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Persistent homology reveals strong phylogenetic signal in 3d protein structures

PNAS NEXUS 2024年第4期3卷 pgae158页

作者： Bou dagher, Lea Madern, dominique Malbos, Philippe Brochier-Armanet, Celine Univ Claude Bernard Lyon 1 CNRS VetAgro Sup Lab Biometrie & Biol Evolut F-69622 Villeurbanne France Univ Claude Bernard Lyon 1 CNRS UMR5208 Inst Camille Jordan F-69622 Villeurbanne France Univ Libanaise Lab Math Ecole Doctorale Sci & Technol POB 5 Hadath Lebanon Univ Grenoble Alpes CEA CNRS IBS F-38000 Grenoble France

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.

关键词： topological data analysis phylogenetics persistent homology protein 3d structure

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Characterizing Secretion System Effector proteins With structure-Aware Graph Neural Networks and Pre-Trained Language Models

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IEEE JOURNAL OF BIOMEdICAL ANd HEALTH INFORMATICS 2024年第9期28卷 5649-5657页

作者： Ran, Zixu Wang, Cong Sun, Heyun Pan, Shirui Li, Fuyi Northwest A&F Univ Coll Informat Engn Xianyang 712100 Peoples R China Univ Adelaide South Australian immunoGEN Canc Inst SAiGENCI Adelaide SA 5000 Australia Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4222 Australia

The Type III Secretion Systems (T3SSs) play a pivotal role in host-pathogen interactions by mediating the secretion of type III secretion system effectors (T3SEs) into host cells. These T3SEs mimic host cell protein functions, influencing interactions between Gram-negative bacterial pathogens and their hosts. Identifying T3SEs is essential in biomedical research for comprehending bacterial pathogenesis and its implications on human cells. This study presents EdIFIER, a novel multi-channel model designed for accurate T3SE prediction. It incorporates a graph structural channel, utilizing graph convolutional networks (GCN) to capture protein 3d structural features and a sequence channel based on the proteinBERT pre-trained model to extract the sequence context features of T3SEs. Rigorous benchmarking tests, including ablation studies and comparative analysis, validate that EdIFIER outperforms current state-of-the-art tools in T3SE prediction. To enhance EdIFIER's accessibility to the broader scientific community, we developed a webserver that is publicly accessible at http://***/. We anticipate EdIFIER will contribute to the field by providing reliable T3SE predictions, thereby advancing our understanding of host-pathogen dynamics.

关键词： proteins Feature extraction Three-dimensional displays Amino acids Solvents Bioinformatics Benchmark testing deep learning host-pathogen interaction protein 3d structure secreted protein

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Rinmaker: a fast, versatile and reliable tool to determine residue interaction networks in proteins

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BMC BIOINFORMATICS 2023年第1期24卷 1-14页

作者： Spano, Alvise Fanton, Lorenzo Pizzolato, davide Moi, Jacopo Vinci, Francesco Pesce, Alberto Foumthuim, Cedrix J. dongmo Giacometti, Achille Simeoni, Marta Univ Ca Foscari Venice Dept Environm Sci Comp Sci & Stat Via Torino 155 I-30172 Venice Italy Univ Ca Foscari Venice Dept Mol Sci & Nanosyst Via Torino 155 I-30172 Venice Italy European Ctr Living Technol ECLT Dorsoduro 3911 I-30123 Venice Italy

Background: Residue Interaction Networks (RINs) map the crystallographic description of a protein into a graph, where amino acids are represented as nodes and noncovalent bonds as edges. determination and visualization of a protein as a RIN provides insights on the topological properties (and hence their related biological functions) of large proteins without dealing with the full complexity of the three-dimensional description, and hence it represents an invaluable tool of modern bioinformatics. Results: We present RINmaker, a fast, flexible, and powerful tool for determining and visualizing RINs that include all standard non-covalent interactions. RINmaker is offered as a cross-platform and open source software that can be used either as a command-line tool or through a web application or a web API service. We benchmark its efficiency against the main alternatives and provide explicit tests to show its performance and its correctness. Conclusions: RINmaker is designed to be fully customizable, from a simple and handy support for experimental research to a sophisticated computational tool that can be embedded into a large computational pipeline. Hence, it paves the way to bridge the gap between data-driven/machine learning approaches and numerical simulations of simple, physically motivated, models.

关键词： protein 3d structure Residue interaction network (RIN) Non-covalent bonds

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deePROG: deep Attention-Based Model for diseased Gene Prognosis by Fusing Multi-Omics data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY ANd BIOINFORMATICS 2022年第5期19卷 2770-2781页

作者： dutta, Pratik Patra, Aditya Prakash Saha, Sriparna Indian Inst Technol Dept Comp Sci & Engn Patna 801103 Bihar India

An in-depth exploration of gene prognosis using different methodologies aids in understanding various biological regulations of genes in disease pathobiology and molecular functions. Interpreting gene functions at biological and molecular levels remains a daunting yet crucial task in domains such as drug design, personalized medicine, and next-generation diagnostics. Recent advancements in omics technologies have produced diverse heterogeneous genomic datasets like micro-array gene expression, miRNA expression, dNA sequence, 3d structures, which are significant resources for understanding the gene functions. In this paper, we propose a novel self-attention based deep multi-modal model, named deePROG, for the prognosis of disease affected genes based on heterogeneous omics data. We use three NCBI datasets covering three modalities, namely gene expression profile, the underlying dNA sequence, and the 3d protein structures. To extract useful features from each modality, we develop several context-specific deep learning models. Besides, we develop three attention-based deep bi-modal architectures along with deePROG to leverage the prognosis of the underlying biomedical data. We assess the performance of the models' in terms of computational assessment of function annotation (CAFA2) metrics. Moreover, we analyze the results in terms of receiver operating characteristics (ROC) curve in high-class imbalance data setting and perform statistical significance tests in terms of Welch's t-test. Experiment results show that deePROG significantly outperforms baseline models across in terms of performance metrics. The source code and all preprocessed datasets used in this study are available at https://***/duttaprat/deePROG.

关键词： Gene expression diseases Three-dimensional displays Prognostics and health management Computer architecture proteins Feature extraction Multi-modal architecture gene prognosis gene expression profile protein 3d structure nucleotide sequence attention mechanism deep learning

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Nanoparticles insert a three dimensional cavity structure of proteins for function inhibition: The Case of CeO2 and SARS-CoV-2

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NANO TOdAY 2024年 55卷

作者： Zhang, Guofang Wang, Xiaofeng Wang, Guocheng Suo, Xiaoman Qiu, Yu Luo, Rong -Hua Liu, Yingnan Li, Qi Luo, Wenhe Shi, Zhenzhen Liu, Fangfang Li, Zhongda Qu, Jing Li, Liang Bastus, Neus G. Himly, Martin Wang, Liming Liu, Feng-Liang Zheng, Yong-Tang Puntes, Victor Li, Yang Chinese Acad Sci Shenzhen Inst Adv Technol Lab Inflammat & Vaccines Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci Lab Immunol & Nanomed Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol China Italy Joint Lab Pharmacobiotechnol Med Immun Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Res Ctr Human Tissues & Organs Degenerat Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci Ctr Biosafety Mega Sci Kunming Inst ZoolKey Lab Anim Models & Human Dis Key Lab Bioact Peptides Yunnan ProvKIZ CUHK Joint Kunming 650223 Peoples R China Paris Lodron Univ Salzburg Dept Biosci & Med Biol A-5020 Salzburg Austria Loyola Univ Chicago Stritch Sch Med Dept Med Div HepatolProgram Expt & Theoret Modeling Maywood IL 60153 USA Southern Univ Sci & Technol Shenzhen 518055 Guangdong Peoples R China CSIC Instiut Catala Nanociencia Nanotecnol ICN2 Campus UAB Barcelona 08193 Spain Barcelona Inst Sci & Technol BIST Campus UAB Barcelona 08193 Spain CIBER Bioingn Biomat & Nanomed CIBER BBN Bellaterra Spain Vall dHebron Inst Recerca VHIR Barcelona 08035 Spain Chinese Acad Sci Inst High Energy Phys CAS Key Lab Biomed Effects Nanomat & Nanosafety Beijing 100049 Peoples R China Vall dHebron Inst Recerca VHIR Barcelona 08035 Spain Inst Catalana Recerca i Estudis Avancats ICREA Barcelona 08010 Spain

The selective interaction of nanomaterials with proteins for protein function suppression has been reported. However, whether the nanomaterials could be used to target a three-dimensional (3d) structure of proteins for the consequent function inhibition is not defined. When SARS-CoV-2 binds to the host cell surface ACE2 receptor, the spike protein trimer changes to an "Open State" which forms a 5 nm cavity structure, consequently exposing the receptor binding domain (RBd) for the following viral infection. We found that the 3 nm cerium oxide nanoparticles (CeO2@3) showed a better anti-SARS-CoV-2 effect than 30 nm cerium oxide nanoparticles (CeO2@30). We performed a series of experiments and demonstrated that the CeO2@3 could target the 5 nm spike protein trimer cavity and tightly bind with the RBd, thus effectively blocking the following virus-cell interaction and rendering CeO2@3 as an effective anti-viral agent. As all coronaviruses possess similar spike protein structures as homologous proteins, CeO2@3 can be used as a broad-sperm anti-coronavirus nanodrug candidate by targeting the spike protein 3d structure. This work, for the first time, demonstrated that rationally engineered inorganic nanomaterials can be used to specifically target a 3d structure of a certain protein for function inhibition, thus providing a novel methodological approach and paving the way for future molecular targeting nanodrug candidate design.

关键词： protein corona protein 3d structure Selective binding protein SARS-CoV-2 Cerium oxide nanoparticles

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protein folding problem: enigma, paradox, solution

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BIOPHYSICAL REVIEWS 2022年第6期14卷 1255-1272页

作者： Finkelstein, Alexei V. Bogatyreva, Natalya S. Ivankov, dmitry N. Garbuzynskiy, Sergiy O. Russian Acad Sci Inst Prot Res Pushchino 142290 Moscow Region Russia Lomonosov Moscow State Univ Biotechnol Dept 4 Inst Skaya Str Pushchino 142290 Moscow Region Russia Lomonosov Moscow State Univ Biol Dept 1-12 Leninskie Gory Moscow 119991 Russia Skolkovo Inst Sci & Technol Ctr Life Sci Moscow 121205 Russia

The ability of protein chains to spontaneously form their three-dimensional structures is a long-standing mystery in molecular biology. The most conceptual aspect of this mystery is how the protein chain can find its native, "working" spatial structure (which, for not too big protein chains, corresponds to the global free energy minimum) in a biologically reasonable time, without exhaustive enumeration of all possible conformations, which would take billions of years. This is the so-called "Levinthal's paradox." In this review, we discuss the key ideas and discoveries leading to the current understanding of protein folding kinetics, including folding landscapes and funnels, free energy barriers at the folding/unfolding pathways, and the solution of Levinthal's paradox. A special role here is played by the "all-or-none" phase transition occurring at protein folding and unfolding and by the point of thermodynamic (and kinetic) equilibrium between the "native" and the "unfolded" phases of the protein chain (where the theory obtains the simplest form). The modern theory provides an understanding of key features of protein folding and, in good agreement with experiments, it (i) outlines the chain length-dependent range of protein folding times, (ii) predicts the observed maximal size of "foldable" proteins and domains. Besides, it predicts the maximal size of proteins and domains that fold under solely thermodynamic (rather than kinetic) control. Complementarily, a theoretical analysis of the number of possible protein folding patterns, performed at the level of formation and assembly of secondary structures, correctly outlines the upper limit of protein folding times.

关键词： protein 3d structure protein folding Levinthal's paradox "All-or-none" phase transition Free energy landscape Folding funnel

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New approach methodologies for risk assessment using deep learning

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EFSA JOURNAL 2024年第S1期22卷 e221105页

作者： Junquera, Enol diaz, Irene Montes, Susana Febbraio, Ferdinando Univ Oviedo Oviedo Spain Natl Res Council CNR Inst Biochem & Cell Biol Naples Italy

The advancement of technologies and the development of more efficient artificial intelligence (AI) enable the processing of large amounts of data in a very short time. Concurrently, the increase in information within biological databases, such as 3d molecular structures or networks of functional macromolecule associations, will facilitate the creation of new methods for risk assessment that can serve as alternatives to animal testing. Specifically, the predictive capabilities of AI as new approach methodologies (NAMs) are poised to revolutionise risk assessment approaches. Our previous studies on molecular docking predictions, using the software Autodock Vina, indicated high-affinity binding of certain toxic chemicals to the 3d structures of human proteins associated with nervous and reproductive functions. Similar approaches revealed potential sublethal interactions of neonicotinoids with proteins linked to the bees' immune system. Building on these findings, we plan to develop an AI-based decision tool that exploits the data available on the toxicity of the most know chemical, such as Ld50, and the data obtainable by their interaction with the human proteins to support risk assessment studies for multiple stressors still not characterised. Our focus will be on utilising these new bioinformatics methodologies to develop specific experimental designs that allow for confident and predictable study of the toxic and sublethal effects of pesticides on humans. We will also validate the developed NAMs by integrating existing in vivo information from scientific literature and technical reports. These approaches will significantly impact toxicity studies, guiding researchers' experiments and greatly reducing the need for animal testing.

关键词： artificial intelligence molecular docking molecular stressors pesticide toxicity protein 3d structure risk assessment software development

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Improving integrative 3d modeling into low- to medium-resolution electron microscopy structures with evolutionary couplings

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protein SCIENCE 2021年第5期30卷 1006-1021页

作者： McCafferty, Caitlyn L. Taylor, david W. Marcotte, Edward M. Univ Texas Austin Dept Mol Biosci Austin TX 78712 USA Univ Texas Austin Ctr Syst & Synthet Biol Austin TX 78712 USA Dell Med Sch LIVESTRONG Canc Inst Austin TX USA

Electron microscopy (EM) continues to provide near-atomic resolution structures for well-behaved proteins and protein complexes. Unfortunately, structures of some complexes are limited to low- to medium-resolution due to biochemical or conformational heterogeneity. Thus, the application of unbiased systematic methods for fitting individual structures into EM maps is important. A method that employs co-evolutionary information obtained solely from sequence data could prove invaluable for quick, confident localization of subunits within these structures. Here, we incorporate the co-evolution of intermolecular amino acids as a new type of distance restraint in the integrative modeling platform in order to build three-dimensional models of atomic structures into EM maps ranging from 10-14 angstrom in resolution. We validate this method using four complexes of known structure, where we highlight the conservation of intermolecular couplings despite dynamic conformational changes using the BAM complex. Finally, we use this method to assemble the subunits of the bacterial holo-translocon into a model that agrees with previous biochemical data. The use of evolutionary couplings in integrative modeling improves systematic, unbiased fitting of atomic models into medium- to low-resolution EM maps, providing additional information to integrative models lacking in spatial data.

关键词： electron microscopy evolutionary couplings integrative modeling protein 3d structure

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Improved structure-related prediction for insufficient homologous proteins using MSA enhancement and pre-trained language model

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BRIEFINGS IN BIOINFORMATICS 2023年第4期24卷 bbad217-bbad217页

作者： Meng, Qiaozhen Guo, Fei Tang, Jijun Tianjin Univ Tianjin Peoples R China Cent South Univ Changsha Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

In recent years, protein structure problems have become a hotspot for understanding protein folding and function mechanisms. It has been observed that most of the protein structure works rely on and benefit from co-evolutionary information obtained by multiple sequence alignment (MSA). As an example, AlphaFold2 (AF2) is a typical MSA-based protein structure tool which is famous for its high *** a consequence, these MSA-based methods are limited by the quality of the MSAs. Especially for orphan proteins that have no homologous sequence, AlphaFold2 performs unsatisfactorily as MSA depth decreases, which may pose a barrier to its widespread application in protein mutation and design problems in which there are no rich homologous sequences and rapid prediction is needed. In this paper, we constructed two standard datasets for orphan and de novo proteins which have insufficient/none homology information, called Orphan62 and design204, respectively, to fairly evaluate the performance of the various methods in this case. Then, depending on whether or not utilizing scarce MSA information, we summarized two approaches, MSA-enhanced and MSA-free methods, to effectively solve the issue without sufficient MSAs. MSA-enhanced model aims to improve poor MSA quality from the data source by knowledge distillation and generation models. MSA-free model directly learns the relationship between residues on enormous protein sequences from pre-trained models, bypassing the step of extracting the residue pair representation from MSA. Next, we evaluated the performance of four MSA-free methods (trRosettaX-Single, TRFold, ESMFold and ProtT5) and MSA-enhanced (Bagging MSA) method compared with a traditional MSA-based method AlphaFold2, in two protein structure-related prediction tasks, respectively. Comparison analyses show that trRosettaX-Single and ESMFold which belong to MSA-free method can achieve fast prediction (similar to 40s) and comparable performance compared with AF2 in ter

关键词： protein 3d structure multiple sequence alignment orphan protein de novo protein

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Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy

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BRIEFINGS IN BIOINFORMATICS 2023年第2期24卷 bbac628-bbac628页

作者： Guo, Binjie Zheng, Hanyu Jiang, Haohan Li, Xiaodan Guan, Naiyu Zuo, Yanming Zhang, Yicheng Yang, Hengfu Wang, Xuhua Zhejiang Univ Sch Med Dept Neurobiol Dept Rehabil Med Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Liangzhu Lab 1369 West Wenyi Rd Hangzhou 311121 Peoples R China Zhejiang Univ NHC & CAMS Key Lab Med Neurobiol Hangzhou 310058 Peoples R China Hunan First Normal Univ Sch Comp Sci Changsha 410205 Hunan Peoples R China Nantong Univ Coinnovat Ctr Neuroregenerat Nantong 226001 Jiangsu Peoples R China

due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.

关键词： compound-protein binding affinity prediction coevolutionary strategy protein multimodal information protein 3d structure computational model artificial intelligence

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