Computer-aided design of amino acid-based therapeutics: a review

Farhadi, Tayebeh; Hashemian, Seyed MohammadReza

The affinity of BH3 peptides to Bcl-2 protein was investigated, and results showed the higher affinity of bound peptides occurred when the corresponding peptides were in a lower degree of disorder in unbound states and vice versa. 111 These results showed that the highly structured peptides could increase their affinity through reducing the entropic loss associated with the binding. Overall, in addition to the electrostatic and hydrophobic forces, protein-peptide interactions can be affected by the entropic effect and conformational flexibility that could be willingly examined with atomistic simulations. 111 Very recently, using a fast molecular dynamics simulation, the energetic and dynamic features of protein-peptide interactions were studied. In most cases, the native binding sites and native-like postures of protein-peptide complexes were recapitulated. Additional investigation showed that insertion of motility and flexibility in the simulation could meaningfully advance the correctness of protein-peptide binding prediction. 112 Peptide affinity prediction Most features of computational peptide design are based on the accuracy and efficacy of affinity prediction. Hence, the fast and reliable prediction of peptide-protein affinity is significant for rational peptide design. 18 In this aspect, two categories of prediction algorithms including sequence-and structure-based approaches were developed. The sequencebased method uses the information derived from primary polypeptide sequences to approximate and evaluate the standards of the binding affinity. The structure-based process takes the information derived from 3D structures of proteinpeptide complexes to predict the binding affinity. 113 At the sequence level, the quantitative structure-activity relationships (QSARs) have been widely utilized to forecast the binding affinity of peptides and conclude the biologic function. 114 To model the statistical correlation between sequence patterns and biologic activities of experimentally assessed peptides, machine-learning methods such as partial least squares (PLS), artificial neural networks (ANN) and support vector machine (SVM) have been used. The obtained correlations have been used to infer experimentally undetermined peptides. 115 The relationship between the biologic activity and molecular structure is an important issue in biology and biochemistry. QSAR is a well-established method employed in pharmaceutical chemistry and has become a standard tool for drug discovery. However, the predictive capacity of QSAR techniques is generally weaker than statistics-based approaches. Therefore, a combination of the QSAR method with a statistic-based technique may bring out the best in each other and can be a trend in future developments of drug discovery. 114 At the structural level, numerous reports on affinity prediction have addressed the MHC-binding peptides. Plentiful MHC-peptide complex structure records have been deposited in the PDB. 116 The significance of domain-peptide recognition has been recently illustrated in the metabolic pathway and cell signaling. 117 To predict the protein-peptide binding potency, a number of strict theories were suggested based on the potential free energy perturbation. The theories computed the alteration of free energies upon the interaction between phosphor-tyrosine-tetra-peptide (pYEEI) and human Lck SH2 domain. 118 Furthermore, to obtain a deep insight into the structural and energetic aspects of peptid