Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Comparison of the anticatabolic effects of leucine and Ca-β-hydroxy-β-methylbutyrate in experimental models of cancer cachexia

Objective: Loss of skeletal muscle is the most debilitating feature of cancer cachexia, and there are few treatments available. The aim of this study was to compare the anticatabolic efficacy of L-leucine and the leucine metabolite β-hydroxy-β-methylbutyrate (Ca-HMB) on muscle protein metabolism, both invitro and invivo. Methods: Studies were conducted in mice bearing the cachexia-inducing murine adenocarcinoma 16 tumor, and in murine C2 C12 myotubes exposed to proteolysis-inducing factor, lipopolysaccharide, and angiotensin II. Results: Both leucine and HMB were found to attenuate the increase in protein degradation and the decrease in protein synthesis in murine myotubes induced by proteolysis-inducing factor, lipopolysaccharide, and angiotensin II. However, HMB was more potent than leucine, because HMB at 50 μM produced essentially the same effect as leucine at 1 mM. Both leucine and HMB reduced the activity of the ubiquitin-proteasome pathway as measured by the functional (chymotrypsin-like) enzyme activity of the proteasome in muscle lysates, as well as Western blot quantitation of protein levels of the structural/enzymatic proteasome subunits (20 S and 19 S) and the ubiquitin ligases (MuRF1 and MAFbx). Invivo studies in mice bearing the murine adenocarcinoma 16 tumor showed a low dose of Ca-HMB (0.25 g/kg) tobe 60% more effective than leucine (1 g/kg) in attenuating loss of body weight over a 4-d period. Conclusion: These results favor the clinical feasibility of using Ca-HMB over high doses of leucine for the treatment of cancer cachexia. © 2014 Elsevier Inc.

Novel, isoform-selective, cholecystokinin A receptor antagonist inhibits colon and pancreatic cancers in preclinical models through novel mechanism of action

Colon and pancreatic cancers contribute to 90,000 deaths each year in the USA. These cancers lack targeted therapeutics due to heterogeneity of the disease and multiple causative factors. One important factor that contributes to increased colon and pancreatic cancer risk is gastrin. Gastrin mediates its actions through two G-protein coupled receptors (GPCRs): cholecystokinin receptor A (CCK-A) and CCK-B/gastrin receptor. Previous studies have indicated that colon cancer predominantly expresses CCK-A and responds to CCK-A isoform antagonists. However, many CCK-A antagonists have failed in the clinic due to poor pharmacokinetic properties or lack of efficacy. In the present study, we synthesized a library of CCK-A isoform-selective antagonists and tested them in various colon and pancreatic cancer preclinical models. The lead CCK-A isoform, selective antagonist PNB-028, bound to CCK-A at 12 nM with a 60-fold selectivity towards CCK-A over CCK-B. Furthermore, it inhibited the proliferation of CCK-A-expressing colon and pancreatic cancer cells without affecting the proliferation of non-cancerous cells. PNB-028 was also extremely effective in inhibiting the growth of MAC-16 and LoVo colon cancer and MIA PaCa pancreatic cancer xenografts in immune-compromised mice. Genomewide microarray and kinase-array studies indicate that PNB-028 inhibited oncogenic kinases and angiogenic factors to inhibit the growth of colon cancer xenografts. Safety pharmacology and toxicology studies have indicated that PNB-028 is extremely safe and has a wide safety margin. These studies suggest that targeting CCK-A selectively renders promise to treat colon and pancreatic cancers and that PNB-028 could become the next-generation treatment option.