BCSearch: fast structural fragment mining over large collections of protein structures

Guyon, Frédéric; Martz, François; Vavruša, Marek; Becot, Jérôme; Rey, Julien; Tufféry, Pierre

doi:10.1093/nar/gkv492

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…We then switch the flanks and search for linker candidates that match those flanks. We employ the method that was previously introduced to mine the database using a Binet-Cauchy (BC) kernel and a Rigidity score 66 (detail in Supporting Materials).…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Exploring a Structural Data Mining Approach to Design Linkers for Head-to-Tail Peptide Cyclization

Karami,

Murail,

Giribaldi

et al. 2023

J. Chem. Inf. Model.

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Peptides have recently regained interest as therapeutic candidates, but their development remains confronted with several limitations including low bioavailability. Backbone head-to-tail cyclization, i.e., setting a covalent peptide bond linking the last amino acid with the first one, is one effective strategy of peptide-based drug design to stabilize the conformation of bioactive peptides while preserving peptide properties in terms of low toxicity, binding affinity, target selectivity, and preventing enzymatic degradation. Starting from an active peptide, it usually requires the design of a linker of a few amino acids to make it possible to cyclize the peptide, possibly preserving the conformation of the initial peptide and not affecting its activity. However, very little is known about the sequence−structure relationship requirements of designing linkers for peptide cyclization in a rational manner. Recently, we have shown that large-scale data-mining of available protein structures can lead to the precise identification of protein loop conformations, even from remote structural classes. Here, we transpose this approach to linkers, allowing head-to-tail peptide cyclization. First we show that given a linker sequence and the conformation of the linear peptide, it is possible to accurately predict the cyclized peptide conformation. Second, and more importantly, we show that it seems possible to elaborate on the information inferred from protein structures to propose effective candidate linker sequences constrained by length and amino acid composition, providing the first framework for the rational design of head-to-tail cyclization linkers. Finally, we illustrate this for two peptides using a limited set of amino-acids likely not to interfere with peptide function. For a linear peptide derived from Nrf2, the peptide cyclized starting from the experimental structure showed a 26-fold increase in the binding affinity. For urotensin II, a peptide already cyclized by a disulfide bond that exerts a broad array of biological activities, we were able, starting from models of the structure, to design a head-to-tail cyclized peptide, the first synthesized bicyclic 14-residue long urotensin II analogue, showing a retention of in vitro activity. Although preliminary, our results strongly suggest that such an approach has strong potential for cyclic peptide-based drug design.

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Section: ■ Materials and Methodsmentioning

confidence: 99%

Exploring a Structural Data Mining Approach to Design Linkers for Head-to-Tail Peptide Cyclization

Karami,

Murail,

Giribaldi

et al. 2023

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We then, switch the flanks and search for linker candidates that match those flanks. We employ the method that was previously introduced to mine the database using a Binet-Cauchy (BC) kernel and a Rigidity score [68] (detail in Supporting Materials ).…”

Section: Methodsmentioning

confidence: 99%

“…We previously introduced the BCLoopSearch protocol, to mine large protein structure datasets and retrieve loop candidates, given two disjoint fragments (loop flanks) [59]. It is based on a Binet-Cauchy (BC) kernel and a Rigidity score:…”

Section: Competing Interestsmentioning

confidence: 99%

Head-to-tail peptide cyclization: new directions and application to urotensin II and Nrf2

Karami

Murail

Giribaldi

et al. 2022

Preprint

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Peptides have recently re-gained interest as therapeutic candidates but their development remains confronted with several limitations including low bioavailability. Backbone head-to-tail cyclization is one effective strategy of peptide-based drug design to stabilize the conformation of bioactive peptides while preserving peptide properties in terms of low toxicity, binding affinity, target selectivity and preventing enzymatic degradation. However, very little is known about the sequence-structure relationship requirements of designing linkers for peptide cyclization in a rational manner. Recently, we have shown that large scale data-mining of available protein structures can lead to the precise identification of protein loop conformations, even from remote structural classes. Here, we transpose this approach to head-to-tail peptide cyclization. Firstly we show that given a linker sequence and the conformation of the linear peptide, it is possible to accurately predict the cyclized peptide conformation improving by over 1 A over pre-existing protocols. Secondly, and more importantly, we show that is is possible to elaborate on the information inferred from protein structures to propose effective candidate linker sequences constrained by length and amino acid composition, providing the first framework for the rational peptide head-to-tail cyclization. As functional validation, we apply it to the design of a head-to-tail cyclized derivative of urotensin II, an 11-residue long peptide which exerts a broad array of biologic activities, making its cognate receptor a valuable and innovative therapeutic or diagnostic target. We propose a three amino acid candidate linker, leading to the first synthesized 14-residue long cyclic UII analogue with excellent retention of in vitro activity.

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“…We previously introduced the BCLoopSearch protocol, to mine large protein structure datasets and retrieve loop candidates, given two disjoint fragments (loop flanks) 49 . It is based on a Binet-Cauchy (BC) kernel and a Rigidity score: where X and Y are C α coordinates of the flanks and dataset fragments, respectively and they are centered at the origin.…”

Section: Methodsmentioning

confidence: 99%

“…We previously introduced a fast and efficient approach to mine large collections of structures using a Binet-Cauchy kernel, to search for similar fragments without gaps 48 . It was extended to the search for loop candidate given loop flanks, BCLoopSearch 49 . However, according to our early tests, the following bottlenecks need to be tackled.…”

Section: Introductionmentioning

confidence: 99%

DaReUS-Loop: accurate loop modeling using fragments from remote or unrelated proteins

Karami¹,

Guyon²,

Vries³

et al. 2018

Sci Rep

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Despite efforts during the past decades, loop modeling remains a difficult part of protein structure modeling. Several approaches have been developed in the framework of crystal structures. However, for homology models, the modeling of loops is still far from being solved. We propose DaReUS-Loop, a data-based approach that identifies loop candidates mining the complete set of experimental structures available in the Protein Data Bank. Candidate filtering relies on local conformation profile-profile comparison, together with physico-chemical scoring. Applied to three different template-based test sets, DaReUS-Loop shows significant increase in the number of high-accuracy loops, and significant enhancement for modeling long loops. A special advantage is that our method proposes a prediction confidence score that correlates well with the expected accuracy of the loops. Strikingly, over 50% of successful loop models are derived from unrelated proteins, indicating that fragments under similar constraints tend to adopt similar structure, beyond mere homology.

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BCSearch: fast structural fragment mining over large collections of protein structures

Cited by 5 publications

References 25 publications

Exploring a Structural Data Mining Approach to Design Linkers for Head-to-Tail Peptide Cyclization

Exploring a Structural Data Mining Approach to Design Linkers for Head-to-Tail Peptide Cyclization

Head-to-tail peptide cyclization: new directions and application to urotensin II and Nrf2

DaReUS-Loop: accurate loop modeling using fragments from remote or unrelated proteins

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