Disulphide Connectivity Prediction in Proteins Based on Secondary Structures and Cysteine Separation
نویسنده
چکیده
The disulphide bonds are important in deciding the final 3D conformation of protein. Knowing disulphide connectivity will help to find out the final protein conformation, as it will limit the conformational search space. Fariselli and Casadio[1] approached problem of predicting disulphide connectivity by equating the problem to a maximum graph matching problem and assigning edge weights based on the residues in the nearest neighborhoods of the cysteines. This paper modifies the weights by adding constraints based on secondary structure and separation between the cysteines in the protein chain. Prediction results show considerable improvement. The prediction results can provide insight into the protein folding and disulphide bond formation as they are supporting the hypothesis on which the objective function is based upon
منابع مشابه
Prediction of cystine connectivity using SVM
One of the major contributors to protein structures is the formation of disulphide bonds between selected pairs of cysteines at oxidized state. Prediction of such disulphide bridges from sequence is challenging given that the possible combination of cysteine pairs as the number of cysteines increases in a protein. Here, we describe a SVM (support vector machine) model for the prediction of cyst...
متن کاملLarge-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural networks, and weighted graph matching.
The formation of disulphide bridges between cysteines plays an important role in protein folding, structure, function, and evolution. Here, we develop new methods for predicting disulphide bridges in proteins. We first build a large curated data set of proteins containing disulphide bridges to extract relevant statistics. We then use kernel methods to predict whether a given protein chain conta...
متن کاملLarge-Scale Prediction of Disulphide Bond Connectivity
The formation of disulphide bridges among cysteines is an important feature of protein structures. Here we develop new methods for the prediction of disulphide bond connectivity. We first build a large curated data set of proteins containing disulphide bridges and then use 2-Dimensional Recursive Neural Networks to predict bonding probabilities between cysteine pairs. These probabilities in tur...
متن کاملCMD: A Database to Store the Bonding States of Cysteine Motifs with Secondary Structures
Computational approaches to the disulphide bonding state and its connectivity pattern prediction are based on various descriptors. One descriptor is the amino acid sequence motifs flanking the cysteine residue motifs. Despite the existence of disulphide bonding information in many databases and applications, there is no complete reference and motif query available at the moment. Cysteine motif ...
متن کاملردیابی و تکثیر ژن های اثرگذار حامل دومین LysMدر ژنوم قارچ F. oxysporum f. sp. lycopersici
During the infection- while the xylem is colonized by the F. oxysporum f. sp. Lycopersici (Fol)- several effector proteins have been secreted into the xylem that suppress the plant’s defense response and enable parasitic colonization. So far, 14 effector proteins have been reported in Fol. However, there are no identified domains in their sequences. LysM effector proteins were identified ...
متن کامل