Success rates of three scoring functions on the benchmark constructed by Huang and Zou [18, 28] using RPDOCK[15].

<p>Success rates of three scoring functions on the benchmark constructed by Huang and Zou [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174662#pone.0174662.ref018" target="_blank">18</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174662#pone.0174662.ref028" target="_blank">28</a>] using RPDOCK[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174662#pone.0174662.ref015" target="_blank">15</a>].</p

Success rates of 3dRPC-Score over the training set for different values of the constant <i>lnP</i>_<i>v</i> in Eq (2).

<p><i>P</i>_<i>v</i> is the probability of class C in the whole conformational space of nucleotide-residue pairs in ideal state. Since the nucleotide-residue pairs are clustered into 10 classes, <i>P</i>_<i>v</i> = 1/10 or ln(<i>P</i>_<i>v</i>) ≈ -2.3 and so the success rate has a dramatic change between -2 and -3.</p

The distribution of RMSDs of nucleotide-residue conformations in relative to their center conformations of 800 classes.

<p>The distribution of RMSDs of nucleotide-residue conformations in relative to their center conformations of 800 classes.</p

Score-LRMSD plots of the decoys of selected RNA-protein complexes.

<p>Score-LRMSD plots of the decoys of selected RNA-protein complexes.</p

Distribution of pair conformations.

<p>Upper: the distribution of the relative distances between the nucleotide and residue in class 9 of MET-G pair (left) and class 7 from ASN-U pair; Lower: comparison of two center conformations of ARG-G (left) and TYR-C (right) pairs whose nucleotide and residue have almost the same distances but their orientations are completely different.</p

Success rates of three scoring functions on the benchmark constructed by Huang and Zou [28] using ZDOCK[30].

<p>Success rates of three scoring functions on the benchmark constructed by Huang and Zou [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174662#pone.0174662.ref028" target="_blank">28</a>] using ZDOCK[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174662#pone.0174662.ref030" target="_blank">30</a>].</p

The distribution of number of conformations in each of 800 classes of nucleotide-residue pairs.

<p>The distribution of number of conformations in each of 800 classes of nucleotide-residue pairs.</p

Success rates of three scoring functions on the benchmark provided by Perez Cano et al [29]and created using RPDOCK[15].

<p>Success rates of three scoring functions on the benchmark provided by Perez Cano et al [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174662#pone.0174662.ref029" target="_blank">29</a>]and created using RPDOCK[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174662#pone.0174662.ref015" target="_blank">15</a>].</p

Stereoselective ZnCl₂‑Catalyzed B–H Bond Insertion of Vinyl Carbenes Generated from Cyclopropenes for the Synthesis of Allylboranes

Zinc-catalyzed insertion of vinyl carbenes generated from cyclopropenes into the B–H bonds of Lewis base–borane adducts for concise and efficient access to allylboranes has been developed. This protocol represents the first zinc-catalyzed B–H bond insertion of carbenes for organoborane compounds. In this protocol, inexpensive ZnCl2, with low toxicity, is used as the catalyst. This simple ligand-free catalytic system affords allylboranes in yields up to 92%, with E/Z ratios of >20:1. Besides, this new protocol offers a broad scope of Lewis base–borane adducts, which are not easily obtained by other catalytic methods for metal carbene insertion into B–H bonds. The potential synthetic applicability of this new methodology is exemplified by a gram-scale experiment and synthetic transformation of the products

Targeting Unoccupied Surfaces on Protein–Protein Interfaces

The use of peptidomimetic scaffolds to target protein–protein interfaces is a promising strategy for inhibitor design. The strategy relies on mimicry of protein motifs that exhibit a concentration of native hot spot residues. To address this constraint, we present a pocket-centric computational design strategy guided by AlphaSpace to identify high-quality pockets near the peptidomimetic motif that are both targetable and unoccupied. Alpha-clusters serve as a spatial representation of pocket space and are used to guide the selection of natural and non-natural amino acid mutations to design inhibitors that optimize pocket occupation across the interface. We tested the strategy against a challenging protein–protein interaction target, KIX/MLL, by optimizing a single helical motif within MLL to compete against the full-length wild-type MLL sequence. Molecular dynamics simulation and experimental fluorescence polarization assays are used to verify the efficacy of the optimized peptide sequence