Abstract Background Altering a protein's function by changing its sequence allows natural proteins to be converted into useful molecular tools.
Current protein engineering methods are limited by a lack of high throughput physical or computational tests that can accurately predict protein activity under conditions relevant to its final application. Here we describe a new synthetic biology approach to protein engineering that avoids these limitations by combining high throughput gene synthesis with machine learning-based design algorithms. Results We selected 24 amino acid substitutions to make in proteinase K from alignments of homologous sequences.
We then designed and synthesized 59 specific proteinase K variants containing different combinations of the selected substitutions.
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Sequence and activity data was analyzed using machine learning algorithms. This analysis was used to design a new set of variants predicted to have increased activity over the training set, that were then synthesized and tested.
By performing two cycles of machine learning analysis and variant design we obtained fold improved proteinase K variants while only testing a total of 95 variant enzymes.
Conclusion The number of protein variants that must be tested to obtain significant functional improvements determines the type of tests that can be performed. Protein engineers wishing to modify the property of a protein to shrink tumours or catalyze chemical reactions under eness matrx 2 22 for binary options conditions have until now been forced to accept high throughput surrogate screens to measure protein properties that they hope will correlate with the functionalities that they intend to modify.
By reducing the number of variants that must be tested to fewer thanmachine learning algorithms make it possible to use more complex and expensive tests so that only eness matrx 2 22 for binary options properties that are directly relevant to the desired application need to be measured.
Protein design algorithms that only require the testing of a small number of variants represent a significant step towards a generic, resource-optimized protein engineering process. Background Protein properties that are relevant to real-world applications are often difficult to manipulate using either of the current protein engineering paradigms [ 1 - 3 ]: structure-based protein design [ 45 ] or directed evolution [ 6 - 8 ].
Both methods have shortcomings and advantages that have been discussed and compared elsewhere [ 1 - 3 ]. Chief amongst the limitations of both methods is the requirement for high throughput computational or physical tests to evaluate protein variants for suitability to a specific application. A common problem with both approaches is that frequently there are no high throughput tests for real applications.
For example, there are no high throughput tests for measuring how well a protease will remove grass stains from jeans, how quickly an antibody will shrink a tumour, or how immunogenic a potential vaccine antigen will be. As a consequence, protein engineers are frequently forced to compromise. Thus a structure-based approach in which the effects of large numbers of amino acid changes on the active site are calculated may require the protein engineer to consider only the affinity of an enzyme for its substrate and product while ignoring the effects that temperature and solvent conditions may have on the enzyme.
Similarly an empirical library based approach in which large numbers of randomly produced viral antigen variants are tested for activity may allow the protein engineer to measure their binding to antibodies already known to how to make 2020 for money neutralizing, but would prohibit direct measurement of the production of such antibodies in animals exposed to eness matrx 2 22 for binary options antigens.
Many non-biotechnological engineering endeavours pose similar challenges to those found in protein engineering: a large number of independent variables and cost-prohibitions against exhaustive search. Such diverse tasks as fuel formulation, clinical trial design and chemical process optimization are solved using experimental designs to combine variables in specific ways, and regression analysis techniques to dissect out the contribution of each variable to the outcome [ 9 ].
The common goal in all these areas of optimization is to keep the total number of activity measurements small enough to allow complex functional tests that are directly relevant to the final application. Multivariate data analysis has been used to optimize small molecules and peptides for nearly a quarter of a century [ 10 - 16 ].
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In their paper describing chemical synthesis of a gene inBenner and colleagues suggested that systematic variation of amino acids could provide an understanding of the relationship between a protein's sequence and its function [ 17 ]. Until recently, however, synthesis of specifically designed individual genes has been sufficiently difficult to effectively preclude the construction of designed gene sets and meaningful testing of analytical predictions.
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Such efforts have thus been largely confined to the synthesis of very small numbers of discrete polynucleotide [ 18 ] or protein variants [ 19 ], or to the analysis of variants produced in a library [ 20 - 22 ]. A synthetic biology approach to protein engineering has been enabled by recent advances in gene synthesis technology [ 23 - 26 ] that permit cost-effective synthesis of individually specified gene sequences instead of relying on creation of libraries of variant sequences [ 2728 ].
The feasibility of producing tens or hundreds of protein variants in which all amino acid changes are precisely specified allows the sequences and activities of these variants to be analyzed using multivariate regression and machine learning techniques adapted from optimization tasks found in other engineering disciplines. We have tested this protein engineering approach by increasing the activity and heat stability of proteinase K.
We selected 24 amino acid substitutions, then designed, synthesized and tested 59 genes containing combinations of these changes. We tested 8 different machine learning algorithms for their ability to identify the amino acid changes with a beneficial effect on proteinase K activity by using them to design new variants with improved combinations of substitutions.
All 8 algorithms produced enzyme designs that were substantially improved over wild type.
Engineering proteinase K using machine learning and synthetic genes
The results show that machine learning models of protein sequence and activity combined with efficient gene synthesis can be valuable tools in engineering proteins with improved properties. Results and Discussion 1. Selection of proteinase K as a test system To test machine learning-based protein engineering we chose to optimize proteinase K-catalyzed hydrolysis of the tetrapeptide N-Succinyl-Ala-Ala-Pro-Leu p-nitroanilide following a heat-treatment of the enzyme. We selected this activity because it mimics a key characteristic of practical protein optimization; target activities frequently result from a combination of protein properties, in this case expression and post-translational processing in a heterologous host, catalytic activity and thermostability.
The gene encoding proteinase K from Tritirachium album [ 29 ] was re-synthesized with an E. The nucleotide and amino acid sequences of this initial "wild-type" proteinase K sequence are shown in Additional file 1.
Engineering proteinase K: design methods 2.