Received 16 February 2021; Accepted 08 March 2021.

8629

**Efficient Secondary Structure Prediction of Qscr -Protein Using ** **Ann Coupled Pso Algorithm **

**1****Saravanan K, **^{2}**Sivakumar S, **^{3}**Sridevi K P **

*1**Department of Physics, AVS Engineering College, Salem, Tamilnadu, INDIA *

*2**Department of Physics, Government Arts College (Autonomous), Salem, *
*Tamilnadu, INDIA *

*3**Department of Physics, Sri Kailash Women's College, Thalaivasal, *
*Salem,Tamilnadu, INDIA *

**ABSTRACT **

Identifying Secondary structure of protein becomes more important in designing drugs. Presently, machine learning algorithms like Artificial Neural Network (ANN) and Support Vector Machine (SVM) has been utilized to identify the Protein Secondary Structure (PSS). However, in order to improve more accuracy of PSS prediction, this work proposed PSO to search the best parameters of theANN predictor developed for QscR protein. From the results, it is found that the proposed topologyexhibits high accuracy than the other existing techniques.

**Keywords: Artificial Neural Network,Protein Secondary Structure, Particle Swarm **
Optimization, QscR.

**1. INTRODUCTION **

Enormous progress in genome sequencing and the consequent attainability of more protein sequences find its usefulness in the field of computational biology and hence, it is made necessary to predict the PSS. Developments in predicting the SS is used in the field of protein engineering and in designing drugs. To determine the 3D structure of protein in huge scale, the present crystallization techniques are too costly and consumes more time [1].Prediction of SS can be used as intermediate to increase the predicting rate of protein structure. It also helps in recognition of genes, categorization of structures, functional patterns and finding the defective structures which are the sources for human diseases.For determining the secondary structure, various computational technologies have been utilized in a successful way [2,3]. It has been proved that machine learning and empirical methods are considered as the most successful method. The prime method GOR relies on information theory [4].

Metamorphic information is utilized for enhanced structure prediction. Neural networks which relies on multiple sequence arrangement is used by the protein predicting server. The PSIBLAST and NNs are used by PSIPRED algorithm.Based on the Jnet algorithm, the Jpred prediction server predicts PSS. By making use of the

currently available structural information and computational methods, SS determination techniques have been developed extensively. To attain better categorization efficiency, it is advised to consider long range interactions as a key factor.Hence, by implementing ANN, an innovative approach for predicting the PSS has been designed. It is a tedious task to frame an ANN, as the fabrication relies on the construction, the chosen transfer function and the learning algorithm which is utilised to train the synaptic weights. Hence, it is advised to use the PSO to enhance the efficiency of ANN classifier by adjusting its specifications like hidden neurons, bias and input weights which are utilized to train the ANN [5,6].

**2. DATA GENERATION **

By deriving the details from relative similarity groups of QscR, which includes both short and long range interactions existing among the AA of proteins, the database is created. The dictionary of database of PSS (DSSP),has 8 categories of protein structures. In these categories, a shortened set of 3 SS termed as α- helix (H), β-Strand (E) and Coil (C) are considered in this work. By using 100 non- homologous protein sequences, a profile matrix is designed.

**2.1 Methods and optimization **

In this, for categorizationANN is used. The variables of the ANN are tuned using the PSO.

**Encoding of ANN **

The input of an ANN has prearranged patterns (Residues). Each pattern is said to have 27 features with values lying between 0 and 1. There are three units in the output and these units correlate any one of the three secondary structure elements. The correlation is defined as 1 for a class of interest and a – 1 for the remaining two classes. At the hidden layer, the given input along with a bias and weights generates an activation function. The hidden layer‘s output associated with a different set of weights earns three outputs [7]. The predicted class which has the maximum value is treated as an output with lowest MSE.To construct a model, the ANN makes use of a set of training samples. At the time of training phase, the variables like weights, bias and deigned hidden neurons of ANN is enhanced using the PSO [8-12]. By doing so, the accuracy of categorization can be improved.The variables are stored and used at the time of the testing stage. Initially, input weights are selected randomly and later on they are adjusted by the PSO. The output weights obtained from the hidden layer are empirically determined by utilizing the pseudo inverse. In the hidden layer, sigmoidal activation function is utilized and regarding the output neurons, linear activation function is implemented [6].

Received 16 February 2021; Accepted 08 March 2021.

8631 In ANN algorithm, the elementary steps involved are as follows

Choose the relevant activation function (G) and number of hidden neurons from the given class labels and training samples.

Choose bias (b) and input weights (V) randomly and the output weights (W).

The class label can be calculated from the determined weights ( W ; V; b) and the inaccuracy between the observed and predicted values should be reduced. The best performances are later tuned by the PSO.

**2.2 PSO **

One of the stochastic optimization methods is PSO. This technique imitator of intelligent social act of colony of birds (or) schools of fish, identified as a particles in a community. In a minimum possible time, these particles by working together give a transparent and excellent solution to a problem. A random set of values called particles initialise the formation of PSO algorithm. These particles contribute collectively to achieve the desired solution. Several parameters defined by these values will increase the performance of the system. To attain the finest possible solution, this method constantly explore a multi- dimensional space set on by a fitness basis, The best values for input weights, hidden layer neurons and bias values are found using a PSO [13,14].

**2.3 PSO trained ANN **

The following strategies are taken into discussion to accommodate ANN with PSO algorithm [15-17]. In the ANN models by using the PSO algorithm, the optimum weights and the bias are realized. The search space of the algorithm with

‗n‘ dimensions is formed by the weights and biases. Here ‗n‘ represents the total number weights and biases that are to be developed. There are n-dimensions of position vector and velocity for every single particle. The letter ‗w‘ denotes both the weights and biases. By flying the particles on all sides of the search space, the exclusive set of weights are acquired. On every repetition, a set of weights with their fitness accessed comes up along with the algorithm. This occurs by applying these weights to the nodes and by determining the value to be achieved. Subsequently, the correctness of the forecasting using the allocated weights is calculated by the variation between the original and forecasted values, the variation must be reduced using the optimization techniques[24][25][26]. Using this view, the particle with best fitness has been attained and until now it is treated as the individual best.Correspondingly, the swarm with the best fitness is treated as the best one globally. The present procedure is reproduced for the definite number of repetitions until the correct weight for the ANN is earned. The procedures for a ANN optimized by PSO is stated below. For a perceptron with three layers, W[1] denotes the relation

among the input layer and the hidden layer whereas W[2] denotes the relation
among the hidden layer and the output layer commonly. Multi-layer perceptron is
trained by using the PSO method. The i^{th} particle can be represented by

𝑊_{𝑖} = 𝑊_{𝑖}^{[1]}, 𝑊_{𝑖}^{[2]} (1)

𝑃_{𝑖} = 𝑃_{𝑖}^{ 1 }, 𝑃_{𝑖}^{ 2 } (2)

𝑃_{𝑏} = 𝑃_{𝑏}^{[1]}, 𝑃_{𝑏}^{[2]} (3)

𝑉_{𝑖} = 𝑉_{𝑖}^{ 1 }, 𝑉_{𝑖}^{ 2 } (4)

where j = 1, 2;

m = 1,. . . ,Mj ;
n = 1,. . . ,N_{j};

Mj&Nj - row and column size W, P, V, r , s - Constants;

a& b - Random numbers ranges within 0 to 1;

t - Time step between observations. Usually it is unity;

V'' & W'' - New values.

Applying Equation,
𝑉_{𝑖}^{"[𝑗 ]} 𝑚, 𝑛 =

𝑉_{𝑖}^{ 𝑗 } 𝑚, 𝑛 + 𝑟𝛼 𝑃_{𝑖}^{ 𝑗 } 𝑚, 𝑛 − 𝑊_{𝑖}^{ 𝑗 } 𝑚, 𝑛 + 𝑠𝛽 𝑃_{𝑏}^{ 𝑗 } 𝑚, 𝑛 − 𝑊_{𝑖}^{ 𝑗 } 𝑚, 𝑛 𝑡 (5)
The current velocity of a particle is calculated by applying its earlier velocity
and the distance of its recent location based on Pbest and Gbest value. On the right
hand side of the equation, the second element serves as the exclusive thinking of the
particle by its own and at the same time the third element, represents the
combination in the midst of the particle as a group. The current location based on the
recent velocity can be resolved by the equation as follows

Received 16 February 2021; Accepted 08 March 2021.

8633
𝑊_{𝑖}^{"[𝑗 ]} = 𝑊_{𝑖}^{[𝑗 ]}+ 𝑉_{𝑖}^{[𝑗 ]}𝑡 (6)

The MSE is the fitness function and is given by
𝑓 = ^{1}

𝑛 ^{𝑛}_{𝑖=1}(𝐾_{𝑜𝑖} − 𝐾_{𝑝𝑖})^{2}n (7)
Where

F - Fitness value, n – No. of data points.

**3. Results and Discussion **

For training 100 protein set and to test 5 protein set are used. All the 3 SS (α- helix, β-strand and coil) exists in the form of a mixture in every sets mentioned above.

**Table 1. The parameter configuration used in PSO **

**Parameters ** **Values **

No. of Particles 100

C1 1

C2 2

Max. Iteration 1000

Among the training data set, 47% were about coil, 31% were strand and 21%

were Helix. In the testing data set, it was about 48% of C, 31% of E and 21% of H.

**Figure 1. Best validation performance curve **

For training, validation and test steps the figure (1) depicts the MSE of ANN model. With reference to the graph at the epoch 6, the least MSE occurs in the validation step and it has best validation performance equivalent to 0.35. Unless the network error on the validation vectors gets decreased, it is important to mention that the training model keeps going. Thus, for the sequence of chain A of QscR, the predicted SS is tabulated below

Received 16 February 2021; Accepted 08 March 2021.

8635
**Table 2. Predicted secondary structure of QscR under different topology **

**Methods ** **Secondary structure **

**Sequence (1-50) ** MHDEREGYLE ILSRITTEEE **FFSLVLEICG ** **NYGFEFFSFG ** ARAPFPLTAP
**Structure ** **DSSP ** ******SHHH HHHH** SHHH HHHHHHHHHH HTT*SEEEEE EE***STTS*

**MLNN ** CHHHHHHHHH HHHHCCCHHH HHHHHHHHHH HHCCCEEEEE EECCCCCCCC
**Proposed PSONN ** CHHHHHHHHH HHHHCCCHHH HHHHHHHHHH HHCCCEEEEE EECCCCCCCC
**Sequence(51-100) ** KYHFLSNYPG EWKSRYISED YTSIDPIVRH GLLEYTPLIW NGEDFQENRF
**Structure ** **DSSP ** *EEEEE*** H HHHHHHHHTT GGGT*HHHHH HHHS*S* EEE ETTT*SS*HH

**MLNN ** CEEEECCCCH HHHHHHHHHC CHHHCHHHHH HHHCCCCEEE CCCCCHHHHH
**Proposed PSONN ** HEEEECCCCH HHHHHHHHHC CHHHHHHHHH HHHCCCHEEE CCCCHHHHHH
**Sequence(101-150) ** FWEEALHHGI RHGWSIPVRG KYGLISMLSL VRSSESIAAT EILEKESFLL
**Structure ** **DSSP ** HHHHHHHTT* *EEEEEEEE* GGG*EEEEEE EESSS*** HH HHHHHHHHHH

**MLNN ** HHHHHHHHCC CCEEEEEEEC CCCCEEEEEE ECCCCCCCHH HHHHHHHHHH
**Proposed PSONN ** HHHHHHHHCC CCEEEEEEEC CCCCEEEEEE ECCCCCCCHH HHHHHHHHHH
**Sequence(151-200) ** WITSMLQATF GDLLAPRIVP ESNVRLTARE TEMLKWTAVG KTYGEIGLIL
**Structure ** **DSSP ** HHHHHHHHHH HHHHHHHHSG GGG**** HHH HHHHHHHHTT **HHHHHHHH

**MLNN ** HHHHHHHHHH HHHHCCCCCC CCCCCCCHHH HHHHHHHHCC CCHHHHHHHH
**Proposed PSONN ** HHHHHHHHHH HHHHCCCCCH CCCCCCCHHH HHHHHHHHHE HCHHHHHHHH
**Sequence(201-237) ** SIDQRTVKFH IVNAMRKLNS SNKAEATMKA YAIGLLN

**Structure ** **DSSP ** TS*HHHHHHH HHHHHHHTT* SSHHHHHHHH HHTT***

**MLNN ** CCCHHHHHHH HHHHHHHHCC CCHHHHHHHH HHHCCCC
**Proposed PSONN ** CCCHHHHHHH HHHHHHHHCC CCHHHHHHHH HHHCCCC

To conclude, with reference to the above table it is observed that the suggested PSOANN shows better accuracy in predicting PSS than that of the other conventional methods. Apart from the detection of SS of QscR, the domination of the configuration of the amino acids is also investigated for determining the structures.The results obtained from many SS prediction methods are expressed in terms of commonly used three SS. At the AA level, it is strange to notice an investigation of the results with few deviations. To recognize the designs of prediction accuracies in connection to various AAs, the results of categorization from many SS prediction servers are observed and reanalysed [18-21]. Thus, the influence of the composition and physicochemical properties of AAs is investigated.In SS analysis, the AAs are not present in uniform quantities[22-23]. Very few amino acids such as Glutamine, Leucine and Isoleucine possess the highest number of helix residues while Cysteine as well as Proline possesses the exact number helix residues in them. The Strand, Serine, Leucine as well as Phenylalanine are having the highest content. On the other hand, most of the acids like Asparagine, Aspartic acid do not exist strand. Similarly, Cysteine and Glutamine does not exhibit coil property. Serine and Proline are the acids which bear more number of residues in the coil structures. Alanine and Asparagine seem to have the same content in the coil and the same is depicted in figure 2.

4.64 3.38

0.84 0.84 0.42

1.27 8.44

3.38 2.11

4.64 7.59

2.95 1.69

2.95

0.84 3.383.80

1.692.11 2.11 1.27

1.69

2.95 1.69

0.00 0.00

1.69

2.53

0.42 2.11

1.69

1.27

0.42 1.27

2.53 2.951.69

0.00 1.69

0.84 0.42

0.84

0.00

0.00

0.00 0.00

0.00

0.84

0.42 1.27

1.69

0.00

0.42 1.69

0.42 1.69

0.42

0.84 0.42

0.84

Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys Met Phe Pro Ser Thr Trp Tyr Val 0

2 4 6 8 10 12

% of Content of SSSC in amino acids

Strand Coil Helix

**Figure 2a. Content of AAs in SS of QscR **

Received 16 February 2021; Accepted 08 March 2021.

8637
**Figure 2b.Percentage of secondary structures in an amino acid **

From the above graph, it is concluded that cys and gln are helix in nature and amino acids such as asn, glu and thr have not exhibited strand nature. From the analysis, it is concluded that helix plays a vital role in QscR protein.Various steps to calculate the forecasting efficiency have been used in this approach. In the three states of secondary structures, the percentage of residues which are correctly predicted is illustrated by Q3 accuracy. By adding together the identified and recognized segments and by calculating their overlap, the precision is furnished by segment overlap measure (SOV) and it is depicted in the table (3).

**Table 3. Performance analysis of Q3 and SOV of the **
**proposed topology **

**Qoverall (%) ** **Q****H**** (%) ** **Q****E**** (%) ** **Q****C**** (%) **

**Q3 ** 78.9 84.6 58.3 85.0

**SOV ** 77.6 85.4 72.9 52.7

Q3-training Q3-testing Sensitivity Specificty

0 20 40 60 80 100

Percent-accuracy

Metrics

Overall Coil Beta Helix

**Figure 3. Accuracy of the proposed topology **

Figure 3 shows the parameters like Q3-training, Q3-testing, Sensitivity, Specificity, for a given set of proteins which is tested. By using the terms of TP correct and FPerror, the achievement of allotment can be checked out. Regarding TN and FN, they are influenced by identical explanation. The forecasted class conforming to a pre-set threshold is determined by the calculated probabilities supplied by the output of a classification. The ROC is graphed by taking the TP rate and FP rate as the co-ordinate pairs. The region below the ROC assists to combine the achievement of all the tasks which are tested.

**5. Comparative Analysis **

Finally, the results obtained using proposed methodology is compared with the performance of other networks which depicted the secondary structure of QscR and is depicted in table 4 and 5 and in figure 4 to 6.

Received 16 February 2021; Accepted 08 March 2021.

8639
**Table 4. Comparative analysis of performance of the other methods in secondary structure **

**prediction **

**Methods Alpha (%) ** **Beta sheet(%) ** **Coil (%) **

**DSSP ** 54 13 -

**STRIDE ** 54 11 -

**MLNN ** 56.96 11.39 31.64

**PSO-NN ** 59.07 12.23 28.69

**Figure 4. Comparative analysis of performance of the other methods in secondary **
**structure prediction **

**Table 5. Performance comparison of Q3 and SOV of the proposed topology **

**Qoverall (%) ** **Q**_{H }**(%) ** **Q**_{E}** (%) ** **Q**_{C}** (%) ** **Methodology **

Q3 72.6 83.9 58.3 52.5

MLANN

SOV 75.6 85 72.9 42.6

Q3 78.9 84.6 58.3 85.0

PSOANN

SOV 77.6 85.4 72.9 52.7

**Figure 5. Overall Q3 comparison with other topologies **

Received 16 February 2021; Accepted 08 March 2021.

8641
**Figure 6. Overall SOV comparison with other topologies **

From the overall comparison study, it is concluded that the proposed PSO-NN gives more and better prediction of secondary structure than the other topologies. Similarly, it exhibits greater accuracy and high per-residue accuracy than MLNN topology.

**6. CONCLUSION **

An innovative method, ANN which depends on PSO, is implemented to identify the secondary structure of a QscRprotein. The recommended predictor has achieved encouraging results and has surpassed a lot of other advanced predictors. In an individual dataset, an accuracy of 95% is achieved. The empirical performance determined by the suggested technique put up helpful hands for the detection of major protein modifications and this approach will be dynamic in the research domains where structure of proteins are predicted

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