Protein structure prediction May 30, 2002 Quiz#4 on June 4 Learning objectives-Understand difference between primary secondary and tertiary structure. Learn how to display and manipulate protein structures with Deep View. Learn the steps to protein structure prediction with SIMS and VAST. Understand how the MMDB database. Workshop-Manipulation of the hen lysozyme protein structure with Deep View.

Primary, secondary, supersecondary, and tertiary structure Primary Secondary Supersecondary Tertiary ACFTYPL … ACFTYPL sssccss

Protein structure viewers RasMol Deep View Cn3D WebLabViewer

Steps to tertiary structure prediction Compartive protein modeling Extrapolates new structure based on related family members Steps 1. Identification of modeling templates 2. Alignment 3. Model building

Identification of modeling templates One chooses a cutoff value from FastA or BLAST search Up to ten templates can be used but the one with the highest sequence similarity is the reference template C  atoms are selected for superimposition

Alignment Optimization of superimposition of templates “Common core” and conserved loops of target sequence is threaded onto the template structure

Building the model Framework construction Average the position of each atom in target, based on the corresponding atoms in template. Areas that do not match the template are constructed by using a “spare part” algorithm Completing the backbone-a library of PDB entries is consulted Side chains are added Model refinement-minimization of energy

Framework construction

Molecular Modeling DB (MMBD) Relies on PDB for data The MMDB data format is based on the Abstract Syntax Notion 1 (ASN.1) data description language that describes the three- dimensional structure of biological macromolecules A piece of software called PDB file parser is used to translate PDB files into ASN.1 MMDB files. Its major feature is that it detects unambiguities in the PDB data format and, if necessary, automatically modifies the sequence data so that they comply with the 3D coordinates. Cn3D uses the descriptions of atoms and bonds as it is in MMDB records, without needing to validate them, a necessary step for viewers of PDB files. As a result, MMDB data files are consistently interpreted and structures are better displayed.

VAST (Vector Alignment Search Tool) MMDB maintains a pre-computed n x n record of "neighboring structures“ All of the stored protein structures have been compared to each other with VAST, to identify similar 3- dimensional substructures. These neighbors often identify distant homologs. Steps to VAST algorithm: 1. Based on coordinate data (x,y,z) all of the alpha helices and beta sheets of the protein are identified. 2. Vectors are calculated based on the position of these secondary structures. 3. The program creates packets of two vectors within a protein. These are called secondary structure elements (SSE’s). For example a coiled-coil. n n

VAST (Vector Alignment Search Tool) (cont. 1) 4. The program attempts to align SSE’s between two proteins based on type (alpha or beta), relative orientation and connectivity 5. A refinement of the alignment is performed using Monte Carlo methods at each residue to optimize. Scoring The program assigns a score where the superposition of the vectors is the greatest. To obtain a high score one must also determine the likelihood the vector superposition would occur by chance.

VAST (Vector Alignment Search Tool) (cont. 2) Note: a tertiary unit is defined as an SSE. Note: VAST is not the best method for determining structural similarities. Reducing substructures to vectors means that you lose some information. However, this is one of the fastest methods.