Algorithmic assessment of missense mutation severity in the Von-Hippel Lindau protein.

Von Hippel-Lindau disease (VHL) is an autosomal dominant rare disease that causes the formation of angiogenic tumors. When functional, pVHL acts as an E3 ubiquitin ligase that negatively regulates hypoxia inducible factor (HIF). Genetic mutations that perturb the structure of pVHL result in dysregulation of HIF, causing a wide array of tumor pathologies including retinal angioma, pheochromocytoma, central nervous system hemangioblastoma, and clear cell renal carcinoma. These VHL-related cancers occur throughout the lifetime of the patient, requiring frequent intervention procedures, such as surgery, to remove the tumors. Although VHL is classified as a rare disease (1 in 39,000 to 1 in 91,000 affected) there is a large heterogeneity in genetic mutations listed for observed pathologies. Understanding how these specific mutations correlate with the myriad of observed pathologies for VHL could provide clinicians insight into the potential severity and onset of disease. Using a select set of 285 ClinVar mutations in VHL, we developed a multiparametric scoring algorithm to evaluate the overall clinical severity of missense mutations in pVHL. The mutations were assessed according to eight weighted parameters as a comprehensive evaluation of protein misfolding and malfunction. Higher mutation scores were strongly associated with pathogenicity. Our approach establishes a novel in silico method by which VHL-specific mutations can be assessed for their severity and effect on the biophysical functions of the VHL protein.

Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains.

Bacteriocins, the ribosomally produced antimicrobial peptides of bacteria, represent an untapped source of promising antibiotic alternatives. However, bacteriocins display diverse mechanisms of action, a narrow spectrum of activity, and inherent challenges in natural product isolation making in vitro verification of putative bacteriocins difficult. A subset of bacteriocins exert their antimicrobial effects through favorable biophysical interactions with the bacterial membrane mediated by the charge, hydrophobicity, and conformation of the peptide. We have developed a pipeline for bacteriocin-derived compound design and testing that combines sequence-free prediction of bacteriocins using machine learning and a simple biophysical trait filter to generate 20 amino acid peptides that can be synthesized and evaluated for activity. We generated 28,895 total 20-mer candidate peptides and scored them for charge, α-helicity, and hydrophobic moment. Of those, we selected 16 sequences for synthesis and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. Peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against Escherichia coli and Pseudomonas aeruginosa. Our combined method incorporates machine learning and biophysical-based minimal region determination to create an original approach to swiftly discover bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.

A large scale prediction of bacteriocin gene blocks suggests a wide functional spectrum for bacteriocins.

BACKGROUND: Bacteriocins are peptide-derived molecules produced by bacteria, whose recently-discovered functions include virulence factors and signaling molecules as well as their better known roles as antibiotics. To date, close to five hundred bacteriocins have been identified and classified. Recent discoveries have shown that bacteriocins are highly diverse and widely distributed among bacterial species. Given the heterogeneity of bacteriocin compounds, many tools struggle with identifying novel bacteriocins due to their vast sequence and structural diversity. Many bacteriocins undergo post-translational processing or modifications necessary for the biosynthesis of the final mature form. Enzymatic modification of bacteriocins as well as their export is achieved by proteins whose genes are often located in a discrete gene cluster proximal to the bacteriocin precursor gene, referred to as context genes in this study. Although bacteriocins themselves are structurally diverse, context genes have been shown to be largely conserved across unrelated species. METHODS: Using this knowledge, we set out to identify new candidates for context genes which may clarify how bacteriocins are synthesized, and identify new candidates for bacteriocins that bear no sequence similarity to known toxins. To achieve these goals, we have developed a software tool, Bacteriocin Operon and gene block Associator (BOA) that can identify homologous bacteriocin associated gene blocks and predict novel ones. BOA generates profile Hidden Markov Models from the clusters of bacteriocin context genes, and uses them to identify novel bacteriocin gene blocks and operons. RESULTS AND CONCLUSIONS: We provide a novel dataset of predicted bacteriocins and context genes. We also discover that several phyla have a strong preference for bacteriocin genes, suggesting distinct functions for this group of molecules. SOFTWARE AVAILABILITY: https://github.com/idoerg/BOA.

A multiparametric computational algorithm for comprehensive assessment of genetic mutations in mucopolysaccharidosis type IIIA (Sanfilippo syndrome).

Mucopolysaccharidosis type IIIA (MPS-IIIA, Sanfilippo syndrome) is a Lysosomal Storage Disease caused by cellular deficiency of N-sulfoglucosamine sulfohydrolase (SGSH). Given the large heterogeneity of genetic mutations responsible for the disease, a comprehensive understanding of the mechanisms by which these mutations affect enzyme function is needed to guide effective therapies. We developed a multiparametric computational algorithm to assess how patient genetic mutations in SGSH affect overall enzyme biogenesis, stability, and function. 107 patient mutations for the SGSH gene were obtained from the Human Gene Mutation Database representing all of the clinical mutations documented for Sanfilippo syndrome. We assessed each mutation individually using ten distinct parameters to give a comprehensive predictive score of the stability and misfolding capacity of the SGSH enzyme resulting from each of these mutations. The predictive score generated by our multiparametric algorithm yielded a standardized quantitative assessment of the severity of a given SGSH genetic mutation toward overall enzyme activity. Application of our algorithm has identified SGSH mutations in which enzymatic malfunction of the gene product is specifically due to impairments in protein folding. These scores provide an assessment of the degree to which a particular mutation could be treated using approaches such as chaperone therapies. Our multiparametric protein biogenesis algorithm advances a key understanding in the overall biochemical mechanism underlying Sanfilippo syndrome. Importantly, the design of our multiparametric algorithm can be tailored to many other diseases of genetic heterogeneity for which protein misfolding phenotypes may constitute a major component of disease manifestation.