IgMAT: immunoglobulin sequence multi-species annotation tool for any species including those with incomplete antibody annotation or unusual characteristics

The advent and continual improvement of high-throughput sequencing technologies has made immunoglobulin repertoire sequencing accessible and informative regardless of study species. However, to fully map changes in polyclonal dynamics, precise annotation of these constantly rearranging genes is pivotal. For this reason, data agnostic tools able to learn from presented data are required. Most sequence annotation tools are designed primarily for use with human and mouse antibody sequences which use databases with fixed species lists, applying very specific assumptions which select against unique structural characteristics. We present IgMAT, which utilises a reduced amino acid alphabet, incorporates multiple HMM alignments into a single consensus and enables the incorporation of user defined databases to better represent their species of interest.Availability and implementationIgMAT has been developed as a python module, and is available on GitHub (https://github.com/TPI-Immunogenetics/igmat) for download under GPLv3 license.Supplementary informationModel Breakdowns

Thousands of protein linear motif classes may still be undiscovered

Linear motifs are short protein subsequences that mediate protein interactions. Hundreds of motif classes including thousands of motif instances are known. Our theory estimates how many motif classes remain undiscovered. As commonly done, we describe motif classes as regular expressions specifying motif length and the allowed amino acids at each motif position. We measure motif specificity for a pair of motif classes by quantifying how many motif-discriminating positions prevent a protein subsequence from matching the two classes at once. We derive theorems for the maximal number of motif classes that can simultaneously maintain a certain number of motif-discriminating positions between all pairs of classes in the motif universe, for a given amino acid alphabet. We also calculate the fraction of all protein subsequences that would belong to a motif class if all potential motif classes came into existence. Naturally occurring pairs of motif classes present most often a single motif-discriminating position. This mild specificity maximizes the potential number of coexisting motif classes, the expansion of the motif universe due to amino acid modifications and the fraction of amino acid sequences that code for a motif instance. As a result, thousands of linear motif classes may remain undiscovered.

Evolution as a Guide to Designing xeno Amino Acid Alphabets

Here, we summarize a line of remarkably simple, theoretical research to better understand the chemical logic by which life’s standard alphabet of 20 genetically encoded amino acids evolved. The connection to the theme of this Special Issue, “Protein Structure Analysis and Prediction with Statistical Scoring Functions”, emerges from the ways in which current bioinformatics currently lacks empirical science when it comes to xenoproteins composed largely or entirely of amino acids from beyond the standard genetic code. Our intent is to present new perspectives on existing data from two different frontiers in order to suggest fresh ways in which their findings complement one another. These frontiers are origins/astrobiology research into the emergence of the standard amino acid alphabet, and empirical xenoprotein synthesis.

DeepRTCP: Predicting ATP-Binding Cassette Transporters Based on 1-Dimensional Convolutional Network

ATP-binding cassette (ABC) transporters can promote cells to absorb nutrients and excrete harmful substances. It plays a vital role in the transmembrane transport of macromolecules. Therefore, the identification of ABC transporters is of great significance for the biological research. This paper will introduce a novel method called DeepRTCP. DeepRTCP uses the deep convolutional neural network and a feature combined of reduced amino acid alphabet based tripeptide composition and PSSM to recognize ABC transporters. We constructed a dataset named ABC_2020. It contains the latest ABC transporters downloaded from Uniprot. We performed 10-fold cross-validation on DeepRTCP, and the average accuracy of DeepRTCP was 95.96%. Compared with the start-of-the-art method for predicting ABC transporters, DeepRTCP improved the accuracy by 9.29%. It is anticipated that DeepRTCP can be used as an effective ABC transporter classifier which provides a reliable guidance for the research of ABC transporters.

Using Reduced Amino Acid Alphabet and Biological Properties to Analyze and Predict Animal Neurotoxin Protein

Background: Because of the high affinity of these animal neurotoxin proteins for some special target site, they were usually used as pharmacological tools and therapeutic agents in medicine to gain deep insights into the function of the nervous system. Background and Objective: The animal neurotoxin proteins are one of the most common functional groups among the animal toxin proteins. Thus, it was very important to characterize and predict the animal neurotoxin proteins. Methods: In this study, the differences between the animal neurotoxin proteins and non-toxin proteins were analyzed. Results: Significant differences were found between them. In addition, the support vector machine was proposed to predict the animal neurotoxin proteins. The predictive results of our classifier achieved the overall accuracy of 96.46%. Furthermore, the random forest and k-nearest neighbors were applied to predict the animal neurotoxin proteins. Conclusion: The compared results indicated that the predictive performances of our classifier were better than other two algorithms.

Enzyme catalysis prior to aromatic residues: reverse engineering of a dephosphoCoA kinase

It is well-known that the large diversity of protein functions and structures is derived from the broad spectrum of physicochemical properties of the 20 canonical amino acids. According to the generally accepted hypothesis, protein evolution was continuously associated with enrichment of this alphabet, increasing stability, specificity and spectrum of catalytic functions. Aromatic amino acids are considered the latest addition to genetic code.The main objective of this study was to test whether enzymatic catalysis can spare the aromatic amino acids (aromatics) by determining the effect of amino acid alphabet reduction on structure and function of dephospho-CoA kinase (DPCK). We designed two mutant variants of a putative DPCK from Aquifex aeolicus by substituting (i) Tyr, Phe and Trp or (ii) all aromatics (including His), i.e. ∼10% of the total sequence. Their structural characterization indicates that removal of aromatic amino acids may support rich secondary structure content although inevitably impairs a firm globular arrangement. Both variants still possess ATPase activity, although with 150-300 times lower efficiency in comparison with the wild-type phosphotransferase activity. The transfer of the phosphate group to the dephospho-CoA substrate is however heavily uncoupled and only one of the variants is still able to perform the reaction.Here we provide support to the hypothesis that proteins in the early stages of life could support at least some enzymatic activities, despite lower efficiencies resulting from the lack of a firm hydrophobic core. Based on the presented data we hypothesize that further protein scaffolding role may be provided by ligands upon binding.SignificanceAll extant proteins rely on the standard coded amino acid alphabet. However, early proteins lacked some of these amino acids that were incorporated into the genetic code only after the evolution of their respective metabolic pathways, aromatic amino acids being among the last additions. This is intriguing because of their crucial role in hydrophobic core packing, indispensable for enzyme catalysis.We designed two aromatics-less variants of a highly conserved enzyme from the CoA synthesis pathway, capable of enzyme catalysis and showing significant ordering upon substrate binding. To our knowledge, this is the first example of enzyme catalysis in complete absence of aromatic amino acids and presents a possible mechanism of how aromatics-less enzymes could potentially support an early biosphere.

Characterization and Prediction of Presynaptic and Postsynaptic Neurotoxin by Reduced Amino Acid and Biological Property.

Background: Presynaptic and postsynaptic neurotoxins are two important neurotoxins. Due to the important role of presynaptic and postsynaptic neurotoxins in pharmacology and neuroscience, identification of them becomes very important in biology. Method: In this study, the statistical test and F-score were used to calculate the difference between amino acids and biological properties. The support vector machine was used to predict the presynaptic and postsynaptic neurotoxins by using the reduced amino acid alphabet types. Results: By using the reduced amino acid alphabet as the input parameters of support vector machine, the overall accuracy of our classifier had increased to 91.07%, which was the highest overall accuracy in this study. When compared with the other published methods, better predictive results were obtained by our classifier. Conclusion: In summary, we analyzed the differences between two neurotoxins in amino acids and biological properties, and constructed a classifier that could predict these two neurotoxins by using the reduced amino acid alphabet.