 Research
 Open Access
 Published:
Research on predicting 2DHP protein folding using reinforcement learning with full state space
BMC Bioinformatics volume 20, Article number: 685 (2019)
Abstract
Background
Protein structure prediction has always been an important issue in bioinformatics. Prediction of the twodimensional structure of proteins based on the hydrophobic polarity model is a typical nondeterministic polynomial hard problem. Currently reported hydrophobic polarity model optimization methods, greedy method, bruteforce method, and genetic algorithm usually cannot converge robustly to the lowest energy conformations. Reinforcement learning with the advantages of continuous Markov optimal decisionmaking and maximizing global cumulative return is especially suitable for solving global optimization problems of biological sequences.
Results
In this study, we proposed a novel hydrophobic polarity model optimization method derived from reinforcement learning which structured the full state space, and designed an energybased reward function and a rigid overlap detection rule. To validate the performance, sixteen sequences were selected from the classical data set. The results indicated that reinforcement learning with full states successfully converged to the lowest energy conformations against all sequences, while the reinforcement learning with partial states folded 50% sequences to the lowest energy conformations. Reinforcement learning with full states hits the lowest energy on an average 5 times, which is 40 and 100% higher than the three and zero hit by the greedy algorithm and reinforcement learning with partial states respectively in the last 100 episodes.
Conclusions
Our results indicate that reinforcement learning with full states is a powerful method for predicting twodimensional hydrophobicpolarity protein structure. It has obvious competitive advantages compared with greedy algorithm and reinforcement learning with partial states.
Background
The biological function of proteins is determined by their spatial folding structure. Understanding the folding process of proteins is one of the most challenging issues in the field of bioinformatics [1]. The bioinformatics hypothesis believes that the protein form found in nature is the most stable form (the lowest free energy). Protein sequences determine protein structure, and protein structure determines protein function [2, 3]. Current research has put forward many computational theoretical models, such as hydrophobic polarity (HP) model, AB offlattice model (Toy model) and continuous model of Euclidean space. The HP model is a widely studied simplified protein folding model with high confidence in the prediction of the protein helical structure. In this model, each amino acid is treated either hydrophobic (H) or hydrophilic (P) and represented as a point on a twodimensional lattice structure. The rationale behind the HP model is that the hydrophobicity of amino acids is the main driving force for small globulins to form a natural conformation [4]. The primary structure analysis of protein sequences involves the analysis of amino acid physicochemical properties (such as hydrophilicity and hydrophobicity) and sequence patterns, so 2DHP protein structure prediction refers to predicting the folding structure based on the primary structural analysis of proteins. Although the HP grid model is a simplified model, solving the protein folding problem of this model is still difficult. This problem has proven to be an NPhard problem, which means that there is no solution algorithm that is both complete and not too slow.
Currently, the methods used to solve the HP model optimization include evolutionary algorithm (EA), genetic algorithm (GA), ant colony optimization (ACO), and supervised classification methods. Genetic algorithm is a method of searching for optimal solutions by simulating natural evolutionary processes. The asymptotic analysis of the computational complexity of the GA and EA is difficult and is usually limited to specific problems [5, 6]. The ACO is a probabilistic algorithm used to find optimized paths. In most cases, the ACO has high computational complexity [7,8,9]. Supervised classification is a method of pattern recognition, and its training process requires external supervision [10,11,12]. The versatility of these methods is not good, especially when calculating the energy minimum, it is easy to fall into the local optimal solution, which makes it difficult to achieve global optimization [13,14,15]. Protein structure prediction has two major problems. The first question is how to abstract the mathematical model that can reflect the interaction between amino acids and how to design its energy function. The second problem is how to find an efficient search method for the exploration of the structure and then find the structure with the lowest energy [16,17,18] within limited central processing unit power and time.
Recently, reinforcement learning has been successfully applied to many aspects of the biological field, such as biological sequence comparisons, genome sequencing and so on, and it has become more extensive in other fields, such as vehicle positioning and recognition, game automation detection and robot simulation [19]. The advantage of reinforcement learning is that the training process does not require external supervision. The agent will conduct autonomous learning based on their interaction experience with the environment, and can find the overall optimal solution based on the reward, and it is not easy to fall into the local optimum [20]. For example, transfer learning in reinforcement learning is considered to be an optimal learning strategy under limited data conditions, especially in areas where labeling data are scarce and distribution is heterogeneous, such as clinical medical diagnosis and animal behavior control [21].
Therefore, this paper proposes an HP model optimization method based on reinforcement learning. In the reinforcement learning framework, the state set and state transition space are given according to the length of the HP sequence to be tested. The agent uses the Qlearning algorithm to select different actions under different conditions to obtain different reward values, and continuously calculates and updates the Qvalue table. At last, the agent selects the optimal solution to obtain the optimal structure according to the converged Qvalue table. This method has strong universality and simple calculation [22]. It can predict the optimal structure well for short length sequences.
Methods
The framework based on reinforcement learning
In recent years, some scholars have proposed some simplified models for protein folding problems. The most typical one is the twodimensional hydrophobicpolarity (2DHP) grid model proposed by Dill et al. [23]. According to the differences in the hydrophilicity and hydrophobicity of each type of amino acid, they are divided into two categories: one is a hydrophobic amino acid (indicated by H, the black circle), and the other is a hydrophilic amino acid (indicated by P, the white circle), so any protein chain can be expressed as a finitelength string of H and P [24, 25]. A legitimate protein space configuration must meet the following three constraints:
① The center of the sphere for each circle in the sequence must be placed on an integer coordinate in two dimensions.
② Any two adjacent circles in the chain must be adjacent to each other in 2D space. That is, the distance between adjacent numbered circles is 1.
③ Each integer grid in 2D space can only represent one circle at most, that is, no two balls overlap.
The reinforcement learning method is used to solve the HP 2D sequence model optimization problem, which can be converted into a Markov decision process and solved by the Qlearning algorithm. The framework is shown in Fig. 1.
Environment
Amino acids are classified into H (hydrophobic) and P (hydrophilic) according to their hydrophilicity and hydrophobicity. In this case, the amino acid sequence is converted into an HP sequence. Using the HP sequence as input data, the entire state set S of the sequence corresponds to the environment part of reinforcement learning.
Action set A
Action set A consists of 4 actions that corresponds to four directions: L (Left), U (Up), R (Right), D (Down), that is A = {a_{1}, a_{2}, a_{3}, a_{4}}, where a_{1} = L, a_{2} = U, a_{3} = R, a_{4} = D.
In the training process, the agent uses εgreedy policy to select the action (ε ∈ [0, 1]), which means that the agent will explore other actions with the probability of ε and the probability of remaining 1 − ε goes “greedy “, which means that the agent takes the best action, and constantly calculates and updates the Q value [26].
Result output pool
The theory shows that, as long as the number of training is enough, the Q value will converge to the optimal value. At the end of training, the agent adopts greedy policy to choose the optimal action in different states according to the converged Q value to further obtain the optimal structure of HP model. Different folded structures with the lowest energy are the final output results.
The full state set S of 2DHP model
The initial state of the agent in the environment is s_{1}. For a twodimensional sequence of length n, its state space S consists of \( \frac{4^n1}{3} \) states. When the state of the first amino acid is fixed, all possible states of the successor of each amino acid are the collection of four states (up, down, left, right) of the previous amino acid, that is, the number of all possible states of subsequent amino acid is four times the number of previous amino acids. The total number of the state set is the sum of the geometric series with an initial value of 1 and an odds ratio of 4, as shown in Eq. (1):
So \( S=\left\{{s}_1,{s}_2,\dots, {s}_{\frac{4^n1}{3}}\right\} \). For example, when there is only one amino acid in the sequence, there is only one state s_{1} in the whole state space. When there are two amino acids, the possible state of the second amino acid consists of four states of the first amino acid s_{2}, s_{3}, s_{4}, s_{5}, so there are 5 states s_{1}, s_{2}, s_{3}, s_{4}, s_{5} in the whole space. Similarly, when there are three amino acids, the possible states of the third amino acid consist of four states (up, down, left, right) of the second amino acid, and the second amino acid may have four states, so the third amino acid may have 16 states, namely s_{6}, s_{7} … s_{20}, s_{21}, and the whole state set has 21 states, and so on, all the states of subsequent amino acids are obtained.
At the same time, we need to define the state transfer function T : s → s^{′} of the HP model, that is, T(s, a) = s^{′}. The process that the agent takes the action a in the state s to the subsequent state s^{′} can be written as the concrete expression as shown in Eq. (2)
where, i ∈ [1, n − 1] is the index of the amino acid in the sequence. k ∈ [1, 4^{i − 1}] represents the kth state of all the states of the i1th amino acid. l ∈ [1, 4] represents the number corresponding to the action.
This means that the agent can move to one of four possible successor states from the state s∈ S by performing one of four possible actions. It should be noted that each state s^{′} ∈ S can be accessed from the state s.
The new definition of full state space of 2DHP model
Further research has found that when the number of actions is reduced to three, a simpler representation of the state space can be obtained. Action set can be described as A = {a_{1}, a_{2}, a_{3}}, where a_{1} = Left, a_{2} = Up, a_{3} = Right. Then for a twodimensional sequence of length n, the state space S has \( \frac{3^n1}{2} \) states. The number of states is calculated in the same as before, as shown in Eq. (3):
So \( S=\left\{{s}_1,{s}_2,\dots, {s}_{\frac{3^n1}{2}}\right\} \). Accordingly, the state transfer function is updated to Eq. (4):
where, i ∈ [1, n − 1] is the index of the amino acid in the sequence. k ∈ [1, 3^{i − 1}] represents the kth state of all the states of the i1th amino acid. l ∈ [1, 3] represents the number corresponding to the action.
Energybased reward function with criterions
The protein folding thermodynamic hypothesis holds that the energy of proteins under natural structures is the lowest [27, 28]. Therefore, the problem of predicting protein folding structures is to find the lowest energy structure of all available structures for a given amino acid sequence. The determination of the energy function is especially important for this paper.
The energy value is only determined by the hydrophobic force. Each pair of hydrophobic amino acids that is not adjacent in sequence but adjacent in 2D space produces energy of − 1, and in other cases, the energy is calculated as 0. The energy value of the entire structure is the sum of energy of each pair of hydrophobic amino acids that meets the requirements mentioned above in the legal configuration. A formal description of the legal configuration energy E of a protein of chain length n is as follows:
where, n is the length of the amino acid sequence. Both i and j are the indices of the amino acids in the sequence. And
where, applicable conditions mean that the ith and jth amino acid are both hydrophobic amino acids and they are not adjacent in the sequence but adjacent in 2D space.
The purpose of reinforcement learning is to maximize the objective function, which is to maximize the reward. However, in the HP model problem, the ultimate goal is to minimize the energy function, so we need to take the absolute value of the energy function to achieve the positive unite. At the same time, using the absolute value of the energy function as a reward after reaching the end state enables the trained structure closer to the ideal structure.
In the training process, the agent tends to put amino acids in the lattice which placed in the amino acid before, which is not allowed in the actual situation. This overlapping problem can be solved by setting the reward function. We define the reward function by flexible and rigid criteria.
Flexible criterion

① When the agent selects the action, they are allowed to place the succeeding amino acid in the lattice position where the amino acid was placed before. A negative reward (which can be defined as a penalty) is given to the agent to judge and optimize to maximize the prize. Before reaching the terminal state, the next state of the amino acid is placed in the invalid position with the reward set to − 10.

② Before reaching the terminal state, the next state of the amino acid is placed in the valid position (blank position) with the reward set to 0.

③ When the terminal state is reached, the absolute value of the energy of the final folded structure is rewarded.
where, n is the length of the amino acid sequence. i is the index of the amino acid in the sequence. E is the sum of the energy formed by the final folded structure. R is the symbolic representation of reward in this article.
Rigid criterion
Compared to the flexible criterion, when the agent places the next amino acid in the selection process, if this action causes the next amino acid to be placed on the lattice of the existing amino acid, the action is called invalid and needs to be reselected until a valid action occurs. The check matrix ‘Check’ is introduced here. For a sequence of length n, the check matrix is a 2D matrix of 2n1 rows and 2n1 columns. The lattice position where the amino acid has been placed is marked (also called invalid position), then in this episode, this position can no longer be placed, that can be expressed as
where, (p, q) indicates the twodimensional coordinates of the placement of the amino acid.
① Before reaching the terminal state, the reward is set to 0.
② When the terminal state is reached, the absolute value of the energy of the resulting structure is rewarded.
where, n is the length of the amino acid sequence. i is the index of the amino acid in the sequence. E is the sum of the energy formed by the final folded structure. R is the symbolic representation of reward in this article.
HP model training algorithm based on reinforcement learning with Qlearning
The algorithm for solving 2DHP protein folding based on reinforcement learning with full states using Qlearning in rigid criterion is shown in Table 1. The program of this method is implemented on PyCharm.
Function approximation
The function approximation theory is an important part of the function theory. The basic problem involved is the approximate representation of the function. In reinforcement learning, for some basic methods such as dynamic programming (DP), Monte Carlo (MC) and temporal difference (TD), there is a basic premise that the state space and the action space are discrete and not too large [29]. Note that the value function of these methods is actually a table. For state value function (V), the index is the state; for stateaction value function (Q), the index is a stateaction pair. The process of iterative update of the value function is an iterative update of this table. If the dimension of the state space is large, or the state space is contiguous, the value function cannot be represented by a table. At this time, it is necessary to represent the value function by means of function approximation [30].
In the value function approximation method, the value function corresponds to an approximation function. From a mathematical point of view, the function approximation method can be divided into parameter and nonparametric approximation. Therefore, the reinforcement learning value function estimation can be divided into parametric and nonparametric approximation. The most commonly used is parameter approximation. When the approximation of the value function structure is determined, then the approximation of the value function is equivalent to the approximation of the parameter. The update of the value function is equivalent to the update of the parameter. In other words, it is time to use experimental data to update parameter values [31].
Results
Comparative experiment between rigid criterion and flexible criterion
According to two different reward settings of rigid and flexible criteria, six paper dataset sequences and ten sequences in the classic Uniref50 database are selected as experimental objects. The known information and test energy information were shown in Table 2. The parameters were set as follows: stepsize parameter α = 0.01, exploration probability ε = 0.5, and learning parameter γ = 0.9.
In Table 3, the first four sequences were chosen to compare the performance of reinforcement learning with rigid and flexible criteria. In order to avoid contingency, the rigid and flexible criteria experiments were repeated five times. The number of training iterations per round was set to 5 million, and the test was performed once every 10,000 times. In training process, the number of episodes required to converge to the lowest energy was counted as shown in Table 3.
Combination of Tables 2 and 3 showed that reinforcement learning with rigid criterion can stably find the lowest energy conformation faster than reinforcement learning with flexible criterion. For the shorter sequences (1 and 2), the number of training episodes required for agent to achieve convergence conformation by flexible criterion was greater than rigid criterion. Reinforcement learning with rigid criterion sampled an average 30,000 and 210,000 episodes to achieve the robust lowest energy conformation, which was 50 and 63% less than 60,000 and 570,000 episodes required by reinforcement learning with rigid criterion. For the longer sequences (3 and 4), reinforcement learning with flexible criterion could not find the lowest energy conformation. One possible reason was that, although flexibility criterion gave a negative reward (or penalty) for states that caused repetition, the states still had some positive Q values, and the Q values of these repeated states in rigid criterion still had an initial value of 0. Therefore, the probability of the repeated states in flexibility criterion being selected was greater than rigid criterion. And as the length of the sequence increased, the number of states that caused repetition in the full state space was also greater, and it was more difficult to find the lowest energy structure.
Comparative experiment with greedy algorithm
Reinforcement learning with full states using rigid criterion was compared with greedy algorithm. The experimental objects were the twelve sequences in the Uniref50 data set. Similarly, in order to avoid accidentality, two methods were trained for five rounds, and the number of training iterations per round was set to 5 million, and the samples were performed once every 10,000 times. We counted the number of times the lowest energy was obtained in the last 100 samples (Table 4).
It can be seen from Table 2 that reinforcement learning with full states using rigid criterion can find the lowest energy for all 16 sequences, but the greedy algorithm can only find 13 of them. From Table 4, the training process with 10 sequences was far superior to the greedy algorithm for the above 12 sequences. And the total number of times that the lowest energy was found was 300, which was greater than 205 for the greedy algorithm.
Comparative experiment with the reinforcement learning with partial states
Reinforcement learning with full states using the rigid criterion was compared with reinforcement learning with partial states. The experimental objects and experimental settings were the same for greedy algorithm above.
In the reinforcement learning with partial states, for an HP sequence of length n, its state space S consists of 1 + 4 (n1) states. Apart from the first amino acid that had only one state, each of the other amino acids had four different actions (up, down, left, and right) to transfer to four different states, so the number of the entire state set was expressed as 1 + 4 (n1), so S = {s_{1}, s_{2}, …, s_{1 + 4(n − 1)}}. For example, the state of the first amino acid is s_{1}. In this state, the four actions of up, down, left, and right were respectively transferred to states s_{2}, s_{3}, s_{4}, s_{5}, which were all possible states of the second amino acid. On the same basis, the four actions of up, down, left and right respectively transferred to the states s_{6}, s_{7}, s_{8} and s_{9}, which were all possible states of the third amino acid, and so on, to find all the states of the subsequent amino acids.
In Table 2, there were 8 sequences that cannot converge to the lowest energy conformations by the reinforcement learning with partial states, while reinforcement learning with full states successfully folded all sequences to the lowest energy conformations. Table 4 showed that in the last 100 episodes, reinforcement learning with full states hits the lowest energy an average five times, which was 40 and 100% higher than the three and zero times hit by the greedy algorithm and reinforcement learning with partial states, respectively. Reinforcement learning with full states achieved lower energy structures on ten out of twelve sequences than the greedy algorithm.
Discussion
Analysis of time complexity and space complexity
In this algorithm, for one sequence, many iterations of training are required to get its lowest energy. Therefore, the time complexity of the algorithm is determined by the length of the amino acid sequence (N) and the number of training iterations (I), that is, the time complexity is O(N × I). The time complexity of the ant colony algorithm for solving HP twodimensional structure prediction is O(N × (N − 1) × M × I/2), where N is the sequence length, I is the number of iterations, and M is the number of ants. The time complexity of particle swarm optimization is O(N × I × M), where N is the sequence length, I is the number of iterations, and M is the number of particles. Obviously, the time complexity of the method in this paper is the smallest of the three methods, and the larger the sequence length, the more prominent the time advantage.
The space complexity is composed of statetransfer function matrix and stateaction value matrix. The rows of both matrices represent states, and the columns all represent actions. The number of rows in new statetransfer function matrix is \( \frac{3^{N1}1}{2} \) and the number of columns is 3. The number of rows in stateaction value matrix is \( \frac{3^N1}{2} \) and the number of columns is 3. So the space complexity is \( O\left(\frac{3^{N1}1}{2}\times 3+\frac{3^N1}{2}\times 3\right) \).
Case study
Sequence 12 is a zinc finger protein 528 (fragment), which is a transcription factor with a fingerlike domain and plays an important role in gene regulation. Taking sequence 12 as an example, a series of optimized structures with the lowest energy obtained by the method of this paper under rigid criterion are given, as shown in Fig. 2ac. The results of the last 100 samples of the method and the greedy algorithm and reinforcement learning with partial states in the training exploration process are given, as shown in Fig. 3ac. The greedy algorithm itself cannot converge, and the convergence of reinforcement learning with full and partial states in the test process is shown in Fig. 4a, b.
For reinforcement learning with full states, the agent can be trained to select the better action to obtain a lower energy structure after training for several million times, and then guarantee that the structure obtained after convergence is the optimal structure, and it can be considered that the training effect of reinforcement learning with full states is stable. However, the greedy algorithm is not ideal for training. Only several structures with the lowest energy are trained occasionally, and the accuracy of the lowest energy structure cannot be guaranteed. As a whole, reinforcement learning with full states is better than the greedy algorithm. This is because, for reinforcement learning, the agent can choose better actions based on the previous interaction with the environment during the exploration process. Therefore, as the number of training increases, the agent can select the optimal action more quickly and accurately. Also, because of the setting of the reward function, the agent is more concerned about the overall situation without being trapped in a local optimum. The calculation of each plot in the greedy algorithm is independent, and the previous experience does not help the development of the current plot. As a result, the calculation amount becomes larger and the correct structure cannot be stably obtained.
From the testing process, it can be found that reinforcement learning with full states can maintain the lowest energy and achieve stable convergence after reaching the minimum energy. In contrast, reinforcement learning with partial states has fluctuations, cannot be stably maintained, and cannot reach the convergence state. This is because each state in the full state space is uniquely determined and can only be transferred by a unique stateaction pair, and the process has Markov properties. However, the state in the partial state space can be transferred by different stateaction pairs, which has partial uncertainty.
Full state space compares to partial state space
The full state space and the partial state space are two different descriptions of the state space in the 2DHP model under reinforcement learning framework. The same point of the full and partial state spaces is that different states corresponding to each amino acid are set in advance, but they differ in the rules of the state setting. For the full state space, the number of states of subsequent amino acids is always three times the number of previous amino acid states. The state of the subsequent amino acid is obtained by a specific action of the previous amino acid in a specific state. That is to say, each state is transferred by a certain stateaction pair, and the whole process has Markov properties. For the partial state space, the number of states for each amino acid except the first amino acid is four. The four states of the subsequent amino acid can be transferred from the four states of the previous amino acid through four different actions, and the whole process does not have Markov properties. The advantage of the full state space is that it can accurately find the lowest energy of the sequence and stabilize the convergence. The disadvantage is that the state space dimension is too high and the memory requirement is high, and the sequence with long length cannot be calculated. The advantage of partial state space is that the required state space is small, and it is possible to calculate a sequence with a long length. The disadvantage is that it cannot converge and cannot find the lowest energy of the sequence.
Function approximation is especially suitable for solving problems with large state space. The method described above for presetting the stateaction value matrix and updating the stateaction value matrix during the training process takes up a large amount of memory. Function approximation can be used to map the stateaction value matrix to an approximation function (such as a parameter approximation function). Updating the parameter values with experimental data during the training process is equivalent to updating the stateaction value, and finally a suitable approximation function is obtained. It can save memory space and solve the problem of sequence length limitation.
Conclusion
This paper proposes a model based on reinforcement learning to solve the problem of HP model prediction. This problem is also a basic problem in computational molecular biology. The state set and state transition space are calculated according to the length of the HP sequence. The reward function is set according to different situations. The agent uses the εgreedy strategy to select the exploration action to be rewarded, and continuously calculates and updates the Q value. Finally, the optimal solution is selected according to the converged Qvalue table to obtain the optimal HP structure. In this paper, sixteen sequences were selected as experimental objects. The experimental results showed that compared with the flexible criterion, the method can converge to the optimal value function under the rigid criterion, and obtain the optimal structure of the HP model. Compared with the greedy algorithm, the algorithm can find the lowest energy more than times in the training process, highlighting the advantages of this method based on previous experience. Compared with reinforcement learning with partial states, the advantages of the stable convergence of the algorithm are highlighted. This article is a new attempt in the field of protein structure prediction with reinforcement learning, which will play an exemplary role in further threedimensional protein structure prediction and other areas of biological information using reinforcement learning.
Besides, reinforcement learning with rigid criterion can robustly converge to the lowest energy conformations; the limitations of the method can be further improved. Firstly, although this method can be calculated for long sequences, it has higher memory requirements for computers; Secondly, in order to obtain accurate results, a large number of training events need to be taken into account, resulting in a slow convergence process and the convergence speed needs to be improved. In the followup study, we will further improve the forecast results from these two aspects. We believe that the reinforcement learning method applied to solve the problem of protein folding deserves further research.
Availability of data and materials
Dataset and source code can be access from http://eie.usts.edu.cn/prj/RLHP/index.html.
Abbreviations
 2D:

Twodimensional
 2DHP:

Twodimensional hydrophobicpolarity
 AB:

Hydrophobic is called A and hydrophilic is called B
 ACO:

Ant colony optimization
 DP:

Dynamic programming
 EA:

Evolutionary algorithm
 GA:

Genetic algorithm
 HP:

Hydrophobic polarity
 MC:

Monte Carlo
 TD:

Temporal difference
References
 1.
MárquezChamorro AE, AsencioCortés G, SantiestebanToca CE, et al. Soft computing methods for the prediction of protein tertiary structures: a survey. Appl Soft Comput. 2015;35:398–410.
 2.
Wu H, Wang K, Lu L, et al. Deep conditional random field approach to transmembrane topology prediction and application to GPCR threedimensional structure modeling. IEEE/ACM Trans Comput Biol Bioinform. 2017;14(5):1106–14.
 3.
Zhao XM, Cheung YM, Huang DS, et al. Analysis of gene expression data using RPEM algorithm in normal mixture model with dynamic adjustment of learning rate. Int J Pattern Recognit Artif Intell. 2010;24(04):651–66.
 4.
Günther F, Möbius A, Schreiber M. Structure optimisation by thermal cycling for the hydrophobicpolar lattice model of protein folding. Eur Phys J Spec Top. 2017;226(4):639–49.
 5.
Tang X, Wang J, Zhong J, et al. Predicting essential proteins based on weighted degree centrality. IEEE/ACM Trans Comput Biol Bioinform. 2014;11(2):407–18.
 6.
Deng SP, Zhu L, Huang DS. Predicting hub genes associated with cervical cancer through gene coexpression networks. IEEE/ACM Trans Comput Biol Bioinform. 2016;13(1):27–35.
 7.
Corrêa LDL, Borguesan B, Krause MJ, et al. Threedimensional protein structure prediction based on memetic algorithms. Comput Oper Res. 2018;91:160–77.
 8.
Li Z, Wang J, Zhang S, et al. A new hybrid coding for protein secondary structure prediction based on primary structure similarity. Gene. 2017;618:8–13.
 9.
Zheng CH, Zhang L, Ng TY, et al. Molecular pattern discovery based on penalized matrix decomposition. IEEE/ACM Trans Comput Biol Bioinform. 2011;8(6):1592–603.
 10.
Wu H, Cao C, Xia X, et al. Unified deep learning architecture for modeling biology sequence. IEEE/ACM Trans Comput Biol Bioinform. 2018;15(5):1445–52.
 11.
Deng SP, Cao S, Huang DS, et al. Identifying stages of kidney renal cell carcinoma by combining gene expression and DNA methylation data. IEEE/ACM Trans Comput Biol Bioinform. 2017;14(5):1147–53.
 12.
Deng SP, Huang DS. SFAPS: an R package for structure/function analysis of protein sequences based on informational spectrum method. Methods. 2014;69(3):207–12.
 13.
Deng SP, Zhu L, Huang DS. Mining the bladder cancerassociated genes by an integrated strategy for the construction and analysis of differential coexpression networks. BMC Genomics. 2015;16(Suppl 3):S4.
 14.
Zhu L, Deng SP, Huang DS. A twostage geometric method for pruning unreliable links in proteinprotein networks. IEEE Trans Nanobioscience. 2015;14(5):528–34.
 15.
Wang SL, Zhu YH, Jia W, et al. Robust classification method of tumor subtype by using correlation filters. IEEE/ACM Trans Comput Biol Bioinform. 2012;9(2):580–91.
 16.
Huang DS, Yu HJ. Normalized feature vectors: a novel alignmentfree sequence comparison method based on the numbers of adjacent amino acids. IEEE/ACM Trans Comput Biol Bioinform. 2013;10(2):457–67.
 17.
Liu KH, Huang DS. Cancer classification using rotation forest. Comput Biol Med. 2008;38(5):601–10.
 18.
Zheng CH, Zhang L, Ng TY, et al. Metasamplebased sparse representation for tumor classification. IEEE/ACM Trans Comput Biol Bioinform. 2011;8(5):1273–82.
 19.
Qiao J, Wang G, Li W, et al. An adaptive deep Qlearning strategy for handwritten digit recognition. Neural Netw. 2018;107:61–71.
 20.
Mendonca MRF, Bernardino HS, Neto RF. Reinforcement learning with optimized reward function for stealth applications. Entertain Comput. 2018;25:37–47.
 21.
Ghazi MM, Yanikoglu B, Aptoula E. Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing. 2017;235:228–35.
 22.
Pan J, Wang X, Cheng Y, et al. Multisource transfer ELMbased Q learning. Neurocomputing. 2014;137:57–64.
 23.
Wu H, Li H, Jiang M, et al. Identify highquality protein structural models by enhanced KMeans. Biomed Res Int. 2017;2017:7294519.
 24.
Boskovic B, Brest J. Genetic algorithm with advanced mechanisms applied to the protein structure prediction in a hydrophobicpolar model and cubic lattice. Appl Soft Comput. 2016;45:61–70.
 25.
Zheng CH, Huang DS, Kong XZ, et al. Gene expression data classification using consensus independent component analysis. Genomics Proteomics Bioinformatics. 2008;6(2):74–82.
 26.
Shah SM, Borkar VS. Qlearning for Markov decision processes with a satisfiability criterion. Syst Control Lett. 2018;113:45–51.
 27.
Zhu L, You Z, Huang D, et al. LSE: a novel robust geometric approach for modeling proteinprotein interaction networks. PLoS One. 2013;8(4):e58368.
 28.
Wang SL, Li X, Zhang S, et al. Tumor classification by combining PNN classifier ensemble with neighborhood rough set based gene reduction. Comput Biol Med. 2010;40(2):179–89.
 29.
Joseph AG, Bhatnagar S. An online prediction algorithm for reinforcement learning with linear function approximation using cross entropy method. Mach Learn. 2018;107:1385–429.
 30.
Ebadzadeh MM, SalimiBadr A. ICFNN: a novel fuzzy neural network with interpretable, intuitive, and correlatedcontours fuzzy rules for function approximation. IEEE Trans Fuzzy Syst. 2018;26(3):1288–302.
 31.
Korda M, Henrion D, Jones CN. Controller design and value function approximation for nonlinear dynamical systems. Automatica. 2016;67:54–66.
 32.
Lu QG, Chen DF, Mao LM, et al. Research on predication of proteins structure based on GA. In: China artificial intelligence annual conference; 2005.
 33.
Chen M. Quasiphysical quasihuman algorithm for protein folding: Huazhong University of Science and Technology. Wuhan; 2007.
 34.
GarzaFabre M, RodriguezTello E, ToscanoPulido G. Constrainthandling through multiobjective optimization: the hydrophobicpolar model for protein structure prediction. Comput Oper Res. 2015;53:128–53.
Acknowledgments
The authors acknowledge and thank the anonymous reviewers for their suggestions that allowed the improvement of our manuscript.
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This article has been published as part of BMC Bioinformatics Volume 20 Supplement 25, 2019: Proceedings of the 2018 International Conference on Intelligent Computing (ICIC 2018) and Intelligent Computing and Biomedical Informatics (ICBI) 2018 conference: bioinformatics. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume20supplement25.
Funding
This work was supported in part by the National Natural Science Foundation of China (No. 61772357, 61902272, 61672371, 61876217, 61902271, 61750110519), and Suzhou Science and Technology Project (SYG201704, SNG201610, SZS201609). The publication costs of this article were funded by the grants of the above foundations and projects.
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HW proposed the original idea. RY and QF designed the framework and the experiments. HL collected the experimental datasets. HW, RY and QF performed the experiments and performed the primary data analysis. HW and YR wrote the manuscript. JC and WL modified the codes and the manuscript. All authors contributed to the manuscript. All authors read and approved the final manuscript.
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Wu, H., Yang, R., Fu, Q. et al. Research on predicting 2DHP protein folding using reinforcement learning with full state space. BMC Bioinformatics 20, 685 (2019). https://doi.org/10.1186/s1285901932596
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DOI: https://doi.org/10.1186/s1285901932596
Keywords
 Reinforcement learning
 HP model
 Structure prediction