Code domains in tandem repetitive DNA sequence structures. Traditionally, many people doing research in molecular biology attribute coding properties to a given DNA sequence if this sequence contains an open reading frame for translation into a sequence of amino acids. This protein coding capability of DNA was detected about 30 years ago. The underlying genetic code is highly conserved and present in every biological species studied so far. Today, it is obvious that DNA has a much larger coding potential for other important tasks. Apart from coding for specific RNA molecules such as rRNA, snRNA and tRNA molecules, specific structural and sequence patterns of the DNA chain itself express distinct codes for the regulation and expression of its genetic activity. A chromatin code has been defined for phasing of the histone-octamer protein complex in the nucleosome. A translation frame code has been shown to exist that determines correct triplet counting at the ribosome during protein synthesis. A loop code seems to organize the single stranded interaction of the nascent RNA chain with proteins during the splicing process, and a splicing code phases successive 5' and 3' splicing sites.

Is there a higher level genetic code that directs evolution? Because the genetic code is redundant for most amino acids, different codons can be used in a given position without altering the structure of the protein for which the gene codes. This flexibility permits information encoding structural, and therefore functional, properties of RNA and DNA to be transmitted simultaneously by a protein-coding sequence of DNA. Among the other messages that might be transmitted, it is proposed, is one modulating the evolution of the DNA itself. Published in Molecular and cellular biochemistry (1984)

Classification of cell receptors. This manuscript constitutes a first attempt to categorize the cell receptors. Based on evolutionary and biological characteristics, it is possible to classify ligand-receptor units into original families. Certain dynamic patterns that could not be classified previously lead us towards the ligand-receptor unit. Biological information is transformed at the receptor by cellular transduction and effector pathways. The dynamic code is compared with the genetic code. The dynamic code controls the biological information patterns necessary for the integral function and structure of cells, tissue, organs, and organisms. The genetic code controls the assembly and molecular structure of proteins. The original families can be assigned certain original functions. A coevolution of ligand-receptor and dynamic code is revealed. For the time being the most useful approach to receptor classification would seem to be based on the organization of the molecular and protein structure of receptors. Two classes of receptor can be described: receptors for cyclic hydrophobic compounds and membrane receptors.

acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli, providing structure to cells and organisms, and transporting molecules from one location to another. Proteins differ from one another primarily Proteins are large biomolecules and macromolecules that comprise one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli, providing structure to cells and organisms, and transporting molecules from one location to another. Proteins differ from one another primarily in their sequence of amino acids, which is dictated by the nucleotide sequence of their genes, and which usually results in protein folding into a specific 3D structure that determines its activity. A linear chain of amino acid residues is called a polypeptide. A protein contains at least one long polypeptide. Short polypeptides, containing less than 20–30 residues, are rarely considered to be proteins and are commonly called peptides. The individual amino acid residues are bonded together by peptide bonds and adjacent amino acid residues. The sequence of amino acid residues in a protein is defined by the sequence of a gene, which is encoded in the genetic code. In general, the genetic code specifies 20 standard amino acids; but in certain organisms the genetic code can include selenocysteine and—in certain archaea—pyrrolysine. Shortly after or even during synthesis, the residues in a protein are often chemically modified by post-translational modification, which alters the physical and chemical properties, folding, stability, activity, and ultimately, the function of the proteins. Some require a non-protein chemical compound or ion for biological activity, these are known as cofactors. Proteins can work together to achieve a particular function, and they often associate to form stable protein complexes. Once formed, proteins only exist for a certain period and are then degraded and recycled by the cell's machinery through the process of protein turnover. A protein's lifespan is measured in terms of its half-life and covers a wide range. They can exist for minutes or years with an average lifespan of 1–2 days in mammalian cells. Abnormal or misfolded proteins are degraded more rapidly, often by the proteasome, which is a large protein assembly itself. These proteins…

The transformer architecture has revolutionized bioinformatics and driven progress in the understanding and prediction of the properties of biomolecules. To date, most biosequence transformers have been trained on single-omic data—either proteins or nucleic acids—and have seen incredible success in downstream tasks in each domain, with particularly noteworthy breakthroughs in protein structural modeling. However, single-omic pretraining limits the ability of these models to capture cross-modal interactions. Here we present OmniBioTE, the largest open-source multi-omic model trained on over 250