Studies in the assessment of folding quality for protein modeling and structure prediction

De novo protein folding on computers. Benefits and challenges

There has been recent success in prediction of the three-dimensional folded native structures of proteins, most famously by the AlphaFold Algorithm running on Google's/Alphabet's DeepMind computer. However, this largely involves machine learning of protein structures and is not a de novo protein structure prediction method for predicting three-dimensional structures from amino acid residue sequences. A de novo approach would be based almost entirely on general principles of energy and entropy that govern protein folding energetics, and importantly do so without the use of the amino acid sequences and structural features of other proteins. Most consider that problem as still unsolved even though it has occupied leading scientists for decades. Many consider that it remains one of the major outstanding issues in modern science. There is crucial continuing help from experimental findings on protein unfolding and refolding in the laboratory, but only to a limited extent because many researchers consider that the speed by which real proteins folds themselves, often from milliseconds to minutes, is itself still not fully understood. This is unfortunate, because a practical solution to the problem would probably have a major effect on personalized medicine, the pharmaceutical industry, biotechnology, and nanotechnology, including for example "smaller" tasks such as better modeling of flexible "unfolded" regions of the SARS-COV-2 spike glycoprotein when interacting with its cell receptor, antibodies, and therapeutic agents. Some important ideas from earlier studies are given before moving on to lessons from periodic and aperiodic crystals, and a possible role for quantum phenomena. The conclusion is that better computation of entropy should be the priority, though that is presented guardedly.

Testing machine learning techniques for general application by using protein secondary structure prediction. A brief survey with studies of pitfalls and benefits using a simple progressive learning approach

Many researchers have recently used the prediction of protein secondary structure (local conformational states of amino acid residues) to test advances in predictive and machine learning technology such as Neural Net Deep Learning. Protein secondary structure prediction continues to be a helpful tool in research in biomedicine and the life sciences, but it is also extremely enticing for testing predictive methods such as neural nets that are intended for different or more general purposes. A complication is highlighted here for researchers testing their methods for other applications. Modern protein databases inevitably contain important clues to the answer, so-called "strong buried clues", though often obscurely; they are hard to avoid. This is because most proteins or parts of proteins in a modern protein data base are related to others by biological evolution. For researchers developing machine learning and predictive methods, this can overstate and so confuse understanding of the true quality of a predictive method. However, for researchers using the algorithms as tools, understanding strong buried clues is of great value, because they need to make maximum use of all information available. A simple method related to the GOR methods but with some features of neural nets in the sense of progressive learning of large numbers of weights, is used to explore this. It can acquire tens of millions and hence gigabytes of weights, but they are learned stably by exhaustive sampling. The significance of the findings is discussed in the light of promising recent results from AlphaFold using Google's DeepMind.

Computers and viral diseases. Preliminary bioinformatics studies on the design of a synthetic vaccine and a preventative peptidomimetic antagonist against the SARS-CoV-2 (2019-nCoV, COVID-19) coronavirus

This paper concerns study of the genome of the Wuhan Seafood Market isolate believed to represent the causative agent of the disease COVID-19. This is to find a short section or sections of viral protein sequence suitable for preliminary design proposal for a peptide synthetic vaccine and a peptidomimetic therapeutic, and to explore some design possibilities. The project was originally directed towards a use case for the Q-UEL language and its implementation in a knowledge management and automated inference system for medicine called the BioIngine, but focus here remains mostly on the virus itself. However, using Q-UEL systems to access relevant and emerging literature, and to interact with standard publically available bioinformatics tools on the Internet, did help quickly identify sequences of amino acids that are well conserved across many coronaviruses including 2019-nCoV. KRSFIEDLLFNKV was found to be particularly well conserved in this study and corresponds to the region around one of the known cleavage sites of the SARS virus that are believed to be required for virus activation for cell entry. This sequence motif and surrounding variations formed the basis for proposing a specific synthetic vaccine epitope and peptidomimetic agent. The work can, nonetheless, be described in traditional bioinformatics terms, and readily reproduced by others, albeit with the caveat that new data and research into 2019-nCoV is emerging and evolving at an explosive pace. Preliminary studies using molecular modeling and docking, and in that context the potential value of certain known herbal extracts, are also described.

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Bioinformatics studies are carried out on the COVID-19 virus.

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A sequence motif KRSFIEDLLFNKV is of particular interest.

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Based on the above, synthetic peptides are designed.

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Preliminary considerations are also given to non-peptide organic molecules.

Clinical and pharmacogenomic data mining: 4. The FANO program and command set as an example of tools for biomedical discovery and evidence based medicine

The culmination of methodology explored and developed in the preceding three papers is described in terms of the FANO program (also known as CliniMiner) and specifically in terms of the contemporary command set for data mining. This provides a more detailed account of how strategies were implemented in applications described elsewhere, in the previous papers in the series and in a paper on the analysis of 667 000 patient records. Although it is not customary to think of a command set as the output of research, it represents the elements and strategies for data mining biomedical and clinical data with many parameters, that is, in a high dimensional space that requires skilful navigation. The intent is not to promote FANO per se, but to report its science and methodologies. Typical example rules from traditional data mining are that A and B and C associate, or IF A & B THEN C. We need much higher complexity rules for clinical data especially with inclusion of proteomics and genomics. FANO's specific goal is to be able routinely to extract from clinical record repositories and other data not only the complex rules required for biomedical research and the clinical practice of evidence based medicine, but to quantify their uncertainty, that is, their essentially probabilistic nature. The underlying information and number theoretic basis previously described is less of an issue here, being "under the hood", although the fundamental role and use of the Incomplete (generalized) Riemann Zeta Function as a general surprise measure is highlighted, along with its covariance or multivariance analogue, as it appears to be a unique and powerful feature. Another characteristic described is the very general tactic of the metadata operator ':='. It allows decomposition of diverse data types such as trees, spreadsheets, biosequences, sets of objects, amorphous data collections with repeating items, XML structures, and so forth into universally atomic data items with or without metadata, and assists in reconstruction of ontology from the associations and numerical correlations so data mined.

Genomic Messaging System Language Including Command Extensions for Clinical Data Categories

This paper, in the area of clinical bioinformatics, highlights relatively efficient means of storing, exchanging, protecting, and searching human and other genomic data, so as to make the data securely accessible to researchers while respecting patient privacy. One important idea is that the GMSL language can be considered as an extension of the way DNA and protein sequences are written so as to carry with them the wishes of the patient in regard to fine-grained consent (as well as retaining the medical experts' cautions, instructions for use, and annotation), and this is carried, whatever environment (e.g., XML) that the data is from or whatever it is going to. At the deepest level, a stream of data expressed in GMSL resembles highly compressed stream of self-checking machine code. For the reader less familiar with the computational aspects, some simple examples illustrate how the raw language looks and works as a raw stream of (interpreted) bytes. The bioinformatics applications are not confined to the clinical domain. This paper completes the initial specification of the language as previously presented and reports on some important extensions including clinical data categories.

Genomic Messaging System and DNA Mark-Up Language for Information-Based Personalized Medicine with Clinical and Proteome Research Applications

The convergence of clinical medicine and the Life Sciences, commencing with opportunities in clinical trials and clinically linked medical research, presents many novel challenges. The Genomic Messaging System (GMS) described here was originally developed as a tool for assembling clinical genomic records of individual and collective patients, and was then generalized to become a flexible workflow component that will link clinical records to a variety of computational biology research tools, for research and ultimately for a more personalized, focused, and preventative healthcare system. Prominent among the applications linked are protein science applications, including the rapid automated modeling of patient proteins with their individual structural polymorphisms. In an initial study, GMS formed the basis of a fully automated system for modeling patient proteins with structural polymorphisms as a basis for drug selection and ultimately design on an individual patient basis.

Rapid subpicogram protein detection on a microchip without denaturing

Protein size separation based on sodium dodecyl sulfate-gel electrophoresis (SDS-GE) requires denaturing, but we propose that denaturing is unnecessary for analysis by microchip electrophoresis (micro-CE). By omitting the protein denaturing process, we achieved not only shortened total analysis time, but also dramatically improved sensitivity without compromising size determination. The detection limit was improved to 0.1 ng/microL under conditions without denaturing and 600 pg (9.0 femtomol) of bovine serum albumin was detectable, which equals levels detectable by Silver stain, although a routine method by microchips in the Coomassie Blue detection level.