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

Farhadi, Tayebeh; Hashemian, Seyed MohammadReza

Different diseases may be caused by pathogens or malfunctioning organs, and using therapeutic agents to heal them has an old recorded history. Small molecules are conventional therapeutic candidates that can be easily synthesized and administered. However, many of these small molecules are not specific to their targets and may lead to side effects. 1 Moreover, a number of diseases are caused due to deficiency in a specific protein or enzyme. Thus, they can be treated using biologically based therapies that are able to recognize a specific target within crowded cells. 2 Under the biologic conditions, some macromolecules such as proteins and peptides are optimized to recognize specific targets. 3 Therefore, they can override the shortcomings of small molecules. 3 Recently, pharmaceutical scientists have shown interest in engineering amino acid-based therapeutics such as proteins, peptides and peptidomimetics. [4] [5] [6] Theoretical and experimental techniques can predict the structure and folding of amino acid sequences and provide an insight into how structure and function are encoded in the sequence. Such predictions may be valuable to interpret genomic information and many life processes. Moreover, engineering of novel proteins or redesigning the existing proteins has opened the ways to achieve novel biologic macromolecules with desirable therapeutic functions. 7 Protein sequences comprise tens to thousands of amino acids. Besides, the backbone and side chain degrees of freedom lead to a large number of configurations for a single amino acid sequence. Protein design techniques give minimal frustration through precise identification of sequences and their characteristics. [8] [9] [10] [11] Considering energy landscape theory, the adequately minimal frustration in natural proteins occurs when their native state is adequately low in energy. 7 The de novo design of a sequence is difficult because there are huge numbers of possible sequences: 20 N for N-residue proteins with only 20 natural amino acids. 12 Peptide design should incorporate computational approaches. It can benefit from searching the more advanced fields used for small molecules and protein design. 13 However, the straightforward adoption of computational approaches employed to small-molecule and protein design has not be accepted as a reasonable solution to the peptide design problem. [14] [15] [16] In the peptide drug design, the conformational space accessible to peptides challenges the small-molecule computational approaches. Besides, the necessity for nonstandard amino acids and various cyclization chemistries challenges the available tools for protein modeling. 13 Furthermore, the aggregation of peptide drugs during production or storage can be an unavoidable problem in the peptide design procedure. Rational design of a peptide ligand is also challenging because of the elusive affinity and intrinsic flexibility of peptides. 17 Peptide-focused in silico methods have been increasingly developed to make testable predictions and refine design hypotheses. Consequently, the peptide-focused approaches decrease the chemical spaces of theoretical peptides to more acceptable focused "drug-like" spaces and reduce the problems associated with aggregation and flexibility. 13, 18 For the discussions that follow, peptides can be defined as relatively small (2-30 residues) polymers of amino acids. 18 In physiological conditions, several problems such as degradation by specific or nons