In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences.
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ABSTRACT:
SUBMITTER: Burnside D
PROVIDER: S-EPMC6348295 | biostudies-literature | 2019 Jan
REPOSITORIES: biostudies-literature
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Project description:MotivationIn this work, we aim to develop a computational approach for predicting DNA-binding sites in proteins from amino acid sequences. To avoid overfitting with this method, all available DNA-binding proteins from the Protein Data Bank (PDB) are used to construct the models. The random forest (RF) algorithm is used because it is fast and has robust performance for different parameter values. A novel hybrid feature is presented which incorporates evolutionary information of the amino acid sequence, secondary structure (SS) information and orthogonal binary vector (OBV) information which reflects the characteristics of 20 kinds of amino acids for two physical-chemical properties (dipoles and volumes of the side chains). The numbers of binding and non-binding residues in proteins are highly unbalanced, so a novel scheme is proposed to deal with the problem of imbalanced datasets by downsizing the majority class.ResultsThe results show that the RF model achieves 91.41% overall accuracy with Matthew's correlation coefficient of 0.70 and an area under the receiver operating characteristic curve (AUC) of 0.913. To our knowledge, the RF method using the hybrid feature is currently the computationally optimal approach for predicting DNA-binding sites in proteins from amino acid sequences without using three-dimensional (3D) structural information. We have demonstrated that the prediction results are useful for understanding protein-DNA interactions.AvailabilityDBindR web server implementation is freely available at http://www.cbi.seu.edu.cn/DBindR/DBindR.htm.
| S-EPMC2638931 | biostudies-literature
Project description:What kinds of amino acid sequences could possibly be protein sequences? From all existing databases that we can find, known proteins are only a small fraction of all possible combinations of amino acids. Beginning with Sanger's first detailed determination of a protein sequence in 1952, previous studies have focused on describing the structure of existing protein sequences in order to construct the protein universe. No one, however, has developed a criteria for determining whether an arbitrary amino acid sequence can be a protein. Here we show that when the collection of arbitrary amino acid sequences is viewed in an appropriate geometric context, the protein sequences cluster together. This leads to a new computational test, described here, that has proved to be remarkably accurate at determining whether an arbitrary amino acid sequence can be a protein. Even more, if the results of this test indicate that the sequence can be a protein, and it is indeed a protein sequence, then its identity as a protein sequence is uniquely defined. We anticipate our computational test will be useful for those who are attempting to complete the job of discovering all proteins, or constructing the protein universe.
| S-EPMC4302309 | biostudies-literature
Project description:The existence of over- and under-represented sequence motifs in genomes provides evidence of selective evolutionary pressures on biological mechanisms such as transcription, translation, ligand-substrate binding, and host immunity. In order to accurately identify motifs and other genome-scale patterns of interest, it is essential to be able to generate accurate null models that are appropriate for the sequences under study. While many tools have been developed to create random nucleotide sequences, protein coding sequences are subject to a unique set of constraints that complicates the process of generating appropriate null models. There are currently no tools available that allow users to create random coding sequences with specified amino acid composition and GC content for the purpose of hypothesis testing. Using the principle of maximum entropy, we developed a method that generates unbiased random sequences with pre-specified amino acid and GC content, which we have developed into a python package. Our method is the simplest way to obtain maximally unbiased random sequences that are subject to GC usage and primary amino acid sequence constraints. Furthermore, this approach can easily be expanded to create unbiased random sequences that incorporate more complicated constraints such as individual nucleotide usage or even di-nucleotide frequencies. The ability to generate correctly specified null models will allow researchers to accurately identify sequence motifs which will lead to a better understanding of biological processes as well as more effective engineering of biological systems.
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Project description:As the number of known proteins has expanded, how to accurately identify DNA binding proteins has become a significant biological challenge. At present, various computational methods have been proposed to recognize DNA-binding proteins from only amino acid sequences, such as SVM, DNABP and CNN-RNN. However, these methods do not consider the context in amino acid sequences, which makes it difficult for them to adequately capture sequence features. In this study, a new method that coordinates a bidirectional long-term memory recurrent neural network and a convolutional neural network, called CNN-BiLSTM, is proposed to identify DNA binding proteins. The CNN-BiLSTM model can explore the potential contextual relationships of amino acid sequences and obtain more features than can traditional models. The experimental results show that the CNN-BiLSTM achieves a validation set prediction accuracy of 96.5%-7.8% higher than that of SVM, 9.6% higher than that of DNABP and 3.7% higher than that of CNN-RNN. After testing on 20,000 independent samples provided by UniProt that were not involved in model training, the accuracy of CNN-BiLSTM reached 94.5%-12% higher than that of SVM, 4.9% higher than that of DNABP and 4% higher than that of CNN-RNN. We visualized and compared the model training process of CNN-BiLSTM with that of CNN-RNN and found that the former is capable of better generalization from the training dataset, showing that CNN-BiLSTM has a wider range of adaptations to protein sequences. On the test set, CNN-BiLSTM has better credibility because its predicted scores are closer to the sample labels than are those of CNN-RNN. Therefore, the proposed CNN-BiLSTM is a more powerful method for identifying DNA-binding proteins.
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Project description:Nucleic acid-binding proteins are traditionally divided into two categories: With the ability to bind DNA or RNA. In the light of new knowledge, such categorizing should be overcome because a large proportion of proteins can bind both DNA and RNA. Another even more important features of nucleic acid-binding proteins are so-called sequence or structure specificities. Proteins able to bind nucleic acids in a sequence-specific manner usually contain one or more of the well-defined structural motifs (zinc-fingers, leucine zipper, helix-turn-helix, or helix-loop-helix). In contrast, many proteins do not recognize nucleic acid sequence but rather local DNA or RNA structures (G-quadruplexes, i-motifs, triplexes, cruciforms, left-handed DNA/RNA form, and others). Finally, there are also proteins recognizing both sequence and local structural properties of nucleic acids (e.g., famous tumor suppressor p53). In this mini-review, we aim to summarize current knowledge about the amino acid composition of various types of nucleic acid-binding proteins with a special focus on significant enrichment and/or depletion in each category.
| S-EPMC7831508 | biostudies-literature
Project description:The identification of metal ion binding sites is important for protein function annotation and the design of new drug molecules. This study presents an effective method of analyzing and identifying the binding residues of metal ions based solely on sequence information. Ten metal ions were extracted from the BioLip database: Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, K+ and Co2+. The analysis showed that Zn2+, Cu2+, Fe2+, Fe3+, and Co2+ were sensitive to the conservation of amino acids at binding sites, and promising results can be achieved using the Position Weight Scoring Matrix algorithm, with an accuracy of over 79.9% and a Matthews correlation coefficient of over 0.6. The binding sites of other metals can also be accurately identified using the Support Vector Machine algorithm with multifeature parameters as input. In addition, we found that Ca2+ was insensitive to hydrophobicity and hydrophilicity information and Mn2+ was insensitive to polarization charge information. An online server was constructed based on the framework of the proposed method and is freely available at http://60.31.198.140:8081/metal/HomePage/HomePage.html.
| S-EPMC5576659 | biostudies-literature
Project description:There is an urgent need for affinity reagents that target phospho-modified sites on individual proteins; however, generating such reagents remains a significant challenge. Here, we describe a genetic selection strategy for routine laboratory isolation of phospho-specific designed ankyrin repeat proteins (DARPins) by linking in vivo affinity capture of a phosphorylated target protein with antibiotic resistance of Escherichia coli cells. The assay is validated using an existing panel of DARPins that selectively bind the nonphosphorylated (inactive) form of extracellular signal-regulated kinase 2 (ERK2) or its doubly phosphorylated (active) form (pERK2). We then use the selection to affinity-mature a phospho-specific DARPin without compromising its selectivity for pERK2 over ERK2 and to reprogram the substrate specificity of the same DARPin towards non-cognate ERK2. Collectively, these results establish our genetic selection as a useful and potentially generalizable protein engineering tool for studying phospho-specific binding proteins and customizing their affinity and selectivity.
| S-EPMC6478843 | biostudies-literature
Project description:Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.We start with a Naïve Bayes classifier trained to predict whether a given amino acid residue is a DNA-binding residue based on its identity and the identities of its sequence neighbors. The input to the classifier consists of the identities of the target residue and 4 sequence neighbors on each side of the target residue. The classifier is trained and evaluated (using leave-one-out cross-validation) on a non-redundant set of 171 proteins. Our results indicate the feasibility of identifying interface residues based on local sequence information. The classifier achieves 71% overall accuracy with a correlation coefficient of 0.24, 35% specificity and 53% sensitivity in identifying interface residues as evaluated by leave-one-out cross-validation. We show that the performance of the classifier is improved by using sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) as additional input. The classifier achieves 78% overall accuracy with a correlation coefficient of 0.28, 44% specificity and 41% sensitivity in identifying interface residues. Examination of the predictions in the context of 3-dimensional structures of proteins demonstrates the effectiveness of this method in identifying DNA-binding sites from sequence information. In 33% (56 out of 171) of the proteins, the classifier identifies the interaction sites by correctly recognizing at least half of the interface residues. In 87% (149 out of 171) of the proteins, the classifier correctly identifies at least 20% of the interface residues. This suggests the possibility of using such classifiers to identify potential DNA-binding motifs and to gain potentially useful insights into sequence correlates of protein-DNA interactions.Naïve Bayes classifiers trained to identify DNA-binding residues using sequence information offer a computationally efficient approach to identifying putative DNA-binding sites in DNA-binding proteins and recognizing potential DNA-binding motifs.
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Project description:Mutagenesis of protein-encoding sequences occurs ubiquitously; it enables evolution, accumulates during aging, and is associated with disease. Many biotechnological methods exploit random mutations to evolve novel proteins. To quantitate protein tolerance to random change, it is vital to understand the probability that a random amino acid replacement will lead to a protein's functional inactivation. We define this probability as the "x factor." Here, we develop a broadly applicable approach to calculate x factors and demonstrate this method using the human DNA repair enzyme 3-methyladenine DNA glycosylase (AAG). Three gene-wide mutagenesis libraries were created, each with 10(5) diversity and averaging 2.2, 4.6, and 6.2 random amino acid changes per mutant. After determining the percentage of functional mutants in each library using high-stringency selection (>19,000-fold), the x factor was found to be 34% +/- 6%. Remarkably, reanalysis of data from studies of diverse proteins reveals similar inactivation probabilities. To delineate the nature of tolerated amino acid substitutions, we sequenced 244 surviving AAG mutants. The 920 tolerated substitutions were characterized by substitutability index and mapped onto the AAG primary, secondary, and known tertiary structures. Evolutionarily conserved residues show low substitutability indices. In AAG, beta strands are on average less substitutable than alpha helices; and surface loops that are not involved in DNA binding are the most substitutable. Our results are relevant to such diverse topics as applied molecular evolution, the rate of introduction of deleterious alleles into genomes in evolutionary history, and organisms' tolerance of mutational burden.
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Project description:The ability to site-specifically incorporate non-canonical amino acids (ncAAs) into proteins has made possible the study of protein structure and function in fundamentally new ways, as well as the bio synthesis of unnatural polymers. However, the task of site-specifically incorporating multiple ncAAs into proteins with high purity and yield continues to present a challenge. At the heart of this challenge lies the lower efficiency of engineered orthogonal translation system components compared to their natural counterparts (e.g., translation elements that specifically use a ncAA and do not interact with the cell's natural translation apparatus). Here, we show that evolving and tuning expression levels of multiple components of an engineered translation system together as a whole enhances ncAA incorporation efficiency. Specifically, we increase protein yield when incorporating multiple p-azido-phenylalanine(pAzF) residues into proteins by (i) evolving the Methanocaldococcus jannaschii p-azido-phenylalanyl-tRNA synthetase anti-codon binding domain, (ii) evolving the elongation factor Tu amino acid-binding pocket, and (iii) tuning the expression of evolved translation machinery components in a single vector. Use of the evolved translation machinery in a genomically recoded organism lacking release factor one enabled enhanced multi-site ncAA incorporation into proteins. We anticipate that our approach to orthogonal translation system development will accelerate and expand our ability to site-specifically incorporate multiple ncAAs into proteins and biopolymers, advancing new horizons for synthetic and chemical biotechnology. Biotechnol. Bioeng. 2017;114: 1074-1086. © 2016 Wiley Periodicals, Inc.
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