protein structure prediction methods. homology modelling, fold recognition, threading, ab initio methods. in short and easy form slides. after one time read you can easily understand methods for protein structure prediction.
Prediction of the three dimensional structure of a given protein sequence i.e. target protein from the amino acid sequence of a homologous (template) protein for which an X-ray or NMR structure is available based on an alignment to one or more known protein structures
The Protein Data Bank (PDB) is a database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. This presentation deals with what, why, how, where and who of PDB. In this presentation we have also included briefing about various file formats available in PDB with emphasis on PDB file format
Prediction of the three dimensional structure of a given protein sequence i.e. target protein from the amino acid sequence of a homologous (template) protein for which an X-ray or NMR structure is available based on an alignment to one or more known protein structures
The Protein Data Bank (PDB) is a database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. This presentation deals with what, why, how, where and who of PDB. In this presentation we have also included briefing about various file formats available in PDB with emphasis on PDB file format
Scoring system is a set of values for qualifying the set of one residue being substituted by another in an alignment.
It is also known as substitution matrix.
Scoring matrix of nucleotide is relatively simple.
A positive value or a high score is given for a match & negative value or a low score is given for a mismatch.
Scoring matrices for amino acids are more complicated because scoring has to reflect the physicochemical properties of amino acid residues.
Secondary Structure Prediction of proteins Vijay Hemmadi
Secondary structure prediction has been around for almost a quarter of a century. The early methods suffered from a lack of data. Predictions were performed on single sequences rather than families of homologous sequences, and there were relatively few known 3D structures from which to derive parameters. Probably the most famous early methods are those of Chou & Fasman, Garnier, Osguthorbe & Robson (GOR) and Lim. Although the authors originally claimed quite high accuracies (70-80 %), under careful examination, the methods were shown to be only between 56 and 60% accurate (see Kabsch & Sander, 1984 given below). An early problem in secondary structure prediction had been the inclusion of structures used to derive parameters in the set of structures used to assess the accuracy of the method.
Some good references on the subject:
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
Scoring system is a set of values for qualifying the set of one residue being substituted by another in an alignment.
It is also known as substitution matrix.
Scoring matrix of nucleotide is relatively simple.
A positive value or a high score is given for a match & negative value or a low score is given for a mismatch.
Scoring matrices for amino acids are more complicated because scoring has to reflect the physicochemical properties of amino acid residues.
Secondary Structure Prediction of proteins Vijay Hemmadi
Secondary structure prediction has been around for almost a quarter of a century. The early methods suffered from a lack of data. Predictions were performed on single sequences rather than families of homologous sequences, and there were relatively few known 3D structures from which to derive parameters. Probably the most famous early methods are those of Chou & Fasman, Garnier, Osguthorbe & Robson (GOR) and Lim. Although the authors originally claimed quite high accuracies (70-80 %), under careful examination, the methods were shown to be only between 56 and 60% accurate (see Kabsch & Sander, 1984 given below). An early problem in secondary structure prediction had been the inclusion of structures used to derive parameters in the set of structures used to assess the accuracy of the method.
Some good references on the subject:
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
HERE IN THIS PRESENTATION HY HOMOLOGY MODELING IS EXPLAIN , WITH EXAMPLES OF PROTEIN PRIMARY AND SECONDARY, SHOWING THE IMAGES FORM WHICH MAKES EASY TO UNDERSTAND
Proteins : is made of chain of amino acids ( amino acid= monomers) therefor the protein is polymers .
The proteins are made up of carbon, hydrogen, oxygen, and nitrogen.
Amino acid :
Similar to methods for protein structure prediction (20)
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
3. METHODS FOR PROTEIN STRUCTURE PREDICTION
1. EXPERIMANTAL METHODS
2. COMPUTIONAL METHODS
3
4. Experimental Protein StructureExperimental Protein Structure
DeterminationDetermination
• X-ray crystallographyX-ray crystallography
– most accuratemost accurate
– in vitroin vitro
– needs crystalsneeds crystals
– ~$100-200K per structure~$100-200K per structure
– time consuming and expansive.time consuming and expansive.
• NMRNMR
– fairly accuratefairly accurate
– in vivoin vivo
– no need for crystalsno need for crystals
– limited to very small proteinslimited to very small proteins
– time consuming and hardly .time consuming and hardly .
• Electron-microscopyElectron-microscopy
– imaging technologyimaging technology
– low resolutionlow resolution
– not more observable.not more observable.
4
5. Computational method
• Major Techniques
– Template Modeling
• Homology Modeling
• Threading
• Both are use known protein structure
– Template-Free Modeling
• ab initio Methods
– Physics-Based
– Knowledge-Based
– without use of known protein structure
5
6. Homology Modelling
• also called comparitive modeling.
• predict protein structures based on sequence
homology with known structure.
• Principle:-
• if two proteins share a high enough sequence
similarity,they are likely to have very similar
three dimensional structure.
• modeling server:-modbase,swiss-model etc.
• Fail in absence of homology
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7. Homology Modelling
six steps:-
1.template selection (BLAST and FASTA)
2.sequence alignment (T-coffee and PRALINE)
3.model building (CODA)
(a)backbone model building
(b)loop modeling
4.side chain refinement (SCWRL)
5.model refinement using energy function (GROMOS)
6.model evalution (PROCHECK & WHAT IF)
• Has been observed that even proteins with 30% sequence identity
fold into similar structures
• Does not work for remote homologs (< 30% pairwise identity)
7
8. Homology Modeling: How it works
o Find template
o Align target sequence
with template
o Generate model:
- add loops
- add sidechains
o Refine model
8
9. Threading
• Given:
– sequence of protein 'P 'with unknown structure
– Database of known folds
• Find:
– Most plausible fold for 'P'
– Evaluate quality of such arrangement
• Places the residues of unknown 'P' along the
backbone of a known structure and determines
stability of side chains in that arrangement
9
10. Threading and fold recognition
• predicts the structural fold of unknown protein
sequences by fitting the sequence into a
structural database and selecting the best
fitting fold.
• we can identify structurally similar proteins
even without detectablesequence similarity.
• two algorithms:-
1.pairwise energy based method
2.profile based method
10
11. 1.Pairwise energy based method (threading)
• Searched for a structural fold database to find the best
matching structural fold using energy based criteria.
• Using dynamic programming and heuristic approaches.
• Calculate energy for raw model.
• Lowest energy fold that correspond to the structurally a
group of most compatible fold.
2.profile based method (fold recognition)
• A profile is constructed for related protein structures.
• Generated by superimposition of the structures to
expose corresponding residues.
• Secondary structure type,polarity,hydrophobicity.
• The protein fold to be predicted does not exist in the
fold library,method will fail. 3D-PSSM
http://www.sbg.bio.ic.ac.uk/~3dpssm/
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13. 13
Ab InitioAb Initio Protein Structure PredictionProtein Structure Prediction
• Ab initio protein structure prediction methods build protein 3D
structures from sequence based on physical principles.
• Importance
– The ab initio methods are important even though they are
computationally demanding
– Ab initio methods predict protein structure based on physical models,
they are indispensable complementary methods to Knowledge-based
approach.
Knowledge-based approach would fail in following conditions:
• Structure homologues are not available
• Possible undiscovered new fold exists.
Anfinsen’s theory: Protein native structure corresponds to the
state with the lowest free energy of the protein-solvent system.
14. Rosetta
• web server for protein 3d structure prediction.
• mini threading method.
breaks down the quary sequence into many short segments (3
to 9).
predicts the secondary structure of small segments using
HMMSTR.
Segments with assigned secondary structure are subsequently
assembled into a 3D configuration.
random combination of fragments, a large number of models
are built and their over all energy potential calculated.
conformation with lowest free energy is choosen as the best
model.
14