Consensus RAPDF rTAD Refinement Successes & Failures Jeremy Horst Ram Samudrala’s CompBio Group University of Washington.

Slides:

Advertisements

Similar presentations

Recommender System A Brief Survey.

Advertisements

Fields and Isolines.

Research skills. OUTLINE Mission and Vision What is Research? Ten Steps for Good Research Resources of Research Types of research Skills (Top_5 Skills)

Using the Optimizer to Generate an Effective Regression Suite: A First Step Murali M. Krishna Presented by Harumi Kuno HP.

Cyclical vs. Stochastic Visual Search Models Andrew Elias 11 Aug 2011 NRL Code 7440 branch meeting How a Target’s Effect on the Eye Varies with Distance.

Rosetta Energy Function Glenn Butterfoss. Rosetta Energy Function Major Classes: 1. Low resolution: Reduced atom representation Simple energy function.

State-Space Searches. State spaces A state space consists of –A (possibly infinite) set of states The start state represents the initial problem Each.

Alex Aiken Chess Kickoff Meeting1 Program Analysis for Embedded Systems Alex Aiken.

A Fully Automated Object Extraction System for the World Wide Web a paper by David Buttler, Ling Liu and Calton Pu, Georgia Tech.

Jianlin Cheng Computer Science Department & Informatics Institute

1 A theory-based decision heuristic for DPLL(T) Dan Goldwasser Ofer Strichman Shai Fine Haifa university TechnionIBM-HRL.

1 Integration of Background Modeling and Object Tracking Yu-Ting Chen, Chu-Song Chen, Yi-Ping Hung IEEE ICME, 2006.

Hybrid Protein Model Quality Assessment Jianlin Cheng Computer Science Department & Informatics Institute University of Missouri, Columbia, MO, USA.

(c) 2007 Mauro Pezzè & Michal Young Ch 10, slide 1 Functional testing.

SCIENTIFIC NOTATION Scientific Notation should be used whenever a situation calls for the use of very large or very small numbers, such as distances in.

Multiple Sequence Alignments

Learning at Low False Positive Rate Scott Wen-tau Yih Joshua Goodman Learning for Messaging and Adversarial Problems Microsoft Research Geoff Hulten Microsoft.

How Search Engines Work. Any ideas? Building an index Dan taylor Flickr Creative Commons.

Absolute Value Equalities and Inequalities Absolute value: The distance from zero on the number line. Example: The absolute value of 7, written as |7|,

Modelling, comparison, and analysis of proteomes Ram Samudrala University of Washington.

Modelling binding site with 3DLigandSite Mark Wass

Modelling proteomes An integrated computational framework for systems biology research Ram Samudrala University of Washington How does the genome of an.

COMPUTATIONAL VACCINE DESIGN RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How can we design vaccines based on conformational epitopes and.

Searching for Extremes Among Distributed Data Sources with Optimal Probing Zhenyu (Victor) Liu Computer Science Department, UCLA.

Modelling Genome Structure and Function Ram Samudrala University of Washington.

Pseudocode AStar. Heuristics - how to determine what's a good next step? As mentioned already, the A* algorithm depends on evaluating the best next step.

Jianlin Jack Cheng Computer Science Department University of Missouri, Columbia, USA Mexico, 2014.

Heuristics in Search-Space CSE 574 April 11, 2003 Dan Weld.

An Integrated Computational Framework for Systems Biology Ram Samudrala University of Washington How does the genome of an organism specify its behaviour.

INTERACTOMICS RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON NIH DIRECTOR’S PIONEER AWARD 2010 How does the genome of an organism specify its.

Domains or not domains? ShuoYong Shi, Indraneel Majumdar and Nick V. Grishin Howard Hughes Medical Institute, Department of Biochemistry, University of.

Computational engineering of bionanostructures Ram Samudrala University of Washington How can we analyse, design, & engineer peptides capable of specific.

Modelling protein tertiary structure Ram Samudrala University of Washington.

Schreiber, Yevgeny. Value-Ordering Heuristics: Search Performance vs. Solution Diversity. In: D. Cohen (Ed.) CP 2010, LNCS 6308, pp Springer-

THERAPUETIC DISCOVERY BY MODELLING INTERACTOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How does the genome of an organism specify its.

“Isolating Failure Causes through Test Case Generation “ Jeremias Rößler Gordon Fraser Andreas Zeller Alessandro Orso Presented by John-Paul Ore.

COMPUTATIONAL ENGINEERING OF BIONANOSTRUCTURES

MODELLING INTERACTOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How does the genome of an organism specify its behaviour and characteristics?

Modelling proteomes Ram Samudrala Department of Microbiology How does the genome of an organism specify its behaviour and characteristics?

Testing. Have you contacted your project members lately?

COMPUTATIONAL BIOLOGY IN DRUG DISCOVERY RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How can we computationally screen compounds against.

MODELLING PROTEOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How does the genome of an organism specify its behaviour and characteristics?

Automated Planning and Decision Making Prof. Ronen Brafman Automated Planning and Decision Making Fully Observable MDP.

Modelling proteomes Ram Samudrala University of Washington How does the genome of an organism specify its behaviour and characteristics?

MODELLING INTERACTOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How does the genome of an organism specify its behaviour and characteristics?

SHOTGUN STRUCTURAL PROTEOMICS RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON Given a heterogeneous mixture of proteins, how can we determine.

RAPPER Nick Furnham Blundell Group – Department of Biochemistry Cambridge University UK

Objective: To find the opposite and the absolute value of an integer.

Modelling proteomes Ram Samudrala University of Washington.

Modelling proteomes: Application to understanding HIV disease progression Ram Samudrala Department of Microbiology University of Washington How does the.

Modelling genome structure and function Ram Samudrala University of Washington.

Modelling Genome Structure and Function Ram Samudrala University of Washington.

Modelling proteomes Ram Samudrala University of Washington How does the genome of an organism specify its behaviour and characteristics?

MODELLING PROTEOMES RAM SAMUDRALA ASSOCIATE PROFESSOR UNIVERSITY OF WASHINGTON How does the genome of an organism specify its behaviour and characteristics?

FM Model Assessment: Old scores and New Combinations ShuoYong Shi Nick Grishin Lab.

How does the genome of an organism

University of Washington

MODELLING INTERACTOMES

Modelling the rice proteome

MODELLING INTERACTOMES

University of Washington

Matt Menke, Tufts Bonnie Berger, MIT Lenore Cowen, Tufts

Assessment of CASP11 Contact-Assisted Predictions

How does the genome of an organism

Rosetta: De Novo determination of protein structure

University of Washington

Atomic-Level Protein Structure Refinement Using Fragment-Guided Molecular Dynamics Conformation Sampling Jian Zhang, Yu Liang, Yang Zhang Structure

CS639: Data Management for Data Science

Presentation transcript:

Consensus RAPDF rTAD Refinement Successes & Failures Jeremy Horst Ram Samudrala’s CompBio Group University of Washington

Derive interatomic distances Nonredundant structure set Atom type Distance bin Bayesian probabilities Consensus RAPDF rTAD Refinement J.Horst & R.Samudrala

2.11  1.46 aRMSD 0.87  0.91 GDT-TS J.Horst & R.Samudrala Consensus RAPDF rTAD Refinement - RESULTS TR  3.51 aaRMSD 0.61  0.68 GDT-TS TR464

Consensus RAPDF rTAD Refinement – SIDE CHAIN PACKING J.Horst & R.Samudrala

CASP8R Perks + no focus on specific regions; automated + never much worse (454) CASP8 FM targets Perks + absolute best on 5/96 targets (407,409,414,455,510) + always better than 2 nd best initial model (on ~all servers)

Needs - Larger loop search space (looser/less constraints) - De-constrain problem areas (Seq vs. Str entropy) - Functional sites (?functional refinement target?) Advice  Benchmark on past CASPs(in papers)  Do not avoid hard targets(show limits)  Expensive methods should be iteratable  Combine Methods  Focus physics based methods here.  We need a new way to move atoms (according to Nicolay Grishin)  Are heuristics and regression / SVM okay?

Acknowledgements Folks who wrote code Ram Samudrala Tianyun Liu Charles Mader Ling-Hong Hung Idea bouncers ++ Michal Guerquin Brady Bernard Weerayuth Kittichotirat Stewart Moughon