Protein structures can be aligned and compared using computational methods like structural alignment. Structural alignment finds the optimal rotation and translation that superimposes one protein structure onto another to maximize structural similarity. This is done by treating protein structures as sets of points defined by atom coordinates and finding the transformation that minimizes the root-mean-square deviation between corresponding atoms in the two structures. While useful, structural alignment has limitations like not accounting for differences in amino acid attributes and treating all atoms equally.
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:
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
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
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:
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
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time.
A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results.
Chou-Fasman algorithm is an empirical algorithm developed for the prediction of protein secondary structure
Protein structure classification/domain prediction: SCOP and CATH (Bioinforma...SELF-EXPLANATORY
This pdf is about the protein structure classification/domain prediction: SCOP and CATH (Bioinformatics).
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Genome annotation, NGS sequence data, decoding sequence information, The genome contains all the biological information required to build and maintain any given living organism.
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time.
A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results.
Chou-Fasman algorithm is an empirical algorithm developed for the prediction of protein secondary structure
Protein structure classification/domain prediction: SCOP and CATH (Bioinforma...SELF-EXPLANATORY
This pdf is about the protein structure classification/domain prediction: SCOP and CATH (Bioinformatics).
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Genome annotation, NGS sequence data, decoding sequence information, The genome contains all the biological information required to build and maintain any given living organism.
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Deep Learning Meets Biology: How Does a Protein Helix Know Where to Start and...Melissa Moody
UVA Data Science Institute Master of Science in Data Science students Sean Mullane, Ruoyan Chen and Sri Vaishnavi Vemulapalli were motivated to apply data science tools and techniques to the problem, and see if protein structures can be quantitatively described, compared and otherwise analyzed in a more robust, efficient and automated manner. Potential applications include more effectively designed drugs to inhibit disease-related proteins, or even newly engineered ones.
The researchers received the award for Best Paper in the Data Science for Health category at the 2019 Systems & Information Design Symposium (SIEDS) meeting. Their project, "Machine Learning for Classification of Protein Helix Capping Motifs," focused on small segments of a protein called secondary structural elements. These structural elements are the basic molecular-scale building blocks that all proteins—and therefore life—build upon.
A QSAR is a mathematical relationship between a biological activity of a molecular system and its geometric and chemical characteristics.
QSAR attempts to find consistent relationship between biological activity and molecular properties, so that these “rules” can be used to evaluate the activity of new compounds.
Drug discovery take years to decade for discovering a new drug and very costly
Effort to cut down the research timeline and cost by reducing wet-lab experiment use computer modeling
Others have done the work. Some have used the work. I have spoken only on behalf of their behalf.
Comparative sequence studies of the repeat elements in diverse insect species can provide useful information on how to make use of them for developing abundant markers that can be used in those species;
$ At the moment, a total of 8 species are in genome assembly stages and another 35 are in progress for genome sequencing;
$ Different molecular marker systems in the field of entomology are expected to provide new directions to study insect genomes in an unprecedented way in the years to come
Olfaction is very important for us and also for other animals.
Dog’s sense of smell is 1000 times more than humans. People use dog’s keen sense of smell in many ways---
Govt. agencies use specially trained dogs in search and rescue missio
Detection of narcotics.
Detection of forensic cadaver material.
Due to lack of smell the following disorders may be seen---
Anosmia : lack of ability to smell
Hyposmia- decreased ability to smell
Phantosmia- [“hallucinated smell”] often unpleasant in nature
Dysosmia- things smell differently than they should.
Hyperosmia- an abnormally acute sense of smell
Some times olfaction serve as marker for Perkinson’s diseases. Some illness can be diagnosed by their associated smell( e.g. acetone and diabetes). So smell therapy and clinical use of odour is an area for future.
Synthesis and Actions of Juvenile Hormones In Insect Development (MS Power…Saramita De Chakravarti
A morphogenetic hormone.
Has multiple functions and a primary role of JH in insect development is to modulate ecdysone action.
Maintains the current commitment of the tissues and cells whereas ecdysone causes both predifferentiative and differentiative cellular events that are necessary for the moult.
When JH is present, a moult to a larval stage ensures.
If JH is absent at the onset of the moult, morphogenesis occurs.
Further studies and researches are still going on that can elucidate new
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
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Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
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It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
3. 3
Why Proteins Structure ?Why Proteins Structure ?
Proteins are fundamental components of all living
cells, performing a variety of biological tasks.
Each protein has a particular 3D structure that
determines its function.
Protein structure is more conserved than protein
sequence, and more closely related to function.
4. 4
Protein core - usually conserved.
Protein loops - variable regions
Hydrophobic core
Surface loops
Protein Structure
8. 8
• Two conserved sequences similar structures
• Two similar structures conserved sequences?
Structure – Sequence RelationshipsStructure – Sequence Relationships
There are cases of proteins with the same
structure but no clear sequence similarity.
9. 9
Principles of Protein Structure
•Today's proteins reflect millions of years of
evolution.
•3D structure is better conserved than sequence
during evolution.
•Similarities among sequences or among
structures may reveal information about shared
biological functions of a protein family.
10. 10
The Levinthal paradox
Assume a protein is comprised of 100 AAs and that
each AA can take up 10 different conformations.
Altogether we get:10100
(i.e. google( conformations.
If each conformation were sampled in the shortest
possible time (time of a molecular vibration ~ 10-13
s(
it would take an astronomical amount of time (~1077
years( to sample all possible conformations, in order
to find the Native State.
11. 11
The Levinthal paradox
Luckily, nature works out with these sorts of
numbers and the correct conformation of a protein
is reached within seconds.
12. 12
How is the 3D Structure Determined ?How is the 3D Structure Determined ?
Experimental methods (Best approach(:Experimental methods (Best approach(:
• X-rays crystallography.
• NMR.
• Others (e.g., neutron diffraction(.
13. 13
How is the 3D Structure Determined ?How is the 3D Structure Determined ?
In-silico methodsIn-silico methods
Ab-initio structure prediction given only the
sequence as input - not always successful.
14. 14
A note on ab-initio predictions: The
current state is that “failure can no
longer be guaranteed”…
15. 15
A note on ab-initio secondary structure
prediction: Success ~70%.
16. 16
How is the 3D Structure Determined ?How is the 3D Structure Determined ?
In-silico methodsIn-silico methods
Threading = Sequence-structure alignment. The
idea is to search for a structure and sequence in
existing databases of 3D structure, and use
similarity of sequences + information on the
structures to find best predicted structures.
17. 17
Comments
• X-ray crystallography is the most widely
used method.
• Quaternary structure of large proteins
(ribosomes, virus particles, etc) can be
determined by electron microscopes
(cryoEM).
19. 19
PDB: Protein Data Bank
• Holds 3D models of biological macromolecules
(protein, RNA, DNA).
• All data are available to the public.
• Obtained by X-Ray crystallography (84%) or NMR
spectroscopy (16%).
• Submitted by biologists and biochemists from
around the world.
20. 20
PDB: Protein Data Bank
•Founded in 1971 by Brookhaven National
Laboratory, New York.
•Transferred to the Research Collaboratory
for Structural Bioinformatics (RCSB) in 1998.
•Currently it holds > 49,426 released
structures.
61695
21. 21
PDB - model
• A model defines the 3D positions of atoms in
one or more molecules.
• There are models of proteins, protein
complexes, proteins and DNA, protein
segments, etc …
• The models also include the positions of ligand
molecules, solvent molecules, metal ions, etc.
24. 24
The PDB file – textThe PDB file – text formatformat
ATOM:
Usually protein
or DNA
HETATM:
Usually Ligand,
ion, water
chain
Residue
identity
Residue
number
Atom
number
Atom
identity
The coordinates
for each residue in
the structure
X Y Z
26. 26
Why structural alignment?
• Structural similarity can point to remote
evolutionary relationship
• Shared structural motifs among proteins
suggest similar biological function
• Getting insight into sequence-structure
mapping (e.g., which parts of the protein
structure are conserved among related
organisms).
27. 27
As in any alignment problem, we can
search for GLOBAL ALIGNMENT or for
LOCAL ALIGNMENT
29. 29
What is the best transformation thatWhat is the best transformation that
superimposes the unicorn on the lion?superimposes the unicorn on the lion?
37. 37
We represent a protein as a geometric
object in the plane.
The object consists of points represented
by coordinates (x, y, z).
Thr
Lys
Met Gly
Glu
Ala
38. 38
The aim:
Given two proteins
Find the transformation that produces
the best Superimposition of one protein
onto the other
42. 42
Simple case – two closely related proteins with the
same number of amino acids.
Question:
how do we asses the
quality of the
transformation?
+
43. 43
Scoring the Alignment
Two point sets: A={ai} i=1…n
B={bj} j=1…m
• Pairwise Correspondence:
(ak1,bt1) (ak2,bt2)… (akN,btN)
(1) Bottleneck max ||aki – bti||
(2) RMSD (Root Mean Square Distance)
Sqrt( Σ||aki – bti||2
/N)
44. 44
RMSD – Root Mean Square
Deviation
Given two sets of 3-D points :
P={pi}, Q={qi} , i=1,…,n;
rmsd(P,Q) = √ Σ i|pi - qi |2
/n
Find a 3-D transformation T*
such that:
rmsd( T*
(P), Q ) = minT √ Σ i|T(pi) - qi |2
/n
Find the highest number of atoms aligned with the lowest RMSD
45. 45
Pitfalls of RMSD
• all atoms are treated equally
(residues on the surface have a higher degree of
freedom than those in the core)
• best alignment does not always mean minimal
RMSD
• does not take into account the attributes of the
amino acids
48. 48
Does the fact that all proteins have alpha-
helix indicates that they are all evolutionary
related?
No. Alpha helices reflect physical constraints,
as do beta sheets.
For structures – it is difficult sometimes
to separate convergent evolution from
evolutionary relatedness.
49. 49
Structural genomics: solve or predict 3D of
all proteins of a given organism (X-ray, NMR,
and homology modelling).
Unlike traditional structural biology, 3D is
often solved before anything is known on
the protein in question. A new challenge
emerged: predict a protein’s function from
its 3D structure.
50. 50
CASP: a competition for predicting 3D
structures.
Instead of running to publish a new 3D
structure, the AA sequence is published and
each group is invited to give their
predictions.
52. 52
Homology modeling: predicting the
structure from a closely related known
structure.
This can be important for example to
predict how a mutation influences the
structure