Molecular mimicry, where foreign and self-peptides contain similar epitopes, can induce autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (EINF) and T1DM epitopes (ET1D) that demonstrated sequence homology; however, structural homology was not considered. Correlating sequence homology with structural properties is important for streamlining translational investigation of potential molecular mimics. Therefore, the purpose of this work is to compare sequence homology with structural homology by calculating the structures and electrostatic potentials of 35 pairs of epitopes identified in previous work from our laboratory. For each epitope pair the root mean square deviation (RMSD) was calculated between their predicted structures and their electrostatic potentials were compared. Structures were predicted using the AlphaFold and I-TASSER software programs. We considered a structural match of EINF and ET1D pairs successful if the RMSD wasINF/ET1Dstructurally unmatched pairs. Despite structural differences, these four EINF/ET1Dpairs show similar electrostatic distributions, indicating that they may still bind to the same protein targets, major histocompatibility complex molecules, for T1DM. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to strong candidates for further study.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

BACKGROUND: The expansion of polyglutamine (poly-Q) repeats in several unrelated proteins is associated with at least ten neurodegenerative diseases. The length of the poly-Q regions plays an important role in the progression of the diseases. The number of glutamines (Q) is inversely related to the onset age of these polyglutamine diseases, and the expansion of poly-Q repeats has been associated with protein misfolding. However, very little is known about the structural changes induced by the expansion of the repeats. Computational methods can provide an alternative to determine the structure of these poly-Q proteins, but it is important to evaluate their performance before large scale prediction work is done. RESULTS: In this paper, two popular protein structure prediction programs, I-TASSER and Rosetta, have been used to predict the structure of the N-terminal fragment of a protein associated with Huntington's disease with 17 glutamines. Results show that both programs have the ability to find the native structures, but I-TASSER performs better for the overall task. CONCLUSIONS: Both I-TASSER and Rosetta can be used for structure prediction of proteins with poly-Q repeats. Knowledge of poly-Q structure may significantly contribute to development of therapeutic strategies for poly-Q diseases.

High-performance computing centers (HPC) traditionally have far less restrictive privacy management policies than those encountered in healthcare. We show how an HPC can be re-engineered to accommodate clinical data while retaining its utility in computationally intensive tasks such as data mining, machine learning, and statistics. We also discuss deploying protected virtual machines. A critical planning step was to engage the university's information security operations and the information security and privacy office. Access to the environment requires a double authentication mechanism. The first level of authentication requires access to the university's virtual private network and the second requires that the users be listed in the HPC network information service directory. The physical hardware resides in a data center with controlled room access. All employees of the HPC and its users take the university's local Health Insurance Portability and Accountability Act training series. In the first 3 years, researcher count has increased from 6 to 58.

Public health data could be used to assist with public health surveillance and decision support. However, in most cases data has to be transformed into a coded format to make it computable and amiable to quasi real time analytical processing. Natural language processing (NLP) systems, which aim to accurately extract and encode biomedical information in a standard format, have a great potential in surveillance. NLP methods are complex, difficult, and expensive to implement. Its implementation, in most cases, is well beyond the technical expertise and resources available in Public Health organizations. Making NLP systems available as a service can greatly improve access to this methodology by public health officials and potentially enhance disease surveillance. MetaMap is a comprehensive biomedical NLP system, and has been shown to perform well for numerous applications. We describe how we have implemented MetaMap as a grid service to make it available to the public health community.

The rapid advance of gene sequencing technologies has produced an unprecedented rate of discovery of genome variation in humans. A growing number of authoritative clinical repositories archive gene variants and disease phenotypes, yet there are currently many more gene variants that lack clear annotation or disease association. To date, there has been very limited coverage of gene-specific predictors in the literature. Here the evaluation is presented of "gene-specific" predictor models based on a naïve Bayesian classifier for 20 gene-disease datasets, containing 3986 variants with clinically characterized patient conditions. The utility of gene-specific prediction is then compared with "all-gene" generalized prediction and also with existing popular predictors. Gene-specific computational prediction models derived from clinically curated gene variant disease datasets often outperform established generalized algorithms for novel and uncertain gene variants.

Currently users of high performance computers are overwhelmed with non-scalable tasks such as job submission and monitoring. Many users are limited by the number of jobs they can submit to one High Performance Computing (HPC) resource at a time, which results in very long queue times. Digital Sherpa is a grid application for executing jobs on many separate HPC resources at a time, which can reduce total queue time. It automates non-scalable tasks such as job submission and monitoring, and includes recovery features such as resubmission of failed jobs. Digital Sherpa has been implemented for MGAC, a parallel distributed application for the prediction of atomic clusters and crystal structures using Genetic Algorithms. Success has been found using Digital Sherpa in a prototype of an HPC oriented combustion simulation application as well as on the TeraGrid. The high level goal is to allow Digital Sherpa to interoperate with any HPC application.