Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.
Protein folding is the physical process where a protein chain is translated into its native three-dimensional structure, typically a "folded" conformation, by which the protein becomes biologically functional. Via an expeditious and reproducible process, a polypeptide folds into its characteristic three-dimensional structure from a random coil. Each protein exists first as an unfolded polypeptide or random coil after being translated from a sequence of mRNA into a linear chain of amino acids. At this stage, the polypeptide lacks any stable three-dimensional structure. As the polypeptide chain is being synthesized by a ribosome, the linear chain begins to fold into its three-dimensional structure.
Structural genomics seeks to describe the 3-dimensional structure of every protein encoded by a given genome. This genome-based approach allows for a high-throughput method of structure determination by a combination of experimental and modeling approaches. The principal difference between structural genomics and traditional structural prediction is that structural genomics attempts to determine the structure of every protein encoded by the genome, rather than focusing on one particular protein. With full-genome sequences available, structure prediction can be done more quickly through a combination of experimental and modeling approaches, especially because the availability of large number of sequenced genomes and previously solved protein structures allows scientists to model protein structure on the structures of previously solved homologs.
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; and it is important in medicine and biotechnology.
Levinthal's paradox is a thought experiment in the field of computational protein structure prediction where an algorithmic search for a minimum energy configuration is vastly slower than the actual process by which stable configurations are reached in protein folding. In 1969, Cyrus Levinthal noted that, because of the very large number of degrees of freedom in an unfolded polypeptide chain, the molecule has an astronomical number of possible conformations. An estimate of 10300 was made in one of his papers (often incorrectly cited as the 1968 paper). For example, a polypeptide of 100 residues will have 99 peptide bonds, and therefore 198 different phi and psi bond angles. If each of these bond angles can be in one of three stable conformations, the protein may misfold into a maximum of 3198 different conformations (including any possible folding redundancy). Therefore, if a protein were to attain its correctly folded configuration by sequentially sampling all the possible conformations, it would require a time longer than the age of the universe to arrive at its correct native conformation. This is true even if conformations are sampled at rapid (nanosecond or picosecond) rates. The "paradox" is that most small proteins fold spontaneously on a millisecond or even microsecond time scale. The solution to this paradox has been established by computational approaches to protein structure prediction.
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.
In molecular biology, protein threading, also known as fold recognition, is a method of protein modeling which is used to model those proteins which have the same fold as proteins of known structures, but do not have homologous proteins with known structure. It differs from the homology modeling method of structure prediction as it is used for proteins which do not have their homologous protein structures deposited in the Protein Data Bank (PDB), whereas homology modeling is used for those proteins which do. Threading works by using statistical knowledge of the relationship between the structures deposited in the PDB and the sequence of the protein which one wishes to model.
Rosetta@home is a volunteer computing project researching protein structure prediction on the Berkeley Open Infrastructure for Network Computing (BOINC) platform, run by the Baker lab. Rosetta@home aims to predict protein–protein docking and design new proteins with the help of about fifty-five thousand active volunteered computers processing at over 487,946 GigaFLOPS on average as of September 19, 2020. Foldit, a Rosetta@home videogame, aims to reach these goals with a crowdsourcing approach. Though much of the project is oriented toward basic research to improve the accuracy and robustness of proteomics methods, Rosetta@home also does applied research on malaria, Alzheimer's disease, and other pathologies.
The TIM barrel, also known as an alpha/beta barrel, is a conserved protein fold consisting of eight alpha helices (α-helices) and eight parallel beta strands (β-strands) that alternate along the peptide backbone. The structure is named after triose-phosphate isomerase, a conserved metabolic enzyme. TIM barrels are ubiquitous, with approximately 10% of all enzymes adopting this fold. Further, five of seven enzyme commission (EC) enzyme classes include TIM barrel proteins. The TIM barrel fold is evolutionarily ancient, with many of its members possessing little similarity today, instead falling within the twilight zone of sequence similarity.
David Baker is an American biochemist and computational biologist who has pioneered methods to predict and design the three-dimensional structures of proteins. He is the Henrietta and Aubrey Davis Endowed Professor in Biochemistry and an adjunct professor of genome sciences, bioengineering, chemical engineering, computer science, and physics at the University of Washington. He serves as the director of the Rosetta Commons, a consortium of labs and researchers that develop biomolecular structure prediction and design software. The problem of protein structure prediction to which Baker has contributed significantly has now been largely solved by DeepMind using artificial intelligence. Baker is a Howard Hughes Medical Institute investigator and a member of the United States National Academy of Sciences. He is also the director of the University of Washington's Institute for Protein Design.
In computational biology, de novo protein structure prediction refers to an algorithmic process by which protein tertiary structure is predicted from its amino acid primary sequence. The problem itself has occupied leading scientists for decades while still remaining unsolved. According to Science, the problem remains one of the top 125 outstanding issues in modern science. At present, some of the most successful methods have a reasonable probability of predicting the folds of small, single-domain proteins within 1.5 angstroms over the entire structure.
Eterna is a browser-based "game with a purpose", developed by scientists at Carnegie Mellon University and Stanford University, that engages users to solve puzzles related to the folding of RNA molecules. The project is supported by the Bill and Melinda Gates Foundation, Stanford University, and the National Institutes of Health. Prior funders include the National Science Foundation.
CS-ROSETTA is a framework for structure calculation of biological macromolecules on the basis of conformational information from NMR, which is built on top of the biomolecular modeling and design software called ROSETTA. The name CS-ROSETTA for this branch of ROSETTA stems from its origin in combining NMR chemical shift (CS) data with ROSETTA structure prediction protocols. The software package was later extended to include additional NMR conformational parameters, such as Residual Dipolar Couplings (RDC), NOE distance restraints, pseudocontact chemical shifts (PCS) and restraints derived from homologous proteins. This software can be used together with other molecular modeling protocols, such as docking to model protein oligomers. In addition, CS-ROSETTA can be combined with chemical shift resonance assignment algorithms to create a fully automated NMR structure determination pipeline. The CS-ROSETTA software is freely available for academic use and can be licensed for commercial use. A software manual and tutorials are provided on the supporting website https://csrosetta.chemistry.ucsc.edu/.
CS23D is a web server to generate 3D structural models from NMR chemical shifts. CS23D combines maximal fragment assembly with chemical shift threading, de novo structure generation, chemical shift-based torsion angle prediction, and chemical shift refinement. CS23D makes use of RefDB and ShiftX.
I-TASSER is a bioinformatics method for predicting three-dimensional structure model of protein molecules from amino acid sequences. It detects structure templates from the Protein Data Bank by a technique called fold recognition. The full-length structure models are constructed by reassembling structural fragments from threading templates using replica exchange Monte Carlo simulations. I-TASSER is one of the most successful protein structure prediction methods in the community-wide CASP experiments.
A neutral network is a set of genes all related by point mutations that have equivalent function or fitness. Each node represents a gene sequence and each line represents the mutation connecting two sequences. Neutral networks can be thought of as high, flat plateaus in a fitness landscape. During neutral evolution, genes can randomly move through neutral networks and traverse regions of sequence space which may have consequences for robustness and evolvability.
AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. The program is designed as a deep learning system.
In biochemistry, a backbone-dependent rotamer library provides the frequencies, mean dihedral angles, and standard deviations of the discrete conformations of the amino acid side chains in proteins as a function of the backbone dihedral angles φ and ψ of the Ramachandran map. By contrast, backbone-independent rotamer libraries express the frequencies and mean dihedral angles for all side chains in proteins, regardless of the backbone conformation of each residue type. Backbone-dependent rotamer libraries have been shown to have significant advantages over backbone-independent rotamer libraries, principally when used as an energy term, by speeding up search times of side-chain packing algorithms used in protein structure prediction and protein design.
John Michael Jumper is a senior research scientist at DeepMind Technologies. Jumper and his colleagues created AlphaFold, an artificial intelligence (AI) model to predict protein structures from their amino acid sequence with high accuracy. Jumper has stated that the AlphaFold team plans to release 100 million protein structures. The scientific journal Nature included Jumper as one of the ten "people who mattered" in science in their annual listing of Nature's 10 in 2021.
