Prediction Of Amino Acid Sequence Research Articles

Protein class prediction based on Count Vectorizer and long short term memory

Proteins class and function prediction is one of the most significant task in computational bioinformatics. The information about the protein functions and class plays a vital role in understanding biological cells and has a great impact on human life in factors such as personalized medicine. The technical advancement in the areas of biological aspects and understanding of biological processes results in features and characteristics of important Proteins. Prediction of amino acid sequence involves prediction of amino sequence folding and its structures from the primary sequence obtained. In this work, Machine learning prediction algorithms have applied for protein class prediction. This method takes consideration of macromolecules of biological significances. Later the solution focuses on the understanding of different protein family, subsequently classify the protein family type sequence. This is achieved through machine learning algorithms Naive Bayes (NB) and Random forest (RF) algorithms with count vectorized feature and LSTM. These algorithms are used to classify the protein family on its protein sequence. Finally, result shows that LSTM predicts the protein class more accurately than the RF, and NB algorithm. LSTM achieves an accuracy of 96% whereas RF & NB with an accuracy of 91% and 86%.

Computational methods in preformulation study for pharmaceutical solid dosage forms of therapeutic proteins

AbstractDesign and delivery of protein-based biopharmaceuticals needs detailed planning and strict monitoring of intermediate processing steps, storage conditions and container-closure system to ensure a stable, elegant and biopharmaceutically acceptable dosage form. Selection of manufacturing process variables and conditions along with packaging specifications can be achieved through properly designed preformulation study protocol for the formulation. Thermodynamic stability and biological activity of therapeutic proteins depend on folding–unfolding and three-dimensional packing dynamics of amino acid network in the protein molecule. Lack of favourable environment may cause protein aggregation with loss in activity and even fatal immunological reaction. Although lyophilization can enhance the stability of protein-based formulations in the solid state, it can induce protein unfolding leading to thermodynamic instability. Formulation stabilizers such as preservatives can also result in aggregation of therapeutic proteins. Modern instrumental techniques in conjunction with computational tools enable rapid and accurate prediction of amino acid sequence, thermodynamic parameters associated with protein folding and detection of aggregation “hot-spots.” Globular proteins pose a challenge during investigations on their aggregation propensity. Biobetter therapeutic monoclonal antibodies with enhanced stability, solubility and reduced immunogenic potential can be designed through mutation of aggregation-prone zones. The objective of the present review article is to focus on the various analytical methods and computational approaches used in the study of thermodynamic stability and aggregation tendency of therapeutic proteins, with an aim to develop optimal and marketable formulation. Knowledge of protein dynamics through application of computational tools will provide the essential inputs and relevant information for successful and meaningful completion of preformulation studies on solid dosage forms of therapeutic proteins.

Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation.

Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.

Biosynthetic Gene Cluster for Surugamide A Encompasses an Unrelated Decapeptide, Surugamide F.

Genome mining is a powerful method for finding novel secondary metabolites. In our study on the biosynthetic gene cluster for the cyclic octapeptides surugamides A-E (inhibitors of cathepsin B), we found a putative gene cluster consisting of four successive non-ribosomal peptide synthetase (NRPS) genes, surA, surB, surC, and surD. Prediction of amino acid sequence based on the NRPSs and gene inactivation revealed that surugamides A-E are produced by two NRPS genes, surA and surD, which were separated by two NRPS genes, surB and surC. The latter genes are responsible for the biosynthesis of an unrelated peptide, surugamide F. The pattern of intercalation observed in the sur genes is unprecedented. The structure of surugamide F, a linear decapeptide containing one 3-amino-2-methylpropionic acid (AMPA) residue, was determined by spectroscopic methods and was confirmed by solid-phase peptide synthesis.

A comprehensive mapping of the structure and gene organisation in the sheep MHC class I region.

BackgroundThe major histocompatibility complex (MHC) is a chromosomal region that regulates immune responsiveness in vertebrates. This region is one of the most important for disease resistance because it has been associated with resistance or susceptibility to a wide variety of diseases and because the MHC often accounts for more of the variance than other loci. Selective breeding for disease resistance is becoming increasingly common in livestock industries, and it is important to determine how this will influence MHC polymorphism and resistance to diseases that are not targeted for selection. However, in sheep the order and sequence of the protein coding genes is controversial. Yet this information is needed to determine precisely how the MHC influences resistance and susceptibility to disease.MethodsCHORI bacterial artificial chromosomes (BACs) known to contain sequences from the sheep MHC class I region were sub-cloned, and the clones partially sequenced. The resulting sequences were analysed and re-assembled to identify gene content and organisation within each BAC. The low resolution MHC class I physical map was then compared to the cattle reference genome, the Chinese Merino sheep MHC map published by Gao, et al. (2010) and the recently available sheep reference genome.ResultsImmune related class I genes are clustered into 3 blocks; beta, kappa and a novel block not previously identified in other organisms. The revised map is more similar to Bovidae maps than the previous sheep maps and also includes several genes previously not annotated in the Chinese Merino BAC assembly and others not currently annotated in the sheep reference chromosome 20. In particular, the organisation of nonclassical MHC class I genes is similar to that present in the cattle MHC. Sequence analysis and prediction of amino acid sequences of MHC class I classical and nonclassical genes was performed and it was observed that the map contained one classical and eight nonclassical genes together with three possible pseudogenes.ConclusionsThe comprehensive physical map of the sheep MHC class I region enhances our understanding of the genetic architecture of the class I MHC region in sheep and will facilitate future studies of MHC function.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1992-4) contains supplementary material, which is available to authorized users.

Prediction of amino acid sequence from structure

We have developed a method for the prediction of an amino acid sequence that is compatible with a three-dimensional backbone structure. Using only a backbone structure of a protein as input, the algorithm is capable of designing sequences that closely resemble natural members of the protein family to which the template structure belongs. In general, the predicted sequences are shown to have multiple sequence profile scores that are dramatically higher than those of random sequences, and sometimes better than some of the natural sequences that make up the superfamily. As anticipated, highly conserved but poorly predicted residues are often those that contribute to the functional rather than structural properties of the protein. Overall, our analysis suggests that statistical profile scores of designed sequences are a novel and valuable figure of merit for assessing and improving protein design algorithms.

Isolation of an insulin-like growth factor II cDNA from guinea pig liver: Expression and developmental regulation

Insulin-like growth factor II (IGF-II) cDNA was isolated from adult guinea pig liver by polymerase chain reaction (PCR) screening. A cDNA sequence was obtained corresponding to part of the preproIGF-II, including the signal peptide, the mature IGF-II and 37 amino acids of the acid carboxy-terminal E-domain. Amino acid sequence prediction, based on the cDNA clone, showed that mature guinea pig IGF-II has a high homology with both human and rat IGF-II, 100 and 94% identity, respectively. Levels of IGF-II mRNA in guinea pigs of different ages were analyzed by solution hybridization/RNase protection assay using part of the isolated IGF-II cDNA as a probe. There is a marked developmental regulation of IGF-II after birth. IGF-II mRNA levels were high in fetal livers, and decreased 15- to 30-fold in adults. As in man, but in contrast to rats, adult guinea pigs have significant levels of IGF-II mRNA in the liver. In fetal guinea pigs, the expression of IGF-II mRNA was 5-, 2- and 70-fold lower in kidney, skeletal muscle and brain cortex, respectively, than in liver. IGF-II mRNA levels in kidney and skeletal muscle of fetal guinea pigs were 5- and 4-fold higher, respectively, compared with adults. Similar sizes of IGF-II mRNA transcripts could be observed on Northern blots in newborn rats and in fetal guinea pigs. Our conclusions are that the mature IGF-II peptide in the guinea pig is 100% identical to the mature peptide in the human. IGF-II mRNA is developmentally regulated in the guinea pig and this developmental regulation of IGF-II expression is different when compared with that of rats, guinea pigs having a higher adult IGF-II expression in liver, kidney and skeletal muscle. Finally, individual IGF-II transcripts may be both differentially and developmentally regulated in guinea pig tissues.

Editing of a chloroplast mRNA by creation of an initiation codon.

Primary mRNA transcripts in several systems are edited by single base substitutions, small deletions or insertions to yield functional messenger RNA species. Mitochondrial mRNAs in particular, including those from plants, seem to be the subject of extensive editing, unlike mRNAs encoded by chloroplast DNA, for which the prediction of amino-acid sequence from the corresponding gene sequence is generally unambiguous. Occasionally, however, an ACG codon appears at the 5' terminus of chloroplast genes, where the initiation codon ATG would be expected. Here we present evidence for a C----U editing that is responsible for the conversion of the ACG codon to an AUG initiation codon in the mRNA transcript from the rpl2 gene of the maize plastome, showing that mRNA editing can also occur in chloroplasts.