Structural properties and interaction energies affecting drug design. An approach combining molecular simulations, statistics, interaction energies and neural networks.
In order to elucidate some basic principles for protein-ligand interactions, a subset of 87 structures of human proteins with their ligands was obtained from the PDB databank. After a short molecular dynamics simulation (to ensure structure stability), a variety of interaction energies and structural parameters were extracted. Linear regression was performed to determine which of these parameters have a potentially significant contribution to the protein-ligand interaction. The parameters exhibiting relatively high correlation coefficients were selected. Important factors seem to be the number of ligand atoms, the ratio of N, O and S atoms to total ligand atoms, the hydrophobic/polar aminoacid ratio and the ratio of cavity size to the sum of ligand plus water atoms in the cavity. An important factor also seems to be the immobile water molecules in the cavity. Nine of these parameters were used as known inputs to train a neural network in the prediction of seven other. Eight structures were left out of the training to test the quality of the predictions. After optimization of the neural network, the predictions were fairly accurate given the relatively small number of structures, especially in the prediction of the number of nitrogen and sulfur atoms of the ligand.
Laboratory of Biochemistry, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece.
Dimitris
D
Ioannidis
Department of Biochemistry and Biotechnology, University of Thessaly, Ploutonos 26 & Aeolou Greece, 41221 Larisa, Greece.
Georgios E
GE
Papadopoulos
Laboratory of Medical Informatics, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece.
Georgios
G
Anastassopoulos
Laboratory of Biochemistry, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece.
Alexandros
A
Kortsaris
Laboratory of Biochemistry, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece. Electronic address: kanagnos@med.duth.gr.
Konstantinos
K
Anagnostopoulos
D015195:Drug Design; D006801:Humans; D008024:Ligands; D056004:Molecular Dynamics Simulation; D016571:Neural Networks, Computer; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D013816:Thermodynamics; D014867:Water / Q000737:chemistry
D008024:Ligands; D011506:Proteins; D014867:Water
Drug design; Interaction energy; Molecular dynamics simulation; Neural networks