Discussion of Protein Disorder Prediction

Slides:



Advertisements
Similar presentations
© University of Reading Dr Liam J. McGuffin RCUK Academic Fellow 20 April 2014 McGuffin Group.
Advertisements

Structural bioinformatics
Chapter 9 Structure Prediction. Motivation Given a protein, can you predict molecular structure Want to avoid repeated x-ray crystallography, but want.
Protein secondary structure prediction methods TDVEAAVNSLVNLYLQASYLS “From sequence to structure”
Biomolecular Nuclear Magnetic Resonance Spectroscopy BIOCHEMISTRY BEYOND STRUCTURE Protein dynamics from NMR Analytical Biochemistry Comparative Analysis.
Biomolecular Nuclear Magnetic Resonance Spectroscopy BIOCHEMISTRY BEYOND STRUCTURE Protein dynamics from NMR Analytical biochemistry Comparative analysis.
MULTICOM – A Combination Pipeline for Protein Structure Prediction
Bridging the solution divide: comprehensive structural analyses of dynamic RNA, DNA, and protein assemblies by small-angle X-ray scattering By Rambo and.
. Protein Structure Prediction [Based on Structural Bioinformatics, section VII]
Bioinf. Data Analysis & Tools Molecular Simulations & Sampling Techniques117 Jan 2006 Bioinformatics Data Analysis & Tools Molecular simulations & sampling.
Protein modelling ● Protein structure is the key to understanding protein function ● Protein structure ● Topics in modelling and computational methods.
Energy landscape for proteins With statistical mechanics, one can try to understand protein folding kinetics. An extension from theories for glasses and.
Computer-Assisted Drug Design (1) i)Random Screening ii)Lead Development and Optimization using Multivariate Statistical Analyses. iii)Lead Generation.
Practical session 2b Introduction to 3D Modelling and threading 9:30am-10:00am 3D modeling and threading 10:00am-10:30am Analysis of mutations in MYH6.
CRB Journal Club February 13, 2006 Jenny Gu. Selected for a Reason Residues selected by evolution for a reason, but conservation is not distinguished.
Protein Structure Prediction. Historical Perspective Protein Folding: From the Levinthal Paradox to Structure Prediction, Barry Honig, 1999 A personal.
Lecture 10 – protein structure prediction. A protein sequence.
Example: Critical Evaluation of a Paper Barthe et al. (2009) Dynamic and Structural Characterization of a Bacterial FHA Protein Reveals a New Autoinhibition.
RNA Secondary Structure Prediction Spring Objectives  Can we predict the structure of an RNA?  Can we predict the structure of a protein?
Prediction of protein disorder Zsuzsanna Dosztányi MTA-ELTE Momentum Bioinformatics Group Department of Biochemistry Eotvos Lorand University, Budapest,
De novo Protein Design Presented by Alison Fraser, Christine Lee, Pradhuman Jhala, Corban Rivera.
Protein Secondary Structure Prediction. Input: protein sequence Output: for each residue its associated Secondary structure (SS): alpha-helix, beta-strand,
Prediction of protein disorder Zsuzsanna Dosztányi Institute of Enzymology, Budapest, Hungary
Neural Networks for Protein Structure Prediction Brown, JMB 1999 CS 466 Saurabh Sinha.
Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study George Chikenji*, Yoshimi Fujitsuka, and Shoji Takada*
Jianlin Jack Cheng Computer Science Department University of Missouri, Columbia, USA Mexico, 2014.
Last Tuesday and Beyond Common 2° structural elements: influenced by 1° structure –alpha helices –beta strands –beta turns Structure vs. function –Fibrous.
Protein Folding and Modeling Carol K. Hall Chemical and Biomolecular Engineering North Carolina State University.
Protein-Protein Interaction Hotspots Carved into Sequences Yanay Ofran 1,2, Burkhard Rost 1,2,3 1.Department of Biochemistry and Molecular Biophysics,
Tertiary Structure Globular proteins (enzymes, molecular machines)  Variety of secondary structures  Approximately spherical shape  Water soluble 
Math – What is a Function? 1. 2 input output function.
Introduction to Protein Structure Prediction BMI/CS 576 Colin Dewey Fall 2008.
Matching Protein  -Sheet Partners by Feedforward and Recurrent Neural Network Proceedings of Eighth International Conference on Intelligent Systems for.
FlexWeb Nassim Sohaee. FlexWeb 2 Proteins The ability of proteins to change their conformation is important to their function as biological machines.
Comparative methods Basic logics: The 3D structure of the protein is deduced from: 1.Similarities between the protein and other proteins 2.Statistical.
Structural classification of Proteins SCOP Classification: consists of a database Family Evolutionarily related with a significant sequence identity Superfamily.
How NMR is Used for the Study of Biomacromolecules Analytical biochemistry Comparative analysis Interactions between biomolecules Structure determination.
Intrinsically disordered proteins Zsuzsanna Dosztányi EMBO course Budapest, 3 June 2016.
Statistical Machine Learning Methods for Bioinformatics IV
Topics for today: 1) A few comments on using NMR to investigate internal motions in biomolecules. 2) “MRI”, or Magnetic Resonance Imaging (The last day.
Biological networks CS 5263 Bioinformatics.
SMA5422: Special Topics in Biotechnology
Extra Tree Classifier-WS3 Bagging Classifier-WS3
חיזוי ואפיון אתרי קישור של חלבון לדנ"א מתוך הרצף
Yuchun Tang (1), Preeti Singh (1), Yanqing Zhang (1),
Distribution of disorder in the cytosolic phosphoproteome
Enzyme Kinetics & Protein Folding 9/7/2004
Protein Folding and Protein Threading
Protein folding.
Protein Structures.
Protein structure prediction.
Protein Disorder Prediction
Combining Predictors for Short and Long Protein Disorder
Understanding protein folding via free-energy surfaces from theory and experiment  Aaron R Dinner, Andrej Šali, Lorna J Smith, Christopher M Dobson, Martin.
Structural and Dynamic Properties of the Human Prion Protein
Free Energy Diagrams for Protein Function
Volume 23, Issue 5, Pages (May 2015)
Structural Flexibility of CaV1. 2 and CaV2
Paul Robustelli, Kai Kohlhoff, Andrea Cavalli, Michele Vendruscolo 
Volume 104, Issue 9, Pages (May 2013)
Protein Folding and Unfolding at Atomic Resolution
Prediction of the Number of Residue Contacts in Proteins
LC8 is structurally variable but conserved in sequence.
Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.
Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.
Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.
Mr.Halavath Ramesh 16-MCH-001 Dept. of Chemistry Loyola College University of Madras-Chennai.
Volume 109, Issue 7, Pages (October 2015)
Xiaodong Pang, Huan-Xiang Zhou  Biophysical Journal 
Conserved motifs in the ABC
Presentation transcript:

Discussion of Protein Disorder Prediction Jianlin Cheng University of Missouri, Columbia, MO, USA (MULTICOM-CMFR & MULITCOM)

Question 1 In you analysis of disorder do you treat short disordered regions, e.g. a missing loop in a crystal structure, differently than a disordered domain or an entirely disordered protein? No. Two reasons (laziness and principle)

Question 2 Can you briefly describe your disorder analysis, i.e. is it based on physical principals, machine learning or a combination of both? Machine learning – 1D-Recursive Neural Network Input: sequence profile, predicted secondary structure, relative solvent accessibility Output: disorder (+), order (-)

Question 3 Does your analysis of disorder prediction affect your template free modeling, i.e. does the disorder prediction aid your free model prediction? If so, in what way, in practice, did you use your disorder prediction for free modeling? Occasionally. T0500 (800 residues) Should be useful for both template-based and template-free modeling

Question 4 Can your disorder prediction distinguish between regions predicted to be fully disordered, i.e. 'cooked spaghetti', or alternatively an ensemble of a few alternative conformations? Maybe. Strength of signal?

Disorder Ensemble Some disorder regions may be not fully disordered. Most likely a discrete distribution of a number of conformations Disorder regions switch from one conformation to another according to probability

NMR to Determine Ensemble Conformations New NMR techniques can gather local conformations and long-range interactions even under strongly denaturing conditions to obtain plausible all-atom models of the unfolded state at increasing accuracy. S. Meier et al. J. Chemical Physics, 2008

Energy Landscape of Ordered Globular Protein Chan and Dill, Nature Structure Biology, 1997

Energy Landscape of Disordered Regions Shallow, unstable energy landscape

Contacts in Ensemble Essential contacts (conserved long-range interactions) Non-essential contacts (transient contacts) How to predict essential contacts?

Prediction of Ensemble Protein conformation space is significantly reduced due to essential contacts Predict ensemble conformations using template-free modeling Predict ensemble conformations using constrained molecular dynamics