L-12 Modeling protein Structure
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L-12 Modeling protein Structure - Leaderboard
L-12 Modeling protein Structure - Details
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| 🇬🇧 | 🇬🇧 |
| How accurate is Predicting Secondary Structure From Primary Structure? | Accuracy 64- 75% higher accuracy for -helices than for -sheets predictions of engineered (artificial) proteins are less accurate accuracy is dependent on protein famIly |
| Why is molecular dynamics important? | • Provides a way to observe the motion of large molecules such as proteins at the atomic level – dynamic simulation • Newton’s second law applied to molecules |
| Importance of Potential energy function? | • Molecular coordinates • Force on all atoms can be calculated, given this function • Trajectory of motion of molecule can be determined |
| What does Knowledge Based Approaches include? | -Homology Modelling -Threading Based Methods |
| How does homology Modelling work? | Need homologues of known protein structure • Backbone modelling • Side chain modelling • Fail in absence of homology |
| How does threading based methods work? | • New way of fold recognition • Sequence is tried to fit in known structures • Motif recognition • Loop & Side chain modelling • Fail in absence of known example |
| What are the properties of homology modeling? | Simplest, reliable approach Basis: proteins with similar sequences tend to fold into similar structures Has been observed that even proteins with 25% sequence identity fold into similar structures Does not work for remote homologs (< 25% pairwise identity) |
| What is the approach of homology modeling? | • Given: • A query sequence Q • A database of known protein structures • Find protein P such that P has high sequence similarity to Q • Return P’s structure as an approximation to Q’s structure |
| What does secondary structure prediction assume? | • The entire information for forming secondary structure is contained in the primary sequence • Side groups of residues will determine structure • Examining windows of 13-17 residues is sufficient to predict secondary structure • -helices 5–40 residues long • -strands 5–10 residues long |
| What are the Measures of prediction accuracy? | Qindex and Q3 Correlation coefficient |
| Methods of secondary structure prediction? | First generation methods: single residue statistics Second generation methods: segment statistics The GOR method Third generation methods |
| Explain the first generation method single residue statistics? | Chou & Fasman (1974 & 1978) : Some residues have particular secondary-structure preferences. Based on empirical frequencies of residues in -helices, -sheets, and coils. Examples: Glu α-helix Val β-strand Accuracy: Q3 = 50-60% |
| Explain Second generation methods segment statistics? | Similar to single-residue methods, but incorporating additional information (adjacent residues, segmental statistics). • Problems: • Low accuracy - Q3 below 66% (results). • Q3 of -strands (E): 28% - 48%. • Predicted structures were too Short. |
| What is the GOR method? | • developed by Garnier, Osguthorpe & Robson • build on Chou-Fasman Pij values • evaluate each residue PLUS adjacent 8 N-terminal and 8 carboxyl-terminal residues • sliding window of 17 residues • underpredicts -strand regions • GOR method accuracy Q3 = ~64% |
| What ideas Third generation methods are based on? | • Third generation methods reached 77% accuracy. • They are based on two new ideas: 1. A biological idea – Using evolutionary information based on conservation analysis of multiple sequence alignments. 2. A technological idea – Using neural networks |
| What is Artificial Neural Networks? | An attempt to imitate the human brain (assuming that this is the way it works |
| What does proteins consist of? | Proteins consist of amino acids linked by peptide bonds |
| What is the significance of secondary structure prediction? | • Historically first structure prediction methods predicted secondary structure • Can be used to improve alignment accuracy • Can be used to detect domain boundaries within proteins with remote sequence homology • Often the first step towards 3D structure prediction • Informative for mutagenesis studies |
| What are the limitations of Energy Minimization Techniques? | • large number of degree of freedom, CPU power not adequate • Interaction potential is not good enough to model |
| How does Dynamical Minimization Methods work? | • Motions of atoms also considered • Monte Carlo simulation (stochastics in nature, time is not considered) • Molecular Dynamics (time, quantum mechanical, classical equ.) |
| How does Static Minimization Methods work? | • Classical many potential-potential can be constructed • Assume that atoms in protein is in static form • Problems (large number of variables & minima and validity of potentials) |
| What are the Energy Minimization Techniques? | Energy Minimization based methods in their pure form, make no priori assumptions and attempt to locate global minima. |
| How does the Knowledge Based approaches in protein structure prediction work? | • Homology Based Approach • Threading Protein Sequence • Hierarchical Methods |
| How does Computer simulation based on energy calculation work? | • Based on physio-chemical principles • Thermodynamic equilibrium with a minimum free energy • Global minimum free energy of protein surface |
| What are the techniques of structure prediction? | -Computer simulation based on energy calculation -Knowledge Based approaches |
| What are the levels of protein structural complexity? | • Primary structure (AA sequence) • Secondary structure • Spatial arrangement of a polypeptide’s backbone atoms without regard to side-chain conformations • alpha, beta, coil, turns (Venkatachalam, 1968) • Super-secondary structure • alpha, beta, alpha/beta, alpha+Beta (Rao and Rassman, 1973) • Tertiary structure • 3-D structure of an entire polypeptide • Quaternary structure • Spatial arrangement of subunits (2 or more polypeptide chains) |
| What are the exceptions in protein folding? | Chaperone proteins assist folding • Abnormally folded Prion proteins can catalyze misfolding of normal prion proteins that then aggregate |
| What did the Anfinsen’s experiments in 1957 demonstrate? | Anfinsen’s experiments in 1957 demonstrated that proteins can fold spontaneously into their native conformations under physiological conditions. This implies that primary structure does indeed determine folding or 3-D structure. |
| What are the limitations of predicting proteins structure? | • Not all proteins or parts of proteins assume a well- defined 3D structure in solution. • Protein structure is not static, there are various degrees of thermal motion for different parts of the structure. • There may be a number of slightly different conformations in solution. • Some proteins undergo conformational changes when interacting with STUFF. |
| Why is predicting protein structures important? | Structural knowledge = some understanding of function and mechanism of action • Predicted structures can be used in structure-based drug design • It can help us understand the effects of mutations on structure and function • It is a very interesting scientific problem (still unsolved in its most general form after more than 50 years of effort) |
| How can you distinguish amino acids ? | Differences in side chains distinguish the various amino acids |
| What does amino acids consists of? | • a central carbon atom • an amino group • a carboxyl group and • a side chain |