Prediction of Disordered Regions in Proteins Using Physicochemical Properties of Amino Acids

Gök, Murat; Koçal, Osman Hilmi; Genç, Sevdanur

doi:10.1007/s10989-015-9481-9

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…They analyzed heart-rate variability and electroencephalography data to detect a congestive heart failure and seizures consecutively. For the detection of irregular regions in proteins, Gök et al (2016) developed a new feature coding technique that connects physicochemical properties using the LE. The LE was used for anomaly detection (Ruiz and Finke, 2019) and impulsive control (Li et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Response of Lyapunov exponents to diffusion state of biological networks

Altuntaş¹,

Gök²,

Koçal³

2020

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The topologies of protein-protein interaction networks are uncertain and noisy. The network topology determines the reliability of computational knowledge acquired from noisy networks and can impose the deterministic and non-deterministic character of the resulting data. In this study, we analyze the effect of the network topology on Lyapunov exponents and its relationship with network stability. We define the methodology to convert the network data into signal data and obtain the Lyapunov exponents for a variety of networks. We then compare the Lyapunov exponent response and the stability results. Our technique can be applied to all types of network topologies as demonstrated with our experiments, conducted on both synthetic and real networks from public databases. For the first time, this article presents findings where Lyapunov exponents are evaluated under topological mutations and used for network analysis. Experimental results show that Lyapunov exponents have a strong correlation with network stability and both are correlatively affected by the network model. Hence we develop a novel coefficient, termed LEC, to measure the robustness of biological networks. LEC can be applied to real or synthetic biological networks rapidly. Results are a striking indication that the Lyapunov exponent is a potential candidate measure for network analysis.

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Section: Introductionmentioning

confidence: 99%

Response of Lyapunov exponents to diffusion state of biological networks

Altuntaş¹,

Gök²,

Koçal³

2020

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“…Like the emerged part of an iceberg, the intricated symbol set of an encoded protein sequence can be seen as a footprint of a wide range of covert biochemical interactions within the protein. Then, there are numerous encoder models that try to reflect the reality accurately using a conversion rule related to physicochemical and biochemical properties [4][5][6]. Beyond the symbol combination and arrangement of the protein sequence, understanding the nature and the organization of the symbols is very challenging in protein biology.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Yu et al [14] have made a comparative study of structure and intrinsic disorder between 10,000 natural and random protein sequences and found that natural sequences have more long disordered regions than random sequences. In addition, Gök et al [5] have used the Lyapunov exponent and test four classifier algorithms (Bayesian network, Naïve Bayes, k-means, and SVM) to identify the disordered protein regions. Long short-term memory (LSTM) recurrent neural networks is a deep learning algorithm that has gained some interest for tracking the long-range interactions between sequences [1,15].…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Dynamics Analysis of Protein Sequences. Application to CYP450

Cadet

Dehak

Chin

et al. 2019

Entropy

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The nature of changes involved in crossed-sequence scale and inner-sequence scale is very challenging in protein biology. This study is a new attempt to assess with a phenomenological approach the non-stationary and nonlinear fluctuation of changes encountered in protein sequence. We have computed fluctuations from an encoded amino acid index dataset using cumulative sum technique and extracted the departure from the linear trend found in each protein sequence. For inner-sequence analysis, we found that the fluctuations of changes statistically follow a −5/3 Kolmogorov power and behave like an incremental Brownian process. The pattern of the changes in the inner sequence seems to be monofractal in essence and to be bounded between Hurst exponent [1/3,1/2] range, which respectively corresponds to the Kolmogorov and Brownian monofractal process. In addition, the changes in the inner sequence exhibit moderate complexity and chaos, which seems to be coherent with the monofractal and stochastic process highlighted previously in the study. The crossed-sequence changes analysis was achieved using an external parameter, which is the activity available for each protein sequence, and some results obtained for the inner sequence, specifically the drift and Kolmogorov complexity spectrum. We found a significant linear relationship between activity changes and drift changes, and also between activity and Kolmogorov complexity. An analysis of the mean square displacement of trajectories in the bivariate space (drift, activity) and (Kolmogorov complexity spectrum, activity) seems to present a superdiffusive law with a 1.6 power law value.

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Prediction of Disordered Regions in Proteins Using Physicochemical Properties of Amino Acids

Cited by 2 publications

References 23 publications

Response of Lyapunov exponents to diffusion state of biological networks

Response of Lyapunov exponents to diffusion state of biological networks

Non-Linear Dynamics Analysis of Protein Sequences. Application to CYP450

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