Quantitative Assessment of Affinity Selection Performance by Using DNA-Encoded Chemical Libraries.

DNA-encoded chemical libraries are often used for the discovery of ligands against protein targets of interest. These large collections of DNA-barcoded chemical compounds are typically screened by using affinity capture methodologies followed by PCR amplification and DNA sequencing procedures. However, the performance of individual steps in the selection procedures has been scarcely investigated, so far. Herein, the quantitative analysis of selection experiments, by using three ligands with different affinity to carbonic anhydrase IX as model compounds, is described. In the first set of experiments, quantitative PCR (qPCR) procedures are used to evaluate the recovery and selectivity for affinity capture procedures performed on different solid-phase supports, which are commonly used for library screening. In the second step, both qPCR and analysis of DNA sequencing results are used to assess the recovery and selectivity of individual carbonic anhydrase IX ligands in a library, containing 360 000 compounds. Collectively, this study reveals that selection procedures can be efficient for ligands with sub-micromolar dissociation constants to the target protein of interest, but also that selection performance dramatically drops if 104 copies per library member are used as the input.

Using postural synergies to animate a low-dimensional hand avatar in haptic simulation.

A technique to animate a realistic hand avatar with 20 DoFs based on the biomechanics of the human hand is presented. The animation does not use any sensor glove or advanced tracker with markers. The proposed approach is based on the knowledge of a set of kinematic constraints on the model of the hand, referred to as postural synergies, which allows to represent the hand posture using a number of variables lower than the number of joints of the hand model. This low-dimensional set of parameters is estimated from direct measurement of the motion of thumb and index finger tracked using two haptic devices. A kinematic inversion algorithm has been developed, which takes synergies into account and estimates the kinematic configuration of the whole hand, i.e., also of the fingers whose end tips are not directly tracked by the two haptic devices. The hand skin is deformable and its deformation is computed using a linear vertex blending technique. The proposed synergy-based animation of the hand avatar involves only algebraic computations and is suitable for real-time implementation as required in haptics.