In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences

Burnside, Daniel; Schoenrock, Andrew; Moteshareie, Houman; Hooshyar, M. A.; Basra, Prabh; Hajikarimlou, Maryam; Dick, Kevin; Barnes, Brad; Kazmirchuk, Tom; Jessulat, Matthew; Pitre, Sylvain; Samanfar, Bahram; Babu, M. Madan; Green, James R.; Wong, Alex; Dehne, Frank; Biggar, Kyle K.; Golshani, Ashkan

doi:10.1016/j.isci.2018.11.038

Cited by 11 publications

(27 citation statements)

References 55 publications

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“…Many biological functions depend on specific protein-protein interactions. Protein engineering offers the possibility to tune these interactions by developing de novo binding partners [ 1 , 2 , 3 ] or by mutating the interaction partners using computational design [ 4 , 5 ]. A powerful tool of protein engineering is to generate protein binders by the in vitro directed selection techniques [ 6 , 7 , 8 ] or to use evolution-based approaches to increase the stability of recombinant proteins [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Protein Binder (ProBi) as a New Class of Structurally Robust Non-Antibody Protein Scaffold for Directed Evolution

Pham

Huličiak

Biedermannová

et al. 2021

Viruses

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Engineered small non-antibody protein scaffolds are a promising alternative to antibodies and are especially attractive for use in protein therapeutics and diagnostics. The advantages include smaller size and a more robust, single-domain structural framework with a defined binding surface amenable to mutation. This calls for a more systematic approach in designing new scaffolds suitable for use in one or more methods of directed evolution. We hereby describe a process based on an analysis of protein structures from the Protein Data Bank and their experimental examination. The candidate protein scaffolds were subjected to a thorough screening including computational evaluation of the mutability, and experimental determination of their expression yield in E. coli, solubility, and thermostability. In the next step, we examined several variants of the candidate scaffolds including their wild types and alanine mutants. We proved the applicability of this systematic procedure by selecting a monomeric single-domain human protein with a fold different from previously known scaffolds. The newly developed scaffold, called ProBi (Protein Binder), contains two independently mutable surface patches. We demonstrated its functionality by training it as a binder against human interleukin-10, a medically important cytokine. The procedure yielded scaffold-related variants with nanomolar affinity.

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Section: Introductionmentioning

confidence: 99%

Protein Binder (ProBi) as a New Class of Structurally Robust Non-Antibody Protein Scaffold for Directed Evolution

Pham

Huličiak

Biedermannová

et al. 2021

Viruses

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show abstract

“…Surprisingly few target-specific peptide engineering approaches have been developed 10 , 11 . This is unsurprising, given that validated protein–peptide interactions are relatively scarce in comparison with protein–protein interactions.…”

Section: Introductionmentioning

confidence: 99%

“…As such, it is difficult to develop general-purpose peptide engineering algorithms that could be broadly applied to design a peptide that interacts with one’s protein target of choice. To the best of our knowledge, InSiPS 11 is the only entirely in silico method that addresses this problem as of today.…”

Section: Introductionmentioning

confidence: 99%

“…The PIPE sequence-based PPI predictor 16 has been successfully used to design novel peptides that interact with a selected target. The In Silico Peptide Synthesizer (InSiPS) 11 , built around PIPE, uses genetic algorithms to explore the peptide space and maximize peptide fitness. The fitness function is designed such that the predicted interaction score between the peptide and the target is maximized, but the interaction score between the peptide and all other proteins is minimized.…”

Section: Introductionmentioning

confidence: 99%

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Assessing sequence-based protein–protein interaction predictors for use in therapeutic peptide engineering

Charih

Biggar

Green

2022

Sci Rep

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Engineering peptides to achieve a desired therapeutic effect through the inhibition of a specific target activity or protein interaction is a non-trivial task. Few of the existing in silico peptide design algorithms generate target-specific peptides. Instead, many methods produce peptides that achieve a desired effect through an unknown mechanism. In contrast with resource-intensive high-throughput experiments, in silico screening is a cost-effective alternative that can prune the space of candidates when engineering target-specific peptides. Using a set of FDA-approved peptides we curated specifically for this task, we assess the applicability of several sequence-based protein–protein interaction predictors as a screening tool within the context of peptide therapeutic engineering. We show that similarity-based protein–protein interaction predictors are more suitable for this purpose than the state-of-the-art deep learning methods publicly available at the time of writing. We also show that this approach is mostly useful when designing new peptides against targets for which naturally-occurring interactors are already known, and that deploying it for de novo peptide engineering tasks may require gathering additional target-specific training data. Taken together, this work offers evidence that supports the use of similarity-based protein–protein interaction predictors for peptide therapeutic engineering, especially peptide analogs.

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“…IC 50 in [6]). Surprisingly few target-specific peptide engineering approaches have been developed [10,11]. This is unsurprising, given that validated protein-peptide interactions are relatively scarce in comparison with protein-protein interactions.…”

Section: Introductionmentioning

confidence: 99%

Assessing sequence-based protein-protein interaction predictors for use in therapeutic peptide engineering

Charih

Green

2021

Preprint

Self Cite

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Engineering peptides to achieve a desired therapeutic effect through the inhibition of a specific target activity or protein interaction is a non-trivial task. Few of the existing in silico peptide design algorithms generate target-specific peptides. Instead, many methods produce peptides that achieve a desired effect through an unknown mechanism. In contrast with resource-intensive high-throughput experiments, in silico screening is a cost-effective alternative that can prune the space of candidates when engineering target-specific peptides. Using a set of FDA-approved peptides we curated specifically for this task, we assess the applicability of several sequence-based protein-protein interaction predictors as a screening tool within the context of peptide therapeutic engineering. We show that similarity-based protein-protein interaction predictors are more suitable for this purpose than the current state-of-the-art deep learning methods. We also show that this approach is mostly useful when designing new peptides against targets for which naturally-occurring interactors are already known, and that deploying it for de novo peptide engineering tasks may require gathering additional target-specific training data. Taken together, this work offers evidence that supports the use of similarity-based protein-protein interaction predictors for peptide therapeutic engineering, especially peptide analogs.

show abstract

In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences

Cited by 11 publications

References 55 publications

Protein Binder (ProBi) as a New Class of Structurally Robust Non-Antibody Protein Scaffold for Directed Evolution

Protein Binder (ProBi) as a New Class of Structurally Robust Non-Antibody Protein Scaffold for Directed Evolution

Assessing sequence-based protein–protein interaction predictors for use in therapeutic peptide engineering

Assessing sequence-based protein-protein interaction predictors for use in therapeutic peptide engineering

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