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.