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1.
J Chem Inf Model ; 64(11): 4392-4409, 2024 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-38815246

RESUMO

By accelerating time-consuming processes with high efficiency, computing has become an essential part of many modern chemical pipelines. Machine learning is a class of computing methods that can discover patterns within chemical data and utilize this knowledge for a wide variety of downstream tasks, such as property prediction or substance generation. The complex and diverse chemical space requires complex machine learning architectures with great learning power. Recently, learning models based on transformer architectures have revolutionized multiple domains of machine learning, including natural language processing and computer vision. Naturally, there have been ongoing endeavors in adopting these techniques to the chemical domain, resulting in a surge of publications within a short period. The diversity of chemical structures, use cases, and learning models necessitate a comprehensive summarization of existing works. In this paper, we review recent innovations in adapting transformers to solve learning problems in chemistry. Because chemical data is diverse and complex, we structure our discussion based on chemical representations. Specifically, we highlight the strengths and weaknesses of each representation, the current progress of adapting transformer architectures, and future directions.


Assuntos
Quimioinformática , Aprendizado de Máquina , Quimioinformática/métodos
2.
ACS Chem Biol ; 19(4): 938-952, 2024 04 19.
Artigo em Inglês | MEDLINE | ID: mdl-38565185

RESUMO

Phenotypic assays have become an established approach to drug discovery. Greater disease relevance is often achieved through cellular models with increased complexity and more detailed readouts, such as gene expression or advanced imaging. However, the intricate nature and cost of these assays impose limitations on their screening capacity, often restricting screens to well-characterized small compound sets such as chemogenomics libraries. Here, we outline a cheminformatics approach to identify a small set of compounds with likely novel mechanisms of action (MoAs), expanding the MoA search space for throughput limited phenotypic assays. Our approach is based on mining existing large-scale, phenotypic high-throughput screening (HTS) data. It enables the identification of chemotypes that exhibit selectivity across multiple cell-based assays, which are characterized by persistent and broad structure activity relationships (SAR). We validate the effectiveness of our approach in broad cellular profiling assays (Cell Painting, DRUG-seq, and Promotor Signature Profiling) and chemical proteomics experiments. These experiments revealed that the compounds behave similarly to known chemogenetic libraries, but with a notable bias toward novel protein targets. To foster collaboration and advance research in this area, we have curated a public set of such compounds based on the PubChem BioAssay dataset and made it available for use by the scientific community.


Assuntos
Descoberta de Drogas , Ensaios de Triagem em Larga Escala , Bibliotecas de Moléculas Pequenas , Descoberta de Drogas/métodos , Ensaios de Triagem em Larga Escala/métodos , Quimioinformática/métodos , Bibliotecas de Moléculas Pequenas/química , Relação Estrutura-Atividade
4.
Sci Rep ; 14(1): 9801, 2024 04 29.
Artigo em Inglês | MEDLINE | ID: mdl-38684706

RESUMO

The Covid-19 pandemic outbreak has accelerated tremendous efforts to discover a therapeutic strategy that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to control viral infection. Various viral proteins have been identified as potential drug targets, however, to date, no specific therapeutic cure is available against the SARS-CoV-2. To address this issue, the present work reports a systematic cheminformatic approach to identify the potent andrographolide derivatives that can target methyltransferases of SARS-CoV-2, i.e. nsp14 and nsp16 which are crucial for the replication of the virus and host immune evasion. A consensus of cheminformatics methodologies including virtual screening, molecular docking, ADMET profiling, molecular dynamics simulations, free-energy landscape analysis, molecular mechanics generalized born surface area (MM-GBSA), and density functional theory (DFT) was utilized. Our study reveals two new andrographolide derivatives (PubChem CID: 2734589 and 138968421) as natural bioactive molecules that can form stable complexes with both proteins via hydrophobic interactions, hydrogen bonds and electrostatic interactions. The toxicity analysis predicts class four toxicity for both compounds with LD50 value in the range of 500-700 mg/kg. MD simulation reveals the stable formation of the complex for both the compounds and their average trajectory values were found to be lower than the control inhibitor and protein alone. MMGBSA analysis corroborates the MD simulation result and showed the lowest energy for the compounds 2734589 and 138968421. The DFT and MEP analysis also predicts the better reactivity and stability of both the hit compounds. Overall, both andrographolide derivatives exhibit good potential as potent inhibitors for both nsp14 and nsp16 proteins, however, in-vitro and in vivo assessment would be required to prove their efficacy and safety in clinical settings. Moreover, the drug discovery strategy aiming at the dual target approach might serve as a useful model for inventing novel drug molecules for various other diseases.


Assuntos
Antivirais , Diterpenos , Metiltransferases , Simulação de Acoplamento Molecular , Simulação de Dinâmica Molecular , SARS-CoV-2 , Proteínas não Estruturais Virais , Diterpenos/farmacologia , Diterpenos/química , SARS-CoV-2/efeitos dos fármacos , SARS-CoV-2/enzimologia , Metiltransferases/antagonistas & inibidores , Metiltransferases/química , Metiltransferases/metabolismo , Antivirais/farmacologia , Antivirais/química , Humanos , Proteínas não Estruturais Virais/antagonistas & inibidores , Proteínas não Estruturais Virais/química , Proteínas não Estruturais Virais/metabolismo , Quimioinformática/métodos , COVID-19/virologia , Inibidores Enzimáticos/química , Inibidores Enzimáticos/farmacologia , Tratamento Farmacológico da COVID-19
5.
J Am Chem Soc ; 146(12): 8016-8030, 2024 Mar 27.
Artigo em Inglês | MEDLINE | ID: mdl-38470819

RESUMO

There have been significant advances in the flexibility and power of in vitro cell-free translation systems. The increasing ability to incorporate noncanonical amino acids and complement translation with recombinant enzymes has enabled cell-free production of peptide-based natural products (NPs) and NP-like molecules. We anticipate that many more such compounds and analogs might be accessed in this way. To assess the peptide NP space that is directly accessible to current cell-free technologies, we developed a peptide parsing algorithm that breaks down peptide NPs into building blocks based on ribosomal translation logic. Using the resultant data set, we broadly analyze the biophysical properties of these privileged compounds and perform a retrobiosynthetic analysis to predict which peptide NPs could be directly synthesized in augmented cell-free translation reactions. We then tested these predictions by preparing a library of highly modified peptide NPs. Two macrocyclases, PatG and PCY1, were used to effect the head-to-tail macrocyclization of candidate NPs. This retrobiosynthetic analysis identified a collection of high-priority building blocks that are enriched throughout peptide NPs, yet they had not previously been tested in cell-free translation. To expand the cell-free toolbox into this space, we established, optimized, and characterized the flexizyme-enabled ribosomal incorporation of piperazic acids. Overall, these results demonstrate the feasibility of cell-free translation for peptide NP total synthesis while expanding the limits of the technology. This work provides a novel computational tool for exploration of peptide NP chemical space, that could be expanded in the future to allow design of ribosomal biosynthetic pathways for NPs and NP-like molecules.


Assuntos
Produtos Biológicos , Produtos Biológicos/química , Quimioinformática , Peptídeos/química , Biossíntese Peptídica , Aminoácidos
6.
SLAS Discov ; 29(4): 100155, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38518955

RESUMO

In June 2022, EU-OS came to the decision to make public a solubility data set of 100+K compounds obtained from several of the EU-OS proprietary screening compound collections. Leveraging on the interest of SLAS for screening scientific development it was decided to launch a joint EUOS-SLAS competition within the chemoinformatics and machine learning (ML) communities. The competition was open to real world computation experts, for the best, most predictive, classification model of compound solubility. The aim of the competition was multiple: from a practical side, the winning model should then serve as a cornerstone for future solubility predictions having used the largest training set so far publicly available. From a higher project perspective, the intent was to focus the energies and experiences, even if professionally not precisely coming from Pharma R&D; to address the issue of how to predict compound solubility. Here we report how the competition was ideated and the practical aspects of conducting it within the Kaggle framework, leveraging of the versatility and the open-source nature of this data science platform. Consideration on results and challenges encountered have been also examined.


Assuntos
Aprendizado de Máquina , Solubilidade , Quimioinformática/métodos , Humanos , Descoberta de Drogas/métodos
7.
Adv Protein Chem Struct Biol ; 139: 27-55, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38448138

RESUMO

The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.


Assuntos
Quimioinformática , Descoberta de Drogas , Medicina de Precisão
8.
J Chem Inf Model ; 64(8): 2948-2954, 2024 Apr 22.
Artigo em Inglês | MEDLINE | ID: mdl-38488634

RESUMO

SMARTS is a widely used language in cheminformatics for defining substructural queries for database lookups, reaction templates for chemical transformations, and other applications. As an extension to SMILES, many SMARTS patterns can represent the same query. Despite this, no canonicalization algorithm invariant of the line notation sequence or atomic numbering is publicly available. Here, we introduce RDCanon, an open-source Python package that can be used to standardize SMARTS queries. RDCanon is designed to ensure that the sequence of atomic queries remains consistent for all graphs representing the same substructure query and to ensure a canonical sequence of primitives within each individual atom query; furthermore, the algorithm can be applied to canonicalize the order of reactants, agents, and products and their atom map numbers in reaction SMARTS templates. As part of its canonicalization algorithm, RDCanon provides a mechanism in which the canonicalized SMARTS is optimized for speed against specific molecular databases. Several case studies are provided to showcase improved efficiency in substructure matching and retrosynthetic analysis.


Assuntos
Algoritmos , Software , Linguagens de Programação , Quimioinformática/métodos , Bases de Dados de Compostos Químicos
9.
J Chem Inf Model ; 64(8): 3173-3179, 2024 Apr 22.
Artigo em Inglês | MEDLINE | ID: mdl-38554112

RESUMO

In this work, we propose a versatile molecule and reaction encoding binary data format that aims to bridge the gap between the advantages of SMILES, like local stereo- and implicit hydrogen encoding, and block-structured MDL MOL with a 2D layout and explicit bond encoding, while addressing their respective limitations. Our new format introduces a balance between size efficiency, processing speed, and comprehensive representation, making it well-suited for various applications in cheminformatics, including deep learning, data storage, and searching. By offering an explicit approach to store atom connectivity (including implicit hydrogens), electronic state, stereochemistry, and other crucial molecular attributes, our proposal seeks to enhance data storage efficiency and promote interoperability among different software tools.


Assuntos
Quimioinformática , Software , Quimioinformática/métodos , Estrutura Molecular
10.
J Chem Inf Model ; 64(6): 1966-1974, 2024 Mar 25.
Artigo em Inglês | MEDLINE | ID: mdl-38437714

RESUMO

Chemical diversity is challenging to describe objectively. Despite this, various notions of chemical diversity are used throughout the medicinal chemistry optimization process in drug discovery. In this work, we show the usefulness of considering exploited vectors during different phases of the drug design process to provide a quantitative and objective description of chemical diversity. We have developed a concise and fast approach to enumerate and analyze the exploited vector patterns (EVPs) of molecular compound series, which can then be used in archetypal compound selection tasks, from hit matter identification to hit expansion and lead optimization. We first show that EVPs can be used to assess the progressibility of compounds in a fragment library design exercise. By considering EVPs, we then show how a set of compounds can be prioritized for hit expansion using EVP-based, customizable diversity sampling approaches, reducing the time taken and mitigating human biases. We also show that EVPs are a useful tool to analyze SAR data, offering the chance to uncover correlations between different vectors without predetermining the molecular scaffold structures. The codes used to perform these tasks are presented as easy-to-use Jupyter notebooks, which can be readily adapted for further related tasks.


Assuntos
Quimioinformática , Descoberta de Drogas , Humanos , Desenho de Fármacos , Estrutura Molecular , Química Farmacêutica
11.
J Org Chem ; 89(7): 4932-4946, 2024 04 05.
Artigo em Inglês | MEDLINE | ID: mdl-38451837

RESUMO

The concise synthesis of a small library of fluorinated piperidines from readily available dihydropyridinone derivatives has been described. The effect of the fluorination on different positions has then been evaluated by chemoinformatic tools. In particular, the compounds' pKa's have been calculated, revealing that the fluorine atoms notably lowered their basicity, which is correlated to the affinity for hERG channels resulting in cardiac toxicity. The "lead-likeness" and three-dimensionality have also been evaluated to assess their ability as useful fragments for drug design. A random screening on a panel of representative proteolytic enzymes was then carried out and revealed that one scaffold is recognized by the catalytic pocket of 3CLPro (main protease of SARS-CoV-2 coronavirus).


Assuntos
Quimioinformática , Descoberta de Drogas , SARS-CoV-2 , Desenho de Fármacos , Inibidores de Proteases/farmacologia , Antivirais/farmacologia
12.
Cell ; 187(9): 2194-2208.e22, 2024 Apr 25.
Artigo em Inglês | MEDLINE | ID: mdl-38552625

RESUMO

Effective treatments for complex central nervous system (CNS) disorders require drugs with polypharmacology and multifunctionality, yet designing such drugs remains a challenge. Here, we present a flexible scaffold-based cheminformatics approach (FSCA) for the rational design of polypharmacological drugs. FSCA involves fitting a flexible scaffold to different receptors using different binding poses, as exemplified by IHCH-7179, which adopted a "bending-down" binding pose at 5-HT2AR to act as an antagonist and a "stretching-up" binding pose at 5-HT1AR to function as an agonist. IHCH-7179 demonstrated promising results in alleviating cognitive deficits and psychoactive symptoms in mice by blocking 5-HT2AR for psychoactive symptoms and activating 5-HT1AR to alleviate cognitive deficits. By analyzing aminergic receptor structures, we identified two featured motifs, the "agonist filter" and "conformation shaper," which determine ligand binding pose and predict activity at aminergic receptors. With these motifs, FSCA can be applied to the design of polypharmacological ligands at other receptors.


Assuntos
Quimioinformática , Desenho de Fármacos , Polifarmacologia , Animais , Camundongos , Humanos , Quimioinformática/métodos , Ligantes , Receptor 5-HT2A de Serotonina/metabolismo , Receptor 5-HT2A de Serotonina/química , Receptor 5-HT1A de Serotonina/metabolismo , Receptor 5-HT1A de Serotonina/química , Masculino , Sítios de Ligação
13.
Expert Opin Drug Discov ; 19(4): 403-414, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38300511

RESUMO

INTRODUCTION: Large chemical spaces (CSs) include traditional large compound collections, combinatorial libraries covering billions to trillions of molecules, DNA-encoded chemical libraries comprising complete combinatorial CSs in a single mixture, and virtual CSs explored by generative models. The diverse nature of these types of CSs require different chemoinformatic approaches for navigation. AREAS COVERED: An overview of different types of large CSs is provided. Molecular representations and similarity metrics suitable for large CS exploration are discussed. A summary of navigation of CSs in generative models is provided. Methods for characterizing and comparing CSs are discussed. EXPERT OPINION: The size of large CSs might restrict navigation to specialized algorithms and limit it to considering neighborhoods of structurally similar molecules. Efficient navigation of large CSs not only requires methods that scale with size but also requires smart approaches that focus on better but not necessarily larger molecule selections. Deep generative models aim to provide such approaches by implicitly learning features relevant for targeted biological properties. It is unclear whether these models can fulfill this ideal as validation is difficult as long as the covered CSs remain mainly virtual without experimental verification.


Assuntos
Algoritmos , Quimioinformática , Humanos
14.
Food Chem ; 442: 138525, 2024 Jun 01.
Artigo em Inglês | MEDLINE | ID: mdl-38271906

RESUMO

Species mislabeling of commercial loliginidae squid can undermine important conservation efforts and prevent consumers from making informed decisions. A comprehensive lipidomic fingerprint of Uroteuthis singhalensis, Uroteuthis edulis, and Uroteuthis duvauceli rings was established using high-resolution mass spectrometry-based lipidomics and chemoinformatics analysis. The principal component analysis showed a clear separation of sample groups, with R2X and Q2 values of 0.97 and 0.85 for ESI+ and 0.96 and 0.86 for ESI-, indicating a good model fit. The optimized OPLS-DA and PLS-DA models could discriminate the species identity of validation samples with 100 % accuracy. A total of 67 and 90 lipid molecules were putatively identified as biomarkers in ESI+ and ESI-, respectively. Identified lipids, including PC(40:6), C14 sphingomyelin, PS(O-36:0), and PE(41:4), played an important role in species discrimination. For the first time, this study provides a detailed lipidomics profile of commercially important loliginidae squid and establishes a faster workflow for species authentication.


Assuntos
Lipidômica , Espectrometria de Massas em Tandem , Cromatografia Líquida de Alta Pressão , Quimioinformática
15.
J Chem Inf Model ; 64(3): 638-652, 2024 Feb 12.
Artigo em Inglês | MEDLINE | ID: mdl-38294781

RESUMO

A simple approach was developed to computationally construct a polymer dataset by combining simplified molecular-input line-entry system (SMILES) strings of a targeted polymer backbone and a variety of molecular fragments. This method was used to create 14 polymer datasets by combining seven polymer backbones and molecules from two large molecular datasets (MOSES and QM9). Polymer backbones that were studied include four polydimethylsiloxane (PDMS) based backbones, poly(ethylene oxide) (PEO), poly(allyl glycidyl ether) (PAGE), and polyphosphazene (PPZ). The generated polymer datasets can be used for various cheminformatics tasks, including high-throughput screening for gas permeability and selectivity. This study utilized machine learning (ML) models to screen the polymers for CO2/CH4 and CO2/N2 gas separation using membranes. Several polymers of interest were identified. The results highlight that employing an ML model fitted to polymer selectivities leads to higher accuracy in predicting polymer selectivity compared to using the ratio of predicted permeabilities.


Assuntos
Dióxido de Carbono , Polímeros , Polietilenoglicóis , Quimioinformática , Ensaios de Triagem em Larga Escala
16.
Mol Inform ; 43(1): e202300190, 2024 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-37885368

RESUMO

GUIDEMOL is a Python computer program based on the RDKit software to process molecular structures and calculate molecular descriptors with a graphical user interface using the tkinter package. It can calculate descriptors already implemented in RDKit as well as grid representations of 3D molecular structures using the electrostatic potential or voxels. The GUIDEMOL app provides easy access to RDKit tools for chemoinformatics users with no programming skills and can be adapted to calculate other descriptors or to trigger other procedures. A command line interface (CLI) is also provided for the calculation of grid representations. The source code is available at https://github.com/jairesdesousa/guidemol.


Assuntos
Quimioinformática , Software , Proteínas Adaptadoras de Transdução de Sinal
17.
J Biomol Struct Dyn ; 42(1): 298-313, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-36974951

RESUMO

Antibacterial resistance to ß-lactams in microorganisms has been attributed majorly to alterations in penicillin-binding proteins (PBPs) coupled with ß-lactams' inactivation by ß-lactamase. Consequently, the identification of a novel class of therapeutics with improved modulatory action on the PBPs is imperative and plant secondary metabolites, including phenolics, have found relevance in this regard. For the first time in this study, the over 10,000 phenolics currently known were computationally evaluated against PBP3 of Pseudomonas aeruginosa, a superbug implicated in several nosocomial infections. In doing this, a library of phenolics with an affinity for PBP3 of P. aeruginosa was screened using structure-activity relationship-based pharmacophore and molecular docking approaches. Subsequent thermodynamic screening of the top five phenolics with higher docking scores, more drug-likeness attributes, and feasible synthetic accessibility was achieved through a 120 ns molecular dynamic (MD) simulation. Four of the top five hits had higher binding free energy than cefotaxime (-18.72 kcal/mol), with catechin-3-rhamside having the highest affinity (-28.99 kcal/mol). All the hits were stable at the active site of the PBP3, with catechin-3-rhamside being the most stable (2.14 Å), and established important interactions with Ser294, implicated in the catalytic activity of PBP3. Also, PBP3 became more compact with less fluctuation of the active site amino acid residues following the binding of the hits. These observations are indicative of the potential of the test compounds as PBP3 inhibitors, with catechin-3-rhamside being the most prominent of the compounds that could be further improved for enhanced druggability against PBP3 in vitro and in vivo.Communicated by Ramaswamy H. Sarma.


Assuntos
Catequina , Pseudomonas aeruginosa , Proteínas de Ligação às Penicilinas/química , Pseudomonas aeruginosa/metabolismo , Simulação de Acoplamento Molecular , Quimioinformática , Antibacterianos/farmacologia , Antibacterianos/química , beta-Lactamas/farmacologia , beta-Lactamas/química , beta-Lactamas/metabolismo
18.
J Chem Inf Model ; 64(1): 42-56, 2024 Jan 08.
Artigo em Inglês | MEDLINE | ID: mdl-38116926

RESUMO

Machine Learning (ML) techniques face significant challenges when predicting advanced chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction conditions. These challenges stem from the high-dimensional nature of the prediction task and the myriad essential variables involved, ranging from reactants and reagents to catalysts, temperature, and purification processes. Successfully developing a reliable predictive model not only holds the potential for optimizing high-throughput experiments but can also elevate existing retrosynthetic predictive approaches and bolster a plethora of applications within the field. In this review, we systematically evaluate the efficacy of current ML methodologies in chemoinformatics, shedding light on their milestones and inherent limitations. Additionally, a detailed examination of a representative case study provides insights into the prevailing issues related to data availability and transferability in the discipline.


Assuntos
Quimioinformática , Aprendizado de Máquina
19.
Biomolecules ; 13(11)2023 11 20.
Artigo em Inglês | MEDLINE | ID: mdl-38002355

RESUMO

Many natural products have been acquired from plants for their helpful properties. Medicinal plants are used for treating a variety of pathologies or symptoms. The axes of many pathological processes are inflammation, oxidative stress, and senescence. This work is focused on identifying Mexican medicinal plants with potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects through network analysis and chemoinformatic screening of their phytochemicals. We used computational methods to analyze drug-like phytochemicals in Mexican medicinal plants, multi-target compounds, and signaling pathways related to anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence mechanisms. A total of 1373 phytochemicals are found in 1025 Mexican medicinal plants, and 148 compounds showed no harmful functionalities. These compounds displayed comparable structures with reference molecules. Based on their capacity to interact with pharmacological targets, three clusters of Mexican medicinal plants have been established. Curatella americana, Ximenia americana, Malvastrum coromandelianum, and Manilkara zapota all have anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. Plumeria rubra, Lonchocarpus yucatanensis, and Salvia polystachya contained phytochemicals with anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence reported activity. Lonchocarpus guatemalensis, Vallesia glabra, Erythrina oaxacana, and Erythrina sousae have drug-like phytochemicals with potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. Between the drug-like phytochemicals, lonchocarpin, vallesine, and erysotrine exhibit potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. For the first time, we conducted an initial virtual screening of selected Mexican medicinal plants, which was subsequently confirmed in vivo, evaluating the anti-inflammatory activity of Lonchocarpus guatemalensis Benth in mice.


Assuntos
Plantas Medicinais , Animais , Camundongos , Plantas Medicinais/química , Antioxidantes/farmacologia , Quimioinformática , Envelhecimento , Anti-Inflamatórios/farmacologia , Compostos Fitoquímicos/química , Extratos Vegetais/química
20.
PLoS One ; 18(11): e0289773, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-37992050

RESUMO

Shigella sonnei is a gram-negative bacterium and is the primary cause of shigellosis in advanced countries. An exceptional rise in the prevalence of the disease has been reported in Asia, the Middle East, and Latin America. To date, no preventive vaccine is available against S. sonnei infections. This pathogen has shown resistances towards both first- and second-line antibiotics. Therefore, an effective broad spectrum vaccine development against shigellosis is indispensable. In the present study, vaccinomics-aided immunoinformatics strategies were pursued to identify potential vaccine candidates from the S. sonnei whole proteome data. Pathogen essential proteins that are non-homologous to human and human gut microbiome proteome set, are feasible candidates for this purpose. Three antigenic outer membrane proteins were prioritized to predict lead epitopes based on reverse vaccinology approach. Multi-epitope-based chimeric vaccines was designed using lead B- and T-cell epitopes combined with suitable linker and adjuvant peptide sequences to enhance immune responses against the designed vaccine. The SS-MEVC construct was prioritized based on multiple physicochemical, immunological properties, and immune-receptors docking scores. Immune simulation analysis predicted strong immunogenic response capability of the designed vaccine construct. The Molecular dynamic simulations analysis ensured stable molecular interactions of lead vaccine construct with the host receptors. In silico restriction and cloning analysis predicted feasible cloning capability of the SS-MEVC construct within the E. coli expression system. The proposed vaccine construct is predicted to be more safe, effective and capable of inducing robust immune responses against S. sonnei infections and may be worthy of examination via in vitro/in vivo assays.


Assuntos
Disenteria Bacilar , Shigella sonnei , Humanos , Shigella sonnei/genética , Disenteria Bacilar/prevenção & controle , Disenteria Bacilar/microbiologia , Proteoma/metabolismo , Escherichia coli/metabolismo , Quimioinformática , Simulação de Acoplamento Molecular , Vacinas Bacterianas , Vacinas de Subunidades Antigênicas , Epitopos de Linfócito T , Simulação de Dinâmica Molecular , Biologia Computacional , Epitopos de Linfócito B
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