{"id":557,"date":"2024-03-20T07:45:51","date_gmt":"2024-03-20T07:45:51","guid":{"rendered":"https:\/\/www.biosynsis.org\/blog\/?p=557"},"modified":"2024-03-20T07:49:28","modified_gmt":"2024-03-20T07:49:28","slug":"pnas-a-simple-and-robust-experimental-process-for-protein-engineering","status":"publish","type":"post","link":"https:\/\/www.biosynsis.com\/blog\/pnas-a-simple-and-robust-experimental-process-for-protein-engineering\/","title":{"rendered":"PNAS: A Simple and Robust Experimental Process for Protein Engineering"},"content":{"rendered":"

According to a new study by researchers at the University of Michigan, a protein engineering method using simple, cost-effective experiments and machine learning models can predict which proteins are effective for specific purposes.<\/p>\n

\"\"<\/p>\n

This method has profound potential in assembling proteins and peptides, and can be used for applications ranging from industrial tools to therapeutic methods. For example, this technology can help accelerate the development of stable peptides to treat diseases in ways that current drugs cannot, including improving the exclusive binding of antibodies to targets in immunotherapy.
\n“The rules that control how proteins work, from sequence to structure to function, are so complex. Contributing to the interpretability of protein engineering is particularly exciting,” said Marshall Keys, the lead author of this study.<\/p>\n

\n\n\n<\/p>\n\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n<\/p>\n
Catalog Number<\/th>\nProduct Name<\/th>\nProduct Size<\/th>\nApplications<\/th>\nPrice<\/th>\n<\/tr>\n<\/thead>\n
GE0017S<\/td>\n2-Hydroxy-dATP<\/a><\/td>\n10 \u03bcl<\/td>\nCleanup of sgRNA and Cas9 mRNA.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0017L<\/td>\n2-Hydroxy-dATP<\/a><\/td>\n5 \u00d7 10 \u03bcl<\/td>\nCleanup of sgRNA and Cas9 mRNA.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0018S<\/td>\n5-Bromo-dUTP<\/a><\/td>\n50 \u03bcl<\/td>\nFunction as a programmable DNA endonuclease.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0018L<\/td>\n5-Bromo-dUTP<\/a><\/td>\n5 \u00d7 50 \u03bcl<\/td>\nFunction as a programmable DNA endonuclease.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0019S<\/td>\n8-Oxo-dATP<\/a><\/td>\n10 \u03bcl<\/td>\nIn vitro<\/i> binding of DNA for visualization
\nand target enrichment.<\/td>\n		Online Inquiry<\/a><\/td>\n<\/tr>\n
GE0019L<\/td>\n8-Oxo-dATP<\/a><\/td>\n5 \u00d7 10 \u03bcl<\/td>\nIn vitro<\/i> binding of DNA for visualization
\nand target enrichment.<\/td>\n		Online Inquiry<\/a><\/td>\n<\/tr>\n
GE0020S<\/td>\n8-Oxo-dGTP<\/a><\/td>\n30 \u03bcl<\/td>\nIn vitro<\/i> digestion of dsDNA.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0020L<\/td>\n8-Oxo-dGTP<\/a><\/td>\n5 \u00d7 30 \u03bcl<\/td>\nIn vitro<\/i> digestion of dsDNA.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0021S<\/td>\ndITP Solution<\/a><\/td>\n1 ml<\/td>\nFunction as a programmable DNA endonuclease.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0021L<\/td>\ndITP Solution<\/a><\/td>\n10 ml<\/td>\nFunction as a programmable DNA endonuclease.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0022<\/td>\nDNA Shuffling Kit<\/a><\/td>\n15 Reactions<\/td>\nRapid synthesis of guide RNA.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0023<\/td>\ndNTP Random Mutagenesis Kit<\/a><\/td>\n15 Reactions<\/td>\nGeneration of microgram quantities of custom sgRNA.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0024S<\/td>\ndPTP<\/a><\/td>\n30 \u03bcl<\/td>\nTarget-specific DNA nicking.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0024L<\/td>\ndPTP<\/a><\/td>\n5 \u00d7 30 \u03bcl<\/td>\nTarget-specific DNA nicking.<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n
GE0025<\/td>\nError Prone Kit<\/a><\/td>\n15 Reactions<\/td>\nFunction as an RNA-guided endonuclease. Ideal for direct introduction of Cas9\/sgRNA complexes<\/td>\nOnline Inquiry<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

 <\/p>\n

Currently, most protein engineering experiments use complex, labor-intensive methods and expensive instruments to obtain very accurate data. The lengthy process limits the amount of data that can be obtained, and complex methods are challenging for learning and execution – this is a trade-off of accuracy.<\/p>\n

“Our approach suggests that in many applications, you can avoid these complex methods,” said Case.
\nThe updated method first divides cells into two groups, called binary sorting, based on whether they express the desired features – such as binding to fluorescent molecules – or not. Then, the cells are sequenced to obtain potential DNA codes for the proteins of interest. Then, machine learning algorithms will reduce the noise in sequencing data to identify the best proteins.<\/p>\n

Greg Thurber, Associate Professor of Chemical Engineering at the University of Michigan and corresponding author of the paper, said, “Instead of choosing the ‘best book’ from the library, it’s better to read many books and then piece together different pages of different stories to find the best book possible, even if it’s not in your original library.” “I was surprised to see the robustness of this technique using simple binary sorting data.”<\/p>\n

This method further enhances its accessibility by using linear machine learning models, which are easier to interpret compared to models with dozens of parameters.
\nKeith said, “Because we can understand the physical rules of how proteins actually work, we can use linear equations to simulate nonlinear protein behavior and create better drugs.”
\nThis study was conducted at the advanced genomics core, structural biology center, biological mass spectrometry equipment, and proteomics and peptide synthesis core.<\/p>\n

\n

Related Services<\/h2>\n

Protein Engineering\u00a0and Optimization<\/a><\/p>\n

Synthetic DNA Library Construction<\/a><\/p>\n

Protein Directed Evolution<\/a><\/p>\n

Site-Directed Mutagenesis Service<\/a><\/p>\n

Ribosome Display Service<\/a><\/p>\n

Rational Protein Design<\/a><\/p>\n<\/div>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

According to a new study by researchers at the University of Michigan, a protein engineering method using simple, cost-effective experiments and machine learning models can predict which proteins are effective for specific purposes. This method has profound potential in assembling proteins and peptides, and can be used for applications ranging from industrial tools to therapeutic…<\/p>\n","protected":false},"author":1,"featured_media":558,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/posts\/557"}],"collection":[{"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/comments?post=557"}],"version-history":[{"count":109,"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/posts\/557\/revisions"}],"predecessor-version":[{"id":559,"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/posts\/557\/revisions\/559"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/media\/558"}],"wp:attachment":[{"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/media?parent=557"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/categories?post=557"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.biosynsis.com\/blog\/wp-json\/wp\/v2\/tags?post=557"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}