RESUMEN
Proteins are some of the most fascinating and challenging molecules in the universe, and they pose a big challenge for artificial intelligence. The implementation of machine learning/AI in protein science gives rise to a world of knowledge adventures in the workhorse of the cell and proteome homeostasis, which are essential for making life possible. This opens up epistemic horizons thanks to a coupling of human tacit-explicit knowledge with machine learning power, the benefits of which are already tangible, such as important advances in protein structure prediction. Moreover, the driving force behind the protein processes of self-organization, adjustment, and fitness requires a space corresponding to gigabytes of life data in its order of magnitude. There are many tasks such as novel protein design, protein folding pathways, and synthetic metabolic routes, as well as protein-aggregation mechanisms, pathogenesis of protein misfolding and disease, and proteostasis networks that are currently unexplored or unrevealed. In this systematic review and biochemical meta-analysis, we aim to contribute to bridging the gap between what we call binomial artificial intelligence (AI) and protein science (PS), a growing research enterprise with exciting and promising biotechnological and biomedical applications. We undertake our task by exploring "the state of the art" in AI and machine learning (ML) applications to protein science in the scientific literature to address some critical research questions in this domain, including What kind of tasks are already explored by ML approaches to protein sciences? What are the most common ML algorithms and databases used? What is the situational diagnostic of the AI-PS inter-field? What do ML processing steps have in common? We also formulate novel questions such as Is it possible to discover what the rules of protein evolution are with the binomial AI-PS? How do protein folding pathways evolve? What are the rules that dictate the folds? What are the minimal nuclear protein structures? How do protein aggregates form and why do they exhibit different toxicities? What are the structural properties of amyloid proteins? How can we design an effective proteostasis network to deal with misfolded proteins? We are a cross-functional group of scientists from several academic disciplines, and we have conducted the systematic review using a variant of the PICO and PRISMA approaches. The search was carried out in four databases (PubMed, Bireme, OVID, and EBSCO Web of Science), resulting in 144 research articles. After three rounds of quality screening, 93 articles were finally selected for further analysis. A summary of our findings is as follows: regarding AI applications, there are mainly four types: 1) genomics, 2) protein structure and function, 3) protein design and evolution, and 4) drug design. In terms of the ML algorithms and databases used, supervised learning was the most common approach (85%). As for the databases used for the ML models, PDB and UniprotKB/Swissprot were the most common ones (21 and 8%, respectively). Moreover, we identified that approximately 63% of the articles organized their results into three steps, which we labeled pre-process, process, and post-process. A few studies combined data from several databases or created their own databases after the pre-process. Our main finding is that, as of today, there are no research road maps serving as guides to address gaps in our knowledge of the AI-PS binomial. All research efforts to collect, integrate multidimensional data features, and then analyze and validate them are, so far, uncoordinated and scattered throughout the scientific literature without a clear epistemic goal or connection between the studies. Therefore, our main contribution to the scientific literature is to offer a road map to help solve problems in drug design, protein structures, design, and function prediction while also presenting the "state of the art" on research in the AI-PS binomial until February 2021. Thus, we pave the way toward future advances in the synthetic redesign of novel proteins and protein networks and artificial metabolic pathways, learning lessons from nature for the welfare of humankind. Many of the novel proteins and metabolic pathways are currently non-existent in nature, nor are they used in the chemical industry or biomedical field.
RESUMEN
BACKGROUND AND AIMS: This is the first time that obesity and diabetes mellitus (DM) as protein conformational diseases (PCD) are reported in children and they are typically diagnosed too late, when ß-cell damage is evident. Here we wanted to investigate the level of naturally-ocurring or real (not synthetic) oligomeric aggregates of the human islet amyloid polypeptide (hIAPP) that we called RIAO in sera of pediatric patients with obesity and diabetes. We aimed to reduce the gap between basic biomedical research, clinical practice-health decision making and to explore whether RIAO work as a potential biomarker of early ß-cell damage. MATERIALS AND METHODS: We performed a multicentric collaborative, cross-sectional, analytical, ambispective and blinded study; the RIAO from pretreated samples (PTS) of sera of 146 pediatric patients with obesity or DM and 16 healthy children, were isolated, measured by sound indirect ELISA with novel anti-hIAPP cytotoxic oligomers polyclonal antibody (MEX1). We carried out morphological and functional studied and cluster-clinical data driven analysis. RESULTS: We demonstrated by western blot, Transmission Electron Microscopy and cell viability experiments that RIAO circulate in the blood and can be measured by ELISA; are elevated in serum of childhood obesity and diabetes; are neurotoxics and works as biomarkers of early ß-cell failure. We explored the range of evidence-based medicine clusters that included the RIAO level, which allowed us to classify and stratify the obesity patients with high cardiometabolic risk. CONCLUSIONS: RIAO level increases as the number of complications rises; RIAOs > 3.35 µg/ml is a predictor of changes in the current indicators of ß-cell damage. We proposed a novel physio-pathological pathway and shows that PCD affect not only elderly patients but also children. Here we reduced the gap between basic biomedical research, clinical practice and health decision making.