Boitechnology

The default 3D structure modeling tool used in RAPTOR is OWL. Three-dimensional structure modeling involves two steps. The first step is loop modeling which models regions in the target sequence that map to nothing in the template. After all the loops are modeled and the backbone is ready, side chains are attached to the backbone and packed up. For loop modeling, a cyclic coordinate descent algorithm is used to fill the loops and avoid clashes. For side chain packing, a tree decomposition algorithm is used to pack up all the side chains and avoid any clashes. OWL is automatically called in RAPTOR to generate the 3D output.

If a researcher has MODELLER, they can also set up RAPTOR to call MODELLER automatically. RAPTOR can also generate ICM-Pro input files, with which people run ICM-Pro by themselves.

NoCore, NPCore and IP are the three different threading engines implemented in RAPTOR. NoCore and NPCore are based on dynamic programming and faster than IP. The difference between them is that in NPCore, a template is parsed into many “core” regions. A core is a structurally conserved region. IP is RAPTOR’s unique integer programming-based threading engine. It produces better alignments and models than the other two threading engines. People can always start with NoCore and NPCore. If their predictions are not good enough, IP may be a better choice. After all three methods are run, a simple consensus may help to find the best prediction.

The integer programming approach to RAPTOR produces higher quality models than other protein threading methods. Most threading software use dynamic programming to optimize their scoring functions when aligning a sequence with a template. Dynamic programming is much easier to implement than integer programming; however if a scoring function has pairwise contact potential included, dynamic programming cannot globally optimize such a scoring function and instead just generates a local optimal alignment.

Pairwise contacts are very conserved in protein structure and crucial for prediction accuracy. Integer programming can globally optimize a scoring function with pairwise contact potential and produce a global optimal alignment.

Researchers attempting to solve a protein’s structure start their a study with little more than a protein sequence. Initial steps may include performing a PSI-BLAST or PatternHunter search to locate a similar sequences with a known structure in the Protein Data Bank (PDB). If there are highly similar sequences with known structures, there is a high probability that this protein’s structure will be very similar to those known structures as well as functions. If there is no homology found, the researcher must perform either X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, both of which cost approximately $100,000 per sample to solve. Where these techniques are too expensive, time-consuming or limited in scope, researchers can use protein threading software, such as RAPTOR to create a highly reliable model of the protein.

Protein threading is more effective than homology modeling, especially for proteins which have few homologs detectable by sequence alignment. The two methods both predict protein structure from a template. Given a protein sequence, protein threading first aligns (threads) the sequence to each template in a structure library by optimizing a scoring function that measures the fitness of a sequence-structure alignment. The selected best template is used to build the structure model. Unlike homology modeling, which selects template purely based on homology information (sequence alignments), the scoring function used in protein threading utilizes both homology and structure information (sequence structure alignments).

If a sequence has no significant homology found, homology modeling may not give reliable prediction in this case. Without homology information, protein threading can still use structure information to produce good prediction. Failed attempts to obtain a good template with BLAST often result in users processing results through RAPTOR.

RAPTOR is protein threading software used for protein structure prediction, given a primary sequence.

QuteMol is an open source, interactive, molecular visualization system. QuteMol utilizes the current capabilities of modern GPUs through OpenGL shaders to offer an array of innovative visual effects. QuteMol visualization techniques are aimed at improving clarity and an easier understanding of the 3D shape and structure of large molecules or complex proteins.
Real Time ambient occlusion
Depth Aware Silhouette Enhancement
Ball and Sticks, space-filling and Liquorice visualization modes
High resolution antialiased snapshots for creating publication quality renderings
Interactive rendering of large molecules and protein (100k atoms)
Standard Protein Data Bank input.

On August 1, 2006, DeLano Scientific adopted a controlled-access download system for precompiled PyMOL builds (including betas) distributed by the company. Access to these executables is now limited to paying customers but is free for students and teachers. However, current source code continues to be available at no cost, as are older precompiled builds. While the build systems for other platforms are open, the win32 build system is not. Non-students and teachers can either compile an executable from the source code or pay for a subscription to the support services to obtain access to pre-compiled executables.

PyMOL is an open-source, user-sponsored, molecular visualization system created by Warren Lyford DeLano and commercialized by DeLano Scientific LLC, which is a private software company dedicated to creating useful tools that become universally accessible to scientific and educational communities. It is well suited to producing high quality 3D images of small molecules and biological macromolecules such as proteins. According to the author, almost a quarter of all published images of 3D protein structures in the scientific literature were made using PyMOL.

PyMOL is one of few open source visualization tools available for use in structural biology. The Py portion of the software’s name refers to the fact that it extends, and is extensible by the Python programming language.

NOCH (also known as NOC) - is a free and open source molecular explorer for protein structure visualization. It allows for import of molecular structures described in the Protein Data Bank file format.

Visualization of residues (ribbon or schematic)
Complete control over the generation of molecular surfaces (bounding box and resolution)
Visualization of the following surfaces:
orbitals
Isosurface from electron density data
Isosurface from Gaussian cube grid data
Solvent-accesible surface (SAS)
Solvent excluded surface (SES)
Van del Waals radii
Animation of molecular surfaces
Export to PostScript or TIFF