Evolutionary decision rules for predicting protein contact maps

Abstract

Protein structure prediction is currently one of the main open challenges in Bioinformatics. The protein contact map is an useful, and commonly used, representation for protein 3D structure and represents binary proximities (contact or non-contact) between each pair of amino acids of a protein. In this work, we propose a multiobjective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. The rules obtained by the algorithm impose a set of conditions based on amino acid properties to predict contacts. We present results obtained by our approach on four different protein data sets. A statistical study was also performed to extract valid conclusions from the set of prediction rules generated by our algorithm. Results obtained confirm the validity of our proposal.

Improving the Efficiency of MECoMaP: A Protein Residue-Residue Contact Predictor

“…This experiment was performed with the aim of validating our representation and confirms that the new encoding provides enough information for a good performance of a learning classifier. Moreover, we can also notice that MECoMaP achieved the best results for this experiment and improve the results for DS1 and DS2 data set shown in [4]. Table 2.…”

“…As stated in [4], we consider two objectives to be optimized: coverage and accuracy. Coverage represents the number of predicted contacts and accuracy evaluates the real predicted contacts rate.…”

“…The aim of this work consists of improving our proposal MECoMaP (Multiobjective Evolutionary Contact Map Predictor) [4] in order to increase the efficiency of the protein contact map prediction. The prediction is based on three physico-chemical properties: hydrophobicity (H), polarity (P) and charge (C), structural features: solvent accessibility (SA) and secondary structure (SS) and evolutionary information in form of Position Specific Scoring Matrix (PSSM).…”

A new multiple classifier system for the prediction of protein's contacts map

“…It is computed the frequency of each property for the different amino acid residues present in the sub-sequence formed between the target residues. In this segment are included the Kyte-Doolittle hydropathy profile, the Grantham profile for polarity and the Klein scale for net charge [14]. This segment has been included to analyze the influence of the hydrophobicity, polarity and charge properties of the amino acids in the folding process.…”

Run-time prediction of business process indicators using evolutionary decision rules

“…The use of an evolutionary algorithm may be justified for four different reasons ( Fogel, 1997 ): (a) it can handle continuous and discrete attributes and automatically discretizes the continuous features; (b) it also handle missing attribute values and noise; (c) it can build models that can be easily interpreted by humans and finally (d) it finds a sub-set of the features that are relevant to the classification without the use of feature selection. In addition, EAs have shown the capacity of finding suboptimal solutions in search spaces when the search space is characterized by high dimensionality ( Marquez-Chamorro, Asencio-Cortes, Divina, & Aguilar-Ruiz, 2014 ). In this case, the set of possible state conditions of a process, encoding in decision rules, determine the search space and fulfil these requirements.…”