Verify3D is a web-based tool that evaluates the correctness of a protein structure model based on its 3D structural profile. It works by assigning structural classes to residues based on their location and environment, then comparing the results to profiles of good protein structures. The tool generates plots representing the average and raw scores for each residue, with higher average scores across residues indicating a more correct model structure. Verify3D is useful for protein structure prediction as it can verify models based on how well their 3D profiles match their amino acid sequences.
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:
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:
Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its folding and its secondary and tertiary structure from its primary structure. Structure prediction is fundamentally different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes).
A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. In many cases, the input set of query sequences are assumed to have an evolutionary relationship by which they share a linkage and are descended from a common ancestor. From the resulting MSA, sequence homology can be inferred and phylogenetic analysis can be conducted to assess the sequences' shared evolutionary origins.
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 (the "template"). Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence has been shown that protein structures are more conserved than protein sequences amongst homologues, but sequences falling below a 20% sequence identity can have very different structure.
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.
Multiple Alignment Sequence using Clustal Omega/ Shumaila RiazShumailaRiaz6
Alignment of three or more biological sequences of Protein, DNA, RNA of similar length
Clustal Omega is tool for analyzing the Multiple sequence alignments of proteins
Validation is the process of checking that your model is consistent with stereochemical standards i.e., validation is the process of evaluating reliability
In this presentation various aspects of validation are discussed
Ab Initio Protein Structure Prediction is a method to determine the tertiary structure of protein in the absence of experimentally solved structure of a similar/homologous protein. This method builds protein structure guided by energy function.
I had prepared this presentation for an internal project during my masters degree course.
Protein structure determination of insulin of zebra fish (Danio rerio) by hom...IOSR Journals
The protein sequence of insulin of zebra fish is obtained from UniProt. Due to lack of their structure, structure prediction is necessary, because the structure of protein plays an important role in their function. Our work is based on the production of two protein structure, from the same sequence, by computational approach and finally validates these generated structures. In this work two different widely acceptable online web tool are used for generating structure from the protein sequences of insulin of zebra fish. These are Swiss Model web server and ESyPred3D web server. After getting structure from this two web tool, the structures are passed by a series of quality tests. ProQ web software is used for checking quality of these generated structures. 3d-ss web tool is used for superimposition between two generated structures. It can compare between two structures. The Ramachandran plot is calculated by using VegaZZ software. CASTp (Computer Atlas of Surface Topology of protein) is a web tool, used to predict active sides with their respective volume and area. Finally ProFunc tool is used for analysis of two structures
Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its folding and its secondary and tertiary structure from its primary structure. Structure prediction is fundamentally different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes).
A multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences, generally protein, DNA, or RNA. In many cases, the input set of query sequences are assumed to have an evolutionary relationship by which they share a linkage and are descended from a common ancestor. From the resulting MSA, sequence homology can be inferred and phylogenetic analysis can be conducted to assess the sequences' shared evolutionary origins.
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 (the "template"). Homology modeling relies on the identification of one or more known protein structures likely to resemble the structure of the query sequence, and on the production of an alignment that maps residues in the query sequence to residues in the template sequence has been shown that protein structures are more conserved than protein sequences amongst homologues, but sequences falling below a 20% sequence identity can have very different structure.
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.
Multiple Alignment Sequence using Clustal Omega/ Shumaila RiazShumailaRiaz6
Alignment of three or more biological sequences of Protein, DNA, RNA of similar length
Clustal Omega is tool for analyzing the Multiple sequence alignments of proteins
Validation is the process of checking that your model is consistent with stereochemical standards i.e., validation is the process of evaluating reliability
In this presentation various aspects of validation are discussed
Ab Initio Protein Structure Prediction is a method to determine the tertiary structure of protein in the absence of experimentally solved structure of a similar/homologous protein. This method builds protein structure guided by energy function.
I had prepared this presentation for an internal project during my masters degree course.
Protein structure determination of insulin of zebra fish (Danio rerio) by hom...IOSR Journals
The protein sequence of insulin of zebra fish is obtained from UniProt. Due to lack of their structure, structure prediction is necessary, because the structure of protein plays an important role in their function. Our work is based on the production of two protein structure, from the same sequence, by computational approach and finally validates these generated structures. In this work two different widely acceptable online web tool are used for generating structure from the protein sequences of insulin of zebra fish. These are Swiss Model web server and ESyPred3D web server. After getting structure from this two web tool, the structures are passed by a series of quality tests. ProQ web software is used for checking quality of these generated structures. 3d-ss web tool is used for superimposition between two generated structures. It can compare between two structures. The Ramachandran plot is calculated by using VegaZZ software. CASTp (Computer Atlas of Surface Topology of protein) is a web tool, used to predict active sides with their respective volume and area. Finally ProFunc tool is used for analysis of two structures
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is an open access international journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Multibiometric Secure Index Value Code Generation for Authentication and Retr...ijsrd.com
The use of multiple biometric sources for human recognition, referred to as multibiometrics, mitigates some of the limitations of unimodal biometric systems by increasing recognition accuracy, improving population coverage, imparting fault-tolerance, and enhancing security. In a biometric identification system, the identity corresponding to the input data (probe) is typically determined by comparing it against the templates of all identities in a database (gallery). An alternative e approach is to limit the number of identities against which matching is performed based on criteria that are fast to evaluate. We propose a method for generating fixed-length codes for indexing biometric databases. An index code is constructed by computing match scores between a biometric image and a fixed set of reference images. Candidate identities are retrieved based on the similarity between the index code of the probe image and those of the identities in the database. The number of multibiometric systems deployed on a national scale is increasing and the sizes of the underlying databases are growing. These databases are used extensively, thereby requiring efficient ways for searching and retrieving relevant identities. Searching a biometric database for an identity is usually done by comparing the probe image against every enrolled identity in the database and generating a ranked list of candidate identities. Depending on the nature of the matching algorithm, the matching speed in some systems can be slow. The proposed technique can be easily extended to retrieve pertinent identities from multimodal databases. Experiments on a chimeric face and fingerprint bimodal database resulted in an 84% average reduction in the search space at a hit rate of 100%. These results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification. New representation schemes that allow for faster search and, therefore, shorter response time are needed.
PROGRAM TEST DATA GENERATION FOR BRANCH COVERAGE WITH GENETIC ALGORITHM: COMP...cscpconf
In search based test data generation, the problem of test data generation is reduced to that of
function minimization or maximization.Traditionally, for branch testing, the problem of test data
generation has been formulated as a minimization problem. In this paper we define an alternate
maximization formulation and experimentally compare it with the minimization formulation. We
use a genetic algorithm as the search technique and in addition to the usual genetic algorithm
operators we also employ the path prefix strategy as a branch ordering strategy and memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the path prefix strategy, memory and elitism.
Homology Modelling through modeller and its analysis using Ramachandran Plot
Modeller practical. Full tutorial created by Zarlish Attique
https://salilab.org/modeller/
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Comparative analysis of dynamic programming algorithms to find similarity in ...eSAT Journals
Abstract There exist many computational methods for finding similarity in gene sequence, finding suitable methods that gives optimal similarity is difficult task. Objective of this project is to find an appropriate method to compute similarity in gene/protein sequence, both within the families and across the families. Many dynamic programming algorithms like Levenshtein edit distance; Longest Common Subsequence and Smith-waterman have used dynamic programming approach to find similarities between two sequences. But none of the method mentioned above have used real benchmark data sets. They have only used dynamic programming algorithms for synthetic data. We proposed a new method to compute similarity. The performance of the proposed algorithm is evaluated using number of data sets from various families, and similarity value is calculated both within the family and across the families. A comparative analysis and time complexity of the proposed method reveal that Smith-waterman approach is appropriate method when gene/protein sequence belongs to same family and Longest Common Subsequence is best suited when sequence belong to two different families. Keywords - Bioinformatics, Gene, Gene Sequencing, Edit distance, String Similarity.
How to Make a Field invisible in Odoo 17Celine George
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.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
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.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
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.
3. Verify3D determines the
compatibility of an atomic
model (3D) with its own amino
acid sequence (1D) by
assigning a structural class
based on its location and
environment (alpha, beta,
loop, polar, nonpolar) and
comparing the results to good
structures.
Introduction
4. “VERIFY3D (Assessment of Protein Models
With Three-Dimensional Profiles) tool is
utilized to evaluate the correctness of a
protein model by its 3D profile.”
5. Objectives
Available on
Verify3D is a web
based tool that can
accessed through any
web browser.
Research
Outcome
The correctness of a
protein model can be
verified by its 3D
profile.
Analyzes
Verify3D is utilized
for protein model
evaluation by its 3D
Profile.
6. Input
The server requires
to upload the file
containing the
target protein
structure in PDB
format.
The server results in
plots depending
upon the chains
present in the
query protein.
Output
Input & Output
7. Plotting
Generates number
of plots depends
upon the number of
chains
Verify3D Test
Good predicted structure
of a protein must have
80% or more average
score.
Evaluation
Utilized for protein
model evaluation by its
3D Profile.
Plots
Greater the number of
chains present in the
protein model, greater
will be the number of
plots
Correctness
Correctness of a
protein model can be
verified by its 3D
profile
Methodology
3D Profiles
3D profile of a protein
structure can be used to
score the compatibility of
the 3D structure
8. This is how Verify3D evaluates the
protein models, generating plot
which represents averaged score
and raw score for each residue in
the predicted or experimentally
determined model.
9. Dots Interpretation
1st Green Raw Score (Score for each amino
acid residue)
2nd Blue Average Score (Score for overall
residues)
Results Analysis
Table represents the interpretation of plots:
10. Verify3D utilizes the three-
dimensional profiles
computed from correct
protein structures match
their own sequences with
high scores for protein
model evaluation.
Conclusion
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