Protein secondary structure is the three dimensional form of local segments of proteins. The two most common secondary structural elements are alpha helices and beta sheets, though beta turns and omega loops occur as well. Secondary structure elements typically spontaneously form as an intermediate before the protein folds into its three dimensional tertiary structure.
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 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 tertiary structures may fold into a quaternary structure.
In bioinformatics, sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. Methodologies used include sequence alignment, searches against biological databases, and others. Since the development of methods of high-throughput production of gene and protein sequences, the rate of addition of new sequences to the databases increased exponentially. Such a collection of sequences does not, by itself, increase the scientist's understanding of the biology of organisms. However, comparing these new sequences to those with known functions is a key way of understanding the biology of an organism from which the new sequence comes. Thus, sequence analysis can be used to assign function to genes and proteins by the study of the similarities between the compared sequences. Nowadays, there are many tools and techniques that provide the sequence comparisons and analyze the alignment product to understand its biology.
Critical Assessment of protein Structure Prediction, or CASP, is a community-wide, worldwide experiment for protein structure prediction taking place every two years since 1994. CASP provides research groups with an opportunity to objectively test their structure prediction methods and delivers an independent assessment of the state of the art in protein structure modeling to the research community and software users. Even though the primary goal of CASP is to help advance the methods of identifying protein three-dimensional structure from its amino acid sequence, many view the experiment more as a “world championship” in this field of science. More than 100 research groups from all over the world participate in CASP on a regular basis and it is not uncommon for entire groups to suspend their other research for months while they focus on getting their servers ready for the experiment and on performing the detailed predictions.
Macromolecular docking is the computational modelling of the quaternary structure of complexes formed by two or more interacting biological macromolecules. Protein–protein complexes are the most commonly attempted targets of such modelling, followed by protein–nucleic acid complexes.
Rosetta@home is a distributed computing project for protein structure prediction on the Berkeley Open Infrastructure for Network Computing (BOINC) platform, run by the Baker laboratory at the University of Washington. Rosetta@home aims to predict protein–protein docking and design new proteins with the help of about sixty thousand active volunteered computers processing at over 210 teraFLOPS on average as of July 29, 2016. 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 global distance test (GDT), also written as GDT TS to represent "total score", is a measure of similarity between two protein structures with known amino acid correspondences but different tertiary structures. It is most commonly used to compare the results of protein structure prediction to the experimentally determined structure as measured by X-ray crystallography or protein NMR. The metric is intended as a more accurate measurement than the more common RMSD metric, which is sensitive to outlier regions created by poor modeling of individual loop regions in a structure that is otherwise reasonably accurate. GDT_TS measurements are used as major assessment criteria in the production of results from the Critical Assessment of Structure Prediction (CASP), a large-scale experiment in the structure prediction community dedicated to assessing current modeling techniques and identifying their primary deficiencies. In general, the higher GDT_TS is, the better a given model is in comparison to reference structure.
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. He 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.
EVA was a continuously running benchmark project for assessing the quality and value of protein structure prediction and secondary structure prediction methods. Methods for predicting both secondary structure and tertiary structure - including homology modeling, protein threading, and contact order prediction - were compared to results from each week's newly solved protein structures deposited in the Protein Data Bank. The project aimed to determine the prediction accuracy that would be expected for non-expert users of common, publicly available prediction webservers; this is similar to the related LiveBench project and stands in contrast to the bi-yearly benchmark CASP, which aims to identify the maximum accuracy achievable by prediction experts.
CAFASP, or the Critical Assessment of Fully Automated Structure Prediction, is a large-scale blind experiment in protein structure prediction that studies the performance of automated structure prediction webservers in homology modeling, fold recognition, and ab initio prediction of protein tertiary structures based only on amino acid sequence. The experiment runs once every two years in parallel with CASP, which focuses on predictions that incorporate human intervention and expertise. Compared to related benchmarking techniques LiveBench and EVA, which run weekly against newly solved protein structures deposited in the Protein Data Bank, CAFASP generates much less data, but has the advantage of producing predictions that are directly comparable to those produced by human prediction experts. Recently CAFASP has been run essentially integrated into the CASP results rather than as a separate experiment.
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.
Phyre and Phyre2 are web-based services for protein structure prediction that are free for non-commercial use. Phyre is among the most popular methods for protein structure prediction having been cited over 1500 times. Like other remote homology recognition techniques, it is able to regularly generate reliable protein models when other widely used methods such as PSI-BLAST cannot. Phyre2 has been designed to ensure a user-friendly interface for users inexpert in protein structure prediction methods.
RaptorX for protein structure modeling and function prediction
SWISS-MODEL is a structural bioinformatics web-server dedicated to homology modeling of 3D protein structures. Homology modeling is currently the most accurate method to generate reliable three-dimensional protein structure models and is routinely used in many practical applications. Homology modelling methods make use of experimental protein structures ("templates") to build models for evolutionary related proteins ("targets").
The HH-suite is an open-source software package for sensitive protein sequence searching. It contains programs that can search for similar protein sequences in protein sequence databases. Sequence searches are a standard tool in modern biology with which the function of unknown proteins can be inferred from the functions of proteins with similar sequences. HHsearch and HHblits are two main programs in the package and the entry point to its search function, the latter being a faster iteration. HHpred is an online server for protein structure prediction that uses homology information from HH-suite.
Burkhard Rost is a scientist leading the Department for Computational Biology & Bioinformatics at the Faculty of Informatics of the Technical University of Munich (TUM). Rost chairs the Study Section Bioinformatics Munich involving the TUM and the Ludwig Maximilian University of Munich (LMU) in Munich. From 2007-2014 Rost was President of the International Society for Computational Biology (ISCB).
Continuous Automated Model EvaluatiOn (CAMEO) is a community-wide project to continuously evaluate the accuracy and reliability of protein structure prediction servers in a fully automated manner. CAMEO is a continuous and fully automated complement to the bi-annual CASP experiment. Currently, CAMEO evaluates predictions for predicted three-dimensional protein structures (3D), ligand binding site predictions in proteins (LB), and model quality estimation tools (QE).
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.
