AUTHOR==David E Kim
METADATA==This document was last updated on November, 2010 by David E Kim <dekim@uw.edu>. The PI is David Baker <dabaker@uw.edu>. The AbinitioRelax application was developed by numerous Rosetta Commons members, primarily:Kim Simons, Richard Bonneau, Kira Misura, Phil Bradley, Oliver Lange, Michael Tyka, Robert Vernon
EXAMPLES==The ab initio executable is in rosetta_source/src/apps/public/AbinitioRelax.cc. The source code for the ab initio protocol is in rosetta_source/src/protocols/abinitio/AbrelaxApplication.cc. See the rosetta_demos/abinitio directory for an example ab initio run which includes input files, expected output files, and an example run log. The example command exists in rosetta_demos/abinitio/readme.txt. Input files exist in rosetta_demos/abinitio/input_files. Expected output files exist in rosetta_demos/abinitio/output_files. An example run log exist in rosetta_demos/abinitio/log.
REFERENCES==Srivatsan Raman, Robert Vernon, James Thompson, Michael Tyka, Ruslan Sadreyev,Jimin Pei, David Kim, Elizabeth Kellogg, Frank DiMaio, Oliver Lange, Lisa Kinch, Will Sheffler, Bong-Hyun Kim, Rhiju Das, Nick V. Grishin, and David Baker. (2009) Structure prediction for CASP8 with all-atom refinement using Rosetta. Proteins 77 Suppl 9:89-99\nBradley P, Misura KM, Baker D (2005). Toward high-resolution de novo structure prediction for small proteins. Science 309, 1868-71\nBonneau R, Strauss CE, Rohl CA, Chivian D, Bradley P, Malmstrom L, Robertson T, Baker D. (2002) De novo prediction of three-dimensional structures for major protein families. J Mol Biol 322(1):65-78.\nBonneau R, Tsai J, Ruczinski I, Chivian D, Rohl C, Strauss CE, Baker D. (2001) Rosetta in CASP4: progress in ab initio protein structure prediction. Proteins Suppl 5:119-26.\nSimons KT, Ruczinski I, Kooperberg C, Fox B, Bystroff C, Baker D. (1999) Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins 34(1) 82-95\nSimons KT, Kooperberg C, Huang E, Baker, D. (1997) Assembly of protein tertiary structures from fragments with similar local sequences using simulate anealing and Bayesian scoring functions. J Mol Biol 268:209-25.
DESCRIPTION==Abinitio Structure Prediction - The AbinitioRelax application consists of two main steps. The first step is a coarse-grained fragment-based search through conformational space using a knowledge-based "centroid" score function that favors protein-like features (Abinitio). The second optional step is all-atom refinement using the Rosetta full-atom forcefield (Relax). 
ALGORITHM==The "Relax" step is considerably more compute-intensive and time-consuming than the first step. The example run described above in the Code and Demo section takes around 8 minutes to generate one model of a 117 residue protein on a modern computer. A single AbinitioRelax run can generate a user defined number of models via a command line option (see Options section below). For increased conformational sampling, this application is easily parallelized by executing numerous jobs each using a unique random number seed (see Options section below). This is typically done by submitting multiple jobs to a computer cluster or distributed grid. Since the full-atom energy function is very sensitive to imperfect atomic interactions and more noise will exist with insufficient sampling, convergence towards the native structure may require a significant amount of sampling. Additionally, to increase your chance of sampling the correct topology, a diverse set of homologous sequences, preferably with sequence changes that may have a greater impact on sampling like deletions and differences in conserved positions, may also be run since a homologue may converge towards the native structure with significantly less sampling (see Bradley et al reference).
INPUT==Fasta file. Contains the amino acid protein sequence in fasta format. Example: rosetta_demos/abinitio/input_files/1elwA.fasta. Create using the GUI or by hand.  Fragments files. Generate structural fragment libraries using either the publicly available webserver (http://robetta.bakerlab.org/fragmentsubmit.jsp) or the GUI.  Native structure (optional). The native PDB structure may be used for benchmarking. When used, the RMSD to native is calculated for each model and provided as an extra column in the score line.  Psipred secondary structure prediction psipred_ss2 file (optional). The Psipred secondary structure prediction file is necessary when the -use_filters and -kill_hairpins options are used (see below). Note: the fragment webserver runs Psipred and provides the psipred_ss2 output file. Can make this using the GUI as well.
TIPS==The AbinitioRelax application performs best for small monomeric proteins that are less than 100 residues in length. It is possible to get accurate predictions for some proteins up to around 150 residues, however, larger proteins require significantly more computing as the conformational space is vastly increased. It is not uncommon to sample in the range of 20,000 to 200,000 models in order to converge towards the native structure. The following references provide information relevant to the sampling problem:\nBradley P, Misura KM, Baker D (2005). Toward high-resolution de novo structure prediction for small proteins. Science 309, 1868-71.\nKim DE, Blum B, Bradley P, Baker D (2009). Sampling bottlenecks in de novo protein structure prediction. J Mol Biol 393, 249-60.\n\n  As stated above, it is beneficial to try to identify homologous sequences to run along with the target sequence (see Bradley et al reference). Homologs can be identified using search tools like PSI-BLAST to search the non-redundant sequence database (NCBI nr database) or Pfam. Using a sequence alignment viewer like Jalview is very useful to help select an optimal set of homologs to run and also to aid in model selection. Typically we look for a diverse set of homologs (up to 10) with differences in conserved positions and deletions which may represent a truncated loop or disordered region. Small changes in sequence can have a large impact on the topologies that are sampled, for example, a polar residue at a conserved hydrophobic position can have a big effect, i.e. the native topology may not be sampled because the full-atom Rosetta score will highly disfavor a polar residue buried in a hydrophobic core. It is also important to identify and trim disordered termini using publicly available programs like Disopred or metaPrDOS. Signal sequences should also be identified and trimmed using publicly available programs like SignalP. This protocol is not developed for membrane proteins. If transmembrane helices are predicted using programs like TMHMM, please refer to our Membrane ab initio application.
OUTPUTS==Generates pdb files and an energy file, or a silent output file. Example: rosetta_demos/abinitio/input_files/S_00000001.pdb, rosetta_demos/abinitio/input_files/score.fsc, and rosetta_demos/abinitio/output_files/default.out (silent output file).
ANALYSIS==We recommend generating up to 20,000 to 30,000 models of the target sequence and of up to 10 homologs and then using the Cluster application to identify the most frequently sampled conformations. In a general case, at least one of the top 5-10 clusters by size may have models with the lowest rmsd to the native structure.\nIn an ideal case, your sequence will have many homologs identified by search tools like PSI-BLAST. Sequence alignments can be extremely helpful in model selection. For example, conserved hydrophobic positions most likely represent the core of the protein so models that have sidechains exposed in such positions may be discarded. The same logic applies to conserved polar positions which are most likely on the surface. Additionally, conserved cysteine pairs may represent disulphides. Tools like Jalview to view alignments and PyMOL to view models are extremely helpful for model selection in this respect.\nScore versus RMSD plots may be helpful for identifying convergence towards the native structure for the target sequence and homologs. For example, the lowest scoring model can be used for the in:file:native input option when rescoring models with the score.linuxgccrelease score application. A score versus RMSD plot from the resulting score file may show convergence (an energy funnel) towards the lowest scoring model. If an energy funnel exists, the lowest scoring model has a greater chance of being near-native.\nLowest scoring models that are in a cluster and that have a topology represented in the PDB also have a greater chance of being correct. Structure-structure comparison tools like Dali or Mammoth can be used to search against the PDB database.
