High-resolution structures of inhibitor complexes of human indoleamine 2,3-dioxygenase 1 in a new crystal form.

Human indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-dependent enzyme with important roles in many cellular processes and is a potential target for drug discovery against cancer and other diseases. Crystal structures of IDO1 in complex with various inhibitors have been reported. Many of these crystals belong to the same crystal form and most of the reported structures have resolutions in the range 3.2-2.3 Å. Here, three new crystal forms of human IDO1 obtained by introducing a surface mutation, K116A/K117A, distant from the active site are reported. One of these crystal forms diffracted to 1.5 Šresolution and can be readily used for soaking experiments to determine high-resolution structures of IDO1 in complex with the substrate tryptophan or inhibitors that coordinate the heme. In addition, this mutant was used to produce crystals of a complex with an inhibitor that targets the apo form of the enzyme under the same conditions; the structure of this complex was determined at 1.7 Šresolution. Overall, this mutant represents a robust platform for determining the structures of inhibitor and substrate complexes of IDO1 at high resolution.

Swarm intelligence for mixed-variable design optimization.

Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence approach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.