A protein mapping method based on physicochemical properties and dimension reduction

Protein sequence comparison based on representation on a finite dimensional unit hypercube

Numerous techniques are used to compare protein sequences based on the values of the physiochemical properties of amino acids. In this work, a single physical/chemical property value based non-binary representation of protein sequences is obtained on a 20 × 20-dimensional unit hypercube. The represented vector expressed in the matrix form is taken as the descriptor. The generalized NTV metric, which is an extension of the NTV metric used for polynucleotide space is taken as a distance measure. Based on this distance measure, a distance matrix is obtained for protein sequence comparison. Using this distance matrix, phylogenetic trees are drawn by using Molecular Evolutionary Genetics Analysis 11 (MEGA11) software applying the neighbor-joining method. Data sets used in this current work are 9-ND4, 9-ND5, 9-ND6, 24 TF-LF proteins, 27 different viruses and 127 proteins from the protein kinase C (PKC) family. Two sets of phylogenetic trees are obtained - one based on property value of polarity and the other based on property value of molecular weight. They are found to be exactly the same. Similar results also hold for other single property value based representation. The present trees are individually tested for efficiency based on the criterion of rationalized perception and computational time. The results of the present method are compared with those obtained earlier by other methods on the same protein sequences using assessment criteria of Symmetric distance (SD), Correlation coefficient, and Rationalized perception. In all the cases, the present results are found to be better than the results of other methods under comparison.Communicated by Ramaswamy H. Sarma.

Mathematical Approach to Protein Sequence Comparison Based on Physiochemical Properties

The difficult aspect of developing new protein sequence comparison techniques is coming up with a method that can quickly and effectively handle huge data sets of various lengths in a timely manner. In this work, we first obtain two numerical representations of protein sequences separately based on one physical property and one chemical property of amino acids. The lengths of all the sequences under comparison are made equal by appending the required number of zeroes. Then, fast Fourier transform is applied to this numerical time series to obtain the corresponding spectrum. Next, the spectrum values are reduced by the standard inter coefficient difference method. Finally, the corresponding normalized values of the reduced spectrum are selected as the descriptors for protein sequence comparison. Using these descriptors, the distance matrices are obtained using Euclidian distance. They are subsequently used to draw the phylogenetic trees using the UPGMA algorithm. Phylogenetic trees are first constructed for 9 ND4, 9 ND5, and 9 ND6 proteins using the polarity value as the chemical property and the molecular weight as the physical property. They are compared, and it is seen that polarity is a better choice than molecular weight in protein sequence comparison. Next, using the polarity property, phylogenetic trees are obtained for 12 baculovirus and 24 transferrin proteins. The results are compared with those obtained earlier on the identical sequences by other methods. Three assessment criteria are considered for comparison of the results-quality based on rationalized perception, quantitative measures based on symmetric distance, and computational speed. In all the cases, the results are found to be more satisfactory.

A 2D Non-degeneracy Graphical Representation of Protein Sequence and Its Applications

Background:

The comparison of the protein sequences is an important research filed in bioinformatics. Many alignment-free methods have been proposed.

Objective:

In order to mining the more information of the protein sequence, this study focus on a new alignment-free method based on physiochemical properties of amino acids.

Methods:

Average physiochemical value (Apv) has been defined. For a given protein sequence, a 2D curve was outlined based on Apv and position of the amino acid, and there is not loop and intersection on the curve. According to the curve, the similarity/dissimilarity of the protein sequences can be analyzed.

Results and Conclusion:

Two groups of protein sequences are taken as examples to illustrate the new methods, the protein sequences can be classified correctly, and the results are highly correlated with that of ClustalW. The new method is simple and effective.

A Generalized Iterative Map for Analysis of Protein Sequences

AIM AND OBJECTIVE

The similarities comparison of biological sequences is an important task in bioinformatics. The methods of the similarities comparison for biological sequences are divided into two classes: sequence alignment method and alignment-free method. The graphical representation of biological sequences is a kind of alignment-free method, which constitutes a tool for analyzing and visualizing the biological sequences. In this article, a generalized iterative map of protein sequences was suggested to analyze the similarities of biological sequences.

MATERIALS AND METHODS

Based on the normalized physicochemical indexes of 20 amino acids, each amino acid can be mapped into a point in 5D space. A generalized iterative function system was introduced to outline a generalized iterative map of protein sequences, which can not only reflect various physicochemical properties of amino acids but also incorporate with different compression ratios of the component of a generalized iterative map. Several properties were proved to illustrate the advantage of the generalized iterative map. The mathematical description of the generalized iterative map was suggested to compare the similarities and dissimilarities of protein sequences. Based on this method, similarities/dissimilarities were compared among ND5 protein sequences, as well as ND6 protein sequences of ten different species.

RESULTS

By correlation analysis, the ClustalW results were compared with our similarity/dissimilarity results and other graphical representation results to show the utility of our approach. The comparison results show that our approach has better correlations with ClustalW for all species than other approaches and illustrate the effectiveness of our approach.

CONCLUSION

Two examples show that our method not only has good performances and effects in the similarity/dissimilarity analysis of protein sequences but also does not require complex computation.

Novel Protein Sequence Comparison Method Based on Transition Probability Graph and Information Entropy

AIM AND OBJECTIVE

Sequence analysis is one of the foundations in bioinformatics. It is widely used to find out the feature metric hidden in the sequence. Otherwise, the graphical representation of biologic sequence is an important tool for sequencing analysis. This study is undertaken to find out a new graphical representation of biosequences.

MATERIALS AND METHODS

The transition probability is used to describe amino acid combinations of protein sequences. The combinations are composed of amino acids directly adjacent to each other or separated by multiple amino acids. The transition probability graph is built up by the transition probabilities of amino acid combinations. Next, a map is defined as a representation from transition probability graph to transition probability vector by k-order transition probability graph. Transition entropy vectors are developed by the transition probability vector and information entropy. Finally, the proposed method is applied to two separate applications, 499 HA genes of H1N1, and 95 coronaviruses.

RESULTS

By constructing a phylogenetic tree, we find that the results of each application are consistent with other studies.

CONCLUSION

the graphical representation proposed in this article is a practical and correct method.

ProtPCV: A Fixed Dimensional Numerical Representation of Protein Sequence to Significantly Reduce Sequence Search Time

Protein sequence is a wealth of experimental information which is yet to be exploited to extract information on protein homologues. Consequently, it is observed from publications that dynamic programming, heuristics and HMM profile-based alignment techniques along with the alignment free techniques do not directly utilize ordered profile of physicochemical properties of a protein to identify its homologue. Also, it is found that these works lack crucial bench-marking or validation in absence of which their incorporation in search engines may appears to be questionable. In this direction this research approach offers fixed dimensional numerical representation of protein sequences extending the concept of periodicity count value of nucleotide types (2017) to accommodate Euclidean distance as direct similarity measure between two proteins. Instead of bench-marking with BLAST and PSI-BLAST only, this new similarity measure was also compared with Needleman-Wunsch and Smith-Waterman. For enhancing the strength of comparison, this work for the first time introduces two novel benchmarking methods based on correlation of "similarity scores" and "proximity of ranked outputs from a standard sequence alignment method" between all possible pairs of search techniques including the new one presented in this paper. It is found that the novel and unique numerical representation of a protein can reduce computational complexity of protein sequence search to the tune of O(log(n)). It may also help implementation of various other similarity-based operation possible, such as clustering, phylogenetic analysis and classification of proteins on the basis of the properties used to build this numerical representation of protein.

Novel Method of 3-Dimensional Graphical Representation for Proteins and Its Application

In this article, we propose a 3-dimensional graphical representation of protein sequences based on 10 physicochemical properties of 20 amino acids and the BLOSUM62 matrix. It contains evolutionary information and provides intuitive visualization. To further analyze the similarity of proteins, we extract a specific vector from the graphical representation curve. The vector is used to calculate the similarity distance between 2 protein sequences. To prove the effectiveness of our approach, we apply it to 3 real data sets. The results are consistent with the known evolution fact and show that our method is effective in phylogenetic analysis.

Protein Sequence Comparison Based on Physicochemical Properties and the Position-Feature Energy Matrix

We develop a novel position-feature-based model for protein sequences by employing physicochemical properties of 20 amino acids and the measure of graph energy. The method puts the emphasis on sequence order information and describes local dynamic distributions of sequences, from which one can get a characteristic B-vector. Afterwards, we apply the relative entropy to the sequences representing B-vectors to measure their similarity/dissimilarity. The numerical results obtained in this study show that the proposed methods leads to meaningful results compared with competitors such as Clustal W.