Conformational Search for the Building Block of Proteins Based on the Gradient Gravitational Search Algorithm...

Proteins are linear polymers built from a repertoire of 20 different amino acids, which are considered building blocks of proteins. The diversity and versatility of these 20 building blocks with regard to their conformations are key to adopting three-dimensional structures that facilitate proteins to undergo important mechanistic biological processes in living systems. The present investigation reports a conformational search of 20 different amino acids, building blocks of proteins, using three different force fields, CHARMM, AMBER, and OPLS-AA, implemented in the gradient gravitational search algorithm. The search technique (ConfGGS) includes the contribution from both bonded and nonbonded terms using Cartesian coordinates. The efficiency of such conformational searches has also been compared with other optimization algorithms: DE/Best, DE/Rand, and PSO algorithms with respect to computational time and accuracy based on the minimum number of iteration steps and computed lowest mean absolute error (MAE) and mean standard deviation (MSD) values for dihedral angles of respective near-optimal structures. Moreover, the ConfGGS technique has also been extended to an ordered protein fragment (PQITL) extracted from HIV-1 protease (PDB ID: 1YTH), an intrinsically disordered protein fragment, i.e., an amyloid-forming segment (AVVTGVTAV), from the NAC domain of Parkinson's disease protein α-synuclein, residues 69-77 (PDB ID: 4RIK), the experimental NMR atomic-resolution structure of α-synuclein fibrils (PDB ID: 2N0A), and a disulfide bond-containing protein fragment sequence (PCYGWPVCY), residues 59-67 (PDB ID: 6Y4F) toward structure prediction as a close homologue compared with experimental accuracy, using the CHARMM force field. The MolProbity validation results for the protein fragment (PQITL) obtained by ConfGGS/CHARMM are in better agreement with the native protein fragment structure of HIV-1 protease (PDB ID: 1YTH). Furthermore, the computed results have also been compared with the coordinates obtained from the AlphaFold network.