Biology subjects

Avila, N.

Publications and source records attributed to Avila, N..

2 recordsLinked to original sources

bioRxiv2024

The GenPPI tool enhanced Protein Interaction Network Generation with Machine Learning-Based Protein Similarity Inference

AbstractO_ST_ABSBackgroundC_ST_ABSComputational prediction of protein-protein interactions (PPIs) is crucial for understanding cell biology and drug development, offering an alternative to costly experimental methods. The original GenPPi software advanced ab initio PPI network prediction from bacterial genomes but was limited by its reliance on high sequence similarity. This work introduces GenPPi 1.5 to enhance these predictive capabilities. ResultsGenPPi 1.5 incorporates a Random Forest (RF) algorithm, trained on 60 biophysical features from amino acid propensity indices, to classify protein similarity even in low sequence identity scenarios (targeting >65% identity). To manage computational complexity from the increased interactions generated by the RF model, especially in extensive conserved phylogenetic profiles, we developed and integrated the Reduced Interaction Sampling (RIS) algorithm. RIS stochastically samples interactions within these profiles, optimizing performance for complete genome analysis. Extensive simulations across various configurations validated the methodology. RF integration significantly broadened GenPPis predictive power; application to Buchnera aphidicola showed up to 62% overlap with STRING database interactions. Analysis of RIS demonstrated that while introducing some randomness, critical node identification remains robust, particularly for Top N values[≥] 100, indicating minimal compromise to network integrity. ConclusionThe combination of Machine Learning (RF) and the RIS algorithm in GenPPi 1.5 represents a significant advancement. It overcomes the highsimilarity dependency of the previous version while efficiently handling complex genomes. GenPPi 1.5 provides a robust and scalable alignment-free PPI prediction solution, enabling users to train custom models tailored to specific genomic contexts. GenPPi is freely available on our website https://genppi.facom.ufu.br/, its source code is hosted on GitHub https://github.com/santosardr/genppi, and it can be easily installed via the Python Package Index using the command pip install genppipy.

bioinformatics↗

bioRxiv2019

First report on the effective intraperitoneal therapy of insulin-dependent diabetes mellitus in pet dogs using \"Neo-Islets\", aggregates of adipose stem and pancreatic islet cells

We previously reported that allogeneic, intraperitoneally administered \"Neo-Islets,\" composed of cultured pancreatic islet cells co-aggregated with high numbers of immunoprotective and cytoprotective Adipose-derived Stem Cells, reestablished, through omental engraftment, redifferentiation and splenic and omental up-regulation of Regulatory T-cells, normoglycemia in autoimmune Type-1 Diabetic Non-Obese Diabetic (NOD) mice without the use of immunosuppressive agents or encapsulation devices. Based on these observations, we are currently testing this Neo-Islet technology in an FDA guided Pilot Study (INAD 012-776) in insulin-dependent, spontaneously diabetic pet dogs by the intraperitoneal administration of 2x10e5 Neo-Islets/kilogram body weight to metabolically controlled (blood glucose, triglycerides, thyroid and adrenal functions) animals under sedation and local anesthesia and ultrasound guidance. We report here initial observations on the first 4 Neo-Islet-treated, insulin dependent pet dogs that are now in the intermediate-term follow-up phase of the study (> 6 months post treatment). Current results indicate that in dogs, Neo-Islets appear to engraft, redifferentiate and physiologically produce insulin, and are neither rejected by auto- or allo-immune attacks, as evidenced by (a) an absent IgG response to the allogeneic cells contained in the administered Neo-Islets, and (b) progressively improved glycemic control that achieves up to a 50% reduction in daily insulin needs paralleled by a significant fall in serum glucose levels. This is accomplished without the use of anti-rejection drugs or encapsulation devices. No adverse or serious adverse events related to the Neo-Islet administration have been observed to date. We conclude that this minimally invasive therapy has significant translational relevance to veterinary and clinical Type 1 Diabetes Mellitus by achieving complete and at this point partial glycemic control in two species, i.e., diabetic mice and dogs, respectively.

cell biology↗