Hybrid method to solve HP model on 3D lattice and to probe protein stability upon amino acid mutations

Abstract

BackgroundPredicting protein structure from amino acid sequence is a prominent problem in computational biology. The long range interactions (or non-local interactions) are known as the main source of complexity for protein folding and dynamics and play the dominant role in the compact architecture. Some simple but exact model, such as HP model, captures the pain point for this difficult problem and has important implications to understand the mapping between protein sequence and structure.ResultsIn this paper, we formulate the biological problem into optimization model to study the hydrophobic-hydrophilic model on 3D square lattice. This is a combinatorial optimization problem and known as NP-hard. Particle swarm optimization is utilized as the heuristic framework to solve the hard problem. To avoid premature in computation, we incorporated the Tabu search strategy. In addition, a pulling strategy was designed to accelerate the convergence of algorithm based on the characteristic of native protein structure. Together a novel hybrid method combining particle swarm optimization, Tabu strategy, and pulling strategy can fold the amino acid sequences on 3D square lattice efficiently. Promising results are reported in several examples by comparing with existing methods. This allows us to use this tool to study the protein stability upon amino acid mutation on 3D lattice. In particular, we evaluate the effect of single amino acid mutation and double amino acids mutation via 3D HP lattice model and some useful insights are derived.ConclusionWe propose a novel hybrid method to combine several heuristic strategies to study HP model on 3D lattice. The results indicate that our hybrid method can predict protein structure more accurately and efficiently. Furthermore, it serves as a useful tools to probe the protein stability on 3D lattice and provides some biological insights.

Neural‐augmented two‐stage Monte Carlo tree search with over‐sampling for protein folding in HP Model

“…The second group contains those relatively recent methods. They are usually improved heuristic approaches, such as Hybrid EDA [27], MO + FR GA [29], TPPSO [30] and extended heuristic algorithm (EHA) [31].…”

An Adaptive Discrete Brain Storm Algorithm Solves 3D Protein Structure Prediction

“…BSO-HP method was compared with other methods to exhibit the strength of the method. Table XIII presents the comparison between theBSO-HP method with MCMPSO-TS [28], HGA-PSO [29], and TPPSO [27] based on reaching the optimal solution. MCMPSO-TS method was tested on p1, p2, p3, p4, p5, p6, p8, p10, and p11 and focused on the small HP lengths.…”

“…Gabrial [17] presented an evolutionary strategy to solve the 3D HP model. Few papers have tried to solve the PSP problem as a mathematical model [26] and [27]. We treat this problem as a simpler mathematical model than other methods; because the our mathematical model is more accurate in finding the solution and is more time efficient.…”

Protein folding simulations in the hydrophobic-polar model using a hybrid cuckoo search algorithm

“…To properly evaluate our HCSA algorithm performance, we also tested it using the instances of the HP 3D benchmark presented in Table 2. We have compared the best results obtained by HCSA with those of three metaheuristic algorithms known in the literature, namely the Genetic Algorithm (GA) [48], two-hybrid Particle Swarm Optimization methods (T P P SO 1 , T P P SO 2 ) [49], and the Elastic Network Algorithm (EN) [42]. Based on the results in Table 4, we can see that the suggested algorithm achieved the optimal energy value for the 11 benchmark instances.…”