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Translocator protein (TSPO) is an 18 kDa transmembrane protein, localized primarily on the outer mitochondrial membrane. It has been found to be involved in various physiological processes and pathophysiological conditions. Though studies on its structure have been performed only recently, there is little information on the nature of dynamics and doubts about some structures referenced in the literature, especially the NMR structure of mouse TSPO. In the present work, we thoroughly study the dynamics of mouse TSPO protein by means of atomistic molecular dynamics simulations, in presence as well as in absence of the diagnostic ligand PKA. We considered two starting structures: the NMR structure and a homology model (HM) generated on the basis of X-ray structures from bacterial TSPO. We examine the conformational landscape in both the modes for both starting points, in presence and absence of the ligand, in order to measure its impact for both structures. The analysis highlights high flexibility of the protein globally, but NMR simulations show a surprisingly flexibility even in the presence of the ligand. Interestingly, this is not the case for HM calculations, to the point that the ligand seems not so stable as in the NMR system and an unbinding event process is partially sampled. All those results tend to show that the NMR structure of mTSPO seems not deficient but is just in another portion of the global conformation space of TSPO.
Assuntos
Isoquinolinas , Receptores de GABA , Animais , Camundongos , Sítios de Ligação , Isoquinolinas/química , Isoquinolinas/metabolismo , Ligantes , Simulação de Dinâmica Molecular , Ligação Proteica , Receptores de GABA/metabolismo , Receptores de GABA/químicaRESUMO
Important bacterial pathogens such as Pseudomonas aeruginosa produce several exoproducts such as siderophores, degradative enzymes, biosurfactants, and exopolysaccharides that are used extracellularly, benefiting all members of the population, hence being public goods. Since the production of public goods is a cooperative trait, it is in principle susceptible to cheating by individuals in the population who do not invest in their production, but use their benefits, hence increasing their fitness at the expense of the cooperators' fitness. Among the most studied virulence factors susceptible to cheating are siderophores and exoproteases, with several studies in vitro and some in animal infection models. In addition to these two well-known examples, cheating with other virulence factors such as exopolysaccharides, biosurfactants, eDNA production, secretion systems, and biofilm formation has also been studied. In this review, we discuss the evidence of the susceptibility of each of those virulence factors to cheating, as well as the mechanisms that counteract this behavior and the possible consequences for bacterial virulence.
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Sideróforos , Fatores de Virulência , Humanos , Fatores de Virulência/genética , Pseudomonas aeruginosa/genética , Biofilmes , Percepção de QuorumRESUMO
Mutation Q345F in sucrose phosphorylase from Bifidobacterium adolescentis (BaSP) has shown to allow efficient (+)-catechin glucosylation yielding a regioisomeric mixture: (+)-catechin-3'-O-α-D-glucopyranoside, (+)-catechin-5-O-α-D-glucopyranoside and (+)-catechin-3',5-O-α-D-diglucopyranoside with a ratio of 51 : 25 : 24. Here, we efficiently increased the control of (+)-catechin glucosylation regioselectivity with a new variant Q345F/P134D. The same products were obtained with a ratio of 82 : 9 : 9. Thanks to bioinformatics models, we successfully explained the glucosylation favoured at the OH-3' position due to the mutation P134D.
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Bifidobacterium adolescentis , Catequina , Bifidobacterium adolescentis/genética , Glucosiltransferases/genética , MutaçãoRESUMO
Conformational flexibility plays an essential role in antibodies' functional and structural stability. They facilitate and determine the strength of antigen-antibody interactions. Camelidae express an interesting subtype of single-chain antibody, named Heavy Chain only Antibody. They have only one N-terminal Variable domain (VHH) per chain, composed of Frameworks (FRs) and Complementarity Determining regions (CDRs) like their VH and VL counterparts in IgG. Even when expressed independently, VHH domains display excellent solubility and (thermo)stability, which helps them to retain their impressive interaction capabilities. Sequence and structural features of VHH domains contributing to these abilities have already been studied compared to classical antibodies. To have the broadest view and understand the changes in dynamics of these macromolecules, large-scale molecular dynamics simulations for a large number of non-redundant VHH structures have been performed for the first time. This analysis reveals the most prevalent movements in these domains. It reveals the four main classes of VHHs dynamics. Diverse local changes were observed in CDRs with various intensities. Similarly, different types of constraints were observed in CDRs, while FRs close to CDRs were sometimes primarily impacted. This study sheds light on the changes in flexibility in different regions of VHH that may impact their in silico design.
Assuntos
Camelidae , Região Variável de Imunoglobulina , Animais , Região Variável de Imunoglobulina/química , Regiões Determinantes de Complementaridade/química , Cadeias Pesadas de Imunoglobulinas/química , Simulação de Dinâmica MolecularRESUMO
Heavy Chain Only Antibodies are specific to Camelid species. Despite the lack of the light chain variable domain, their heavy chain variable domain (VH) domain, named VHH or nanobody, has promising potential applications in research and therapeutic fields. The structural study of VHH is therefore of great interest. Unfortunately, considering the huge amount of sequences that might be produced, only about one thousand of VHH experimental structures are publicly available in the Protein Data Bank, implying that structural model prediction of VHH is a necessary alternative to obtaining 3D information besides its sequence. The present study aims to assess and compare the quality of predictions from different modelling methodologies. Established comparative & homology modelling approaches to recent Deep Learning-based modelling strategies were applied, i.e. Modeller using single or multiple structural templates, ModWeb, SwissModel (with two evaluation schema), RoseTTAfold, AlphaFold 2 and NanoNet. The prediction accuracy was evaluated using RMSD, TM-score, GDT-TS, GDT-HA and Protein Blocks distance metrics. Besides the global structure assessment, we performed specific analyses of Frameworks and CDRs structures. We observed that AlphaFold 2 and especially NanoNet performed better than the other evaluated softwares. Importantly, we performed molecular dynamics simulations of an experimental structure and a NanoNet predicted model of a VHH in order to compare the global structural flexibility and local conformations using Protein Blocks. Despite rather similar structures, substantial differences in dynamical properties were observed, which underlies the complexity of the task of model evaluation.Communicated by Ramaswamy H. Sarma.
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Cadeias Pesadas de Imunoglobulinas , Região Variável de Imunoglobulina , Região Variável de Imunoglobulina/química , Cadeias Pesadas de Imunoglobulinas/químicaRESUMO
Background: Bacteriophage therapy is becoming part of mainstream Western medicine since antibiotics of clinical use tend to fail. It involves applying lytic bacteriophages that self-replicate and induce cell lysis, thus killing their hosts. Nevertheless, bacterial killing promotes the selection of resistant clones which sometimes may exhibit a decrease in bacterial virulence or antibiotic resistance. Methods: In this work, we studied the Pseudomonas aeruginosa lytic phage φDCL-PA6 and its variant φDCL-PA6α. Additionally, we characterized and evaluated the production of virulence factors and the virulence in a Galleria mellonella model of resistant mutants against each phage for PA14 and two clinical strains. Results: Phage φDCL-PA6α differs from the original by only two amino acids: one in the baseplate wedge subunit and another in the tail fiber protein. According to genomic data and cross-resistance experiments, these changes may promote the change of the phage receptor from the O-antigen to the core lipopolysaccharide. Interestingly, the host range of the two phages differs as determined against the Pseudomonas aeruginosa reference strains PA14 and PAO1 and against nine multidrug-resistant isolates from ventilator associated pneumonia. Conclusions: We show as well that phage resistance impacts virulence factor production. Specifically, phage resistance led to decreased biofilm formation, swarming, and type III secretion; therefore, the virulence towards Galleria mellonella was dramatically attenuated. Furthermore, antibiotic resistance decreased for one clinical strain. Our study highlights important potential advantages of phage therapy's evolutionary impact that may be exploited to generate robust therapy schemes.
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Bacteriófagos , Mariposas , Terapia por Fagos , Fagos de Pseudomonas , Animais , Virulência , Pseudomonas aeruginosa , Fagos de Pseudomonas/genética , Fatores de Virulência/genética , Resistência Microbiana a Medicamentos , Antibacterianos/farmacologiaRESUMO
Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.
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Evolução Molecular Direcionada , Engenharia de Proteínas , Evolução Molecular Direcionada/métodos , Engenharia de Proteínas/métodos , Proteínas/genética , Biologia SintéticaRESUMO
The use of machine learning (ML) in life sciences has gained wide interest over the past years, as it speeds up the development of high performing models. Important modeling tools in biology have proven their worth for pathway design, such as mechanistic models and metabolic networks, as they allow better understanding of mechanisms involved in the functioning of organisms. However, little has been done on the use of ML to model metabolic pathways, and the degree of non-linearity associated with them is not clear. Here, we report the construction of different metabolic pathways with several linear and non-linear ML models. Different types of data are used; they lead to the prediction of important biological data, such as pathway flux and final product concentration. A comparison reveals that the data features impact model performance and highlight the effectiveness of non-linear models (e.g., QRF: RMSE = 0.021 nmol·min-1 and R2 = 1 vs. Bayesian GLM: RMSE = 1.379 nmol·min-1 R2 = 0.823). It turns out that the greater the degree of non-linearity of the pathway, the better suited a non-linear model will be. Therefore, a decision-making support for pathway modeling is established. These findings generally support the hypothesis that non-linear aspects predominate within the metabolic pathways. This must be taken into account when devising possible applications of these pathways for the identification of biomarkers of diseases (e.g., infections, cancer, neurodegenerative diseases) or the optimization of industrial production processes.
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VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.
Assuntos
Camelídeos Americanos , Cadeias Pesadas de Imunoglobulinas , Sequência de Aminoácidos , Animais , Anticorpos , Cadeias Pesadas de Imunoglobulinas/química , Modelos EstruturaisRESUMO
In the particular case of the Camelidae family, immunoglobulin proteins have evolved into a unique and more simplified architecture with only heavy chains. The variable domains of these chains, named VHHs, have a number of Complementary Determining Regions (CDRs) reduced by half, and can function as single domains making them good candidates for molecular tools. 3D structure prediction of these domains is a beneficial and advantageous step to advance their developability as molecular tools. Nonetheless, the conformations of CDRs loops in these domains remain difficult to predict due to their higher conformational diversity. In addition to CDRs loop diversity, our earlier study has established that Framework Regions (FRs) are also not entirely conformationally conserved which establishes a need for more rigorous analyses of these regions that could assist in template selection. In the current study, VHHs models using different template selection strategies for comparative modeling using Modeller have been extensively assessed. This study analyses the conformational changes in both CDRs and FRs using an original strategy of conformational discretization based on a structural alphabet. Conformational sampling in selected cases is precisely reported. Some interesting outcomes of the structural analyses of models also draw attention towards the distinct difficulty in 3D structure prediction of VHH domains.
Assuntos
Cadeias Pesadas de Imunoglobulinas/química , Região Variável de Imunoglobulina/química , Modelos Moleculares , Conformação Proteica , Sequência de Aminoácidos , Regiões Determinantes de Complementaridade/química , Humanos , Ligação Proteica , Relação Estrutura-AtividadeRESUMO
Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside-the-box, predictions not found in other state-of-the-art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.
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Epóxido Hidrolases/química , Epóxido Hidrolases/metabolismo , Aprendizado de Máquina , Mutação , Dobramento de Proteína , Multimerização Proteica , Rhodococcus/enzimologia , Epóxido Hidrolases/genética , Limoneno/química , Limoneno/metabolismo , Simulação de Dinâmica Molecular , Engenharia de ProteínasRESUMO
Metabolic pathway modeling plays an increasing role in drug design by allowing better understanding of the underlying regulation and controlling networks in the metabolism of living organisms. However, despite rapid progress in this area, pathway modeling can become a real nightmare for researchers, notably when few experimental data are available or when the pathway is highly complex. Here, three different approaches were developed to model the second part of glycolysis of E. histolytica as an application example, and have succeeded in predicting the final pathway flux: one including detailed kinetic information (white-box), another with an added adjustment term (grey-box) and the last one using an artificial neural network method (black-box). Afterwards, each model was used for metabolic control analysis and flux control coefficient determination. The first two enzymes of this pathway are identified as the key enzymes playing a role in flux control. This study revealed the significance of the three methods for building suitable models adjusted to the available data in the field of metabolic pathway modeling, and could be useful to biologists and modelers.
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Glicólise/fisiologia , Redes e Vias Metabólicas/fisiologia , Simulação por Computador , Entamoeba histolytica/metabolismo , Cinética , Modelos Biológicos , Modelos Teóricos , Fenômenos FísicosRESUMO
Translocator protein (TSPO), a mitochondrial membrane protein, has been extensively studied, and its role is still debated and continues to be enigmatic. From a structural perspective, despite availability of atomic structures from different species, the possible oligomeric state and its 3D structure remains elusive. In the present study, we attempted to study dynamics of TSPO from the perspective of oligomerization. In this aim, we examined if and how TSPO monomers could assemble to form a dimer. Accordingly, we performed several coarse-grained molecular dynamics simulations considering two different initial configurations, one with a pair of TSPO monomers distantly placed in a model of a bilayer composed of DMPC/cholesterol mixture and the other with preformed dimer models with different starting interactions. We identify stable TSPO dimers with diverse interfaces, some of which were consistent with earlier experimental observations on putative TSPO oligomer interfaces. For most of the stable ones, interactions between aromatic residues were significantly overrepresented in diverse oligomeric organizations. Interestingly, we identified different communication pathways that involve dimer interfaces. Additionally, we observed that cholesterol molecules in close interaction with the TSPO dimer were able to translocate through the bilayer. This phenomenon might be related to the putative mechanism of cholesterol transport and could be increased and favored by the dimer formation. Overall, our observations shed new light on TSPO oligomerization and bring new perspectives on its dynamics, as well its interactions with protein and ligand partners.
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Simulação de Dinâmica Molecular , Receptores de GABA , Proteínas de Transporte , Colesterol , Dimerização , Receptores de GABA/metabolismoRESUMO
Prediction of protein structures using computational approaches has been explored for over two decades, paving a way for more focused research and development of algorithms in comparative modelling, ab intio modelling and structure refinement protocols. A tremendous success has been witnessed in template-based modelling protocols, whereas strategies that involve template-free modelling still lag behind, specifically for larger proteins (>150 a.a.). Various improvements have been observed in ab initio protein structure prediction methodologies overtime, with recent ones attributed to the usage of deep learning approaches to construct protein backbone structure from its amino acid sequence. This review highlights the major strategies undertaken for template-free modelling of protein structures while discussing few tools developed under each strategy. It will also briefly comment on the progress observed in the field of ab initio modelling of proteins over the course of time as seen through the evolution of CASP platform.
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Algoritmos , Biologia Computacional , Bases de Dados de Proteínas , Modelos Moleculares , Dobramento de Proteína , Proteínas/química , Conformação Proteica , Proteínas/genéticaRESUMO
Antigen binding by antibodies requires precise orientation of the complementarity- determining region (CDR) loops in the variable domain to establish the correct contact surface. Members of the family Camelidae have a modified form of immunoglobulin gamma (IgG) with only heavy chains, called Heavy Chain only Antibodies (HCAb). Antigen binding in HCAbs is mediated by only three CDR loops from the single variable domain (VHH) at the N-terminus of each heavy chain. This feature of the VHH, along with their other important features, e.g., easy expression, small size, thermo-stability and hydrophilicity, made them promising candidates for therapeutics and diagnostics. Thus, to design better VHH domains, it is important to thoroughly understand their sequence and structure characteristics and relationship. In this study, sequence characteristics of VHH domains have been analysed in depth, along with their structural features using innovative approaches, namely a structural alphabet. An elaborate summary of various studies proposing structural models of VHH domains showed diversity in the algorithms used. Finally, a case study to elucidate the differences in structural models from single and multiple templates is presented. In this case study, along with the above-mentioned aspects of VHH, an exciting view of various factors in structure prediction of VHH, like template framework selection, is also discussed.
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Enzymes are biological catalysts with many industrial applications, but natural enzymes are usually unsuitable for industrial processes because they are not optimized for the process conditions. The properties of enzymes can be improved by directed evolution, which involves multiple rounds of mutagenesis and screening. By using mathematical models to predict the structure-activity relationship of an enzyme, and by defining the optimal combination of mutations in silico, we can significantly reduce the number of bench experiments needed, and hence the time and investment required to develop an optimized product. Here, we applied our innovative sequence-activity relationship methodology (innov'SAR) to improve glucose oxidase activity in the presence of different mediators across a range of pH values. Using this machine learning approach, a predictive model was developed and the optimal combination of mutations was determined, leading to a glucose oxidase mutant (P1) with greater specificity for the mediators ferrocene-methanol (12-fold) and nitrosoaniline (8-fold), compared to the wild-type enzyme, and better performance in three pH-adjusted buffers. The kcat /KM ratio of P1 increased by up to 121 folds compared to the wild type enzyme at pH 5.5 in the presence of ferrocene methanol.
Assuntos
Evolução Molecular Direcionada/métodos , Glucose Oxidase , Aprendizado de Máquina , Mutagênese Sítio-Dirigida/métodos , Mutação , Sequência de Aminoácidos , Compostos Ferrosos/metabolismo , Glucose/metabolismo , Glucose Oxidase/química , Glucose Oxidase/genética , Glucose Oxidase/metabolismo , Concentração de Íons de Hidrogênio , Cinética , Modelos Estatísticos , Nitrosaminas/metabolismoRESUMO
Understanding the structural plasticity of proteins is key to understanding the intricacies of their functions and mechanistic basis. In the current study, we analyzed the available multiple crystal structures of the same protein for the structural differences. For this purpose we used an abstraction of protein structures referred as Protein Blocks (PBs) that was previously established. We also characterized the nature of the structural variations for a few proteins using molecular dynamics simulations. In both the cases, the structural variations were summarized in the form of substitution matrices of PBs. We show that certain conformational states are preferably replaced by other specific conformational states. Interestingly, these structural variations are highly similar to those previously observed across structures of homologous proteins (r2â¯=â¯0.923) or across the ensemble of conformations from NMR data (r2â¯=â¯0.919). Thus our study quantitatively shows that overall trends of structural changes in a given protein are nearly identical to the trends of structural differences that occur in the topologically equivalent positions in homologous proteins. Specific case studies are used to illustrate the nature of these structural variations.