithms for Modeling and Simulation of Nanosystems (NANO-D), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), JiangSu University, Chemical Research Support [Rehovot], Weizmann Institute of Science [Rehovot, Israël], University of Gdańsk (UG), Department of Environmental Analytics [Univ Gdańsk], Faculty of Chemistry [Univ Gdańsk], University of Gdańsk (UG)-University of Gdańsk (UG), Institute of Physics [Warsaw] (IFPAN), Polish Academy of Sciences (PAN), Department of Computer Science [Davis] (UC Davis), University of California [Davis] (UC Davis), University of California-University of California, Moscow Institute of Physics and Technology [Moscow] (MIPT), Huazhong University of Science and Technology [Wuhan] (HUST), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), Purdue University [West Lafayette], Kitasato University, Stony Brook University [SUNY] (SBU), State University of New York (SUNY), Department of Biomedical Engineering [Boston], Boston University [Boston] (BU), Computational Algorithms for Protein Structures and Interactions (CAPSID), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), King Abdullah University of Science and Technology (KAUST), University of Naples Federico II, Service des Recherches Métallurgiques Appliquées (SRMA), Département des Matériaux pour le Nucléaire (DMN), CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-CEA-Direction des Energies (ex-Direction de l'Energie Nucléaire) (CEA-DES (ex-DEN)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Department of Chemistry, Seoul National University [Seoul] (SNU), The Hebrew University of Jerusalem (HUJ), Vilnius University [Vilnius], Department of Molecular Biosciences [Lawrence], University of Kansas [Lawrence] (KU), University of Kansas [Kansas City], University of Massachusetts Medical School [Worcester] (UMASS), University of Massachusetts System (UMASS), Program in Bioinformatics and Integrative Biology [Worcester], University of Massachusetts System (UMASS)-University of Massachusetts System (UMASS), University of Maryland [Baltimore], Beijing University of Technology, University of Missouri [Columbia] (Mizzou), University of Missouri System, Bijvoet Center for Biomolecular Research [Utrecht], Utrecht University [Utrecht], Dalton Cardiovascular Research Center [Columbia], University of Missouri System-University of Missouri System, Shandong University, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), University of California [Santa Cruz] (UCSC), University of California, The Hospital for sick children [Toronto] (SickKids), Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 (UGSF), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), Institut des Hautes Etudes Scientifiques (IHES), IHES, The Francis Crick Institute, Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut National Polytechnique de Grenoble (INPG), Weizmann Institute of Science, University of Gdańsk, Department of Environmental Analytics [Gdańsk], Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Technische Universität München [München] (TUM), CEA-Direction de l'Energie Nucléaire (CEA-DEN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-CEA-Direction de l'Energie Nucléaire (CEA-DEN), University of Missouri [Columbia], Université Paris-Sud - Paris 11 (UP11)-Université Paris-Saclay-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), Université de Lille, CNRS, Unité de Glycobiologie Structurale et Fonctionnelle UMR 8576 [UGSF], Agence Nationale de la Recherche (France), Cancer Research UK, European Commission, Medical Research Council (UK), National Institutes of Health (US), National Natural Science Foundation of China, National Research Foundation of Korea, National Science Foundation (US), Ministerio de Economía y Competitividad (España), Università degli Studi di Napoli PARTHENOPE, Wellcome Trust, Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Algorithms for Modeling and Simulating Nanosystems [2018-...] (NANO-D-POST [2018-2020]), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Department of Computer Science [Univ California Davis] (CS - UC Davis), University of California (UC)-University of California (UC), University of Naples Federico II = Università degli studi di Napoli Federico II, University of California [Santa Cruz] (UC Santa Cruz), University of California (UC), and Grudinin, Sergei

Subjects
Models, Molecular, Computer science, Protein Conformation, Protein complexes, Template‐based modeling, Oligomeric state, Biochemistry, Docking, protein-protein interaction, Structural Biology, Protein Interaction Mapping, Taverne, Blind prediction, Protein assemblies, Databases, Protein, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 0303 health sciences, oligomeric state, 030302 biochemistry & molecular biology, protein assemblies, computer.file_format, 3. Good health, CASP, docking, blind prediction, Protein‐protein interaction, CAPRI, proteincomplexes, Algorithms, Protein Binding, protein complexes, template-based modeling, Computational biology, Article, Protein–protein interaction, 03 medical and health sciences, protein‐protein interaction, template‐based modeling, Molecular Biology, 030304 developmental biology, Binding Sites, Computational Biology, Proteins, Protein Data Bank, Docking (molecular), Structural Homology, Protein, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, Software
Abstract

We present the results for CAPRI Round 46, the third joint CASP‐CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo‐oligomers and 6 heterocomplexes. Eight of the homo‐oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher‐order assemblies. These were more difficult to model, as their prediction mainly involved “ab‐initio” docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance “gap” was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template‐based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements., Agence Nationale de la Recherche, Grant/Award Number: ANR‐15‐CE11‐0029‐03; Cancer Research UK, Grant/Award Number: FC001003; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Lietuvos Mokslo Taryba, Grant/Award Number: S‐MIP‐17‐60; Medical Research Council, Grant/Award Number: FC001003; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM123055, R35GM124952; National Natural Science Foundation of China, Grant/Award Number: 31670724; National Research Foundation of Korea, Grant/Award Number: 2016M3C4A7952630; National Science Foundation, Grant/Award Number: DBI1565107; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Number: 718.015.001; Spanish >Programma Estatal I+D+i>, Grant/Award Number: BIO2016‐79930‐R; University Parthenope, Grant/Award Number: Finanziamento per il Sostegno alla Ricerca Individ; Wellcome Trust, Grant/Award Number: FC001003

Published
2019
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2. Assembly of Protein Complexes In and On the Membrane with Predicted Spatial Arrangement Constraints.

Author

Christoffer C, Harini K, Archit G, and Kihara D

Abstract

Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org., Competing Interests: DECLARATIONS OF INTEREST The authors declare no conflict of interest.

Published
2023
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3. Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Author

Schweke H, Xu Q, Tauriello G, Pantolini L, Schwede T, Cazals F, Lhéritier A, Fernandez-Recio J, Rodríguez-Lumbreras LA, Schueler-Furman O, Varga JK, Jiménez-García B, Réau MF, Bonvin AMJJ, Savojardo C, Martelli PL, Casadio R, Tubiana J, Wolfson HJ, Oliva R, Barradas-Bautista D, Ricciardelli T, Cavallo L, Venclovas Č, Olechnovič K, Guerois R, Andreani J, Martin J, Wang X, Terashi G, Sarkar D, Christoffer C, Aderinwale T, Verburgt J, Kihara D, Marchand A, Correia BE, Duan R, Qiu L, Xu X, Zhang S, Zou X, Dey S, Dunbrack RL, Levy ED, and Wodak SJ

Subjects
Reproducibility of Results, Protein Binding, Proteins metabolism
Abstract

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy., (© 2023 Wiley-VCH GmbH.)

Published
2023
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4. Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

Author

Lensink MF, Brysbaert G, Mauri T, Nadzirin N, Velankar S, Chaleil RAG, Clarence T, Bates PA, Kong R, Liu B, Yang G, Liu M, Shi H, Lu X, Chang S, Roy RS, Quadir F, Liu J, Cheng J, Antoniak A, Czaplewski C, Giełdoń A, Kogut M, Lipska AG, Liwo A, Lubecka EA, Maszota-Zieleniak M, Sieradzan AK, Ślusarz R, Wesołowski PA, Zięba K, Del Carpio Muñoz CA, Ichiishi E, Harmalkar A, Gray JJ, Bonvin AMJJ, Ambrosetti F, Vargas Honorato R, Jandova Z, Jiménez-García B, Koukos PI, Van Keulen S, Van Noort CW, Réau M, Roel-Touris J, Kotelnikov S, Padhorny D, Porter KA, Alekseenko A, Ignatov M, Desta I, Ashizawa R, Sun Z, Ghani U, Hashemi N, Vajda S, Kozakov D, Rosell M, Rodríguez-Lumbreras LA, Fernandez-Recio J, Karczynska A, Grudinin S, Yan Y, Li H, Lin P, Huang SY, Christoffer C, Terashi G, Verburgt J, Sarkar D, Aderinwale T, Wang X, Kihara D, Nakamura T, Hanazono Y, Gowthaman R, Guest JD, Yin R, Taherzadeh G, Pierce BG, Barradas-Bautista D, Cao Z, Cavallo L, Oliva R, Sun Y, Zhu S, Shen Y, Park T, Woo H, Yang J, Kwon S, Won J, Seok C, Kiyota Y, Kobayashi S, Harada Y, Takeda-Shitaka M, Kundrotas PJ, Singh A, Vakser IA, Dapkūnas J, Olechnovič K, Venclovas Č, Duan R, Qiu L, Xu X, Zhang S, Zou X, and Wodak SJ

Subjects
Binding Sites, Molecular Docking Simulation, Protein Interaction Domains and Motifs, Sequence Analysis, Protein, Computational Biology methods, Models, Molecular, Proteins chemistry, Proteins metabolism, Software
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., (© 2021 Wiley Periodicals LLC.)

Published
2021
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5. VESPER: global and local cryo-EM map alignment using local density vectors.

Author

Han X, Terashi G, Christoffer C, Chen S, and Kihara D

Subjects
Models, Molecular, Protein Conformation, Proteins chemistry, Cryoelectron Microscopy methods, Databases, Factual, Models, Structural, Software
Abstract

An increasing number of density maps of biological macromolecules have been determined by cryo-electron microscopy (cryo-EM) and stored in the public database, EMDB. To interpret the structural information contained in EM density maps, alignment of maps is an essential step for structure modeling, comparison of maps, and for database search. Here, we developed VESPER, which captures the similarity of underlying molecular structures embedded in density maps by taking local gradient directions into consideration. Compared to existing methods, VESPER achieved substantially more accurate global and local alignment of maps as well as database retrieval.

Published
2021
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6. Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46.

Author

Christoffer C, Terashi G, Shin WH, Aderinwale T, Maddhuri Venkata Subramaniya SR, Peterson L, Verburgt J, and Kihara D

Subjects
Algorithms, Amino Acid Sequence, Binding Sites, Humans, Ligands, Peptides metabolism, Protein Binding, Protein Conformation, alpha-Helical, Protein Conformation, beta-Strand, Protein Interaction Domains and Motifs, Protein Interaction Mapping, Proteins metabolism, Research Design, Structural Homology, Protein, Molecular Docking Simulation, Peptides chemistry, Proteins chemistry, Software
Abstract

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group., (© 2019 Wiley Periodicals, Inc.)

Published
2020
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7. A global map of the protein shape universe.

Author

Han X, Sit A, Christoffer C, Chen S, and Kihara D

Subjects
Computational Biology methods, Databases, Protein, Models, Molecular, Protein Conformation, Software, Protein Structural Elements physiology, Proteins chemistry, Proteins classification
Abstract

Proteins are involved in almost all functions in a living cell, and functions of proteins are realized by their tertiary structures. Obtaining a global perspective of the variety and distribution of protein structures lays a foundation for our understanding of the building principle of protein structures. In light of the rapid accumulation of low-resolution structure data from electron tomography and cryo-electron microscopy, here we map and classify three-dimensional (3D) surface shapes of proteins into a similarity space. Surface shapes of proteins were represented with 3D Zernike descriptors, mathematical moment-based invariants, which have previously been demonstrated effective for biomolecular structure similarity search. In addition to single chains of proteins, we have also analyzed the shape space occupied by protein complexes. From the mapping, we have obtained various new insights into the relationship between shapes, main-chain folds, and complex formation. The unique view obtained from shape mapping opens up new ways to understand design principles, functions, and evolution of proteins., Competing Interests: The authors have declared that no competing interests exist.

Published
2019
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