Genetic Algorithm Embedded with a Search Space Dimension Reduction Scheme for Efficient Peptide Structure Predictions

Xiao, Ru; Lin, Zijing

doi:10.1021/acs.jpcb.1c01255

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…15 PEPstr 16 (and its extension to nonstandard amino acid, PEPstrMod 17 ) utilizes the observation on the prevalence of β-turn secondary structure to add constraints on molecular dynamics simulation to predict peptide tertiary structure. In the parallel microgenetic algorithm (PMGA) 18 techniques, peptides' structure predictions are done by utilizing a genetic algorithm with backbone dihedral angle correlations for sampling a density functional theory derived fitness function. Finally, the recently developed APPTest 19 was developed by combining distance/angle constraints derived by a neural network with simulated annealing, resulting in great structural predictions for small peptides.…”

Section: ■ Introductionmentioning

confidence: 99%

A Generalized Attraction–Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction

Binette

Mousseau

Tufféry

2022

J. Chem. Theory Comput.

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Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for nonbonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small β-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.

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Section: ■ Introductionmentioning

confidence: 99%

A Generalized Attraction–Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction

Binette

Mousseau

Tufféry

2022

J. Chem. Theory Comput.

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“…A significant task of rational drug design is to determine the three-dimensional (3D) structure of a drug candidate that binds to a particular bio-macro-molecule. Due to the complexity of the bio-complex potential energy landscape involving a gigantic number of local minima, 1,2 the binding mode structure is often determined by molecular docking simulations. 3 A docking algorithm generates possible ligand conformations within the receptor-binding site by some conformational sampling technique, 1,4 and a quickto-evaluate empirical scoring function estimates the corresponding binding free energy energies.…”

Section: Introductionmentioning

confidence: 99%

Combining classical molecular docking with self-consistent charge density-functional tight-binding computations for the efficient and quality prediction of ligand binding structure

Al-Ansi

Lin

2022

Journal of Chemical Research

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To improve the successful prediction rate of the existing molecular docking methods, a new docking approach is proposed that consists of three steps: generating an ensemble of docked poses with a conventional docking method, performing clustering analysis of the ensemble to select the representative poses, and optimizing the representative structures with a low-cost quantum mechanics method. Three quantum mechanics methods, self-consistent charge density-functional tight-binding, ONIOM(DFT:PM6), and ONIOM(SCC-DFTB:PM6), are tested on 18 ligand-receptor bio-complexes. The rate of successful binding pose predictions by the proposed self-consistent charge density-functional tight-binding docking method is the highest, at 67%. The self-consistent charge density-functional tight-binding docking method should be useful for the structure-based drug design.

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Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy

et al. 2022

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Motivation Cyclization is a common strategy to enhance the therapeutic potential of peptides. Many cyclic peptide drugs have been approved for clinical use, in which the disulfide-driven cyclic peptide is one of the most prevalent categories. Molecular docking is a powerful computational method to predict the binding modes of molecules. For protein-cyclic peptide docking, a big challenge is considering the flexibility of peptides with conformers constrained by cyclization. Results Integrating our efficient peptide 3D conformation sampling algorithm MODPEP2.0 and knowledge-based scoring function ITScorePP, we have proposed an extended version of our hierarchical peptide docking algorithm, named HPEPDOCK2.0, to predict the binding modes of the peptide cyclized through a disulfide against a protein. Our HPEPDOCK2.0 approach was extensively evaluated on diverse test sets and compared with the state-of-the-art cyclic peptide docking program AutoDock CrankPep (ADCP). On a benchmark data set of 18 cyclic peptide-protein complexes, HPEPDOCK2.0 obtained a native contact fraction of above 0.5 for 61% of the cases when the top prediction was considered, compared with 39% for ADCP. On a larger test set of 25 cyclic peptide-protein complexes, HPEPDOCK2.0 yielded a success rate of 44% for the top prediction, compared with 20% for ADCP. In addition, HPEPDOCK2.0 was also validated on two other test sets of 10 and 11 complexes with apo and predicted receptor structures, respectively. HPEPDOCK2.0 is computationally efficient and the average running time for docking a cyclic peptide is about 34 minutes on a single CPU core, compared with 496 minutes for ADCP. HPEPDOCK2.0 will facilitate the study of the interaction between cyclic peptides and proteins and the development of therapeutic cyclic peptide drugs. Availability and implementation http://huanglab.phys.hust.edu.cn/hpepdock/. Supplementary information Supplementary data are available at Bioinformatics online.

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Genetic Algorithm Embedded with a Search Space Dimension Reduction Scheme for Efficient Peptide Structure Predictions

Cited by 3 publications

References 35 publications

A Generalized Attraction–Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction

A Generalized Attraction–Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction

Combining classical molecular docking with self-consistent charge density-functional tight-binding computations for the efficient and quality prediction of ligand binding structure

Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy

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