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

Farhadi, Tayebeh; Hashemian, Seyed MohammadReza

Deterministic methods: To achieve a sequence folded into a global minimum energy conformation, deterministic methods search the whole sequence space and identify the global optima. 3, 7 These methods include dead-end elimination (DEE), 46 self-consistent mean field, 47 graph decomposition and linear programming. 48 Stochastic algorithms search the sequence space in an exploratory manner. 3 These algorithms include Monte Carlo algorithms (simulated annealing), 49 graph search methods 50 and genetic algorithms. 51 Some of the most commonly used methods are discussed below. DEE has been considered as a thorough search algorithm. To find and remove sequence-rotameric positions that are not portions of the global minimum energy conformation, DEE compares two amino acid rotamers and removes the one with greater interaction energy. 52 Interaction energies are computed for each rotamer of the test amino acid, along with all rotamers of every other amino acid. 3 The situation is repetitively examined for total amino acid states as well as their rotamers until it no longer holds true. 52, 53 Expanding the sequence length increases the combinatorial complication of DEE exponentially. Therefore, to design sequences of 30 amino acids or larger, application of DEE may be restricted. 54 Details of the theorem are explained elsewhere. 3, 7 Stochastic search algorithms: As mentioned before, deterministic approaches are perfect to design proteins with small sizes, but show the applied disadvantages with extension of sequence size. Stochastic or heuristic methods are valuable to design large proteins. 3 The most widely used method for protein design includes Monte Carlo sampling. 3, 7 Monte Carlo method samples positions of complicated proteins in a way related to a selected probability distribution such as Boltzmann distribution. Boltzmann distribution specially weighs low-energy configurations. The Monte Carlo algorithm performs iterative series of calculations. At the primary step of each search, a partially accidental test sequence is generated, and its energy is calculated via a physical potential. During the primary step, both rotamer state and amino acid identity are adjusted and an efficient temperature controls the probable energy alterations. In the next step, named simulated annealing, the temperature gradually decreases and permits favorable sampling of lowerenergy configurations. 55 Multiple independent calculations are carried out to converge the system to a global minimum. 3, 7 For more explanation about the theorems and details of the formulation of the probability distribution and weights, readers are referred to study previous reports. 3, 7 Probabilistic approach: Probabilistic approaches are frequently employed when thorough information is not accessible for protein design. In a probabilistic approach, sitespecific amino acid probabilities may be utilized, rather than particular sequences. The procedure is partially motivated by the uncertainties to find sequences consistent with a specific structure. Briefly, the backbone atoms are fixed or greatly constrained, side chain conformations are discretely handled, energy functions are estimated and solvation is handled by simple models. 7 However, in order to offer valuable sequence information for design experiments and to find structurally significant amino acids, probabilistic techniques leverage structural characteristics of interatomic interactions. 7 Generally, Monte Carlo methods giv