1
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Bou Dagher L, Madern D, Malbos P, Brochier-Armanet C. Persistent homology reveals strong phylogenetic signal in 3D protein structures. PNAS NEXUS 2024; 3:pgae158. [PMID: 38689707 PMCID: PMC11058471 DOI: 10.1093/pnasnexus/pgae158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 04/01/2024] [Indexed: 05/02/2024]
Abstract
Changes that occur in proteins over time provide a phylogenetic signal that can be used to decipher their evolutionary history and the relationships between organisms. Sequence comparison is the most common way to access this phylogenetic signal, while those based on 3D structure comparisons are still in their infancy. In this study, we propose an effective approach based on Persistent Homology Theory (PH) to extract the phylogenetic information contained in protein structures. PH provides efficient and robust algorithms for extracting and comparing geometric features from noisy datasets at different spatial resolutions. PH has a growing number of applications in the life sciences, including the study of proteins (e.g. classification, folding). However, it has never been used to study the phylogenetic signal they may contain. Here, using 518 protein families, representing 22,940 protein sequences and structures, from 10 major taxonomic groups, we show that distances calculated with PH from protein structures correlate strongly with phylogenetic distances calculated from protein sequences, at both small and large evolutionary scales. We test several methods for calculating PH distances and propose some refinements to improve their relevance for addressing evolutionary questions. This work opens up new perspectives in evolutionary biology by proposing an efficient way to access the phylogenetic signal contained in protein structures, as well as future developments of topological analysis in the life sciences.
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Affiliation(s)
- Léa Bou Dagher
- Université Claude Bernard Lyon 1, CNRS, VetAgro Sup, Laboratoire de Biométrie et BiologieÉvolutive, UMR5558, F-69622 Villeurbanne, France
- Université Claude Bernard Lyon 1, CNRS, Institut Camille Jordan, UMR5208, F-69622 Villeurbanne, France
- Université Libanaise, Laboratoire de Mathématiques, École Doctorale en Science et Technologie, PO BOX 5 Hadath, Liban
| | - Dominique Madern
- University Grenoble Alpes, CEA, CNRS, IBS, 38000 Grenoble, France
| | - Philippe Malbos
- Université Claude Bernard Lyon 1, CNRS, Institut Camille Jordan, UMR5208, F-69622 Villeurbanne, France
| | - Céline Brochier-Armanet
- Université Claude Bernard Lyon 1, CNRS, VetAgro Sup, Laboratoire de Biométrie et BiologieÉvolutive, UMR5558, F-69622 Villeurbanne, France
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2
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Durairaj J, de Ridder D, van Dijk AD. Beyond sequence: Structure-based machine learning. Comput Struct Biotechnol J 2022; 21:630-643. [PMID: 36659927 PMCID: PMC9826903 DOI: 10.1016/j.csbj.2022.12.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 12/21/2022] [Accepted: 12/21/2022] [Indexed: 12/31/2022] Open
Abstract
Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.
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Affiliation(s)
- Janani Durairaj
- Biozentrum, University of Basel, Basel, Switzerland
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
| | - Aalt D.J. van Dijk
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen, the Netherlands
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3
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Timonina DS, Suplatov DA. Analysis of Multiple Protein Alignments Using 3D-Structural Information on the Orientation of Amino Acid Side-Chains. Mol Biol 2022. [DOI: 10.1134/s0026893322040136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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4
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Loop 422–437 in NanA from Streptococcus pneumoniae plays the role of an active site lid and is associated with allosteric regulation. Comput Biol Med 2022; 144:105290. [DOI: 10.1016/j.compbiomed.2022.105290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/20/2022] [Accepted: 02/01/2022] [Indexed: 11/03/2022]
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5
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Xia C, Feng SH, Xia Y, Pan X, Shen HB. Fast protein structure comparison through effective representation learning with contrastive graph neural networks. PLoS Comput Biol 2022; 18:e1009986. [PMID: 35324898 PMCID: PMC8982879 DOI: 10.1371/journal.pcbi.1009986] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 04/05/2022] [Accepted: 03/03/2022] [Indexed: 12/03/2022] Open
Abstract
Protein structure alignment algorithms are often time-consuming, resulting in challenges for large-scale protein structure similarity-based retrieval. There is an urgent need for more efficient structure comparison approaches as the number of protein structures increases rapidly. In this paper, we propose an effective graph-based protein structure representation learning method, GraSR, for fast and accurate structure comparison. In GraSR, a graph is constructed based on the intra-residue distance derived from the tertiary structure. Then, deep graph neural networks (GNNs) with a short-cut connection learn graph representations of the tertiary structures under a contrastive learning framework. To further improve GraSR, a novel dynamic training data partition strategy and length-scaling cosine distance are introduced. We objectively evaluate our method GraSR on SCOPe v2.07 and a new released independent test set from PDB database with a designed comprehensive performance metric. Compared with other state-of-the-art methods, GraSR achieves about 7%-10% improvement on two benchmark datasets. GraSR is also much faster than alignment-based methods. We dig into the model and observe that the superiority of GraSR is mainly brought by the learned discriminative residue-level and global descriptors. The web-server and source code of GraSR are freely available at www.csbio.sjtu.edu.cn/bioinf/GraSR/ for academic use. The size and shape of protein structures vary considerably. Accurate protein structure comparison usually relies on structure alignment algorithms. However, superimposing two protein structures is relatively time-consuming, which makes it inappropriate for large-scale protein structure retrieval. Alignment-free algorithms are proposed for efficient protein structure comparison over the last few decades. These algorithms first transform the coordinates of atoms in two proteins to fixed-length vectors. Then, the comparison can be done by measuring the distance or similarity between two vectors, which is much faster than alignment. In this study, we propose a novel protein structure representation method for efficient structure comparison. Compared with other state-of-the-art alignment-free methods, our method achieves better performance on both ranking and multi-class classification tasks due to the powerful representation ability of deep graph neural networks. We dig into the model and observe that the superiority of our method is mainly brought by the learned discriminative residue-level and global descriptors.
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Affiliation(s)
- Chunqiu Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
| | - Shi-Hao Feng
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
| | - Ying Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
- * E-mail: (XP); (HS)
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
- * E-mail: (XP); (HS)
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6
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Shegay MV, Švedas VK, Voevodin VV, Suplatov DA, Popova NN. Guide tree optimization with genetic algorithm to improve multiple protein 3D-structure alignment. Bioinformatics 2022; 38:985-989. [PMID: 34849594 DOI: 10.1093/bioinformatics/btab798] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/23/2021] [Accepted: 11/19/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION With the increasing availability of 3D-data, the focus of comparative bioinformatic analysis is shifting from protein sequence alignments toward more content-rich 3D-alignments. This raises the need for new ways to improve the accuracy of 3D-superimposition. RESULTS We proposed guide tree optimization with genetic algorithm (GA) as a universal tool to improve the alignment quality of multiple protein 3D-structures systematically. As a proof of concept, we implemented the suggested GA-based approach in popular Matt and Caretta multiple protein 3D-structure alignment (M3DSA) algorithms, leading to a statistically significant improvement of the TM-score quality indicator by up to 220-1523% on 'SABmark Superfamilies' (in 49-77% of cases) and 'SABmark Twilight' (in 59-80% of cases) datasets. The observed improvement in collections of distant homologies highlights the potentials of GA to optimize 3D-alignments of diverse protein superfamilies as one plausible tool to study the structure-function relationship. AVAILABILITY AND IMPLEMENTATION The source codes of patched gaCaretta and gaMatt programs are available open-access at https://github.com/n-canter/gamaps. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Maksim V Shegay
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Vytas K Švedas
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia.,Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Vladimir V Voevodin
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia.,Research Computing Center, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Dmitry A Suplatov
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
| | - Nina N Popova
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Vorobjev Hills, Moscow 119991, Russia
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7
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Abstract
Protein alignment finds its application in refining results of sequence alignment and understanding protein function. A previous study aligned single molecules, making use of the minimization of sums of the squares of eigenvalues, obtained for the antisymmetric Cartesian coordinate distance matrices Dx and Dy. This is used in our program to search for similarities between amino acids by comparing the sums of the squares of eigenvalues associated with the Dx, Dy, and Dz distance matrices. These matrices are obtained by removing atoms that could lead to low similarity. Candidates are aligned, and trilateration is used to attach all previously striped atoms. A TM-score is the scoring function that chooses the best alignment from supplied candidates. Twenty essential amino acids that take many forms in nature are selected for comparison. The correct alignment is taken into account most of the time by the alignment algorithm. It was numerically detected by the TM-score 70% of the time, on average, and 15% more cases with close scores can be easily distinguished by human observation.
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8
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Kamiński K, Ludwiczak J, Jasiński M, Bukala A, Madaj R, Szczepaniak K, Dunin-Horkawicz S. Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins. Brief Bioinform 2021; 23:6375059. [PMID: 34571541 PMCID: PMC8769691 DOI: 10.1093/bib/bbab371] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/04/2021] [Accepted: 08/22/2021] [Indexed: 11/15/2022] Open
Abstract
The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-adenosylmethionine, the oxidoreductases, depending on the family, bind nicotinamide (nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate) or flavin-based (flavin adenine dinucleotide) cofactors. In this study, we showed that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the βαβ motif. A benchmark on two independent test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent.uw.edu.pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox.
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Affiliation(s)
- Kamil Kamiński
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Jan Ludwiczak
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland.,Laboratory of Bioinformatics, Nencki Institute of Experimental Biology, Pasteura 3, 02-093 Warsaw, Poland
| | - Maciej Jasiński
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Adriana Bukala
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Rafal Madaj
- Centre of Molecular and Macromolecular Studies, Polish Academy of Sciences, Sienkiewicza 112, 90-363, Lodz, Poland
| | - Krzysztof Szczepaniak
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
| | - Stanisław Dunin-Horkawicz
- Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland
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9
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Suplatov D, Sharapova Y, Švedas V. Mustguseal and Sister Web-Methods: A Practical Guide to Bioinformatic Analysis of Protein Superfamilies. Methods Mol Biol 2021; 2231:179-200. [PMID: 33289894 DOI: 10.1007/978-1-0716-1036-7_12] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Bioinformatic analysis of functionally diverse superfamilies can help to study the structure-function relationship in proteins, but represents a methodological challenge. The Mustguseal web-server can build large structure-guided sequence alignments of thousands of homologs that cover all currently available sequence variants within a common structural fold. The input to the method is a PDB code of the query protein, which represents the protein superfamily of interest. The collection and subsequent alignment of protein sequences and structures is fully automated and driven by the particular choice of parameters. Four integrated sister web-methods-the Zebra, pocketZebra, visualCMAT, and Yosshi-are available to further analyze the resulting superimposition and identify conserved, subfamily-specific, and co-evolving residues, as well as to classify and study disulfide bonds in protein superfamilies. The integration of these web-based bioinformatic tools provides an out-of-the-box easy-to-use solution, first of its kind, to study protein function and regulation and design improved enzyme variants for practical applications and selective ligands to modulate their functional properties. In this chapter, we provide a step-by-step protocol for a comprehensive bioinformatic analysis of a protein superfamily using a web-browser as the main tool and notes on selecting the appropriate values for the key algorithm parameters depending on your research objective. The web-servers are freely available to all users at https://biokinet.belozersky.msu.ru/m-platform with no login requirement.
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Affiliation(s)
- Dmitry Suplatov
- Belozersky Institute of Physicochemical Biology, Lomonosov Moscow State University, Moscow, Russia.
| | - Yana Sharapova
- Belozersky Institute of Physicochemical Biology, Lomonosov Moscow State University, Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Vytas Švedas
- Belozersky Institute of Physicochemical Biology, Lomonosov Moscow State University, Moscow, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
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10
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Mousavi R, Konuru SH, Lobo D. Inference of dynamic spatial GRN models with multi-GPU evolutionary computation. Brief Bioinform 2021; 22:6217729. [PMID: 33834216 DOI: 10.1093/bib/bbab104] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 02/15/2021] [Accepted: 03/09/2021] [Indexed: 02/06/2023] Open
Abstract
Reverse engineering mechanistic gene regulatory network (GRN) models with a specific dynamic spatial behavior is an inverse problem without analytical solutions in general. Instead, heuristic machine learning algorithms have been proposed to infer the structure and parameters of a system of equations able to recapitulate a given gene expression pattern. However, these algorithms are computationally intensive as they need to simulate millions of candidate models, which limits their applicability and requires high computational resources. Graphics processing unit (GPU) computing is an affordable alternative for accelerating large-scale scientific computation, yet no method is currently available to exploit GPU technology for the reverse engineering of mechanistic GRNs from spatial phenotypes. Here we present an efficient methodology to parallelize evolutionary algorithms using GPU computing for the inference of mechanistic GRNs that can develop a given gene expression pattern in a multicellular tissue area or cell culture. The proposed approach is based on multi-CPU threads running the lightweight crossover, mutation and selection operators and launching GPU kernels asynchronously. Kernels can run in parallel in a single or multiple GPUs and each kernel simulates and scores the error of a model using the thread parallelism of the GPU. We tested this methodology for the inference of spatiotemporal mechanistic gene regulatory networks (GRNs)-including topology and parameters-that can develop a given 2D gene expression pattern. The results show a 700-fold speedup with respect to a single CPU implementation. This approach can streamline the extraction of knowledge from biological and medical datasets and accelerate the automatic design of GRNs for synthetic biology applications.
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Affiliation(s)
- Reza Mousavi
- Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA
| | - Sri Harsha Konuru
- Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA
| | - Daniel Lobo
- Department of Biological Sciences at the University of Maryland, Baltimore, MD 21250, USA
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11
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Timonina D, Sharapova Y, Švedas V, Suplatov D. Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies. Comput Struct Biotechnol J 2021; 19:1302-1311. [PMID: 33738079 PMCID: PMC7933735 DOI: 10.1016/j.csbj.2021.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 02/07/2023] Open
Abstract
Local 3D-structural differences in homologous proteins contribute to functional diversity observed in a superfamily, but so far received little attention as bioinformatic analysis was usually carried out at the level of amino acid sequences. We have developed Zebra3D - the first-of-its-kind bioinformatic software for systematic analysis of 3D-alignments of protein families using machine learning. The new tool identifies subfamily-specific regions (SSRs) - patterns of local 3D-structure (i.e. single residues, loops, or secondary structure fragments) that are spatially equivalent within families/subfamilies, but are different among them, and thus can be associated with functional diversity and function-related conformational plasticity. Bioinformatic analysis of protein superfamilies by Zebra3D can be used to study 3D-determinants of catalytic activity and specific accommodation of ligands, help to prepare focused libraries for directed evolution or assist development of chimeric enzymes with novel properties by exchange of equivalent regions between homologs, and to characterize plasticity in binding sites. A companion Mustguseal web-server is available to automatically construct a 3D-alignment of functionally diverse proteins, thus reducing the minimal input required to operate Zebra3D to a single PDB code. The Zebra3D + Mustguseal combined approach provides the opportunity to systematically explore the value of SSRs in superfamilies and to use this information for protein design and drug discovery. The software is available open-access at https://biokinet.belozersky.msu.ru/Zebra3D.
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Affiliation(s)
- Daria Timonina
- Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Lenin Hills 1-73, Moscow 119234, Russia
| | - Yana Sharapova
- Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Lenin Hills 1-73, Moscow 119234, Russia
- Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Lenin Hills 1-73, Moscow 119234, Russia
| | - Vytas Švedas
- Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Lenin Hills 1-73, Moscow 119234, Russia
- Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Lenin Hills 1-73, Moscow 119234, Russia
| | - Dmitry Suplatov
- Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Lenin Hills 1-73, Moscow 119234, Russia
- Corresponding author.
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12
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Sharapova Y, Švedas V, Suplatov D. Catalytic and lectin domains in neuraminidase A from Streptococcus pneumoniae are capable of an intermolecular assembly: Implications for biofilm formation. FEBS J 2020; 288:3217-3230. [PMID: 33108702 DOI: 10.1111/febs.15610] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 09/25/2020] [Accepted: 10/19/2020] [Indexed: 01/14/2023]
Abstract
Neuraminidase A from Streptococcus pneumoniae (NanA) is a cell wall-bound modular enzyme containing one lectin and one catalytic domain. Unlike homologous NanB and NanC expressed by the same bacterium, the two domains within one NanA molecule do not form a stable interaction and are spatially separated by a 16-amino acid-long flexible linker. In this work, the ability of NanA to form intermolecular assemblies was characterized using the methods of molecular modeling and bioinformatic analysis based on crystallographic data and by bringing together previously published experimental data. It was concluded that two catalytic domains, as well as one catalytic and one lectin domain, originating from two cell wall-bound NanA molecules, can interact through a previously uncharacterized interdomain interface to form complexes stabilized by a network of intermolecular hydrogen bonds and salt bridges. Supercomputer modeling strongly indicated that artocarpin, an earlier experimentally discovered inhibitor of the pneumococcal biofilm formation, is able to bind to a site located in the catalytic domain of one NanA entity and prevent its interaction with the lectin or catalytic domain of another NanA entity, thus directly precluding the generation of intermolecular assemblies. The revealed structural adaptation is discussed as one plausible mechanism of noncatalytic participation of this potentially key pathogenicity enzyme in pneumococcal biofilm formation.
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Affiliation(s)
- Yana Sharapova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia.,Belozersky Institute of Physicochemical Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Vytas Švedas
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia.,Belozersky Institute of Physicochemical Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Dmitry Suplatov
- Belozersky Institute of Physicochemical Biology, Lomonosov Moscow State University, Moscow, Russia
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13
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Akdel M, Durairaj J, de Ridder D, van Dijk ADJ. Caretta - A multiple protein structure alignment and feature extraction suite. Comput Struct Biotechnol J 2020; 18:981-992. [PMID: 32368333 PMCID: PMC7186369 DOI: 10.1016/j.csbj.2020.03.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 02/01/2020] [Accepted: 03/13/2020] [Indexed: 02/06/2023] Open
Abstract
The vast number of protein structures currently available opens exciting opportunities for machine learning on proteins, aimed at predicting and understanding functional properties. In particular, in combination with homology modelling, it is now possible to not only use sequence features as input for machine learning, but also structure features. However, in order to do so, robust multiple structure alignments are imperative. Here we present Caretta, a multiple structure alignment suite meant for homologous but sequentially divergent protein families which consistently returns accurate alignments with a higher coverage than current state-of-the-art tools. Caretta is available as a GUI and command-line application and additionally outputs an aligned structure feature matrix for a given set of input structures, which can readily be used in downstream steps for supervised or unsupervised machine learning. We show Caretta’s performance on two benchmark datasets, and present an example application of Caretta in predicting the conformational state of cyclin-dependent kinases.
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Affiliation(s)
- Mehmet Akdel
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, The Netherlands
| | - Janani Durairaj
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, The Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, The Netherlands
| | - Aalt D J van Dijk
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, The Netherlands.,Mathematical and Statistical Methods - Biometris, Department of Plant Sciences, Wageningen University and Research, The Netherlands
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