A Peptides Prediction Methodology for Tertiary Structure Based on Simulated Annealing

Abstract

The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.

SAIPO-TAIPO and Genetic Algorithms for Investment Portfolios

“…The parameters of this algorithm are the number of iterations n iter , number of steps n steps , and a threshold sequence, as shown in Algorithm 3. Both SA and TA algorithms have been applied to successfully solve different problems [45,46]. T k = T i 3:…”

A Peptides Prediction Methodology with Fragments and CNN for Tertiary Structure Based on GRSA2

“…Algorithm 2 shows the perturbation process which determines a new solution. GRSA2 was applied to a set of peptides and mini proteins in the GRSA2-SSP [18] algorithm and compared with the state-of-the-art. GRSA2 is an algorithm that has been able to refine peptide structures with good results [17].…”

“…GRSA2 is an algorithm that has been able to refine peptide structures with good results [17]. However, this application was limited to small peptides in the alpha class [18]-i.e., when applied to peptides of class none and beta, GRSA2 obtained poor quality results. return Sj 12: end Function SA and HSA algorithms are used in the refinement process of protein prediction.…”

“…Another important process is the refinement method for assembling structures; Simulated Annealing (SA) is commonly used in this process [12,13]. Hybrid Simulated Annealing (HSA) algorithms have obtained good results in the prediction of small proteins or peptides; for example, the methods that include Monte Carlo or Simulated Annealing algorithms are I-TASSER [8], Rossetta [9], QUARK [14], PEP-FOLD3 [15], GRSA [16], GRSA2 [17], GRSA2-SSP [18], and AlphaFold [10]. An important aspect of HSA algorithms is that the computational cost increases in proportion to the length of the amino acid sequence.…”

“…An important aspect of HSA algorithms is that the computational cost increases in proportion to the length of the amino acid sequence. Conversely, the works in the references [16][17][18] are all based on another HSA algorithm called Golden Ratio Simulated Annealing (GRSA), which has a cooling scheme that improves the computational times compared to the classical SA.…”