Hai-Xia Lin

Protein–ligand binding affinity reflects the equilibrium thermodynamics of the protein–ligand binding process. Binding/unbinding kinetics is the other side of the coin. Computational models for interpreting the quantitative structure–kinetics relationship (QSKR) aim at predicting protein–ligand binding/unbinding kinetics based on protein structure, ligand structure, or their complex structure, which in principle can provide a more rational basis for structure-based drug design. Thus far, most of the public data sets used for deriving such QSKR models are rather limited in sample size and structural diversity. To tackle this problem, we have compiled a set of 680 protein–ligand complexes with experimental dissociation rate constants (koff), which were mainly curated from the references accumulated for updating our PDBbind database. Three-dimensional structure of each protein–ligand complex in this data set was either retrieved from the Protein Data Bank or carefully modeled based on a proper template. The entire data set covers 155 types of protein, with their dissociation kinetic constants (koff) spanning nearly 10 orders of magnitude. To the best of our knowledge, this data set is the largest of its kind reported publicly. Utilizing this data set, we derived a random forest (RF) model based on protein–ligand atom pair descriptors for predicting koff values. We also demonstrated that utilizing modeled structures as additional training samples will benefit the model performance. The RF model with mixed structures can serve as a baseline for testifying other more sophisticated QSKR models. The whole data set, namely, PDBbind-koff-2020, is available for free download at our PDBbind-CN web site (http://www.pdbbind.org.cn/download.php).

A new series of C-3′-N-sulfonyl paclitaxel analogs were designed and synthesized from 1-deoxybaccatin VI and their structures were confirmed by ¹H NMR, ¹³C NMR and high resolution MS. The synthesized compounds were evaluated for their in vitro anti-Hepatocellular carcinoma (HCC) activity against human hepatoma (HepG2) cell line. Bioassay results showed that compounds 17c, 17d and 17f exhibited more potent inhibitory activity against HepG2 cell line in comparison with paclitaxel. It is suggested that paclitaxel analogs containing the C-3′-N-sulfonyl could be considered as a precursor structure for further synthesis of more potent analogues.

The Bcl‐2 family proteins are key regulators of the intrinsic apoptotic pathway and are among the validated targets for developing anticancer drugs. Protein–protein interactions between the pro‐ and antiapoptotic members of this family determine mitochondrial outer‐membrane permeabilization. Elucidating such protein–protein interactions in a quantitative way is helpful for network pharmacology studies on the Bcl‐2 family, which, in turn, will provide valuable guidance for developing new anticancer therapies. In this study, the binding affinities of the BH3 peptides derived from eight proapoptotic BH3‐only proteins (i.e., Bid, Bim, Puma, Noxa, Bad, Bmf, Bik, Hrk) against five well‐studied antiapoptotic proteins (i.e., Bcl‐x_L, Bcl‐2, Mcl‐1, Bcl‐w, Bfl‐1) in the Bcl‐2 family have been measured. Three different types of binding assay (i.e., surface plasmon resonance, fluorescence polarization, and homogeneous time‐resolved fluorescence) were employed for cross‐validation. The results confirmed that each proapoptotic BH3 peptide exhibited a distinct binding profile against the five antiapoptotic proteins. The binding data obtained herein serve as a fresh update or correction to existing knowledge. It is expected that such binding data will be helpful for building more accurate mathematical network models for depicting the complex protein–protein interactions within the Bcl‐2 family.