The document discusses function prediction for unknown proteins. It begins with an overview of common methods for function prediction, including sequence and structure similarity, domains and motifs, gene expression, and interactions. It then uses a protein called Msa as a case study, analyzing it with various tools and finding evidence it may function as a signal transducer in bacterial response to environment. Finally, it briefly discusses another protein M46 and challenges in evaluating prediction accuracy.
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.
Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein.
Yeast two-hybrid is based on the reconstitution of a functional transcription factor (TF) when two proteins or polypeptides of interest interact. Upon interaction between the bait and the prey, the DBD and AD are brought in close proximity and a functional TF is reconstituted upstream of the reporter gene.
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
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.
Homology modeling, also known as comparative modeling of protein, refers to constructing an atomic-resolution model of the "target" protein from its amino acid sequence and an experimental three-dimensional structure of a related homologous protein.
Yeast two-hybrid is based on the reconstitution of a functional transcription factor (TF) when two proteins or polypeptides of interest interact. Upon interaction between the bait and the prey, the DBD and AD are brought in close proximity and a functional TF is reconstituted upstream of the reporter gene.
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
DNA Protein interaction occur when a protein binds a molecule of DNA, often to regulate the biological function of DNA, usually the expression of a gene. DNA Protein interactions play very vital roles in any living cell. It controls various cellular processes which are very essential for living beings, viz. replication, transcription, recombination, DNA repair etc. There are several types of proteins found in a cell.Direct recognition occurs when the amino acid side chains of a protein interact with specific DNA bases.
Most protein-DNA interactions are mediated by direct physical interaction (hydrogen bonding or hydrophobic interactions) between the protein and the DNA base pairs.
DNA-binding proteins can be identified by many experimental techniques such as chromatin immunoprecipitation on microarrays, X-ray crystallography and nuclear magnetic resonance (NMR).
A detail ppt about Genome organization with focus on all levels of organization. Most recent research and findings about CT is also added in this ppt. Detail account of 30nm fiber and its ultra structure and types is also included.
The first genome to be sequenced was that of Haemophilus influenzae in 1995.
The E. coli genome was completely sequenced in 1997.
Yeast (Saccharomyces cerevisiae) (12.8 x 106 bp) and worm (Caenorhabditis elegans) genomes were the first eukaryotic genomes to be sequenced in 1999.
Genomes of Drosophila melanogaster and Arabidopsis thaliana were sequenced in 2000.
DNA SEQUENCING METHODS AND STRATEGIES FOR GENOME SEQUENCINGPuneet Kulyana
This presentation will give you a brief idea about the various DNA sequencing methods and various strategies used for genome sequencing and much more vital information related to gene expression and analysis
DNA Protein interaction occur when a protein binds a molecule of DNA, often to regulate the biological function of DNA, usually the expression of a gene. DNA Protein interactions play very vital roles in any living cell. It controls various cellular processes which are very essential for living beings, viz. replication, transcription, recombination, DNA repair etc. There are several types of proteins found in a cell.Direct recognition occurs when the amino acid side chains of a protein interact with specific DNA bases.
Most protein-DNA interactions are mediated by direct physical interaction (hydrogen bonding or hydrophobic interactions) between the protein and the DNA base pairs.
DNA-binding proteins can be identified by many experimental techniques such as chromatin immunoprecipitation on microarrays, X-ray crystallography and nuclear magnetic resonance (NMR).
A detail ppt about Genome organization with focus on all levels of organization. Most recent research and findings about CT is also added in this ppt. Detail account of 30nm fiber and its ultra structure and types is also included.
The first genome to be sequenced was that of Haemophilus influenzae in 1995.
The E. coli genome was completely sequenced in 1997.
Yeast (Saccharomyces cerevisiae) (12.8 x 106 bp) and worm (Caenorhabditis elegans) genomes were the first eukaryotic genomes to be sequenced in 1999.
Genomes of Drosophila melanogaster and Arabidopsis thaliana were sequenced in 2000.
DNA SEQUENCING METHODS AND STRATEGIES FOR GENOME SEQUENCINGPuneet Kulyana
This presentation will give you a brief idea about the various DNA sequencing methods and various strategies used for genome sequencing and much more vital information related to gene expression and analysis
Creation, curation and analysis of RNA and Protein alignments with JalviewJim Procter
Talk given at the Scottish Phylogeny Discussion Group on Feb 18th, 2013 at the James Hutton Institute in Invergowrie, Scotland, UK. It reviews the biological sequence and alignment data visualization.
Full summary and blog post at:
http://www.jalview.org/Community/Community-news/Jalview-and-2013-Google-Summer-of-Code-at-the-Scottish-Phylogeny-Discussion
PomBase conventions for improving annotation depth, breadth, consistency and ...Valerie Wood
PomBase uses a combination of annotation conventions and QC mechanisms. In addition to identifying annotation inconsistencies and errors, these combined methods improve information content, annotation coverage, depth or specificity and redundancy.
Fly chromatin dynamics using bidirectional hidden markov modelSanju K. Sinha
Analysis of various Chromatin states like Promoter, Enhancer, early gene etc using a HMM model(BDHMM).
This HMM model can include the specific trait of a state, "Direction", which makes this HMM special and could help us find interesting discoveries.
Here, We have developed a simple computational model to find unstable transcription via Contiguous States combination. :)
Webinar about JASPAR BioPython module and MANTA.amathelier
In early 2014 we upgraded JASPAR, the largest open-access, manually curated, database storing transcription factor (TF) binding profiles (PMID:24194598), and are in the process of preparing the 2016 release. A new BioPython module dedicated to accessing and using TF binding profiles stored in JASPAR is available, which we will introduce in the first portion of the webinar.
In the second part of the webinar, we will introduce the MANTA (Mongodb for the ANalysis of Tfbs Alteration) database we used for the analysis of cis-regulatory somatic mutations in B-cell lymphomas (PMID:25903198). The database stores positions of predicted TFBSs in ChIP-seq data using JASPAR TF binding profiles. We will describe the database and how to access and use it.
Introduction
Cre-lox recombination
Cre-lox system- Cre recombinase , loxP site
FLP-FRT recombination
FLP-FRT system- FLP recombinase , FRT site
Mechanism of Cre-lox and FLP-FRT recombination
Binding
Synapsis , cleavage and strand exchange
Three type of arrangement
Inversion
Translocation/ Insersion
Deletion
Application of Cre-lox and FLP-FRT recombination
Disadvantage of FLP-FRT
Advantage and disadvantage of Cre-lox
Conclusion
References
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
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.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
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.
Richard's entangled aventures in wonderlandRichard 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.
4. § The function prediction problem
§ Methods and approaches
§ MSA case study
§ M46 : a different direction
§ How good are the predictions…?
Outline
21.
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane
22. § Physicochemical properties
§ Amino acid scale representation
• GRAVY (Grand Average of Hydropathy)
— Msa - 1.021 (Hydrophobic)
Lasergene
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic..?
The next step…?
27. +1
0
+5
-1
Von Heijne, J Mol Biol. 225(2): 487-494. 1992
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic/insoluble/non-enzyme
Signal peptide 1-20
TM1, TM2, TM3 – N-inside ✔
N
N
N
N
C
C
C
C
Positive-Inside rule and
Charge bias
28. http://pbil.ibcp.fr/htm/index.php
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic/insoluble/non-enzyme
Signal peptide 1-20
TM1, TM2, TM3 – N-inside ✔
Three Transmembrane HELIX
Network Protein Sequence
Analysis
30. PreATP-‐grasp
domain
in
Msa.
§ Structural
Classifica:on
of
Proteins
(SCOP)
entry:
d1gsa
1
§ Usually
precedes
the
ATP-‐grasp
domain
and
could
contain
a
substrate-‐binding
func:on.
§ Located
between
the
85th
and
116th
residue.
§ Interes:ngly
this
loca:on
is
predicted
to
be
in
a
cytoplasmic
loop
region
of
Msa
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic/insoluble/non-enzyme
Signal peptide 1-20
TM1, TM2, TM3 – N-inside ✔
Three Transmembrane HELIX
PreATP-grasp domain
Functional sites
33. http://www.ebi.ac.uk/intact/site/index.jsf
Any interactions…?
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic/insoluble/non-enzyme
Signal peptide 1-20
TM1, TM2, TM3 – N-inside ✔
Three Transmembrane HELIX
PreATP-grasp domain
Responds to environment…?
34. http://www.bioinformatics.org/sammd/
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic/insoluble/non-enzyme
Signal peptide 1-20
TM1, TM2, TM3 – N-inside ✔
Three Transmembrane HELIX
PreATP-grasp domain
Responds to environment ✔
Gene expression
38. • Homology Modeling
• No homologous structures in PDB
• FOLD recognition
• Phyre
Structure based predictions
39. § Swiss-Pdb Viewer
– Energy minimization
– PHI/PSI angle
– Loop
§ Structure validation
– Verify3D
Model of Msa
40. Ramachandran plot for the predicted tertiary structure of the
Msa protein pre (A) and post (B) refinement
Quality of the model
41.
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic/insoluble/non-enzyme
Signal peptide 1-20
TM1, TM2, TM3 – N-inside ✔
Three Transmembrane HELIX
PreATP-grasp domain
Responds to environment ✔
Phosphorylation sites 48, 49, 99
Predicted 3D structure of Msa
42. Binding site predictions for the Msa protein. (A) ProFunc
predicted binding site (red); (B) PINUP predicted binding
site (interface in green); (C) Q-SiteFinder predicted binding
site and binding residues (pink)
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic/insoluble/non-enzyme
Signal peptide 1-20
TM1, TM2, TM3 – N-inside ✔
Three Transmembrane HELIX
PreATP-grasp domain
Responds to environment ✔
Phosphorylation sites 48, 49, 99
Binding sites - cytoplasmic region?
Binding sites
43. ProFunc
– “nest” near the putative phosphorylation site (47-50)
– 47-50; predicted outside membrane
– All residues conserved at the “nest”
– “nest” shows features of anion-binding site
– “nest” characteristic functional motifs in ATP or GTP
binding proteins
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic/insoluble/non-enzyme
Signal peptide 1-20
TM1, TM2, TM3 – N-inside ✔
Three Transmembrane HELIX
PreATP-grasp domain
Responds to environment ✔
Phosphorylation sites 48, 49, 99
Binding sites - cytoplasmic region?
Function site (“nest”) – outside..?
Predicted “nest”
44. § Multiple sequence alignment (ClustalW) of Msa protein
sequence from 11 different strains
– 12 variations in strain RF122
§ 1 replacement, 11 substitutions
• 1 substitution in pre-ATP grasp domain
– 7 variations in strain MRSA252
§ 2 replacements, 5 substitutions
• 1 replacement in pre-ATP grasp domain
• Replacement
• Hydrophilic -> Hydrophobic
• Ser -> Gly
• Substitution
• Hydrophilic -> Hydrophilic
• Ser -> Glu
Function motifs: conserved…?
45. – Variation at aa positions 111, 131, 133 common
– None in Phosphorylation sites, signal peptide, “nest”
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic/insoluble/non-enzyme
Signal peptide 1-20
TM1, TM2, TM3 – N-inside ✔
Three Transmembrane HELIX
PreATP-grasp domain
Responds to environment ✔
Phosphorylation sites 48, 49, 99
Binding sites - cytoplasmic region?
Function site (“nest”) – outside..?
Functional sites highly conserved..?
Function motifs: conserved…?
46.
List of Observations
First 40 – pattern.. (DUF?)
Transporter/efflux/membrane..?
Hydrophobic/insoluble/non-enzyme
Signal peptide 1-20
TM1, TM2, TM3 – N-inside ✔
Three Transmembrane HELIX
PreATP-grasp domain
Responds to environment ✔
Phosphorylation sites 48, 49, 99
Binding sites - cytoplasmic region?
Function site (“nest”) – outside..?
Functional sites highly conserved..?
Msa – a putative
signal transducer
51. ü The function prediction problem
ü Methods and approaches
ü MSA case study
§ M46 : a different direction
§ How good are the predictions…?
Any questions…?
52. § Mold specific
• Histoplasma capsulatum
• Only in mold not in yeast
M46 – a different direction
54. § Nucleotide binding
– DNA/RNA
– S-S bonds
§ Look for motifs
– Predict motifs, build HMM, search for similar
§ Localization
– Secreted, ER signal, ER modifications
M46 – the clues
55. ü The function prediction problem
ü Methods and approaches
ü MSA case study
ü M46 : a different direction
§ How good are the
predictions…?
56.
57. • Only
good
if
it
would
make
any
biological
sense
• Only
good
if
it
could
be
supported
by
follow
up
experimental
evidence
• No
hits
• Reduce
threshold
-‐
expect
worst
evalues-‐pvalues
• Cut-‐off
value
paradox
(if
<0.05
is
significant,
what
about
0.051…?)
• It
is
OK
to
look
at
hits
with
poor
evalue-‐pvalue
• Cannot
assign
homology,
can
pick
clues
(your
only
hope)
How good are the predictions…?