This document describes a methodology for improving protein loop structure prediction using PyRosetta. The researchers found that introducing transient amino acid mutations via site-directed mutagenesis helped smooth the energy landscape during conformational searching, leading to lower energy and more accurate predicted structures compared to the wild type sequences. While this approach improved results, the predicted structures still differed somewhat from the actual structures. Future work aims to better understand and linearize the relationship between predicted structure accuracy (RMSD) and energy near the native structure.
This is a Powerpoint for basic understanding regarding Molecular dynamics and NAMD simulation to providing basic information, schematic representation, to understanding the mechanism or process of molecular dynamics ( MD), and NAMD simulation brief discussion.
This is a Powerpoint for basic understanding regarding Molecular dynamics and NAMD simulation to providing basic information, schematic representation, to understanding the mechanism or process of molecular dynamics ( MD), and NAMD simulation brief discussion.
Enhancing the Performance of P3HT/Cdse Solar Cells by Optimal Designing of Ac...IOSRJEEE
The present study examined the influence of different condition like as doping , in active layer, on the performance of P3HT/CdSe Solar cells .In this work, we analyzed the best doping for the configuration of P3HT/ CdSe in order to improve the performance of the solar cell. For this aim, we investigated the current density of electrons, the electric field, the short-circuit current and the open-circuit voltage in different doping . The results indicate that when the doping is increased in P3Ht and is decreased in CdSe, the current density of electrons, the electric field, the short-circuit current, and the open-circuit voltage are increased. Finally, we obtained doping of and for electron and hole donor respectively as the best doping for this configuration
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This talk presents an on line decision support system for structural biologists who are interested in performing multiple protein structure comparisons, via multiple methods, in one go.
Amino acid interaction network prediction using multi objective optimizationcsandit
Protein can be represented by amino acid interaction network. This network is a graph whose
vertices are the proteins amino acids and whose edges are the interactions between them. This
interaction network is the first step of proteins three-dimensional structure prediction. In this
paper we present a multi-objective evolutionary algorithm for interaction prediction and ant
colony probabilistic optimization algorithm is used to confirm the interaction.
The title of the presentation is Tomorrow The World. Its is an impersonation of conducting training for a group of people from a company, mine was Petronas.
Always looking forward towards better and more efficient ways to complete the tasks at hand
Strategy planning and development with Continuous process improvement are areas which I like to work on.
Key Strengths: Ecommerce and Digital business for apparel Retail and Buying
Enhancing the Performance of P3HT/Cdse Solar Cells by Optimal Designing of Ac...IOSRJEEE
The present study examined the influence of different condition like as doping , in active layer, on the performance of P3HT/CdSe Solar cells .In this work, we analyzed the best doping for the configuration of P3HT/ CdSe in order to improve the performance of the solar cell. For this aim, we investigated the current density of electrons, the electric field, the short-circuit current and the open-circuit voltage in different doping . The results indicate that when the doping is increased in P3Ht and is decreased in CdSe, the current density of electrons, the electric field, the short-circuit current, and the open-circuit voltage are increased. Finally, we obtained doping of and for electron and hole donor respectively as the best doping for this configuration
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This talk presents an on line decision support system for structural biologists who are interested in performing multiple protein structure comparisons, via multiple methods, in one go.
Amino acid interaction network prediction using multi objective optimizationcsandit
Protein can be represented by amino acid interaction network. This network is a graph whose
vertices are the proteins amino acids and whose edges are the interactions between them. This
interaction network is the first step of proteins three-dimensional structure prediction. In this
paper we present a multi-objective evolutionary algorithm for interaction prediction and ant
colony probabilistic optimization algorithm is used to confirm the interaction.
The title of the presentation is Tomorrow The World. Its is an impersonation of conducting training for a group of people from a company, mine was Petronas.
Always looking forward towards better and more efficient ways to complete the tasks at hand
Strategy planning and development with Continuous process improvement are areas which I like to work on.
Key Strengths: Ecommerce and Digital business for apparel Retail and Buying
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.
Xia Z., Gardner D.P., Gutell R.R., and Ren P. (2010).
Coarse-Grained Model for Simulation of RNA Three-Dimensional Structures.
The Journal of Physical Chemistry B, 114(42):13497-13506.
AMINO ACID INTERACTION NETWORK PREDICTION USING MULTI-OBJECTIVE OPTIMIZATIONcscpconf
Protein can be represented by amino acid interaction network. This network is a graph whose
vertices are the proteins amino acids and whose edges are the interactions between them. This
interaction network is the first step of proteins three-dimensional structure prediction. In this
paper we present a multi-objective evolutionary algorithm for interaction prediction and ant
colony probabilistic optimization algorithm is used to confirm the interaction.
What is QSAR?, introduction to 3D QSAR, CoMFA, CoMSIA, Case Study on CoMFA contour maps analysis and CoMSIA interactive forces between ligand and receptor, various Statistical techniques involved in QSAR
Protein Structure Prediction Using Support Vector Machine ijsc
Support Vector Machine (SVM) is used for predict the protein structural. Bioinformatics method use to protein structure prediction mostly depends on the amino acid sequence. In this paper, work predicted of 1-D, 2-D, and 3-D protein structure prediction. Protein structure prediction is one of the most important problems in modern computation biology. Support Vector Machine haves shown strong generalization ability protein structure prediction. Binary classification techniques of Support Vector Machine are implemented and RBF kernel function is used in SVM. This Radial Basic Function (RBF) of SVM produces better accuracy in terms of classification and the learning results.
PROTEIN STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINEijsc
Support Vector Machine (SVM) is used for predict the protein structural. Bioinformatics method use to protein structure prediction mostly depends on the amino acid sequence. In this paper, work predicted of 1-
D, 2-D, and 3-D protein structure prediction. Protein structure prediction is one of the most important problems in modern computation biology. Support Vector Machine haves shown strong generalization ability protein structure prediction. Binary classification techniques of Support Vector Machine are implemented and RBF kernel function is used in SVM. This Radial Basic Function (RBF) of SVM produces better accuracy in terms of classification and the learning results.
Wu J.C., Gardner D.P., Ozer S., Gutell R.R. and Ren P. (2009).
Correlation of RNA Secondary Structure Statistics with Thermodynamic Stability and Applications to Folding.
Journal of Molecular Biology, 391(4):769-783.
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.
The major objective of the conformational analysis is to gain insight into the conformational characteristic of flexible biomolecules and drugs and identify the relation between the role of conformational flexibility and their activity.
The significance of conformational analysis not just extends to computational docking and screening but also to lead optimization
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
Similar to CBE_Symposium_Poster_Aparajita - sjp (20)
1. www.buffalo.edu
Introduction
The ability to predict the loop structure in a protein is
useful in many studies, including homology modeling,
protein design and docking.
There are significant challenges in obtaining the high
quality models as the loop length increases.
The current research aims to overcome the challenges
caused by the ruggedness of the energy landscape
around a native protein structure, i.e. the presence of high
energy barriers immediately around the structure by
locally manipulating the shape of the energy landscape
during certain steps of the conformational search.
Methodology
Sequence – Robust Loop Modeling with PyRosetta
Aparajita Dasgupta, Dr. Sheldon Park
Department of Chemical and Biological Engineering, University at Buffalo, SUNY, Email: adasgupt@buffalo.edu, sjpark6@buffalo.edu
PyRosetta is the Python version of Rosetta, a suite of
software to support computational protein structure
analysis. In the context of Rosetta, the kinematic
closure (KIC) loop algorithm, allows prediction of the
structure of loops of up to twelve amino acids with high
accuracy, i.e. < 1 Å (Mandell et al Nature Method 2009,
6:551-2).
We note that protein structure, especially the main
chain conformation, often exhibits robustness against
small sequence variations. Using such transient
mutations which smooth the energy landscape creates
the possibility of improving results during the
conformational search.
Figure 1: Procedure to improve conformational search by introducing transient mutations using KIC loop
protocol in PyRosetta
Results
Most protein structures yielded “funnel – shaped”
continuous graphs while only some diverged from this
trend
Merely increasing the number of wild type structures
(structures without any alanine mutation) did not lead to
improved results
Results Future Work
Citations
Acknowledgments
Figure 2: RMSD vs minimized energy for each of the 20 wild type (non-mutated) proteins. Each graph
represents 600 structures generated by the KIC loop protocol. Note the funnel shaped contour in most
cases. For the proteins where the contour develops differently, prediction of loop structure is very difficult due
to the presence of multiple conformations with different energies at the same RMSD
Figure 3: RMSD vs minimized energy for 3 wild type(1cnv, 1t1d and 1i7p) proteins. Each graph represents
7500 structures generated by the KIC loop protocol for wild type structures. Although the overall energy
surface behaves similar as in the case of 600 structures, there is no marked improvement in either minimizing
energy or predicting loop structure. This leads to the conclusion that site directed mutagenesis is indeed the
right approach. Furthermore, increasing the structures also did not yield the classic “funnel-shaped” energy
contour that is favorable for loop prediction as is evident in the cases of 1cnv and 1i7p. This is due to the fact
that while the number of conformations does indeed increase, the energy landscape is not smoothed and
hence those structures which may be possible but are not calculated due to the presence of a local maxima
are not taken into account in this case as well.
One dimensional analysis of RMSD did not yield any
conclusive results to point out which amino acids (if any)
led to more difficult energy landscapes for modeling
purposes
Mutated structures led to lower energy and resulted in
better structure prediction
Figure 4: Boxplots depicting distribution of LRMSD for each of the 20 amino acids. For each proteins and its
13 versions (12 mutants and 1 wild type), the minimum RMSD was calculated and the mutated residue for
that particular structure was noted. Boxplots were plotted to visualize if any clear trends appeared signifying
which amino acids posed an issue in de-novo modeling. While some amino acids are common in occurrence
as compared to others, a clear trend was not visible while plotting. The main conclusion drawn from this
exercise was that one dimensional analysis does not yield any trends and that a two dimensional analysis of
RMSD with another observable property (Energy, in current experiment) is vital to clearly understand the
bottlenecks associated with loop modeling
Figure 5: RMSD vs minimized energy for all 20 proteins for wild type and mutant structures. Each data point
on each graph represents a single average structure from the cluster which were formed from each type of
mutant. The blue data points are mutant structures while the purple data points are wild type structures. In all
cases the mutated structures had lower energy than the wild type structure. This leads us to the conclusion
that site directed mutagenesis can indeed lead to improved de novo structure prediction when coupled with
the KIC loop protocol. Since energy and RMSD are significantly lower than the wild type structures, the odds
of arriving at a correct structure increase greatly when using these mutated structures.
While applying site directed mutagenesis led to better results,
there are still minor differences in the predicted structure and
the actual structure
Our initial approach was to combine all mutants and wild
type structures together and determine whether this
smoothed the energy landscape further
However, this approach did not yield conclusive results
The current approach is to aim to linearize the RMSD and
energy relationship for each protein near the lowest energy
threshold obtained using linear regression techniques and
neural networks
The authors would like to thank the UB School of Engineering
and Applied Science
Figure 6: 1cnv native structure and minimum energy model mutated back to wild type. The RMSD is 3.3 A for
this system. The current algorithm still leaves a few questions to be answered with regards to the energy
function, the role of each type of amino acid and the characteristic energy landscape for each protein
1. Mandell, J. D., Coutsias, A. E., & Kortemme, T.
(2009). Sub-angstrom accuracy in protein loop
reconstruction by robotics-inspired conformational
sampling. Nature Methods .
2. Baugh, E. H., Lyskov, S., Weitzner, B. D., & Gray, J.
(2011). Real-Time PyMOL Visualization for Rosetta
and PyRosetta. PLOS One .
3. Das R, Baker D (2008) Macromolecular modeling
with Rosetta. Biochemistry 77: 363–382.