Comparative study of in silico protein design techniques
Конференцијски прилог (Објављена верзија)
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© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
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Protein design plays a pivotal role in various scientific and industrial applications, such
as drug development and biotechnology. With the advancement of computational
methods, new tools and algorithms have emerged to facilitate the generation of novel
protein designs. This study presents a comparative analysis of Pepspec and RFdiffusion,
two prominent methods in protein design, to evaluate their effectiveness in designing
peptides with desired properties. Mainly, we aim to design peptides that bind with high
affinity and specificity to a desired protein target.
Pepspec is an application native to the Rosetta software package. It relies on Monte Carlo
sampling of backbone confirmations and residue mutations and a stochastic optimization
based on the Rosetta score – a measure approximating the binding free-energy of the
complex.
On the other hand, a recently developed tool, RFdiffusion, is a denoising diffusion
probabilistic model based on an existing artificial neural netw...ork, RoseTTAFold, developed
for protein structure estimation. It is trained to remove noise from protein structures
on a large database of protein complexes to ultimately be able to generate novel binder
designs based on the target structure.
In this study, we aim to compare the efficiency of these two design tools. As it is common
in generative ML algorithms, the comparison will be made by evaluating both the design
quality and design versatility. The quality will be assessed by using the well-known
AlphaFold2 Machine learning tool to estimate the binding affinity of the peptide-protein
complex while the versatility will be measured using standard sequence based statistical
methods.
RFdiffusion and Pepspec offer distinct approaches to protein design. By assessing
the strengths and limitations of each method in this study, we aim to deepen the
understanding of these methods and allow leveraging these tools effectively in designing
peptides with desired characteristics, contributing to advancements in the field of protein
engineering and biotechnology.
Кључне речи:
rational protein design / AI/ML in biology and medicine / computational bioengineeringИзвор:
4th Belgrade Bioinformatics Conference, 2023, 4, 74-74Издавач:
- Belgrade : Institute of molecular genetics and genetic engineering
Напомена:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
Колекције
Институција/група
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Tanasijević, Ivan AU - Rakić, Branka PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/2014 AB - Protein design plays a pivotal role in various scientific and industrial applications, such as drug development and biotechnology. With the advancement of computational methods, new tools and algorithms have emerged to facilitate the generation of novel protein designs. This study presents a comparative analysis of Pepspec and RFdiffusion, two prominent methods in protein design, to evaluate their effectiveness in designing peptides with desired properties. Mainly, we aim to design peptides that bind with high affinity and specificity to a desired protein target. Pepspec is an application native to the Rosetta software package. It relies on Monte Carlo sampling of backbone confirmations and residue mutations and a stochastic optimization based on the Rosetta score – a measure approximating the binding free-energy of the complex. On the other hand, a recently developed tool, RFdiffusion, is a denoising diffusion probabilistic model based on an existing artificial neural network, RoseTTAFold, developed for protein structure estimation. It is trained to remove noise from protein structures on a large database of protein complexes to ultimately be able to generate novel binder designs based on the target structure. In this study, we aim to compare the efficiency of these two design tools. As it is common in generative ML algorithms, the comparison will be made by evaluating both the design quality and design versatility. The quality will be assessed by using the well-known AlphaFold2 Machine learning tool to estimate the binding affinity of the peptide-protein complex while the versatility will be measured using standard sequence based statistical methods. RFdiffusion and Pepspec offer distinct approaches to protein design. By assessing the strengths and limitations of each method in this study, we aim to deepen the understanding of these methods and allow leveraging these tools effectively in designing peptides with desired characteristics, contributing to advancements in the field of protein engineering and biotechnology. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Comparative study of in silico protein design techniques EP - 74 SP - 74 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_2014 ER -
@conference{ author = "Tanasijević, Ivan and Rakić, Branka", year = "2023", abstract = "Protein design plays a pivotal role in various scientific and industrial applications, such as drug development and biotechnology. With the advancement of computational methods, new tools and algorithms have emerged to facilitate the generation of novel protein designs. This study presents a comparative analysis of Pepspec and RFdiffusion, two prominent methods in protein design, to evaluate their effectiveness in designing peptides with desired properties. Mainly, we aim to design peptides that bind with high affinity and specificity to a desired protein target. Pepspec is an application native to the Rosetta software package. It relies on Monte Carlo sampling of backbone confirmations and residue mutations and a stochastic optimization based on the Rosetta score – a measure approximating the binding free-energy of the complex. On the other hand, a recently developed tool, RFdiffusion, is a denoising diffusion probabilistic model based on an existing artificial neural network, RoseTTAFold, developed for protein structure estimation. It is trained to remove noise from protein structures on a large database of protein complexes to ultimately be able to generate novel binder designs based on the target structure. In this study, we aim to compare the efficiency of these two design tools. As it is common in generative ML algorithms, the comparison will be made by evaluating both the design quality and design versatility. The quality will be assessed by using the well-known AlphaFold2 Machine learning tool to estimate the binding affinity of the peptide-protein complex while the versatility will be measured using standard sequence based statistical methods. RFdiffusion and Pepspec offer distinct approaches to protein design. By assessing the strengths and limitations of each method in this study, we aim to deepen the understanding of these methods and allow leveraging these tools effectively in designing peptides with desired characteristics, contributing to advancements in the field of protein engineering and biotechnology.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Comparative study of in silico protein design techniques", pages = "74-74", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_2014" }
Tanasijević, I.,& Rakić, B.. (2023). Comparative study of in silico protein design techniques. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4, 74-74. https://hdl.handle.net/21.15107/rcub_imagine_2014
Tanasijević I, Rakić B. Comparative study of in silico protein design techniques. in 4th Belgrade Bioinformatics Conference. 2023;4:74-74. https://hdl.handle.net/21.15107/rcub_imagine_2014 .
Tanasijević, Ivan, Rakić, Branka, "Comparative study of in silico protein design techniques" in 4th Belgrade Bioinformatics Conference, 4 (2023):74-74, https://hdl.handle.net/21.15107/rcub_imagine_2014 .