scholarly journals Prediction of the tertiary structure of a protein on a two-dimensional triangular lattice by a hybrid evolutionary algorithm

2021 ◽  
Vol 3 (2) ◽  
pp. 27-32
Author(s):  
I. M. Fefelova ◽  
◽  
V. I. Lytvynenko ◽  
A. O. Fefelov ◽  
◽  
...  

This work discusses the problem of forecasting the tertiary structure of a protein, based on its primary sequence. The problem is that science, with all its computing power and a set of experimental data, has not learned to build models that describe the process of protein molecule coagulation and predict the tertiary structure of a protein, based on its primary structure. However, it is wrong to assume that nothing is happening in this field of science. The regularities of folding (convolution) of the protein are known, methods for its modelling have been developed. Analysis of the current state of research in the field of these problems indicates the presence of shortcomings associated with the accuracy of forecasting and the time necessary to obtain the optimal solution. Consequently, the development of new computational methods, deprived of these shortcomings, seems relevant. In this work, the authors focused on the lattice model, which is a special case of the known hydrophobic-polar dill. protein conformation according to the chosen model, hybrid algorithms of cloning selection, differential are proposed. Since the processes of protein coagulation have not been fully understood, the researchers proposed several simplified models based on the physical properties of molecules and which leads to problems of combinatorial optimization. A hydrophobic-polar simplified model on the planar triangular lattice is chosen as a protein model. From the point of view of the optimization problem, the problem of protein folding comes down to finding a conformation with minimal energy. In lattice models, the conformation is represented as a non-self-cutting pathway. A hybrid artificial immune system in the form of a combination of clonal selection and differential evolution algorithms is proposed to solve this problem. The paper proposes a hybrid method and algorithm to solve the protein folding problem using the HP model on a planar triangular lattice. In this paper, a hybrid method and algorithm for solving the protein folding problem using the HP model on a planar triangular lattice are proposed. The developed hybrid algorithm uses special methods for encoding and decoding individuals, as well as the affinity function, which allows reducing the number of incorrect conformations (self-cutting solutions). Experimental studies on test hp-sequences were conducted to verify the effectiveness of the algorithm. The results of these experiments showed some advantages of the developed algorithm over other known methods. Experiments have been taught to verify the effectiveness of the proposed approach. The results labelled "Best" show the minimum energy values achieved over 30 runs, while the results labelled "Medium" show the robustness of the algorithm to achieve minima. Regarding robustness, the hybrid algorithm also offers an advantage, showing higher results. A comparative analysis of the performance results of the proposed algorithm on test sequences with similar results of other published methods allows us to conclude the high efficiency of the developed method. In particular, the result is more stable, and, in some cases, conformations with lower energy are obtained. Keywords: protein folding; hydrophobic-polar model; clonal selection; differential evolution; artificial immune systems; hydrophobic-polar model.

Author(s):  
Emad Nabil ◽  
Amr Badr ◽  
Ibrahim Farag

The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before them.


1997 ◽  
Vol 4 (3) ◽  
pp. 275-296 ◽  
Author(s):  
RICHA AGARWALA ◽  
SERAFIM BATZOGLOU ◽  
VLADO DANČÍK ◽  
SCOTT E. DECATUR ◽  
SRIDHAR HANNENHALLI ◽  
...  

2003 ◽  
Vol 3 ◽  
pp. 623-635 ◽  
Author(s):  
Ivan Y. Torshin ◽  
Robert W. Harrison

How a unique three-dimensional structure is rapidly formed from the linear sequence of a polypeptide is one of the important questions in contemporary science. Apart from biological context ofin vivoprotein folding (which has been studied only for a few proteins), the roles of the fundamental physical forces in thein vitrofolding remain largely unstudied. Despite a degree of success in using descriptions based on statistical and/or thermodynamic approaches, few of the current models explicitly include more basic physical forces (such as electrostatics and Van Der Waals forces). Moreover, the present-day models rarely take into account that the protein folding is, essentially, a rapid process that produces a highly specific architecture. This review considers several physical models that may provide more direct links between sequence and tertiary structure in terms of the physical forces. In particular, elaboration of such simple models is likely to produce extremely effective computational techniques with value for modern genomics.


Author(s):  
Orhan Bölükbaş ◽  
Harun Uğuz

Artificial immune systems inspired by the natural immune system are used in problems such as classification, optimization, anomaly detection, and error detection. In these problems, clonal selection algorithm, artificial immune network algorithm, and negative selection algorithm are generally used. This chapter aims to solve the problem of correct identification and classification of patients using negative selection (NS) and variable detector negative selection (V-DET NS) algorithms. The authors examine the performance of NSA and V-DET NSA algorithms using three sets of medical data sets from Parkinson, carotid artery doppler, and epilepsy patients. According to the obtained results, NSA achieved 92.45%, 91.46%, and 92.21% detection accuracy and 92.46%, 93.40%, and 90.57% classification accuracy. V-DET NSA achieved 94.34%, 94.52%, and 91.51% classification accuracy and 94.23%, 94.40%, and 89.29% detection accuracy. As can be seen from these values, V-Det NSA yielded a better result. Artificial immune system emerges as an effective and promising system in terms of problem-solving performance.


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