Dataset Information

Primary Sequence and 3D Structure Prediction of the Plant Toxin Stenodactylin.

ABSTRACT: Stenodactylin is one of the most potent type 2 ribosome-inactivating proteins (RIPs); its high toxicity has been demonstrated in several models both in vitro and in vivo. Due to its peculiarities, stenodactylin could have several medical and biotechnological applications in neuroscience and cancer treatment. In this work, we report the complete amino acid sequence of stenodactylin and 3D structure prediction. The comparison between the primary sequence of stenodactylin and other RIPs allowed us to identify homologies/differences and the amino acids involved in RIP toxic activity. Stenodactylin RNA was isolated from plant caudex, reverse transcribed through PCR and the cDNA was amplificated and cloned into a plasmid vector and further analyzed by sequencing. Nucleotide sequence analysis showed that stenodactylin A and B chains contain 251 and 258 amino acids, respectively. The key amino acids of the active site described for ricin and most other RIPs are also conserved in the stenodactylin A chain. Stenodactylin amino acid sequence shows a high identity degree with volkensin (81.7% for A chain, 90.3% for B chain), whilst when compared with other type 2 RIPs the identity degree ranges from 27.7 to 33.0% for the A chain and from 42.1 to 47.7% for the B chain.

SUBMITTER: Iglesias R

PROVIDER: S-EPMC7551084 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Primary Sequence and 3D Structure Prediction of the Plant Toxin Stenodactylin.

Iglesias Rosario R Polito Letizia L Bortolotti Massimo M Pedrazzi Manuela M Citores Lucía L Ferreras José M JM Bolognesi Andrea A

Toxins 20200821 9

Stenodactylin is one of the most potent type 2 ribosome-inactivating proteins (RIPs); its high toxicity has been demonstrated in several models both in vitro and in vivo. Due to its peculiarities, stenodactylin could have several medical and biotechnological applications in neuroscience and cancer treatment. In this work, we report the complete amino acid sequence of stenodactylin and 3D structure prediction. The comparison between the primary sequence of stenodactylin and other RIPs allowed us ...[more]

PMID: 32825611

Similar Datasets

Project description:The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing.In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy.We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues, including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7-4.8 Å C(?)-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes.

Project description:A new monotonicity-constrained maximum likelihood approach, called Partial Order Optimum Likelihood (POOL), is presented and applied to the problem of functional site prediction in protein 3D structures, an important current challenge in genomics. The input consists of electrostatic and geometric properties derived from the 3D structure of the query protein alone. Sequence-based conservation information, where available, may also be incorporated. Electrostatics features from THEMATICS are combined with multidimensional isotonic regression to form maximum likelihood estimates of probabilities that specific residues belong to an active site. This allows likelihood ranking of all ionizable residues in a given protein based on THEMATICS features. The corresponding ROC curves and statistical significance tests demonstrate that this method outperforms prior THEMATICS-based methods, which in turn have been shown previously to outperform other 3D-structure-based methods for identifying active site residues. Then it is shown that the addition of one simple geometric property, the size rank of the cleft in which a given residue is contained, yields improved performance. Extension of the method to include predictions of non-ionizable residues is achieved through the introduction of environment variables. This extension results in even better performance than THEMATICS alone and constitutes to date the best functional site predictor based on 3D structure only, achieving nearly the same level of performance as methods that use both 3D structure and sequence alignment data. Finally, the method also easily incorporates such sequence alignment data, and when this information is included, the resulting method is shown to outperform the best current methods using any combination of sequence alignments and 3D structures. Included is an analysis demonstrating that when THEMATICS features, cleft size rank, and alignment-based conservation scores are used individually or in combination THEMATICS features represent the single most important component of such classifiers.

Project description:BACKGROUND: Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. RESULTS: In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. CONCLUSIONS: The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

Dataset Information

Primary Sequence and 3D Structure Prediction of the Plant Toxin Stenodactylin.

Publications

Primary Sequence and 3D Structure Prediction of the Plant Toxin Stenodactylin.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets