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.
Protein tertiary structure is the three-dimensional shape of a protein. The tertiary structure will have a single polypeptide chain "backbone" with one or more protein secondary structures, the protein domains. Amino acid side chains and the backbone may interact and bond in a number of ways. The interactions and bonds of side chains within a particular protein determine its tertiary structure. The protein tertiary structure is defined by its atomic coordinates. These coordinates may refer either to a protein domain or to the entire tertiary structure. A number of these structures may bind to each other, forming a quaternary structure.
In the fields of biochemistry and pharmacology an allosteric regulator is a substance that binds to a site on an enzyme or receptor distinct from the active site, resulting in a conformational change that alters the protein's activity, either enhancing or inhibiting its function. In contrast, substances that bind directly to an enzyme's active site or the binding site of the endogenous ligand of a receptor are called orthosteric regulators or modulators.
Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by computational biology; it is important in medicine and biotechnology.
Structural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.
In biochemistry and molecular biology, a binding site is a region on a macromolecule such as a protein that binds to another molecule with specificity. The binding partner of the macromolecule is often referred to as a ligand. Ligands may include other proteins, enzyme substrates, second messengers, hormones, or allosteric modulators. The binding event is often, but not always, accompanied by a conformational change that alters the protein's function. Binding to protein binding sites is most often reversible, but can also be covalent reversible or irreversible.
Protein design is the rational design of new protein molecules to design novel activity, behavior, or purpose, and to advance basic understanding of protein function. Proteins can be designed from scratch or by making calculated variants of a known protein structure and its sequence. Rational protein design approaches make protein-sequence predictions that will fold to specific structures. These predicted sequences can then be validated experimentally through methods such as peptide synthesis, site-directed mutagenesis, or artificial gene synthesis.
Chemical biology is a scientific discipline between the fields of chemistry and biology. The discipline involves the application of chemical techniques, analysis, and often small molecules produced through synthetic chemistry, to the study and manipulation of biological systems. Although often confused with biochemistry, which studies the chemistry of biomolecules and regulation of biochemical pathways within and between cells, chemical biology remains distinct by focusing on the application of chemical tools to address biological questions.
In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when a ligand and a target are bound to each other to form a stable complex. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using, for example, scoring functions.
In molecular biology, an intrinsically disordered protein (IDP) is a protein that lacks a fixed or ordered three-dimensional structure, typically in the absence of its macromolecular interaction partners, such as other proteins or RNA. IDPs range from fully unstructured to partially structured and include random coil, molten globule-like aggregates, or flexible linkers in large multi-domain proteins. They are sometimes considered as a separate class of proteins along with globular, fibrous and membrane proteins.
Molecular biophysics is a rapidly evolving interdisciplinary area of research that combines concepts in physics, chemistry, engineering, mathematics and biology. It seeks to understand biomolecular systems and explain biological function in terms of molecular structure, structural organization, and dynamic behaviour at various levels of complexity. This discipline covers topics such as the measurement of molecular forces, molecular associations, allosteric interactions, Brownian motion, and cable theory. Additional areas of study can be found on Outline of Biophysics. The discipline has required development of specialized equipment and procedures capable of imaging and manipulating minute living structures, as well as novel experimental approaches.
JoAnne Stubbe is an American chemist best known for her work on ribonucleotide reductases, for which she was awarded the National Medal of Science in 2009. In 2017, she retired as a Professor of Chemistry and Biology at the Massachusetts Institute of Technology.
Professor Nils Gunnar Hansson von Heijne, born 10 June 1951 in Gothenburg, is a Swedish scientist working on signal peptides, membrane proteins and bioinformatics at the Stockholm Center for Biomembrane Research at Stockholm University.
Ronald J. Williams is professor of computer science at Northeastern University, and one of the pioneers of neural networks. He co-authored a paper on the backpropagation algorithm which triggered a boom in neural network research. He also made fundamental contributions to the fields of recurrent neural networks and reinforcement learning. Together with Wenxu Tong and Mary Jo Ondrechen he developed Partial Order Optimum Likelihood (POOL), a machine learning method used in the prediction of active amino acids in protein structures. POOL is a maximum likelihood method with a monotonicity constraint and is a general predictor of properties that depend monotonically on the input features.
Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways.
The Enzyme Function Initiative (EFI) is a large-scale collaborative project aiming to develop and disseminate a robust strategy to determine enzyme function through an integrated sequence–structure-based approach. The project was funded in May 2010 by the National Institute of General Medical Sciences as a Glue Grant which supports the research of complex biological problems that cannot be solved by a single research group. The EFI was largely spurred by the need to develop methods to identify the functions of the enormous number proteins discovered through genomic sequencing projects.
Jeffrey Skolnick is an American computational biologist. He is currently a Georgia Institute of Technology School of Biology Professor, the Director of the Center for the Study of Systems Biology, the Mary and Maisie Gibson Chair, the Georgia Research Alliance Eminent Scholar in Computational Systems Biology, the Director of the Integrative BioSystems Institute, and was previously the Scientific Advisor at Intellimedix.
Kuo-Chen Chou was a Chinese-American biophysicist and bioinformatician who founded the Gordon Life Science Institute, a non-profit research organization in Boston, Massachusetts. Among other contributions, he developed pseudo amino acid composition (PseAAC), used in computational biology for proteomics analysis and pseudo K-tuple nucleotide composition (PseKNC) for genome analysis. He is the father of James Chou.
Mona Singh is an American computer scientist and an expert in computational molecular biology and bioinformatics. She is the Wang Family Professor in Computer Science in the Lewis-Sigler Institute for Integrative Genomics and the Department of Computer Science at Princeton University. Since 2021, she has been the Editor-in-Chief of the Journal of Computational Biology.
Theoretical Microscopic Anomalous Titration Curve Shapes (THEMATICS) is a computational method for predicting the biochemically active amino acids in a protein three-dimensional structure.
