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Disordered regions of proteins are highly abundant in various biological processes, involving regulation and signaling and also in relation with cancer, cardiovascular, autoimmune diseases and neurodegenerative disorders. Hence, recognizing disordered regions in proteins is a critical task. In this paper, we presented a new feature encoding technique built from physicochemical properties of residues selected as per the chaotic structure of related protein sequence. Our feature vector has been tested with various classification algorithms on an up-to-date data set and also compared to other methods. The proposed method shows better classification performance than many methods in terms of accuracy, sensitivity and specificity. Our results suggest that the new method that links the residues and their physicochemical properties using Lyapunov exponents is highly effective in recognition of disordered regions. [ABSTRACT FROM AUTHOR]

Water has many roles in the context of life on Earth, however throughout the universe, other liquids may be able to support the emergence of life. We looked at the ability of amino acids, peptides, a depsipeptide, and proteins to partition into a non-polar decanol phase, with and without the addition of a phase transfer agent. Partitioning evaluated using UV detection, or with HPLC coupled to either charged aerosol detection or ESI-MS. For amino acids and short peptides, phase transfer agents were used to move the biomolecules to the decanol phase, and this transfer was pH dependent. For larger molecules, phase transfer agents did not seem to affect the transfer. Both the depsipetide, valinomycin, and the protein Taq DNA polymerase had solubility in the decanol phase. Additionally, valinomycin appeared to retain its biological ability to bind to potassium ions. These results show that most terrestrial biological molecules are not compatible with non-polar solvents, but it is possible to find and perhaps evolve polymers that are functional in such phases. [ABSTRACT FROM AUTHOR]

The biological function of protein depends mainly on its tertiary structure which is determined by its amino acid sequence via the process of protein folding. Prediction of protein structure from its amino acid sequence is one of the most prominent problems in computational biology. Two basic methodologies on protein structure prediction are combined: ab initio method (3-D space lattice) and fold recognition method (hidden Markov model). The primary structure of proteins and 3-D coordinates of amino acid residues are put together in one hidden Markov model to learn the path of amino acid residues in 3-D space from the first atom to the last atom of each protein of each fold. Therefore, each model has the information of 3-D path of amino acids of each fold. The proposed method is compared to fold recognition methods which have hidden Markov model as a base of their algorithms having approaches on only amino acid sequence or secondary structure. To validate the proposed method, the models are assessed with three datasets. Results show that the proposed models outperform 7-HMM and 3-HMM in the same dataset. The face-centered cubic lattice which is the most compacted 3-D lattice reached the maximum classification accuracy in all experiments in comparison with the performance of the most effective version of optimized 3-HMM as well as the performance of the latest version of SAM 3.5. Results show that 3-D coordinates of atoms of amino acids in proteins have an important role in prediction. It also has great hidden information as compared to secondary structure of proteins in fold classification. [ABSTRACT FROM AUTHOR]