Physiologically extreme conditions and disease,” Nat. Yung, “ Deformation and failure of protein materials in Buehler, “ Ultrathin free-standing bombyx mori silk nanofibril Ebrahimi et al., “ Silk-its mysteries, how it is made,Īnd how it is used,” ACS Biomater. Proteins consist of 20 naturally occurring amino acid building blocks that are assembled into hierarchical structures across many length-scales. which represents an important category of building materials in living systems with important implications for medicine, engineering, and many other fields. Lin et al., “ Predictive modelling-based designĪnd experiments for synthesis and spinning of bioinspired silk fibres,” In this paper, we focus specifically on protein materials, 4,5 4. We propose that the use of machine learning can be a powerful means to extract features and apply neural network models in the design of novel materials. In spite of nature's extensive examples of material designs, from silk, to bone, to cells and many others, we are yet to have access to methods that can automatically extract design features from such materials and implement them in new materials that do not yet exist in nature. Weinkamer, “ Nature's hierarchical materials,” Buehler, “ Materiomics: An -omics approach to biomaterials Proteins and music,” Nano Today 7, 488 (2012). Wong et al., “ Materials by design: Merging The design of hierarchical materials represents one of the frontiers in materials science. The method provides a tool to design novel protein materials that could find useful applications as materials in biology, medicine, and engineering.
![de novo protein sequence analysis de novo protein sequence analysis](http://www.mtoz-biolabs.com/upload/image-De-Novo-Sequencing-Case-Study6.png)
We validate the newly predicted protein by molecular dynamics equilibration in explicit water and subsequent characterization using a normal mode analysis. We find that the method proposed here can be used to design de novo proteins that do not exist yet, and that the designed proteins fold into specified secondary structures. We use a Basic Local Alignment Search Tool to compare the predicted amino acid sequences against known proteins, and estimate folded protein structures using the Optimized protein fold RecognitION method (ORION) and MODELLER. Using the deep learning model, we then generate de novo musical scores and translate the pitch information and chain lengths into sequences of amino acids. We train a deep learning model whose architecture is composed of several long short-term memory units from data consisting of musical representations of proteins classified by certain features, focused here on alpha-helix rich proteins. The deep neural network model is based on translating protein sequences and structural information into a musical score that features different pitches for each of the amino acids, and variations in note length and note volume reflecting secondary structure information and information about the chain length and distinct protein molecules. We report the use of a deep learning model to design de novo proteins, based on the interplay of elementary building blocks via hierarchical patterns.