Completion of the amino acid sequence of papain.
Ontology highlight
ABSTRACT:
SUBMITTER: Husain SS
PROVIDER: S-EPMC1184853 | biostudies-other | 1969 Sep
REPOSITORIES: biostudies-other
Similar Datasets
Project description:The complete amino acid sequence of the 279-residue CNBr peptide CB8 from the alpha 1 chain of type I calf skin collagen is presented. It was determined by sequencing overlapping fragments of CB8 produced by Staphylococcus aureus V8 proteinase, trypsin, Endoproteinase Arg-C and hydroxylamine. Tryptic cleavages were also made specific for lysine by blocking arginine residues with cyclohexane-1,2-dione. This completes the amino acid sequence analysis of the 1054-residues-long alpha (I) chain of calf skin collagen.
| S-EPMC1152379 | biostudies-other
Project description:MotivationK -mer hashing is a common operation in many foundational bioinformatics problems. However, generic string hashing algorithms are not optimized for this application. Strings in bioinformatics use specific alphabets, a trait leveraged for nucleic acid sequences in earlier work. We note that amino acid sequences, with complexities and context that cannot be captured by generic hashing algorithms, can also benefit from a domain-specific hashing algorithm. Such a hashing algorithm can accelerate and improve the sensitivity of bioinformatics applications developed for protein sequences.ResultsHere, we present aaHash, a recursive hashing algorithm tailored for amino acid sequences. This algorithm utilizes multiple hash levels to represent biochemical similarities between amino acids. aaHash performs ∼10X faster than generic string hashing algorithms in hashing adjacent k -mers.Availability and implementationaaHash is available online at https://github.com/bcgsc/btllib and is free for academic use.
| S-EPMC10197579 | biostudies-literature
Project description:BACKGROUND: Designing novel proteins with site-directed recombination has enormous prospects. By locating effective recombination sites for swapping sequence parts, the probability that hybrid sequences have the desired properties is increased dramatically. The prohibitive requirements for applying current tools led us to investigate machine learning to assist in finding useful recombination sites from amino acid sequence alone. RESULTS: We present STAR, Site Targeted Amino acid Recombination predictor, which produces a score indicating the structural disruption caused by recombination, for each position in an amino acid sequence. Example predictions contrasted with those of alternative tools, illustrate STAR'S utility to assist in determining useful recombination sites. Overall, the correlation coefficient between the output of the experimentally validated protein design algorithm SCHEMA and the prediction of STAR is very high (0.89). CONCLUSION: STAR allows the user to explore useful recombination sites in amino acid sequences with unknown structure and unknown evolutionary origin. The predictor service is available from http://pprowler.itee.uq.edu.au/star.
| S-EPMC1624854 | biostudies-literature
Project description:Blood levels of amino acids are important biomarkers of disease and are influenced by synthesis, protein degradation, and gene-environment interactions. Whole genome sequence analysis of amino acid levels may establish a paradigm for analyzing quantitative risk factors.In a discovery cohort of 1872 African Americans and a replication cohort of 1552 European Americans we sequenced exons and whole genomes and measured serum levels of 70 amino acids. Rare and low-frequency variants (minor allele frequency ?5%) were analyzed by three types of aggregating motifs defined by gene exons, regulatory regions, or genome-wide sliding windows. Common variants (minor allele frequency >5%) were analyzed individually. Over all four analysis strategies, 14 gene-amino acid associations were identified and replicated. The 14 loci accounted for an average of 1.8% of the variance in amino acid levels, which ranged from 0.4 to 9.7%. Among the identified locus-amino acid pairs, four are novel and six have been reported to underlie known Mendelian conditions. These results suggest that there may be substantial genetic effects on amino acid levels in the general population that may underlie inborn errors of metabolism. We also identify a predicted promoter variant in AGA (the gene that encodes aspartylglucosaminidase) that is significantly associated with asparagine levels, with an effect that is independent of any observed coding variants.These data provide insights into genetic influences on circulating amino acid levels by integrating -omic technologies in a multi-ethnic population. The results also help establish a paradigm for whole genome sequence analysis of quantitative traits.
| S-EPMC5123402 | biostudies-literature
Project description:The amino acid sequence of peptide CB-II, the major product (mol.wt. 30 000) of CNBr cleavage of fragment Bb from human complement Factor B, is given. The sequence was obtained from peptides derived by trypsin cleavage of peptide CB-II and clostripain digestion of fragment Bb. Cleavage of two Asn-Gly bonds in peptide CB-II was also found useful. These results, along with those presented in the preceding paper [Gagnon & Christie (1983) Biochem. J. 209, 51-60], yield the complete sequence of the 505 amino acid residues of fragment Bb. The C-terminal half of the molecule shows strong homology of sequence with serine proteinases. Factor B has a catalytic chain (fragment Bb) with a molecular weight twice that of proteinases previously described, suggesting that it is a novel type of serine proteinase, probably with a different activation mechanism.
| S-EPMC1154056 | biostudies-other
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.
| S-EPMC1534068 | biostudies-literature
Project description:BackgroundDue to the ever-expanding gap between the number of proteins being discovered and their functional characterization, protein function inference remains a fundamental challenge in computational biology. Currently, known protein annotations are organized in human-curated ontologies, however, all possible protein functions may not be organized accurately. Meanwhile, recent advancements in natural language processing and machine learning have developed models which embed amino acid sequences as vectors in n-dimensional space. So far, these embeddings have primarily been used to classify protein sequences using manually constructed protein classification schemes.ResultsIn this work, we describe the use of amino acid sequence embeddings as a systematic framework for studying protein ontologies. Using a sequence embedding, we show that the bacterial carbohydrate metabolism class within the SEED annotation system contains 48 clusters of embedded sequences despite this class containing 29 functional labels. Furthermore, by embedding Bacillus amino acid sequences with unknown functions, we show that these unknown sequences form clusters that are likely to have similar biological roles.ConclusionsThis study demonstrates that amino acid sequence embeddings may be a powerful tool for developing more robust ontologies for annotating protein sequence data. In addition, embeddings may be beneficial for clustering protein sequences with unknown functions and selecting optimal candidate proteins to characterize experimentally.
| S-EPMC9502642 | biostudies-literature
Project description:PrDOS is a server that predicts the disordered regions of a protein from its amino acid sequence (http://prdos.hgc.jp). The server accepts a single protein amino acid sequence, in either plain text or FASTA format. The prediction system is composed of two predictors: a predictor based on local amino acid sequence information and one based on template proteins. The server combines the results of the two predictors and returns a two-state prediction (order/disorder) and a disorder probability for each residue. The prediction results are sent by e-mail, and the server also provides a web-interface to check the results.
| S-EPMC1933209 | biostudies-literature
Project description:LingZhi (Ganoderma lucidum) has been used as a therapeutic agent for decades, but the antitumor potency of LingZhi oligopeptides (LZOs) was not well explored. In current study, ten novel LZO amino acid sequences were identified, and anticancer potency was evaluated. We found that LZO-3 [EGHGF] significantly triggered A549 cell apoptosis via mitochondrial dysregulation, as evidenced by caspases activation, mitochondrial membrane potential collapse, Bcl-2/Bax ratio alteration, and cytochrome c release. Further, the down-regulation of Trx/TrxR reductase and the improvement of the MDM2/p53 state also contributed to the LZO-3-induced cell apoptosis. Notably, our findings provide evidence for the novel potency of LZOs, which could be developed as promising chemotherapeutic agents against lung cancer.
| S-EPMC9049989 | biostudies-literature
Project description:RNA-protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA-protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA-protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located in the RNA-protein interface. In leave-one-out cross-validation experiments, RNABindR identifies interface residues with >85% overall accuracy. It can be calibrated by the user to obtain either high specificity or high sensitivity for interface residues. RNABindR, implementing a Naive Bayes classifier, performs as well as a more complex neural network classifier (to our knowledge, the only previously published sequence-based method for RNA binding site prediction) and offers the advantages of speed, simplicity and interpretability of results. RNABindR predictions on the human telomerase protein hTERT are in good agreement with experimental data. The availability of computational tools for predicting which residues in an RNA binding protein are likely to contact RNA should facilitate design of experiments to directly test RNA binding function and contribute to our understanding of the diversity, mechanisms, and regulation of RNA-protein complexes in biological systems. (RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.).
| S-EPMC1524891 | biostudies-literature