Correction: Erlichman et al. Tumor Cell-Autonomous Pro-Metastatic Activities of PD-L1 in Human Breast Cancer Are Mediated by PD-L1-S283 and Chemokine Axes. Cancers 2022, 14, 1042

The authors apologize for several typos/technical inaccuracies in the original article [...].

Alleviation of a polyglucosan storage disorder by enhancement of autophagic glycogen catabolism

This work employs adult polyglucosan body disease (APBD) models to explore the efficacy and mechanism of action of the polyglucosan-reducing compound 144DG11. APBD is a glycogen storage disorder (GSD) caused by glycogen branching enzyme (GBE) deficiency causing accumulation of poorly branched glycogen inclusions called polyglucosans. 144DG11 improved survival and motor parameters in a GBE knockin (Gbeys/ys ) APBD mouse model. 144DG11 reduced polyglucosan and glycogen in brain, liver, heart, and peripheral nerve. Indirect calorimetry experiments revealed that 144DG11 increases carbohydrate burn at the expense of fat burn, suggesting metabolic mobilization of pathogenic polyglucosan. At the cellular level, 144DG11 increased glycolytic, mitochondrial, and total ATP production. The molecular target of 144DG11 is the lysosomal membrane protein LAMP1, whose interaction with the compound, similar to LAMP1 knockdown, enhanced autolysosomal degradation of glycogen and lysosomal acidification. 144DG11 also enhanced mitochondrial activity and modulated lysosomal features as revealed by bioenergetic, image-based phenotyping and proteomics analyses. As an effective lysosomal targeting therapy in a GSD model, 144DG11 could be developed into a safe and efficacious glycogen and lysosomal storage disease therapy.

Computational design of substrate selective inhibition

Most enzymes act on more than a single substrate. There is frequently a need to block the production of a single pathogenic outcome of enzymatic activity on a substrate but to avoid blocking others of its catalytic actions. Full blocking might cause severe side effects because some products of that catalysis may be vital. Substrate selectivity is required but not possible to achieve by blocking the catalytic residues of an enzyme. That is the basis of the need for "Substrate Selective Inhibitors" (SSI), and there are several molecules characterized as SSI. However, none have yet been designed or discovered by computational methods. We demonstrate a computational approach to the discovery of Substrate Selective Inhibitors for one enzyme, Prolyl Oligopeptidase (POP) (E.C 3.4.21.26), a serine protease which cleaves small peptides between Pro and other amino acids. Among those are Thyrotropin Releasing Hormone (TRH) and Angiotensin-III (Ang-III), differing in both their binding (K_m) and in turnover (k_cat). We used our in-house "Iterative Stochastic Elimination" (ISE) algorithm and the structure-based "Pharmacophore" approach to construct two models for identifying SSI of POP. A dataset of ~1.8 million commercially available molecules was initially reduced to less than 12,000 which were screened by these models to a final set of 20 molecules which were sent for experimental validation (five random molecules were tested for comparison). Two molecules out of these 20, one with a high score in the ISE model, the other successful in the pharmacophore model, were confirmed by in vitro measurements. One is a competitive inhibitor of Ang-III (increases its K_m), but non-competitive towards TRH (decreases its V_max).

Many proteins are enzymes—"catalytic machines" performing chemical reactions on "substrates"–which may be small or large molecules. Evolution optimized the speed of enzyme reactions, but mutations or excessive enzyme production could lead to non-controlled, accelerated activity, which must be blocked to avoid a product that promotes disease. Many inhibitors of enzymatic activity became drugs which can block the production of the aberrant product, due to blocking the enzymatic "machinery", the amino acids involved in catalysis. Most enzymes have several substrates and so, those other substrates are blocked too. Those may be vital to the well-being of cells and life and total inhibition is prone to cause serious side effects. It is therefore essential to solve the need for inhibition of a single substrate without inhibiting others. We have thus developed computational methods to block specifically the "culprit" substrate while allowing the enzyme machine to act on other substrates. By applying these computational methods, we predicted candidates for inhibiting one out of two substrates ("substrate selective inhibition") of a well-known enzyme reaction. In collaboration with a research group that excels in studying that specific enzyme (prolyl oligopeptidase) we found that two candidates out of a set of twenty that we picked out of 1.8 million molecules by filtering through computer models—are indeed selective to one substrate vis-a-vis the other (five random molecules were tested for comparison). This may be the first example of a computational method leading to substrate selective inhibitor drugs which could avoid side effects.