Constructing Kinetically Controlled Denaturation Isotherms of Folded Proteins Using Denaturant-Pulse Chaperonin Binding

Methods to assess the kinetic stability of proteins, particularly those that are aggregation prone, are very useful in establishing ligand induced stabilizing effects. Because aggregation prone proteins are by nature difficult to work with, most solution based methods are compromised by this inherent instability. Here, we describe a label-free method that examines the denaturation of immobilized proteins where the dynamic unfolded protein populations are captured and detected by chaperonin binding

Fast docking on graphics processing units via Ray-Casting.

Docking Approach using Ray Casting (DARC) is structure-based computational method for carrying out virtual screening by docking small-molecules into protein surface pockets. In a complementary study we find that DARC can be used to identify known inhibitors from large sets of decoy compounds, and can identify new compounds that are active in biochemical assays. Here, we describe our adaptation of DARC for use on Graphics Processing Units (GPUs), leading to a speedup of approximately 27-fold in typical-use cases over the corresponding calculations carried out using a CPU alone. This dramatic speedup of DARC will enable screening larger compound libraries, screening with more conformations of each compound, and including multiple receptor conformations when screening. We anticipate that all three of these enhanced approaches, which now become tractable, will lead to improved screening results

Docking approach using ray casting.

<p>A schematic diagram of DARC scoring is shown in cross section. A grid is placed at a region of interest on a protein surface, and used to identify “deep pocket” points. Points that are not in direct contact with the protein surface are removed, leaving behind a set of points that map the topography of the protein surface pocket (<i>yellow squares</i>). An adjacent layer of points on the protein surface are then labeled “forbidden” points (<i>red squares</i>). Rays are cast from an origin point within the protein (<i>white square</i>) at each pocket point and forbidden point. To score a docked pose, the same rays are cast at the ligand (<i>blue</i>), and the first intersection (if any) is calculated. The contribution to the total score from each ray is dependent on whether the ray was defined based on a pocket point or a forbidden point, and whether the ray intersects this point before or after it intersects with the ligand. These conditions are described in detail in the main text.</p

Control flow for GPU-enabled DARC.

<p>Control begins on the CPU. The CPU generates the pocket and casts rays at the protein surface, then stores this information on the GPU. The CPU generates 200 “particles” (independent initial ligand orientations to be used in the optimization) and passes each of these docked poses to the GPU. The GPU evaluates the DARC score of each docked pose, and passes these back to the CPU. The CPU uses these scores to update the docked poses accordingly, then sends the new poses to the GPU. This process is repeated 200 times, and the best-scoring particle is reported.</p

Effect of the number of particles and the number of iterations on the “hit” compounds selected.

<p>The most pragmatic measure of convergence is the identity of the “hits” to be advanced for further evaluation. The top scoring 10% of the compound library from the most extensive docking simulations were considered to be the “gold standard” hits. With increasing computationally intensive simulations (by together increasing the number of particles and the number of iterations), an increasing fraction of the hits are members of the “gold standard” set.</p

Dependence on number of atoms in the ligand.

<p>Ligands of varying sizes were docked using DARC. <b>A</b>) Time required to complete the optimization, using a CPU alone or with the GPU. <b>B</b>) Speedup factor, reported as the ratio of the time required using the GPU to the time required using the CPU alone.</p

Runtime dependence on the number of particles and the number of iterations.

<p>A series of optimizations are compared in which the number of calculations (and thus the total time required) on the CPU is constant, and the speedup factor is reported as the ratio of the time required using the GPU to the time required using the CPU alone. The benefit of using the GPU is enhanced when individual GPU tasks are larger (more particles), allowing fewer CPU-GPU communication steps.</p

Comparison of GPU-enabled docking tools.

<p>Docking methods have been adapted for GPU computing using a variety of strategies. These require different degrees of CPU-GPU communication, and accordingly enable varying speedups relative to the analogous CPU-only protocol.</p

Effect of the number of particles and the number of iterations on DARC score.

<p>To determine the number of particles and number of iterations required for reasonable convergence of the DARC score, docking was carried out with (<b>A</b>) an increasing number of iterations while holding the number of particles fixed at 200, and (<b>B</b>) an increasing number of particles while holding the number of iterations fixed at 200. Differences in score are reported relative to the “gold standard,” taken to be the most extensive simulation in the set (i.e. 1000 iterations or 1000 particles).</p

The GPU-enabled speedup facilitates screening of larger libraries, which in turn allows better-scoring ligands to be identified.

<p>Compound libraries of increasing size were screened against interleukin-2 and Mdm2. As expected, screening larger libraries led to identification of compounds with better scores. All scores are reported relative to the lowest scoring ligand in the largest set.</p