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A new approach to model the biomatter dynamics based on the field theory is presented. It is shown that some well known tools in field theory can be utilized to describe the physical phenomena in life matters, in particular at elementary biomatters like DNA and proteins. In this approach, the biomatter dynamics are represented as results of interactions among its elementary matters in the form of lagrangian. Starting from the lagrangian would provide stronger underlying theoretical consideration for further extension. Moreover, it also enables us to acquire rich physical observables using statistical mechanics instead of relying on the space-time dynamics from certain equation of motions which is not solvable due to its nonlinearities. Few examples from previous results are given and explained briefly.

Helices are ubiquitous in art and nature. Independent of their pitch and sense of rotation (handedness), helices in sculpture, painting, architecture, scientific illustrations, conference announcements, logos, and advertising are eye-catching and aesthetically pleasing. Helices can turn either clockwise (right-handed helix) or anti-clockwise (left-handed helix). The $\alpha$-helix formed by l-amino acids and the double helices formed by $\beta$-d-2^′-deoxyribonucleic acid (A- and B-form DNA) and $\beta$-d-ribonucleic acid (A-form RNA) are all right-handed. Artistic license provides the freedom to create helices of any shape and sense; indeed, many helical sculptures do not follow the natural convention observed in proteins and DNA. What is more surprising, given that models of the $\alpha$-helix and the DNA double helix were published over 70 years ago, is how common left-handed DNA double helices are in the context of scientific papers and books as well as in popular science writing and reporting. In all cases except for left-handed Z-DNA, the use of left-handed helices in scientific illustrations or models is incorrect. Here, we revisit the helix types adopted by peptides, DNA, and RNA, and review examples of right and wrong helical models in science, art, and elsewhere.

Sequence alignment is a fundamental and important tool for sequence data analysis in molecular biology. Many applications in molecular biology require the detection of a similarity pattern displayed by a number of DNA and protein sequences. Visual front-ends are useful for an intuitive viewing of alignment and help to analyze the structure, functions, and evolution of the DNA and protein. In this paper, we designed and implemented an interactive system for data visualization in DNA and proteins, which can be used in determining a sequence alignment, similarity search of sequence data, and function interference. Experimental results show that a user can easily operate the system after one hour's practice on the proposed system, which provides a clean output, easy identification of similarity and visualization of alignment data.

Local sequence-structure relationships in the loop regions of proteins were comprehensively estimated using simple prediction tools based on support vector regression (SVR). End-to-end distance was selected as a rough structural property of fragments, and the end-to-end distances of an enormous number of loop fragments from a wide variety of protein folds were directly predicted from sequence information by using SVR. We found that our method was more accurate than random prediction for predicting the structure of fragments comprising 5, 9, and 17 amino acids; moreover, the extended loop fragments could be successfully distinguished from turn structures on the basis of their sequences, which implies that the sequence-structure relationships were significant for loop fragments with a wide range of end-to-end distances. These results suggest that many loop regions as well as helices and strands restrict the conformational space of the entire tertiary structure of proteins to some extent; moreover, our findings throw light on the mechanism of protein folding and prediction of the tertiary structure of proteins without using structural templates.

Identification of small molecule ligands that bind to proteins is a critical step in drug discovery. Computational methods have been developed to accelerate the prediction of protein-ligand binding, but often depend on 3D protein structures. As only a limited number of protein 3D structures have been resolved, the ability to predict protein-ligand interactions without relying on a 3D representation would be highly valuable. We use an interpretable confidence-rated boosting algorithm to predict protein-ligand interactions with high accuracy from ligand chemical substructures and protein 1D sequence motifs, without relying on 3D protein structures. We compare several protein motif definitions, assess generalization of our model’s predictions to unseen proteins and ligands, demonstrate recovery of well established interactions and identify globally predictive protein-ligand motif pairs. By bridging biological and chemical perspectives, we demonstrate that it is possible to predict protein-ligand interactions using only motif-based features and that interpretation of these features can reveal new insights into the molecular mechanics underlying each interaction. Our work also lays a foundation to explore more predictive feature sets and sophisticated machine learning approaches as well as other applications, such as predicting unintended interactions or the effects of mutations.