Engineering protein fragments via evolutionary and protein–protein interaction algorithms: de novo design of peptide inhibitors for FOF1‐ATP synthase

Ruiz‐Blanco, Yasser B.; Ávila-Barrientos, Luis Pablo; Hernández-García, Enrique; Antunes, Agostinho; Agüero-Chapin, Guillermin; García‐Hernández, Enrique

doi:10.1002/1873-3468.13988

Cited by 3 publications

(11 citation statements)

References 72 publications

(94 reference statements)

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“…On the other hand, there is another set of topological descriptors that also comes from the chemoinformatic field that have been applied to the identification and design of AMPs [ 31 , 52 , 56 , 58 ]. They are not formulated by using algebraic forms but rather they rely on descriptive statistics as invariant operators on the aa properties either along the sequence or the 3D protein structure.…”

Section: Non-classical Peptide Features For Bioactivity Predictionmentioning

confidence: 99%

“…ProtDCal’s descriptors have also been involved in the design of new peptides that inhibit the E. coli ATP synthase, as putative antibiotics [ 52 , 76 ]. ProtDCal’s descriptors, implemented in PPI-Detect [ 77 ], were applied to predict interactions between peptides and the main subunits of E. coli ’s (Ec) and human’s (Hs) F1Fo-ATP synthase.…”

Section: Non-classical Peptide Features For Bioactivity Predictionmentioning

confidence: 99%

“…Those peptide with a maximum and a minimum interaction likelihood with EcF1Fo and HsF1Fo were selected for in vitro assays. An overall of three peptides resulted attractive for further optimization steps in the design of new antibiotics [ 52 , 76 ].…”

Section: Non-classical Peptide Features For Bioactivity Predictionmentioning

confidence: 99%

“…Although the SME approach and the generation of oriented libraries toward certain secondary structures, relevant for the interaction with lipid membranes, have represented a step forward in the design and optimization pipeline of AMPs and ACPs [ 130 , 136 ], there still room for improving the simulation of molecular evolution of the offspring peptides. In this sense, algorithms that traditionally have been used for simulating sequence evolution in the field of molecular phylogenetics were recently applied to provide more rationality to the peptide library generation [ 52 ]. These algorithms were initially developed to evaluate the accuracy of MSA and phylogenetic reconstruction tools by generating sets of related simulated protein sequences from known phylogenies.…”

Section: Models Of Sequence Evolution For the Design And Optimization...mentioning

confidence: 99%

“…By other side, evolutionary algorithms that simulate sequence evolution have been recently applied to design/optimize peptides having a pharmaceutical activity [ 51 , 52 ]. Last but not least, computational tools used in proteogenomic analyses are being modified for uncovering cryptic peptides with biological activities in natural sources [ 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

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Emerging Computational Approaches for Antimicrobial Peptide Discovery

et al. 2022

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In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.

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Section: Non-classical Peptide Features For Bioactivity Predictionmentioning

confidence: 99%

Section: Non-classical Peptide Features For Bioactivity Predictionmentioning

confidence: 99%

Section: Non-classical Peptide Features For Bioactivity Predictionmentioning

confidence: 99%

Section: Models Of Sequence Evolution For the Design And Optimization...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emerging Computational Approaches for Antimicrobial Peptide Discovery

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Exploring the druggability of the binding site of aurovertin, an exogenous allosteric inhibitor of FOF1-ATP synthase

et al. 2022

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In addition to playing a central role in the mitochondria as the main producer of ATP, FOF1-ATP synthase performs diverse key regulatory functions in the cell membrane. Its malfunction has been linked to a growing number of human diseases, including hypertension, atherosclerosis, cancer, and some neurodegenerative, autoimmune, and aging diseases. Furthermore, inhibition of this enzyme jeopardizes the survival of several bacterial pathogens of public health concern. Therefore, FOF1-ATP synthase has emerged as a novel drug target both to treat human diseases and to combat antibiotic resistance. In this work, we carried out a computational characterization of the binding sites of the fungal antibiotic aurovertin in the bovine F1 subcomplex, which shares a large identity with the human enzyme. Molecular dynamics simulations showed that although the binding sites can be described as preformed, the inhibitor hinders inter-subunit communications and exerts long-range effects on the dynamics of the catalytic site residues. End-point binding free energy calculations revealed hot spot residues for aurovertin recognition. These residues were also relevant to stabilize solvent sites determined from mixed-solvent molecular dynamics, which mimic the interaction between aurovertin and the enzyme, and could be used as pharmacophore constraints in virtual screening campaigns. To explore the possibility of finding species-specific inhibitors targeting the aurovertin binding site, we performed free energy calculations for two bacterial enzymes with experimentally solved 3D structures. Finally, an analysis of bacterial sequences was carried out to determine conservation of the aurovertin binding site. Taken together, our results constitute a first step in paving the way for structure-based development of new allosteric drugs targeting FOF1-ATP synthase sites of exogenous inhibitors.

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Computational Design of Inhibitors Targeting the Catalytic β Subunit of Escherichia coli FOF1-ATP Synthase

et al. 2022

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With the uncontrolled growth of multidrug-resistant bacteria, there is an urgent need to search for new therapeutic targets, to develop drugs with novel modes of bactericidal action. FoF1-ATP synthase plays a crucial role in bacterial bioenergetic processes, and it has emerged as an attractive antimicrobial target, validated by the pharmaceutical approval of an inhibitor to treat multidrug-resistant tuberculosis. In this work, we aimed to design, through two types of in silico strategies, new allosteric inhibitors of the ATP synthase, by targeting the catalytic β subunit, a centerpiece in communication between rotor subunits and catalytic sites, to drive the rotary mechanism. As a model system, we used the F1 sector of Escherichia coli, a bacterium included in the priority list of multidrug-resistant pathogens. Drug-like molecules and an IF1-derived peptide, designed through molecular dynamics simulations and sequence mining approaches, respectively, exhibited in vitro micromolar inhibitor potency against F1. An analysis of bacterial and Mammalia sequences of the key structural helix-turn-turn motif of the C-terminal domain of the β subunit revealed highly and moderately conserved positions that could be exploited for the development of new species-specific allosteric inhibitors. To our knowledge, these inhibitors are the first binders computationally designed against the catalytic subunit of FOF1-ATP synthase.

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Engineering protein fragments via evolutionary and protein–protein interaction algorithms: de novo design of peptide inhibitors for F_OF₁‐ATP synthase

Cited by 3 publications

References 72 publications

Emerging Computational Approaches for Antimicrobial Peptide Discovery

Emerging Computational Approaches for Antimicrobial Peptide Discovery

Exploring the druggability of the binding site of aurovertin, an exogenous allosteric inhibitor of FOF1-ATP synthase

Computational Design of Inhibitors Targeting the Catalytic β Subunit of Escherichia coli FOF1-ATP Synthase

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