1
|
Liang F, Sun M, Xie L, Zhao X, Liu D, Zhao K, Zhang G. Recent advances and challenges in protein complex model accuracy estimation. Comput Struct Biotechnol J 2024; 23:1824-1832. [PMID: 38707538 PMCID: PMC11066466 DOI: 10.1016/j.csbj.2024.04.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024] Open
Abstract
Estimation of model accuracy plays a crucial role in protein structure prediction, aiming to evaluate the quality of predicted protein structure models accurately and objectively. This process is not only key to screening candidate models that are close to the real structure, but also provides guidance for further optimization of protein structures. With the significant advancements made by AlphaFold2 in monomer structure, the problem of single-domain protein structure prediction has been widely solved. Correspondingly, the importance of assessing the quality of single-domain protein models decreased, and the research focus has shifted to estimation of model accuracy of protein complexes. In this review, our goal is to provide a comprehensive overview of the reference and statistical metrics, as well as representative methods, and the current challenges within four distinct facets (Topology Global Score, Interface Total Score, Interface Residue-Wise Score, and Tertiary Residue-Wise Score) in the field of complex EMA.
Collapse
Affiliation(s)
| | | | - Lei Xie
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xuanfeng Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Dong Liu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Kailong Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| |
Collapse
|
2
|
Nawaz T, Gu L, Gibbons J, Hu Z, Zhou R. Bridging Nature and Engineering: Protein-Derived Materials for Bio-Inspired Applications. Biomimetics (Basel) 2024; 9:373. [PMID: 38921253 PMCID: PMC11201842 DOI: 10.3390/biomimetics9060373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/27/2024] Open
Abstract
The sophisticated, elegant protein-polymers designed by nature can serve as inspiration to redesign and biomanufacture protein-based materials using synthetic biology. Historically, petro-based polymeric materials have dominated industrial activities, consequently transforming our way of living. While this benefits humans, the fabrication and disposal of these materials causes environmental sustainability challenges. Fortunately, protein-based biopolymers can compete with and potentially surpass the performance of petro-based polymers because they can be biologically produced and degraded in an environmentally friendly fashion. This paper reviews four groups of protein-based polymers, including fibrous proteins (collagen, silk fibroin, fibrillin, and keratin), elastomeric proteins (elastin, resilin, and wheat glutenin), adhesive/matrix proteins (spongin and conchiolin), and cyanophycin. We discuss the connection between protein sequence, structure, function, and biomimetic applications. Protein engineering techniques, such as directed evolution and rational design, can be used to improve the functionality of natural protein-based materials. For example, the inclusion of specific protein domains, particularly those observed in structural proteins, such as silk and collagen, enables the creation of novel biomimetic materials with exceptional mechanical properties and adaptability. This review also discusses recent advancements in the production and application of new protein-based materials through the approach of synthetic biology combined biomimetics, providing insight for future research and development of cutting-edge bio-inspired products. Protein-based polymers that utilize nature's designs as a base, then modified by advancements at the intersection of biology and engineering, may provide mankind with more sustainable products.
Collapse
Affiliation(s)
- Taufiq Nawaz
- Department of Biology and Microbiology, South Dakota State University, Brookings, SD 57007, USA;
| | - Liping Gu
- Department of Biology and Microbiology, South Dakota State University, Brookings, SD 57007, USA;
| | | | - Zhong Hu
- Department of Mechanical Engineering, South Dakota State University, Brookings, SD 57007, USA;
| | - Ruanbao Zhou
- Department of Biology and Microbiology, South Dakota State University, Brookings, SD 57007, USA;
| |
Collapse
|
3
|
Martin J. AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility. J Chem Inf Model 2024; 64:1473-1480. [PMID: 38373070 DOI: 10.1021/acs.jcim.3c01805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Predicting whether two proteins physically interact is one of the holy grails of computational biology, galvanized by rapid advancements in deep learning. AlphaFold2, although not developed with this goal, is promising in this respect. Here, I test the prediction capability of AlphaFold2 on a very challenging data set, where proteins are structurally compatible, even when they do not interact. AlphaFold2 achieves high discrimination between interacting and non-interacting proteins, and the cases of misclassifications can either be rescued by revisiting the input sequences or can suggest false positives and negatives in the data set. AlphaFold2 is thus not impaired by the compatibility between protein structures and has the potential to be applied on a large scale.
Collapse
Affiliation(s)
- Juliette Martin
- Univ Lyon, CNRS, UMR 5086 MMSB, 7 passage du Vercors F-69367, Lyon, France
- Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS UMR 5239, Inserm U1293, University Claude Bernard Lyon 1, 69364, Lyon, France
| |
Collapse
|
4
|
Kant R, Kaushik R, Chopra M, Saluja D. Structure-based drug discovery to identify SARS-CoV2 spike protein-ACE2 interaction inhibitors. J Biomol Struct Dyn 2024:1-19. [PMID: 38174578 DOI: 10.1080/07391102.2023.2300060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024]
Abstract
After the emergence of the COVID-19 pandemic in late 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has undergone a dynamic evolution driven by the acquisition of genetic modifications, resulting in several variants that are further classified as variants of interest (VOIs), variants under monitoring (VUM) and variants of concern (VOC) by World Health Organization (WHO). Currently, there are five SARS-CoV-2 VOCs (Alpha, Beta, Delta, Gamma and Omicron), two VOIs (Lambda and Mu) and several other VOIs that have been reported globally. In this study, we report a natural compound, Curcumin, as the potential inhibitor to the interactions between receptor binding domain (RBD(S1)) and human angiotensin-converting enzyme 2 (hACE2) domains and showcased its inhibitory potential for the Delta and Omicron variants through a computational approach by implementing state of the art methods. The study for the first time revealed a higher efficiency of Curcumin, especially for hindering the interaction between RBD(S1) and hACE-2 domains of Delta and Omicron variants as compared to other lead compounds. We investigated that the mutations in the RBD(S1) of VOC especially Delta and Omicron variants affect its structure compared to that of the wild type and other variants and therefore altered its binding to the hACE2 receptor. Molecular docking and molecular dynamics (MD) simulation analyses substantially supported the findings in terms of the stability of the docked complexes. This study offers compelling evidence, warranting a more in-depth exploration into the impact of these alterations on the binding of identified drug molecules with the Spike protein. Further investigation into their potential therapeutic effects in vivo is highly recommended.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Ravi Kant
- Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research &Delhi School of Public Health, IoE, University of Delhi, Delhi, India
| | - Rahul Kaushik
- Biotechology Research Center, Technology Innovation Institute, Masdar City, UAE
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | - Madhu Chopra
- Laboratory of Molecular Modeling and Drug Development, Dr. B. R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Daman Saluja
- Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research &Delhi School of Public Health, IoE, University of Delhi, Delhi, India
| |
Collapse
|
5
|
Lucca MS, Bustamante-Filho IC, Ulguim RR, Gianluppi RDF, Evaristo JAM, Nogueira FCS, Timmers LFSM, Mellagi APG, Wentz I, Bortolozzo FP. Proteomic analysis of boar seminal plasma: Putative markers for fertility based on capacity of semen preservation at 17°C. Mol Reprod Dev 2024; 91:e23735. [PMID: 38282317 DOI: 10.1002/mrd.23735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/20/2023] [Accepted: 01/13/2024] [Indexed: 01/30/2024]
Abstract
Boar seminal plasma (SP) proteins were associated with differences on sperm resistance to cooling at 17°C. However, information about seminal plasma proteins in boars classified by capacity of semen preservation and in vivo fertility remains lacking. Thus, the objective was to evaluate the SP proteome in boars classified by capacity of semen preservation and putative biomarkers for fertility. The ejaculates from high-preservation (HP) showed higher progressive motility during all 5 days than the low-preservation (LP) boars. There was no difference for farrowing rate between ejaculates from LP (89.7%) and HP boars (88.4%). The LP boars presented lower total piglets born (14.0 ± 0.2) than HP (14.8 ± 0.2; p < 0.01). A total of 257 proteins were identified, where 184 were present in both classes of boar, and 41 and 32 were identified only in LP and HP boars, respectively. Nine proteins were differently expressed: five were more abundant in HP (SPMI, ZPBP1, FN1, HPX, and C3) and four in LP boars (B2M, COL1A1, NKX3-2, and MPZL1). The HP boars had an increased abundance of SP proteins related to sperm resistance and fecundation process which explains the better TPB. LP boars had a higher abundance of SP proteins associated with impaired spermatogenesis.
Collapse
Affiliation(s)
- Matheus S Lucca
- Departamento de Medicina Animal, Faculdade de Veterinária, Universidade Federal do Rio Grande do Sul-UFRGS, Setor de Suínos, Porto Alegre, Rio Grande do Sul, Brazil
| | | | - Rafael R Ulguim
- Departamento de Medicina Animal, Faculdade de Veterinária, Universidade Federal do Rio Grande do Sul-UFRGS, Setor de Suínos, Porto Alegre, Rio Grande do Sul, Brazil
| | - Rafael D F Gianluppi
- Departamento de Medicina Animal, Faculdade de Veterinária, Universidade Federal do Rio Grande do Sul-UFRGS, Setor de Suínos, Porto Alegre, Rio Grande do Sul, Brazil
| | - Joseph A M Evaristo
- Laboratory of Proteomics, LADETEC, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fábio C S Nogueira
- Laboratory of Proteomics, LADETEC, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
- Proteomics Unit, Institute of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Luís F S M Timmers
- Laboratório de Biotecnologia, Universidade do Vale do Taquari-Univates, Lajeado, Brazil
| | - Ana P G Mellagi
- Departamento de Medicina Animal, Faculdade de Veterinária, Universidade Federal do Rio Grande do Sul-UFRGS, Setor de Suínos, Porto Alegre, Rio Grande do Sul, Brazil
| | - Ivo Wentz
- Departamento de Medicina Animal, Faculdade de Veterinária, Universidade Federal do Rio Grande do Sul-UFRGS, Setor de Suínos, Porto Alegre, Rio Grande do Sul, Brazil
| | - Fernando P Bortolozzo
- Departamento de Medicina Animal, Faculdade de Veterinária, Universidade Federal do Rio Grande do Sul-UFRGS, Setor de Suínos, Porto Alegre, Rio Grande do Sul, Brazil
| |
Collapse
|
6
|
Olechnovič K, Venclovas Č. VoroIF-GNN: Voronoi tessellation-derived protein-protein interface assessment using a graph neural network. Proteins 2023; 91:1879-1888. [PMID: 37482904 DOI: 10.1002/prot.26554] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/19/2023] [Accepted: 07/01/2023] [Indexed: 07/25/2023]
Abstract
We present VoroIF-GNN (Voronoi InterFace Graph Neural Network), a novel method for assessing inter-subunit interfaces in a structural model of a protein-protein complex, relying solely on the input structure without any additional information. Given a multimeric protein structural model, we derive interface contacts from the Voronoi tessellation of atomic balls, construct a graph of those contacts, and predict the accuracy of every contact using an attention-based GNN. The contact-level predictions are then summarized to produce whole interface-level scores. VoroIF-GNN was blindly tested for its ability to estimate the accuracy of protein complexes during CASP15 and showed strong performance in selecting the best multimeric model out of many. The method implementation is freely available at https://kliment-olechnovic.github.io/voronota/expansion_js/.
Collapse
Affiliation(s)
- Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| |
Collapse
|
7
|
Schweke H, Xu Q, Tauriello G, Pantolini L, Schwede T, Cazals F, Lhéritier A, Fernandez-Recio J, Rodríguez-Lumbreras LÁ, Schueler-Furman O, Varga JK, Jiménez-García B, Réau MF, Bonvin A, Savojardo C, Martelli PL, Casadio R, Tubiana J, Wolfson H, Oliva R, Barradas-Bautista D, Ricciardelli T, Cavallo L, Venclovas Č, Olechnovič K, Guerois R, Andreani J, Martin J, Wang X, Kihara D, Marchand A, Correia B, Zou X, Dey S, Dunbrack R, Levy E, Wodak S. Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study. Proteomics 2023; 23:e2200323. [PMID: 37365936 PMCID: PMC10937251 DOI: 10.1002/pmic.202200323] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 06/28/2023]
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.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Julia K. Varga
- Hebrew University of Jerusalem Institute for Medical Research Israel-Canada
| | | | | | | | | | | | | | - Jérôme Tubiana
- Tel Aviv University Blavatnik School of Computer Science
| | - Haim Wolfson
- Tel Aviv University Blavatnik School of Computer Science
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, Institute for Data Science and Informatics, University of Missouri
| | | | | | | | | |
Collapse
|
8
|
Fereshteh S, Noori Goodarzi N, Kalhor H, Rahimi H, Barzi SM, Badmasti F. Identification of Putative Drug Targets in Highly Resistant Gram-Negative Bacteria; and Drug Discovery Against Glycyl-tRNA Synthetase as a New Target. Bioinform Biol Insights 2023; 17:11779322231152980. [PMID: 36798081 PMCID: PMC9926382 DOI: 10.1177/11779322231152980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/24/2022] [Indexed: 02/17/2023] Open
Abstract
Background Gram-negative bacterial infections are on the rise due to the high prevalence of multidrug-resistant bacteria, and efforts must be made to identify novel drug targets and then new antibiotics. Methods In the upstream part, we retrieved the genome sequences of 4 highly resistant Gram-negative bacteria (e.g., Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterobacter cloacae). The core proteins were assessed to find common, cytoplasmic, and essential proteins with no similarity to the human proteome. Novel drug targets were identified using DrugBank, and their sequence conservancy was evaluated. Protein Data Bank files and STRING interaction networks were assessed. Finally, the aminoacylation cavity of glycyl-tRNA synthetase (GlyQ) was virtually screened to identify novel inhibitors using AutoDock Vina and the StreptomeDB library. Ligands with high binding affinity were clustered, and then the pharmacokinetics properties of therapeutic agents were investigated. Results A total of 6 common proteins (e.g., RP-L28, RP-L30, RP-S20, RP-S21, Rnt, and GlyQ) were selected as novel and widespread drug targets against highly resistant Gram-negative superbugs based on different criteria. In the downstream analysis, virtual screening revealed that Rimocidin, Flavofungin, Chaxamycin, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin, and Platensimycin were promising hit compounds against GlyQ protein. Finally, 11,11'-O-dimethyl-14'-deethyl-14'-methylelaiophylin was identified as the best potential inhibitor of GlyQ protein. This compound showed high absorption capacity in the human intestine. Conclusion The results of this study provide 6 common putative new drug targets against 4 highly resistant and Gram-negative bacteria. Moreover, we presented 5 different hit compounds against GlyQ protein as a novel therapeutic target. However, further in vitro and in vivo studies are needed to explore the bactericidal effects of proposed hit compounds against these superbugs.
Collapse
Affiliation(s)
| | - Narjes Noori Goodarzi
- Department of Pathobiology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Hourieh Kalhor
- Cellular and Molecular Research Center, Qom University of Medical Sciences, Qom, Iran
| | - Hamzeh Rahimi
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
- Texas Biomedical Research Institute, San Antonio, TX, USA
| | | | - Farzad Badmasti
- Department of Bacteriology, Pasteur Institute of Iran, Tehran, Iran
- Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran
- Farzad Badmasti, Department of Bacteriology, Pasteur Institute of Iran, Tehran Province, Tehran, 12 Farvardin St, Tehran 1316943551, Iran.
| |
Collapse
|
9
|
Saha J, Chaudhuri D, Kundu A, Bhattacharya S, Roy S, Giri K. Phylogenetic, structural, functional characterisation and effect of exogenous spermidine on rice ( Oryza sativa) HAK transporters under salt stress. FUNCTIONAL PLANT BIOLOGY : FPB 2023; 50:160-182. [PMID: 36031595 DOI: 10.1071/fp22059] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 07/26/2022] [Indexed: 06/15/2023]
Abstract
The HAK (High-affinity K+ ) family members mediate K+ transport that confers normal plant growth and resistance against unfavourable environmental conditions. Rice (Oryza sativa L.) HAK transporters have been extensively investigated for phylogenetic analyses with other plants species with very few of them functionally characterised. But very little information is known about their evolutionary aspects, overall structural, functional characterisation, and global expression pattern of the complete HAK family members in response to salt stress. In this study, 27 rice transporters were phylogenetically clustered with different dicot and monocot family members. Subsequently, the exon-intron structural patterns, conserved motif analyses, evolutionary divergence based different substitution matrix, orthologous-paralogous relationships were studied elaborately. Structural characterisations included a comparative study of secondary and tertiary structure, post-translational modifications, correspondence analyses, normal mode analyses, K+ /Na+ binding affinities of each of the OsHAK gene members. Global expression profile under salt stress showed clade-specific expression pattern of the proteins. Additionally, five OsHAK genes were chosen for further expression analyses in root and shoot tissues of two rice varieties during short-term salinity in the presence and absence of exogenous spermidine. All the information can be used as first-hand data for dissecting the administrative role of rice HAK transporters under various abiotic stresses.
Collapse
Affiliation(s)
- Jayita Saha
- Department of Botany, Rabindra Mahavidyalaya, Champadanga, Hooghly, West Bengal, India; and Department of Life Sciences, Presidency University, 86/1 College Street, Kolkata 700073, West Bengal, India
| | - Dwaipayan Chaudhuri
- Department of Life Sciences, Presidency University, 86/1 College Street, Kolkata 700073, West Bengal, India
| | - Anirban Kundu
- Plant Genomics and Bioinformatics Laboratory, P.G. Department of Botany, Ramakrishna Mission Vivekananda Centenary College (Autonomous), Rahara, Kolkata 700118, West Bengal, India
| | - Saswati Bhattacharya
- Department of Botany, Dr. A.P.J. Abdul Kalam Government College, New Town, Rajarhat, Kolkata, West Bengal, India
| | - Sudipta Roy
- Department of Botany, University of Kalyani, Kalyani, Nadia, West Bengal, India
| | - Kalyan Giri
- Department of Life Sciences, Presidency University, 86/1 College Street, Kolkata 700073, West Bengal, India
| |
Collapse
|
10
|
Kumar N, Kaushik R, Zhang KYJ, Uversky VN, Sahu U, Sood R, Bhatia S. A novel consensus-based computational pipeline for screening of antibody therapeutics for efficacy against SARS-CoV-2 variants of concern including Omicron variant. Proteins 2023; 91:798-806. [PMID: 36629264 DOI: 10.1002/prot.26467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 11/11/2022] [Accepted: 01/03/2023] [Indexed: 01/12/2023]
Abstract
Multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to evolve carrying flexible amino acid substitutions in the spike protein's receptor binding domain (RBD). These substitutions modify the binding of the SARS-CoV-2 to human angiotensin-converting enzyme 2 (hACE2) receptor and have been implicated in altered host fitness, transmissibility, and efficacy against antibody therapeutics and vaccines. Reliably predicting the binding strength of SARS-CoV-2 variants RBD to hACE2 receptor and neutralizing antibodies (NAbs) can help assessing their fitness, and rapid deployment of effective antibody therapeutics, respectively. Here, we introduced a two-step computational framework with 3-fold validation that first identified dissociation constant as a reliable predictor of binding affinity in hetero- dimeric and trimeric protein complexes. The second step implements dissociation constant as descriptor of the binding strengths of SARS-CoV-2 variants RBD to hACE2 and NAbs. Then, we examined several variants of concerns (VOCs) such as Alpha, Beta, Gamma, Delta, and Omicron and demonstrated that these VOCs RBD bind to the hACE2 with enhanced affinity. Furthermore, the binding affinity of Omicron variant's RBD was reduced with majority of the RBD-directed NAbs, which is highly consistent with the experimental neutralization data. By studying the atomic contacts between RBD and NAbs, we revealed the molecular footprints of four NAbs (GH-12, P2B-1A1, Asarnow_3D11, and C118)-that may likely neutralize the recently emerged Omicron variant-facilitating enhanced binding affinity. Finally, our findings suggest a computational pathway that could aid researchers identify a range of current NAbs that may be effective against emerging SARS-CoV-2 variants.
Collapse
Affiliation(s)
- Naveen Kumar
- Zoonotic Diseases Group, ICAR-National Institute of High Security Animal Diseases, Bhopal, India
| | - Rahul Kaushik
- Biotechnology Research Center, Technology Innovation Institute, Abu Dhabi, UAE.,Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| | - Vladimir N Uversky
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA.,Institute for Biological Instrumentation of the Russian Academy of Sciences, Federal Research Center 'Pushchino Scientific Center for Biological Research of the Russian Academy of Sciences', Pushchino, Russia
| | - Upasana Sahu
- Zoonotic Diseases Group, ICAR-National Institute of High Security Animal Diseases, Bhopal, India
| | - Richa Sood
- Zoonotic Diseases Group, ICAR-National Institute of High Security Animal Diseases, Bhopal, India
| | - Sandeep Bhatia
- Zoonotic Diseases Group, ICAR-National Institute of High Security Animal Diseases, Bhopal, India
| |
Collapse
|
11
|
Abstract
Protein structure modeling is one of the most advanced and complex processes in computational biology. One of the major problems for the protein structure prediction field has been how to estimate the accuracy of the predicted 3D models, on both a local and global level, in the absence of known structures. We must be able to accurately measure the confidence that we have in the quality predicted 3D models of proteins for them to become widely adopted by the general bioscience community. To address this major issue, it was necessary to develop new model quality assessment (MQA) methods and integrate them into our pipelines for building 3D protein models. Our MQA method, called ModFOLD, has been ranked as one of the most accurate MQA tools in independent blind evaluations. This chapter discusses model quality assessment in the protein modeling field, demonstrating both its strengths and limitations. We also present some of the best methods according to independent benchmarking data, which has been gathered in recent years.
Collapse
Affiliation(s)
- Ali H A Maghrabi
- College of Applied Sciences, Umm Al Qura University, Mecca, Saudi Arabia
| | | | - Liam J McGuffin
- School of Biological Sciences, University of Reading, Reading, UK.
| |
Collapse
|
12
|
Murph M, Singh S, Schvarzstein M. A combined in silico and in vivo approach to the structure-function annotation of SPD-2 provides mechanistic insight into its functional diversity. Cell Cycle 2022; 21:1958-1979. [PMID: 35678569 PMCID: PMC9415446 DOI: 10.1080/15384101.2022.2078458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 04/10/2022] [Accepted: 05/04/2022] [Indexed: 11/03/2022] Open
Abstract
Centrosomes are organelles that function as hubs of microtubule nucleation and organization, with key roles in organelle positioning, asymmetric cell division, ciliogenesis, and signaling. Aberrant centrosome number, structure or function is linked to neurodegenerative diseases, developmental abnormalities, ciliopathies, and tumor development. A major regulator of centrosome biogenesis and function in C. elegans is the conserved Spindle-defective protein 2 (SPD-2), a homolog of the human CEP-192 protein. CeSPD-2 is required for centrosome maturation, centriole duplication, spindle assembly and possibly cell polarity establishment. Despite its importance, the specific molecular mechanism of CeSPD-2 regulation and function is poorly understood. Here, we combined computational analysis with cell biology approaches to uncover possible structure-function relationships of CeSPD-2 that may shed mechanistic light on its function. Domain prediction analysis corroborated and refined previously identified coiled-coils and ASH (Aspm-SPD-2 Hydin) domains and identified new domains: a GEF domain, an Ig-like domain, and a PDZ-like domain. In addition to these predicted structural features, CeSPD-2 is also predicted to be intrinsically disordered. Surface electrostatic maps identified a large basic region unique to the ASH domain of CeSPD-2. This basic region overlaps with most of the residues predicted to be involved in protein-protein interactions. In vivo, ASH::GFP localized to centrosomes and centrosome-associated microtubules. Our analysis groups ASH domains, PapD, Usher chaperone domains, and Major Sperm Protein (MSP) domains into a single superfold within the larger Immunoglobulin superfamily. This study lays the groundwork for designing rational hypothesis-based experiments to uncover the mechanisms of CeSPD-2 function in vivo.Abbreviations: AIR, Aurora kinase; ASH, Aspm-SPD-2 Hydin; ASP, Abnormal Spindle Protein; ASPM, Abnormal Spindle-like Microcephaly-associated Protein; CC, coiled-coil; CDK, Cyclin-dependent Kinase; Ce, Caenorhabditis elegans; CEP, Centrosomal Protein; CPAP, centrosomal P4.1-associated protein; D, Drosophila; GAP, GTPase activating protein; GEF, GTPase guanine nucleotide exchange factor; Hs, Homo sapiens/Human; Ig, Immunoglobulin; MAP, Microtubule associated Protein; MSP, Major Sperm Protein; MDP, Major Sperm Domain-Containing Protein; OCRL-1, Golgi endocytic trafficking protein Inositol polyphosphate 5-phosphatase; PAR, abnormal embryonic PARtitioning of the cytosol; PCM, Pericentriolar material; PCMD, pericentriolar matrix deficient; PDZ, PSD95/Dlg-1/zo-1; PLK, Polo like kinase; RMSD, Root Mean Square Deviation; SAS, Spindle assembly abnormal proteins; SPD, Spindle-defective protein; TRAPP, TRAnsport Protein Particle; Xe, Xenopus; ZYG, zygote defective protein.
Collapse
Affiliation(s)
- Mikaela Murph
- Department of Biology, City University of New York, Brooklyn College, New York, NY, USA
| | - Shaneen Singh
- Department of Biology, City University of New York, Brooklyn College, New York, NY, USA
- Department of Biology, The Graduate Center at City University of New York, New York, NY, USA
- Department Biochemistry, The Graduate Center at City University of New York, New York, NY, USA
| | - Mara Schvarzstein
- Department of Biology, City University of New York, Brooklyn College, New York, NY, USA
- Department of Biology, The Graduate Center at City University of New York, New York, NY, USA
- Department Biochemistry, The Graduate Center at City University of New York, New York, NY, USA
| |
Collapse
|
13
|
Kaushik R, Kumar N, Zhang KYJ, Srivastava P, Bhatia S, Malik YS. A novel structure-based approach for identification of vertebrate susceptibility to SARS-CoV-2: Implications for future surveillance programmes. ENVIRONMENTAL RESEARCH 2022; 212:113303. [PMID: 35460633 PMCID: PMC9020514 DOI: 10.1016/j.envres.2022.113303] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 03/09/2022] [Accepted: 04/09/2022] [Indexed: 06/14/2023]
Abstract
Understanding the origin of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a highly debatable and unresolved issue for scientific communities all over the world. Understanding the mechanism of virus entry to the host cells is crucial to deciphering the susceptibility profiles of animal species to SARS-CoV-2. The interaction of SARS-CoV-2 ligands (receptor-binding domain on spike protein) with its host cell receptor, angiotensin-converting enzyme 2 (ACE2), is a critical determinant of host range and cross-species transmission. In this study, we developed and implemented a rigorous computational approach for predicting binding affinity between 299 ACE2 orthologs from diverse vertebrate species and the SARS-CoV-2 spike protein. The findings show that the SARS-CoV-2 spike protein can bind to a wide range of vertebrate species carrying evolutionary divergent ACE2, implying a broad host range at the virus entry level, which may contribute to cross-species transmission and further viral evolution. Furthermore, the current study facilitated the identification of genetic determinants that may differentiate susceptible from resistant host species based on the conservation of ACE2-spike protein interacting residues in vertebrate host species known to facilitate SARS-CoV-2 infection; however, these genetic determinants warrant in vivo experimental confirmation. The molecular interactions associated with varied binding affinity of distinct ACE2 isoforms in a specific bat species were identified using protein structure analysis, implying the existence of diversified bat species' susceptibility to SARS-CoV-2. The current study's findings highlight the importance of intensive surveillance programmes aimed at identifying susceptible hosts, especially those with the potential to transmit zoonotic pathogens, in order to prevent future outbreaks.
Collapse
Affiliation(s)
- Rahul Kaushik
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Yokohama, Kanagawa, 230-0045, Japan
| | - Naveen Kumar
- Zoonotic Diseases Group, ICAR- National Institute of High Security Animal Diseases, Bhopal, 462022, India
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Yokohama, Kanagawa, 230-0045, Japan
| | - Pratiksha Srivastava
- Zoonotic Diseases Group, ICAR- National Institute of High Security Animal Diseases, Bhopal, 462022, India
| | - Sandeep Bhatia
- Zoonotic Diseases Group, ICAR- National Institute of High Security Animal Diseases, Bhopal, 462022, India
| | - Yashpal Singh Malik
- College of Animal Biotechnology, Guru Angad Dev Veterinary and Animal Science University (GADVASU), Ludhiana, 141004, Punjab, India.
| |
Collapse
|
14
|
Davari N, Bakhtiary N, Khajehmohammadi M, Sarkari S, Tolabi H, Ghorbani F, Ghalandari B. Protein-Based Hydrogels: Promising Materials for Tissue Engineering. Polymers (Basel) 2022; 14:986. [PMID: 35267809 PMCID: PMC8914701 DOI: 10.3390/polym14050986] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/19/2022] [Accepted: 02/23/2022] [Indexed: 02/01/2023] Open
Abstract
The successful design of a hydrogel for tissue engineering requires a profound understanding of its constituents' structural and molecular properties, as well as the proper selection of components. If the engineered processes are in line with the procedures that natural materials undergo to achieve the best network structure necessary for the formation of the hydrogel with desired properties, the failure rate of tissue engineering projects will be significantly reduced. In this review, we examine the behavior of proteins as an essential and effective component of hydrogels, and describe the factors that can enhance the protein-based hydrogels' structure. Furthermore, we outline the fabrication route of protein-based hydrogels from protein microstructure and the selection of appropriate materials according to recent research to growth factors, crucial members of the protein family, and their delivery approaches. Finally, the unmet needs and current challenges in developing the ideal biomaterials for protein-based hydrogels are discussed, and emerging strategies in this area are highlighted.
Collapse
Affiliation(s)
- Niyousha Davari
- Department of Life Science Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran 143951561, Iran;
| | - Negar Bakhtiary
- Burn Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran;
- Department of Biomaterials, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran 14115114, Iran
| | - Mehran Khajehmohammadi
- Department of Mechanical Engineering, Faculty of Engineering, Yazd University, Yazd 8174848351, Iran;
- Medical Nanotechnology and Tissue Engineering Research Center, Yazd Reproductive Sciences Institute, Shahid Sadoughi University of Medical Sciences, Yazd 8916877391, Iran
| | - Soulmaz Sarkari
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran;
| | - Hamidreza Tolabi
- New Technologies Research Center (NTRC), Amirkabir University of Technology, Tehran 158754413, Iran;
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 158754413, Iran
| | - Farnaz Ghorbani
- Institute of Biomaterials, Department of Material Science and Engineering, University of Erlangen-Nuremberg, Cauerstraße 6, 91058 Erlangen, Germany
| | - Behafarid Ghalandari
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| |
Collapse
|
15
|
Maritan M, Autin L, Karr J, Covert MW, Olson AJ, Goodsell DS. Building Structural Models of a Whole Mycoplasma Cell. J Mol Biol 2022; 434:167351. [PMID: 34774566 PMCID: PMC8752489 DOI: 10.1016/j.jmb.2021.167351] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 02/01/2023]
Abstract
Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.
Collapse
Affiliation(s)
- Martina Maritan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/MartinaMaritan
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/grinche
| | - Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA; RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
| |
Collapse
|
16
|
Laine E, Eismann S, Elofsson A, Grudinin S. Protein sequence-to-structure learning: Is this the end(-to-end revolution)? Proteins 2021; 89:1770-1786. [PMID: 34519095 DOI: 10.1002/prot.26235] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/16/2021] [Accepted: 09/03/2021] [Indexed: 01/08/2023]
Abstract
The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.
Collapse
Affiliation(s)
- Elodie Laine
- Sorbonne Université, CNRS, IBPS, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), Paris, France
| | - Stephan Eismann
- Department of Computer Science and Applied Physics, Stanford University, Stanford, California, USA
| | - Arne Elofsson
- Department of Biochemistry and Biophysics and Science for Life Laboratory, Stockholm University, Solna, Sweden
| | - Sergei Grudinin
- Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, Grenoble, France
| |
Collapse
|
17
|
Dapkūnas J, Olechnovič K, Venclovas Č. Modeling of protein complexes in CASP14 with emphasis on the interaction interface prediction. Proteins 2021; 89:1834-1843. [PMID: 34176161 PMCID: PMC9292421 DOI: 10.1002/prot.26167] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 01/08/2023]
Abstract
The goal of CASP experiments is to monitor the progress in the protein structure prediction field. During the 14th CASP edition we aimed to test our capabilities of predicting structures of protein complexes. Our protocol for modeling protein assemblies included both template‐based modeling and free docking. Structural templates were identified using sensitive sequence‐based searches. If sequence‐based searches failed, we performed structure‐based template searches using selected CASP server models. In the absence of reliable templates we applied free docking starting from monomers generated by CASP servers. We evaluated and ranked models of protein complexes using an improved version of our protein structure quality assessment method, VoroMQA, taking into account both interaction interface and global structure scores. If reliable templates could be identified, generally accurate models of protein assemblies were generated with the exception of an antibody‐antigen interaction. The success of free docking mainly depended on the accuracy of initial subunit models and on the scoring of docking solutions. To put our overall results in perspective, we analyzed our performance in the context of other CASP groups. Although the subunits in our assembly models often were not of the top quality, these models had, overall, the best‐predicted intersubunit interfaces according to several accuracy measures. We attribute our relative success primarily to the emphasis on the interaction interface when modeling and scoring.
Collapse
Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| |
Collapse
|
18
|
Brysbaert G, Lensink MF. Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein–Protein Binding. FRONTIERS IN BIOINFORMATICS 2021; 1:684970. [PMID: 36303777 PMCID: PMC9581030 DOI: 10.3389/fbinf.2021.684970] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/17/2021] [Indexed: 12/21/2022] Open
Abstract
Residue interaction networks (RINs) describe a protein structure as a network of interacting residues. Central nodes in these networks, identified by centrality analyses, highlight those residues that play a role in the structure and function of the protein. However, little is known about the capability of such analyses to identify residues involved in the formation of macromolecular complexes. Here, we performed six different centrality measures on the RINs generated from the complexes of the SKEMPI 2 database of changes in protein–protein binding upon mutation in order to evaluate the capability of each of these measures to identify major binding residues. The analyses were performed with and without the crystallographic water molecules, in addition to the protein residues. We also investigated the use of a weight factor based on the inter-residue distances to improve the detection of these residues. We show that for the identification of major binding residues, closeness, degree, and PageRank result in good precision, whereas betweenness, eigenvector, and residue centrality analyses give a higher sensitivity. Including water in the analysis improves the sensitivity of all measures without losing precision. Applying weights only slightly raises the sensitivity of eigenvector centrality analysis. We finally show that a combination of multiple centrality analyses is the optimal approach to identify residues that play a role in protein–protein interaction.
Collapse
|
19
|
Igashov I, Olechnovič L, Kadukova M, Venclovas Č, Grudinin S. VoroCNN: Deep convolutional neural network built on 3D Voronoi tessellation of protein structures. Bioinformatics 2021; 37:2332-2339. [PMID: 33620450 DOI: 10.1093/bioinformatics/btab118] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 01/08/2021] [Accepted: 02/22/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Effective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. RESULTS For the first time, we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows us to efficiently introduce both convolution and pooling operations and train the network in an end-to-end fashion without precomputed descriptors. The resultant model, VoroCNN, predicts local qualities of 3D protein folds. The prediction results are competitive to state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in recognition of protein binding interfaces. AVAILABILITY The model, data, and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Ilia Igashov
- Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France.,Moscow Institute of Physics and Technology, 141701 Dolgoprudniy, Russia
| | - Liment Olechnovič
- Institute of Biotechnology Life Sciences Center Vilnius University, Saulėtekio 7, Vilnius, LT 10257, Lithuania
| | - Maria Kadukova
- Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France.,Moscow Institute of Physics and Technology, 141701 Dolgoprudniy, Russia
| | - Česlovas Venclovas
- Institute of Biotechnology Life Sciences Center Vilnius University, Saulėtekio 7, Vilnius, LT 10257, Lithuania
| | - Sergei Grudinin
- Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
| |
Collapse
|
20
|
Aguirre-Plans J, Meseguer A, Molina-Fernandez R, Marín-López MA, Jumde G, Casanova K, Bonet J, Fornes O, Fernandez-Fuentes N, Oliva B. SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions. BMC Bioinformatics 2021; 22:4. [PMID: 33407073 PMCID: PMC7788957 DOI: 10.1186/s12859-020-03770-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 09/20/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. RESULTS Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. CONCLUSIONS While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. SERVER ADDRESS: https://sbi.upf.edu/spserver/ .
Collapse
Grants
- BIO2017-85329-R (FEDER,UE) Ministerio de Economía, Industria y Competitividad, Gobierno de España
- BIO2017-83591-R(FEDER,UE Ministerio de Economía, Industria y Competitividad, Gobierno de España
- RYC-2015-17519 Ministerio de Economía, Industria y Competitividad, Gobierno de España
- MDM-2014-0370 Ministerio de Economía, Industria y Competitividad, Gobierno de España
- FI Agència de Gestió d'Ajuts Universitaris i de Recerca
- 2017 SGR 01020 Agència de Gestió d'Ajuts Universitaris i de Recerca
- PT13/0001/0023 Instituto de Salud Carlos III
- Agència de Gestió d’Ajuts Universitaris i de Recerca
Collapse
Affiliation(s)
- Joaquim Aguirre-Plans
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Alberto Meseguer
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Ruben Molina-Fernandez
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Manuel Alejandro Marín-López
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Gaurav Jumde
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Kevin Casanova
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Jaume Bonet
- Laboratory of Protein Design and Immuno-Enginneering, School of Engineering, Ecole Polytechnique Federale de Lausanne, 1015, Lausanne, Vaud, Switzerland
| | - Oriol Fornes
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Narcis Fernandez-Fuentes
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Vic 08500, Barcelona, Catalonia, Spain
- Institute of Biological, Environ-Mental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK
| | - Baldo Oliva
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain.
| |
Collapse
|
21
|
Intermolecular Interactions in Crystal Structures of Imatinib-Containing Compounds. Int J Mol Sci 2020; 21:ijms21238970. [PMID: 33255944 PMCID: PMC7731260 DOI: 10.3390/ijms21238970] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/23/2020] [Accepted: 11/24/2020] [Indexed: 02/07/2023] Open
Abstract
Imatinib, one of the most used therapeutic agents to treat leukemia, is an inhibitor that specifically blocks the activity of tyrosine kinases. The molecule of imatinib is flexible and contains several functional groups able to take part in H-bonding and hydrophobic interactions. Analysis of molecular conformations for this drug was carried out using density functional theory calculations of rotation potentials along single bonds and by analyzing crystal structures of imatinib-containing compounds taken from the Cambridge Structural Database and the Protein Data Bank. Rotation along the N-C bond in the region of the amide group was found to be the reason for two relatively stable molecular conformations, an extended and a folded one. The role of various types of intermolecular interactions in stabilization of the particular molecular conformation was studied in terms of (i) the likelihood of H-bond formation, and (ii) their contribution to the Voronoi molecular surface. It is shown that experimentally observed hydrogen bonds are in accord with the likelihood of their formation. The number of H-bonds in ligand-receptor complexes surpasses that in imatinib salts due to the large number of donors and acceptors of H-bonding within the binding pocket of tyrosine kinases. Contribution of hydrophilic intermolecular interactions to the Voronoi molecular surface is similar for both conformations, while π...π stacking is more typical for the folded conformation of imatinib.
Collapse
|
22
|
Hameduh T, Haddad Y, Adam V, Heger Z. Homology modeling in the time of collective and artificial intelligence. Comput Struct Biotechnol J 2020; 18:3494-3506. [PMID: 33304450 PMCID: PMC7695898 DOI: 10.1016/j.csbj.2020.11.007] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/04/2020] [Accepted: 11/04/2020] [Indexed: 12/12/2022] Open
Abstract
Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.
Collapse
Affiliation(s)
- Tareq Hameduh
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
| | - Yazan Haddad
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Vojtech Adam
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Zbynek Heger
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| |
Collapse
|
23
|
Carriles AA, Mills A, Muñoz-Alonso MJ, Gutiérrez D, Domínguez JM, Hermoso JA, Gago F. Structural Cues for Understanding eEF1A2 Moonlighting. Chembiochem 2020; 22:374-391. [PMID: 32875694 DOI: 10.1002/cbic.202000516] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/01/2020] [Indexed: 12/16/2022]
Abstract
Spontaneous mutations in the EEF1A2 gene cause epilepsy and severe neurological disabilities in children. The crystal structure of eEF1A2 protein purified from rabbit skeletal muscle reveals a post-translationally modified dimer that provides information about the sites of interaction with numerous binding partners, including itself, and maps these mutations onto the dimer and tetramer interfaces. The spatial locations of the side chain carboxylates of Glu301 and Glu374, to which phosphatidylethanolamine is uniquely attached via an amide bond, define the anchoring points of eEF1A2 to cellular membranes and interorganellar membrane contact sites. Additional bioinformatic and molecular modeling results provide novel structural insight into the demonstrated binding of eEF1A2 to SH3 domains, the common MAPK docking groove, filamentous actin, and phosphatidylinositol-4 kinase IIIβ. In this new light, the role of eEF1A2 as an ancient, multifaceted, and articulated G protein at the crossroads of autophagy, oncogenesis and viral replication appears very distant from the "canonical" one of delivering aminoacyl-tRNAs to the ribosome that has dominated the scene and much of the thinking for many decades.
Collapse
Affiliation(s)
- Alejandra A Carriles
- Department of Crystallography and Structural Biology, Institute of Physical-Chemistry "Rocasolano" CSIC, 28006, Madrid, Spain.,Biocrystallography Unit, Division of Immunology, Transplantation, and Infectious Diseases, IRCCS Scientific Institute San Raffaele, 20132, Milan, Italy
| | - Alberto Mills
- Department of Biomedical Sciences and "Unidad Asociada IQM-CSIC", School of Medicine and Health Sciences, University of Alcalá, 28805, Alcalá de Henares, Madrid, Spain
| | - María-José Muñoz-Alonso
- Department of Cell Biology and Pharmacogenomics, PharmaMar S.A.U., 28770, Colmenar Viejo, Madrid, Spain
| | - Dolores Gutiérrez
- Proteomics Unit, Faculty of Pharmacy, Complutense University, 28040, Madrid, Spain
| | - Juan M Domínguez
- Department of Cell Biology and Pharmacogenomics, PharmaMar S.A.U., 28770, Colmenar Viejo, Madrid, Spain
| | - Juan A Hermoso
- Department of Crystallography and Structural Biology, Institute of Physical-Chemistry "Rocasolano" CSIC, 28006, Madrid, Spain
| | - Federico Gago
- Department of Biomedical Sciences and "Unidad Asociada IQM-CSIC", School of Medicine and Health Sciences, University of Alcalá, 28805, Alcalá de Henares, Madrid, Spain
| |
Collapse
|
24
|
Liu T, Wang Z. MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials. BMC Bioinformatics 2020; 21:246. [PMID: 32631256 PMCID: PMC7336608 DOI: 10.1186/s12859-020-3383-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 01/22/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Protein model quality assessment (QA) is an essential procedure in protein structure prediction. QA methods can predict the qualities of protein models and identify good models from decoys. Clustering-based methods need a certain number of models as input. However, if a pool of models are not available, methods that only need a single model as input are indispensable. RESULTS We developed MASS, a QA method to predict the global qualities of individual protein models using random forests and various novel energy functions. We designed six novel energy functions or statistical potentials that can capture the structural characteristics of a protein model, which can also be used in other protein-related bioinformatics research. MASS potentials demonstrated higher importance than the energy functions of RWplus, GOAP, DFIRE and Rosetta when the scores they generated are used as machine learning features. MASS outperforms almost all of the four CASP11 top-performing single-model methods for global quality assessment in terms of all of the four evaluation criteria officially used by CASP, which measure the abilities to assign relative and absolute scores, identify the best model from decoys, and distinguish between good and bad models. MASS has also achieved comparable performances with the leading QA methods in CASP12 and CASP13. CONCLUSIONS MASS and the source code for all MASS potentials are publicly available at http://dna.cs.miami.edu/MASS/ .
Collapse
Affiliation(s)
- Tong Liu
- Department of Computer Science, University of Miami, 1365 Memorial Drive, P.O. Box 248154, Coral Gables, FL, 33124, USA
| | - Zheng Wang
- Department of Computer Science, University of Miami, 1365 Memorial Drive, P.O. Box 248154, Coral Gables, FL, 33124, USA.
| |
Collapse
|
25
|
Dapkūnas J, Kairys V, Olechnovič K, Venclovas Č. Template-based modeling of diverse protein interactions in CAPRI rounds 38-45. Proteins 2019; 88:939-947. [PMID: 31697420 DOI: 10.1002/prot.25845] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Accepted: 11/03/2019] [Indexed: 11/09/2022]
Abstract
Structures of proteins complexed with other proteins, peptides, or ligands are essential for investigation of molecular mechanisms. However, the experimental structures of protein complexes of interest are often not available. Therefore, computational methods are widely used to predict these structures, and, of those methods, template-based modeling is the most successful. In the rounds 38-45 of the Critical Assessment of PRediction of Interactions (CAPRI), we applied template-based modeling for 9 of 11 protein-protein and protein-peptide interaction targets, resulting in medium and high-quality models for six targets. For the protein-oligosaccharide docking targets, we used constraints derived from template structures, and generated models of at least acceptable quality for most of the targets. Apparently, high flexibility of oligosaccharide molecules was the main cause preventing us from obtaining models of higher quality. We also participated in the CAPRI scoring challenge, the goal of which was to identify the highest quality models from a large pool of decoys. In this experiment, we tested VoroMQA, a scoring method based on interatomic contact areas. The results showed VoroMQA to be quite effective in scoring strongly binding and obligatory protein complexes, but less successful in the case of transient interactions. We extensively used manual intervention in both CAPRI modeling and scoring experiments. This oftentimes allowed us to select the correct templates from available alternatives and to limit the search space during the model scoring.
Collapse
Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Visvaldas Kairys
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| |
Collapse
|
26
|
Dapkūnas J, Olechnovič K, Venclovas Č. Structural modeling of protein complexes: Current capabilities and challenges. Proteins 2019; 87:1222-1232. [PMID: 31294859 DOI: 10.1002/prot.25774] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 06/21/2019] [Accepted: 07/06/2019] [Indexed: 12/27/2022]
Abstract
Proteins frequently interact with each other, and the knowledge of structures of the corresponding protein complexes is necessary to understand how they function. Computational methods are increasingly used to provide structural models of protein complexes. Not surprisingly, community-wide Critical Assessment of protein Structure Prediction (CASP) experiments have recently started monitoring the progress in this research area. We participated in CASP13 with the aim to evaluate our current capabilities in modeling of protein complexes and to gain a better understanding of factors that exert the largest impact on these capabilities. To model protein complexes in CASP13, we applied template-based modeling, free docking and hybrid techniques that enabled us to generate models of the topmost quality for 27 of 42 multimers. If templates for protein complexes could be identified, we modeled the structures with reasonable accuracy by straightforward homology modeling. If only partial templates were available, it was nevertheless possible to predict the interaction interfaces correctly or to generate acceptable models for protein complexes by combining template-based modeling with docking. If no templates were available, we used rigid-body docking with limited success. However, in some free docking models, despite the incorrect subunit orientation and missed interface contacts, the approximate location of protein binding sites was identified correctly. Apparently, our overall performance in docking was limited by the quality of monomer models and by the imperfection of scoring methods. The impact of human intervention on our results in modeling of protein complexes was significant indicating the need for improvements of automatic methods.
Collapse
Affiliation(s)
- Justas Dapkūnas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Kliment Olechnovič
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Česlovas Venclovas
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| |
Collapse
|