Protein predictive methods Flashcards
Why Analyze Protein Sequences?
Central Dogma Limitations:
Challenges in converting DNA to protein sequence.
Understanding protein structure from DNA.
Computational Challenges:
Difficulty in predicting transcripts from DNA.
Experimental approaches required for protein sequence deduction.
Next-Generation Sequencing:
Generates vast raw sequence data.
Outpaces experimental deciphering capabilities.
Sequence-Function Gap:
Increasing gap between known sequences and functions.
Need for improved computational prediction methods.
Predicting 1D Protein Structure?
One-Dimensional (1D) Structure:
Represented as a string of characters for natural amino acids.
The information content is one-dimensional.
Importance of 1D Prediction Methods:
Relevant for protein function assessment.
Key features include membrane helices, protein disorder, and surface residues.
Addressing Sequence-Structure Gap:
Experimental 3D structures are available for <1% of known sequences.
1D predictions are feasible for all 180 million protein sequences.
Role in Functional Prediction:
Inputs for various prediction methods.
Essential for subsequent functional prediction.
PredictProtein Server:
Provides access to these features.
Secondary Structure Prediction?
Secondary structures are local macrostructures formed from short stretches of amino acid residues that organize themselves in specific ways to form the overall 3D structure of a protein.
formation of secondary structure?
Physically, the driving force behind the formation of secondary structures is a complex combination of local and global forces.
Types of secondary structure?
forces. For instance, alpha helices are stabilized by hydrogen bonds between the CO group of one amino
acid and the NH group of the amino acid that is four positions C-terminal. Strands are structures in which the backbone zigzags to create an extended structure. The most common among these is called the beta-strand. Two or more stretches of beta strands often interact with each other, through hydrogen bonds formed between the different strands, to create a planar structure known as a beta-sheet. Structures that are neither helices nor strands are referred to as “coils,” “others,” or “loops”
Importance and Challenges in
secondary Structure Prediction?
Understanding Protein Structure
Function Prediction: Secondary structures play a significant role in determining a protein’s function. For instance, alpha helices are common in membrane-spanning regions, while beta sheets often form the core of protein structures or participate in protein-protein interactions
Secondary structure prediction can validate experimental techniques used to study protein structures. Predicted secondary structures can be compared with experimental data from techniques like X-ray crystallography
challenges in secondary structure prediction?
Numerous prediction methods proposed over decades.
Based on biochemical insights and computational techniques.
Initial methods focused on single amino acids, lacking reliability.
Evolutionary information incorporated to enhance prediction accuracy.
tools used for secondary structure prediction?
PHDSED
PROSITE
PSIPRED
PROTEUS
RAPTOR X PROPERTY
PHDSEC
Utilizes evolutionary information and machine learning.
Based on Homology-derived Secondary Structure of Proteins (HSSP) database.
Predicts three secondary structure states.
PSIPRED
Neural network-based predictor.
Utilizes PSI-BLAST for profile creation.
Improved network architecture over time.
PROTEUS
Transfers secondary structure annotations from homologs.
Incorporates predictions based on the query sequence.
Generates consensus predictions from multiple methods.
SANN
Predicts solvent accessibility using PSI-BLAST-based PSSMs.
Employs a sliding window approach.
Outputs discrete or fractional predictions.
RAPTOR X PROPERTY
Deep learning-based method.
Predicts secondary structure, solvent accessibility, and disorder.
Achieves high performance using sequence profiles as input.
Transmembrane Alpha Helices and Beta Strands
Significance?
Communication between cells and surroundings primarily through transmembrane proteins.
Constitutes 20–30% of all proteins.
Two-thirds of drug targets are transmembrane proteins.
Transmembrane Alpha Helices and Beta Strands challenges?
Experimental determination of structures challenging.
Under-representation in PDB necessitates computational predictions.
