scholarly journals A Machine Learning Study on the Thermostability Prediction of (R)-ω-Selective Amine Transaminase from Aspergillus terreus

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Li-li Jia ◽  
Ting-ting Sun ◽  
Yan Wang ◽  
Yu Shen
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Amino Acid ◽  
Protein Engineering ◽  
Amino Acid Sequence ◽  
High Performance ◽  
Aspergillus Terreus ◽  
Single Point ◽  
Activity Relationship ◽  
Least Squares Regression

Artificial intelligence technologies such as machine learning have been applied to protein engineering, with unique advantages in protein structure, function prediction, catalytic activity, and other issues in recent years. Screening better mutants is still a bottleneck in protein engineering. In this paper, a new sequence-activity relationship method was analyzed for its application in improving the thermal stability of Aspergillus terreus (R)-ω-selective amine transaminase. The experimental data from 6 single-point mutated enzymes were used as a learning dataset to build models and predict the thermostability of 26 mutants. Based on digital signal processing (DSP), this method digitized the amino acid sequence of proteins by fast Fourier transform (FFT) and then established the best model applying partial least squares regression (PLSR) to screen out all possible mutants, especially those with high performance. In protein engineering, the innovative sequence activity relationship (ISAR) method can make a reasonable prediction using limited experimental data and significantly reduce the experimental cost. The half-life ( T 1 / 2 ) of (R)-ω-transaminase was fitted with the amino acid sequence by the ISAR algorithm, resulting in an R 2 of 0.8929 and a cvRMSE of 4.89. At the same time, the mutants with higher T 1 / 2 than the existing ones were predicted, laying the groundwork for better (R)-ω-transaminase in the later stage. The ISAR algorithm is expected to provide a new technique for protein evolution and screening.

scholarly journals Data mining of coronavirus: SARS-CoV-2, SARS-CoV and MERS-CoV

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jung Eun Huh ◽  
Seunghee Han ◽  
Taeseon Yoon
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
High Similarity ◽  
Incubation Periods ◽  
Initial Question ◽  
The Difference ◽  
Blast Program

Abstract Objective In this study we compare the amino acid and codon sequence of SARS-CoV-2, SARS-CoV and MERS-CoV using different statistics programs to understand their characteristics. Specifically, we are interested in how differences in the amino acid and codon sequence can lead to different incubation periods and outbreak periods. Our initial question was to compare SARS-CoV-2 to different viruses in the coronavirus family using BLAST program of NCBI and machine learning algorithms. Results The result of experiments using BLAST, Apriori and Decision Tree has shown that SARS-CoV-2 had high similarity with SARS-CoV while having comparably low similarity with MERS-CoV. We decided to compare the codons of SARS-CoV-2 and MERS-CoV to see the difference. Though the viruses are very alike according to BLAST and Apriori experiments, SVM proved that they can be effectively classified using non-linear kernels. Decision Tree experiment proved several remarkable properties of SARS-CoV-2 amino acid sequence that cannot be found in MERS-CoV amino acid sequence. The consequential purpose of this paper is to minimize the damage on humanity from SARS-CoV-2. Hence, further studies can be focused on the comparison of SARS-CoV-2 virus with other viruses that also can be transmitted during latent periods.

Chemical structure of light chains: amino acid sequence of type K Bence-Jones proteins

1966 ◽  
Vol 166 (1003) ◽  
pp. 124-137 ◽  
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Sequence Data ◽  
Single Point ◽  
Structural Difference ◽  
Light Chains ◽  
Single Point Mutation ◽  
Amino Terminal ◽  
Type K ◽  
Bence Jones Proteins

Bence-Jones proteins are the light chains of the autologous myeloma globulin and are analogous to the light chains of normal human immunoglobulins. Peptide mapping has demonstrated that Bence-Jones proteins share a fixed portion of their sequence (the ‘constant’ portion) and also have a mutable part (the ‘variable’ portion). Sequence analysis and ordering of the tryptic and chymotryptic peptides has provided the tentative complete amino acid sequence of one Bence-Jones protein of antigenic type K. Comparison with partial sequence data for other type K Bence-Jones proteins has revealed many structural differences in the amino terminal half of the molecules, but only one structural difference in the carboxyl terminal half. The latter is strongly correlated with the Inv genetic factor. The points of interchange in the amino terminal half occur in clusters close to the half cystine residues and the ‘switch peptide’ (positions 102 through 105), after which the sequence becomes essentially invariant. This suggests that the major areas subject to sequence variation are part of a single topographical region which may define a portion of the antigen combining site in the light chains of antibodies. Many, but not all, the amino acid interchanges are compatible with a single point mutation. As yet, no single mutational theory suffices to explain the manifold differences in structure of the light chains. Such structural variation, however, could result from the presence of many related genes.

scholarly journals Amino Acid Sequence of the Globin lIB Chain of the Dimeric Haemoglobin of the Bivalve Mollusc Andara trapezia

1984 ◽  
Vol 37 (4) ◽  
pp. 191 ◽  
Author(s):  
WK Fisher ◽  
AT Gilbert ◽  
EOP Thompson
Keyword(s):  
High Performance Liquid Chromatography ◽  
Liquid Chromatography ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
High Performance ◽  
Bivalve Mollusc ◽  
Amino Acid Sequences ◽  
Globin Chain ◽  
Tryptic Peptides

The tryptic peptides of the S-carboxymethylated globin chain ofa dimeric haemoglobin from A. trapezia were purified by high-performance liquid chromatography and their amino acid sequences determined by the dansyl-Edman method.

Die primäre Proteinstruktur von Stämmen des Tabakmosaikvirus

1969 ◽  
Vol 24 (7) ◽  
pp. 877-885 ◽  
Author(s):  
H. G. Wittmann ◽  
I. Hindennach ◽  
B. Wittmann-Liebold
Keyword(s):  
Experimental Data ◽  
Amino Acids ◽  
Amino Acid ◽  
Coat Protein ◽  
Amino Acid Sequence ◽  
Spatial Structure ◽  
Amino Acid Sequences ◽  
The Other ◽  
Tryptic Peptides ◽  
Tmv Strains

Experimental data for determining a) the amino acid sequences of eight tryptic peptides containing 95 amino acids and b) the order of the tryptic peptides are given. Combining the data of this and of a previous paper the complete amino acid sequence of the coat protein of the TMV strain Holmes rib grass (HRG) is established (Fig. 5). It is compared with three other TMV strains the sequences of which have been determined before (Fig. 6).Differences and similarities between the sequences of the four TMV strains are discussed. HRG has a deletion of two amino acids and it is the most distantly related of the four TMV strains. When the sequence of HRG is compared to that of any of the other strains it turns out that in each case more than 50% of the 156 positions contain different amino acids (Fig. 7).The number of positions with the same amino acid in all strains and mutants so far studied is 30 per cent. These positions are not randomly distributed but clustered mainly in two regions. This finding probably reflects the restriction of amino acid exchanges by the spatial structure of the viral rod.

scholarly journals ANTICALIgN: visualizing, editing and analyzing combined nucleotide and amino acid sequence alignments for combinatorial protein engineering

2016 ◽  
Vol 29 (7) ◽  
pp. 263-270 ◽  
Author(s):  
Alexander Jarasch ◽  
Melanie Kopp ◽  
Evelyn Eggenstein ◽  
Antonia Richter ◽  
Michaela Gebauer ◽  
...  
Keyword(s):  
Amino Acid ◽  
Protein Engineering ◽  
Amino Acid Sequence ◽  
Sequence Alignments ◽  
Combinatorial Protein Engineering

scholarly journals Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 18
Author(s):  
Vytautas Ostasevicius ◽  
Ieva Paleviciute ◽  
Agne Paulauskaite-Taraseviciene ◽  
Vytautas Jurenas ◽  
Darius Eidukynas ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Prediction Models ◽  
Single Point ◽  
Sheet Thickness ◽  
Gaussian Process Regression ◽  
Balance Model ◽  
Forming Force ◽  
Friction Forces ◽  
Force Prediction

This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces. This study focuses on the influence of mechanical properties, process parameters and sheet thickness on the maximum forming force. Artificial Neural Network (ANN) and different machine learning (ML) algorithms have been applied to develop an efficient force prediction model. The predicted forces agreed reasonably well with the experimental results. Assuming that the variability of each input function is characterized by a normal distribution, sampling data were generated. The applicability of the models in an industrial environment is due to their relatively high performance and the ability to balance model bias and variance. The results indicate that ANN and Gaussian process regression (GPR) have been identified as the most efficient methods for developing forming force prediction models.

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