Uncovering the Landfill Issue in New York State

Addressing the growing landfill problem in New York State, focusing on toxins like greenhouse gases released into the environment and bodies of water. Steps outlined to tackle the issue include problem identification, evidence gathering, and policy analysis.

• New York
• Landfill
• Environmental Issue
• Waste Management

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Uploaded on Feb 19, 2025 | 0 Views

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1. P.G. V B C - 6 0 3 PROTEIN STRUCTURE PREDICTION AND DESIGN- II 02.01.2021

2. KNOWLEDGE BASED APPROACHES Homology Modelling Need homologues of known protein structure Backbone modelling Side chain modelling Fail in absence of homology Threading Based Methods 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

3. TWO APPROACHES

4. PREDICTING PROTEIN 3D STRUCTURE

5. TEMPLATE-BASED STRUCTURE PREDICTION Template identification Query-template alignment Model generation Model evaluation Model refinement Comparative Modeling or homology modeling: if template is easy to identify Fold recognition: If template is hard to identify, it is often called.

6. HOMOLOGY MODELLING

7. INPUT FOR HOMOLOGY MODELING The sequence of a protein with unknown 3D structure, the "target sequence." A 3D template a structure having the highest sequence identity with the target sequence ( >30% sequence identity) A sequence Alignment between the target sequence and the template sequence

8. HOMOLOGY MODELING Based on the two major observations (and some simplifications): The structure of a protein is uniquely defined by its amino acid sequence. Similar sequences adopt similar structures. (Distantly related sequences may still fold into similar structures.)

9. FOLD RECOGNITION = PROTEIN THREADING Which of the known folds is likely to be similar to the (unknown) fold of a new protein when only its amino-acid sequence is known?

10. AB-INITIO FOLDING Predict structure from first principles Requires: A free energy function, sufficiently close to the true potential A method for searching the conformational space Advantages: Works for novel folds Shows that we understand the process Disadvantages: Applicable to short sequences only

11. PROTEIN MODELLING

12. 3D STRUCTURE PREDICTION TOOLS MULTICOM (http://sysbio.rnet.missouri.edu/multicom_toolbox/index.html ) I-TASSER (http://zhang.bioinformatics.ku.edu/I-TASSER/) HHpred (http://protevo.eb.tuebingen.mpg.de/toolkit/index.php?view=hhpred) Robetta (http://robetta.bakerlab.org/) 3D-Jury (http://bioinfo.pl/Meta/) FFAS (http://ffas.ljcrf.edu/ffas-cgi/cgi/ffas.pl) Pcons (http://pcons.net/) Sparks (http://phyyz4.med.buffalo.edu/hzhou/anonymous-fold-sp3.html) FUGUE (http://www-cryst.bioc.cam.ac.uk/%7Efugue/prfsearch.html) FOLDpro (http://mine5.ics.uci.edu:1026/foldpro.html) SAM (http://www.cse.ucsc.edu/research/compbio/sam.html) Phyre2 (http://www.sbg.bio.ic.ac.uk/~phyre2/) 3D-PSSM (http://www.sbg.bio.ic.ac.uk/3dpssm/) mGenThreader (http://bioinf.cs.ucl.ac.uk/psipred/psiform.html)

13. AUTOMATED WEB-BASED HOMOLOGY MODELLING SWISS Model : http://www.expasy.org/swissmod/SWISS-MODEL.html WHAT IF : http://www.cmbi.kun.nl/swift/servers/ The CPHModels Server : http://www.cbs.dtu.dk/services/CPHmodels/ 3D Jigsaw : http://www.bmm.icnet.uk/~3djigsaw/ SDSC1 : http://cl.sdsc.edu/hm.html EsyPred3D : http://www.fundp.ac.be/urbm/bioinfo/esypred/

14. P RO T E I N M O D E L Q UA L I T Y A S S E S S M E N T http://sysbio.rnet.missouri.edu/apollo/ https://servicesn.mbi.ucla.edu/SAVES/