ProClust: improved clustering of protein sequences with an extended graph-based approach

Aim and Objective:: Given the rapidly increasing number of molecular biology data available, computational methods of low complexity are necessary to infer protein structure, function, and evolution. Method:: In the work, we proposed a novel mthod, FermatS, which based on the global position information and local position representation from the curve and normalized moments of inertia, respectively, to extract features information of protein sequences. Furthermore, we use the generated features by FermatS method to analyze the similarity/dissimilarity of nine ND5 proteins and establish the prediction model of DNA-binding proteins based on logistic regression with 5-fold crossvalidation. Results:: In the similarity/dissimilarity analysis of nine ND5 proteins, the results are consistent with evolutionary theory. Moreover, this method can effectively predict the DNA-binding proteins in realistic situations. Conclusion:: The findings demonstrate that the proposed method is effective for comparing, recognizing and predicting protein sequences. The main code and datasets can download from https://github.com/GaoYa1122/FermatS.

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Computational Analysis of Therapeutic Enzyme Uricase from Different Source Organisms

Current Proteomics ◽

10.2174/1570164616666190617165107 ◽

2020 ◽

Vol 17 (1) ◽

pp. 59-77

Author(s):

Anand Kumar Nelapati ◽

JagadeeshBabu PonnanEttiyappan

Keyword(s):

Uric Acid ◽

Amino Acid ◽

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Protein Sequences ◽

Amino Acid Sequences ◽

Amino Acid Residues ◽

Multiple Sequence ◽

Physiochemical Properties ◽

Pharmaceutical Industries

Background:Hyperuricemia and gout are the conditions, which is a response of accumulation of uric acid in the blood and urine. Uric acid is the product of purine metabolic pathway in humans. Uricase is a therapeutic enzyme that can enzymatically reduces the concentration of uric acid in serum and urine into more a soluble allantoin. Uricases are widely available in several sources like bacteria, fungi, yeast, plants and animals.Objective:The present study is aimed at elucidating the structure and physiochemical properties of uricase by insilico analysis.Methods:A total number of sixty amino acid sequences of uricase belongs to different sources were obtained from NCBI and different analysis like Multiple Sequence Alignment (MSA), homology search, phylogenetic relation, motif search, domain architecture and physiochemical properties including pI, EC, Ai, Ii, and were performed.Results:Multiple sequence alignment of all the selected protein sequences has exhibited distinct difference between bacterial, fungal, plant and animal sources based on the position-specific existence of conserved amino acid residues. The maximum homology of all the selected protein sequences is between 51-388. In singular category, homology is between 16-337 for bacterial uricase, 14-339 for fungal uricase, 12-317 for plants uricase, and 37-361 for animals uricase. The phylogenetic tree constructed based on the amino acid sequences disclosed clusters indicating that uricase is from different source. The physiochemical features revealed that the uricase amino acid residues are in between 300- 338 with a molecular weight as 33-39kDa and theoretical pI ranging from 4.95-8.88. The amino acid composition results showed that valine amino acid has a high average frequency of 8.79 percentage compared to different amino acids in all analyzed species.Conclusion:In the area of bioinformatics field, this work might be informative and a stepping-stone to other researchers to get an idea about the physicochemical features, evolutionary history and structural motifs of uricase that can be widely used in biotechnological and pharmaceutical industries. Therefore, the proposed in silico analysis can be considered for protein engineering work, as well as for gout therapy.

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iAFP-gap-SMOTE: An Efficient Feature Extraction Scheme Gapped Dipeptide Composition is Coupled with an Oversampling Technique for Identification of Antifreeze Proteins

Letters in Organic Chemistry ◽

10.2174/1570178615666180816101653 ◽

2019 ◽

Vol 16 (4) ◽

pp. 294-302 ◽

Cited By ~ 6

Author(s):

Shahid Akbar ◽

Maqsood Hayat ◽

Muhammad Kabir ◽

Muhammad Iqbal

Keyword(s):

Feature Extraction ◽

Amino Acid ◽

Antifreeze Proteins ◽

Protein Sequences ◽

Sampling Technique ◽

Lower Class ◽

Success Rates ◽

Throughput Model ◽

Extraction Scheme ◽

Living Organisms

Antifreeze proteins (AFPs) perform distinguishable roles in maintaining homeostatic conditions of living organisms and protect their cell and body from freezing in extremely cold conditions. Owing to high diversity in protein sequences and structures, the discrimination of AFPs from non- AFPs through experimental approaches is expensive and lengthy. It is, therefore, vastly desirable to propose a computational intelligent and high throughput model that truly reflects AFPs quickly and accurately. In a sequel, a new predictor called “iAFP-gap-SMOTE” is proposed for the identification of AFPs. Protein sequences are expressed by adopting three numerical feature extraction schemes namely; Split Amino Acid Composition, G-gap di-peptide Composition and Reduce Amino Acid alphabet composition. Usually, classification hypothesis biased towards majority class in case of the imbalanced dataset. Oversampling technique Synthetic Minority Over-sampling Technique is employed in order to increase the instances of the lower class and control the biasness. 10-fold cross-validation test is applied to appraise the success rates of “iAFP-gap-SMOTE” model. After the empirical investigation, “iAFP-gap-SMOTE” model obtained 95.02% accuracy. The comparison suggested that the accuracy of” iAFP-gap-SMOTE” model is higher than that of the present techniques in the literature so far. It is greatly recommended that our proposed model “iAFP-gap-SMOTE” might be helpful for the research community and academia.

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Application of Feature Selection Technology Based on Incremental of Diversity in Prediction of Flexible Regions from Protein Sequences

Letters in Organic Chemistry ◽

10.2174/1570178614666170221145333 ◽

2017 ◽

Vol 14 (9) ◽

Cited By ~ 2

Author(s):

Suqing Yang ◽

Shisai Hu ◽

Ying Zhang ◽

Jun Lv

Keyword(s):

Feature Selection ◽

Protein Sequences

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Advances in the Prediction of Protein Subcellular Locations with Machine Learning

Current Bioinformatics ◽

10.2174/1574893614666181217145156 ◽

2019 ◽

Vol 14 (5) ◽

pp. 406-421 ◽

Cited By ~ 3

Author(s):

Ting-He Zhang ◽

Shao-Wu Zhang

Keyword(s):

Machine Learning ◽

Feature Fusion ◽

Protein Sequences ◽

Subcellular Location ◽

Automated Analysis ◽

Cellular Level ◽

Machine Learning Algorithms ◽

Feature Representation ◽

Protein Subcellular Location ◽

Protein Subcellular Locations

Background: Revealing the subcellular location of a newly discovered protein can bring insight into their function and guide research at the cellular level. The experimental methods currently used to identify the protein subcellular locations are both time-consuming and expensive. Thus, it is highly desired to develop computational methods for efficiently and effectively identifying the protein subcellular locations. Especially, the rapidly increasing number of protein sequences entering the genome databases has called for the development of automated analysis methods. Methods: In this review, we will describe the recent advances in predicting the protein subcellular locations with machine learning from the following aspects: i) Protein subcellular location benchmark dataset construction, ii) Protein feature representation and feature descriptors, iii) Common machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web servers. Result & Conclusion: Concomitant with a large number of protein sequences generated by highthroughput technologies, four future directions for predicting protein subcellular locations with machine learning should be paid attention. One direction is the selection of novel and effective features (e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins. Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth one is the protein multiple location sites prediction.

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