MAPK inhibitor - The Quintessential Relaxation!

The distance between a pair of amino acids will correspond to a distance bin giving the same energy value to a range of distances. This enables the sequence selection optimization model to account for backbone movement. Biological constraints, in the form of charge constraints or content constraints, can be included manually by the user as an additional design input. Charge constraints specify a particular charge or range of charges that must be satisfied for the designed sequence or a portion of the designed sequence. The charge is calculated as the sum of the positively charged residues (K and R) minus the sum of the negatively charged residues (D and E). Content constraints specify upper and lower bounds on the occurrence of a particular amino acid in the sequence. Biological Constraints are generally defined through an extensive sequence alignment to the native sequence. This is to capture the known biological limits on charge and amino acid content represented in nature for a family of proteins. Further constraints are manually defined through analysis of known experimental data. Stage One: Sequence Selection: The original sequence selection method was first developed by Klepeis et al.15,16 It selects and ranks amino acid sequences according to their energies in the design template using an Integer Linear Optimization (ILP) model. The method was later improved by the use of a more computationally efficient sequence selection model for rigid (single) templates and expanded through the development of models for flexible templates. This global optimization method does not rely on random mutations and is theoretically guaranteed to search the complete sequence space and determine a global solution. This is a major advantage of our approach compared to all other existing approaches. Single Structure Model: The original form of the sequence selection model proposed by Klepeis et al.15,16 was further refined by Fung et al.28 Its final form is given in Eq. 1. Set i=1,...,n defines the residue positions in the design template. At each position i, mutations are represented by ji=1,...,mi, where mi=20 if position i is allowed to mutate to any of the twenty natural amino acids. The alias sets k��i and l��j, with k>i, are employed to represent all unique pairwise interactions. Binary variables and are introduced to model amino acid mutations. The variable will assume the value of one if the model assigns amino acid j to position i, and the value of zero otherwise (similarly for ). The objective function represents the sum of all pairwise energy interactions in the design template. Parameter which is the energy interaction between position i occupied by amino acid j and position i occupied by amino acid l , depends on the distance between the ��-carbons or side chain check details centroids at the two positions (xi,xj,) as well as the type of amino acids j and l .