Rho family GTPase signaling through type II p21-activated kinases.

Signaling from the Rho family small GTPases controls a wide range of signaling outcomes. Key among the downstream effectors for many of the Rho GTPases are the p21-activated kinases, or PAK group. The PAK family comprises two types, the type I PAKs (PAK1, 2 and 3) and the type II PAKs (PAK4, 5 and 6), which have distinct structures and mechanisms of regulation. In this review, we discuss signal transduction from Rho GTPases with a focus on the type II PAKs. We discuss the role of PAKs in signal transduction pathways and selectivity of Rho GTPases for PAK family members. We consider the less well studied of the Rho GTPases and their PAK-related signaling. We then discuss the molecular basis for kinase domain recognition of substrates and for regulation of signaling. We conclude with a discussion of the role of PAKs in cross talk between Rho family small GTPases and the roles of PAKs in disease.

Machine learning prediction of methionine and tryptophan photooxidation susceptibility.

Photooxidation of methionine (Met) and tryptophan (Trp) residues is common and includes major degradation pathways that often pose a serious threat to the success of therapeutic proteins. Oxidation impacts all steps of protein production, manufacturing, and shelf life. Prediction of oxidation liability as early as possible in development is important because many more candidate drugs are discovered than can be tested experimentally. Undetected oxidation liabilities necessitate expensive and time-consuming remediation strategies in development and may lead to good drugs reaching patients slowly. Conversely, sites mischaracterized as oxidation liabilities could result in overengineering and lead to good drugs never reaching patients. To our knowledge, no predictive model for photooxidation of Met or Trp is currently available. We applied the random forest machine learning algorithm to in-house liquid chromatography-tandem mass spectrometry (LC-MS/MS) datasets (Met, n = 421; Trp, n = 342) of tryptic therapeutic protein peptides to create computational models for Met and Trp photooxidation. We show that our machine learning models predict Met and Trp photooxidation likelihood with 0.926 and 0.860 area under the curve (AUC), respectively, and Met photooxidation rate with a correlation coefficient (Q2) of 0.511 and root-mean-square error (RMSE) of 10.9%. We further identify important physical, chemical, and formulation parameters that influence photooxidation. Improvement of biopharmaceutical liability predictions will result in better, more stable drugs, increasing development throughput, product quality, and likelihood of clinical success.

Recognition of physiological phosphorylation sites by p21-activated kinase 4.

Many serine/threonine protein kinases discriminate between serine and threonine substrates as a filter to control signaling output. Among these, the p21-activated kinase (PAK) group strongly favors phosphorylation of Ser over Thr residues. PAK4, a group II PAK, almost exclusively phosphorylates its substrates on serine residues. The only well documented exception is LIM domain kinase 1 (LIMK1), which is phosphorylated on an activation loop threonine (Thr508) to promote its catalytic activity. To understand the molecular and kinetic basis for PAK4 substrate selectivity we compared its mode of recognition of LIMK1 (Thr508) with that of a known serine substrate, ß-catenin (Ser675). We determined X-ray crystal structures of PAK4 in complex with synthetic peptides corresponding to its phosphorylation sites in LIMK1 and ß-catenin to 1.9 Å and 2.2 Å resolution, respectively. We found that the PAK4 DFG + 1 residue, a key determinant of phosphoacceptor preference, adopts a sub-optimal orientation when bound to LIMK1 compared to ß-catenin. In peptide kinase activity assays, we find that phosphoacceptor identity impacts catalytic efficiency but does not affect the Km value for both phosphorylation sites. Although catalytic efficiency of wild-type LIMK1 and ß-catenin are equivalent, T508S mutation of LIMK1 creates a highly efficient substrate. These results suggest suboptimal phosphorylation of LIMK1 as a mechanism for controlling the dynamics of substrate phosphorylation by PAK4.