Structural Genomics and Protein Structure Determination

Structural Genomics Initiative

• The primary goal of the Structural Genomics Initiative was to determine the 3D structures of a wide range of proteins systematically.

Protein Structure Determination

• Southern blotting is not commonly used for protein structure determination.
• X-ray crystallography, NMR spectroscopy, and Cryo-electron microscopy are commonly used techniques.

Homology Modeling

• Homology modeling is based on structural similarity to a known template.
• It is used to build the structure of a similar protein.

Multiple Sequence Alignments

• Multiple sequence alignments are necessary in protein modeling to facilitate the identification of conserved regions.

Position-Specific Scoring Matrix (PSSM)

• A PSSM represents a matrix that improves the robustness of pairwise alignments in computational biology.

Critical Assessment of Protein Structure Prediction (CASP)

• CASP is a critical assessment event for protein structure prediction methods.

Protein Crystallization

• Not all proteins can be crystallized for X-ray crystallography due to issues like size, complexity, instability, and solubility.

Structural Bioinformatics in Drug Design

• Structural bioinformatics tools aid in identifying drug targets and understanding interactions.

Protein Structure Modeling

• Gel electrophoresis is not a method in protein structure modeling.
• Homology modeling, ab initio modeling, and threading are methods used in protein structure modeling.

Ab Initio Modeling

• Ab initio modeling has the advantage of not requiring a template.

Ramachandran Plot

• The Ramachandran plot is commonly used to check the quality of protein structure predictions.

Meta Prediction

• Meta prediction enhances accuracy by combining results from multiple prediction methods.

Template-Based Modeling

• Templates are used in protein modeling to serve as a reference for building the structure of a similar protein.

Sequence Identity

• Higher sequence identity typically leads to more accurate predictions in rebuilding protein structures.

Ab Initio Calculations

• Ab initio calculations provide insights where other methods cannot be applied, despite their inaccuracies.

Anatomy of Proteins

• VMD software allows measuring dihedral angles within a protein structure.
• Alpha carbon (Cα) is the pivotal atom in each residue of a polypeptide chain.

Ramachandran Plot

• The Ramachandran plot is useful in structural biology for checking sterically allowed regions in protein structures.
• It primarily assesses the φ (phi) and ψ (psi) angles of amino acid residues in protein structures.
• The plot reveals that glycine is absent in certain regions due to steric clashes even at those angles.
• Proline is often found in the cis configuration of the peptide bond, as shown on the plot.

Protein Structure

• Beta-turns are loops connecting two anti-parallel strands in protein structures.
• Hydrogen bonds in a beta-turn provide structural stability by connecting the carbonyl oxygen of the first residue with the amide hydrogen of the fourth.
• Proline residues often cause beta-bulges due to their rigid structure.
• Beta-bulges are significant in protein structures as they can cause local disruptions in regular hydrogen bond patterns.

Visualization Methods

• 3D visualization methods like orthographic projection allow complex 3D structures to be understood in a 2D format.
• These methods are useful in structural biology for visualizing protein structures.

Amino Acid Properties

• Glycine has more conformational freedom in protein structures due to the absence of a bulky side chain.
• Proline's unique ring structure limits its rotational freedom, allowing it to be found in the CIS configuration in proteins.

Secondary Structure Prediction (SSP)

• There are three main classes of secondary structures in proteins discovered by Linus Pauling: alpha-helices, beta-sheets, and turns.

Prediction Methods

• Template-Free Modeling is used when there is no suitable template available for a protein.
• PSIPRED has achieved an accuracy of 81.6% in predicting secondary structures.

Secondary Structures

• Alpha-helices have a periodicity of 3.6 residues per turn.
• In an alpha-helix, amphiphilicity describes the presence of both hydrophobic and hydrophilic regions.
• The (i, i+n) connectivity in protein helices describes the hydrogen bonding pattern between the backbone amides.
• Beta-sheets can exhibit amphiphilic properties, though less commonly than alpha-helices.
• Tertiary folds are not a type of secondary structure commonly predicted by SSP programs.

SSP Prediction Programs

• The typical result format of an SSP prediction program is a sequence annotated with the type of secondary structure for each residue.
• A Q3 score indicates the percentage of residues correctly predicted as either helix, sheet, or coil.

Protein Properties

• Hydrophobic moments measure the periodicity of hydrophobicity in a segment.
• Alpha-helices are important for interactions with lipid bilayers due to their amphipathic nature.
• Conserved regions are identified in SSP by comparing multiple protein sequences.

Representation of Alpha-Helices

• Helical wheels can represent alpha-helices effectively due to their periodicity of 3.6 residues per turn.
• Beta strands have a pseudo periodicity of 2 residues.

Multiple Sequence Alignment (MSA)

• Primary purpose of MSA is to study evolutionary relationships among species.
• Best pairwise sequence alignment does not always correspond to the highest sequence identity; it maximizes the alignment score based on specific scoring functions.

Effects of Evolutionary Changes

• Insertions and deletions in an MSA lead to gaps when sequences are aligned.
• PAM and BLOSUM matrices score substitutions in protein sequences based on evolutionary changes.

Scoring Functions and Matrices

• Example scoring function: +2 for match, -1 for mismatch, -3 for gap.
• A similarity matrix for proteins is 20x20 in size.
• BLOSUM 62 matrix is more reliable for aligning sequences with over 70% identity.
• BLOSUM62 is often the default matrix for BLASTP due to its balance between sensitivity and specificity for a wide range of sequences.

Conserved Regions and MSAs

• Presence of conserved regions in an MSA implies structural or functional importance of those regions.
• Choice of scoring function is a key parameter when generating MSAs to ensure meaningful alignments.

Challenges in MSAs

• Aligning sequences with very low similarity can result in loss of biologically meaningful information.
• MSAs can be dependent on the protein evolution model.

Evolutionary Substitution Matrices

• BLOSUM matrices are often used in MSAs to score the likelihood of one amino acid being replaced by another over evolutionary time.
• BLOSUM matrices are derived from observed substitutions in closely related protein sequences.