The document discusses the author's research interests in integrating omics approaches like proteomics and metabolomics to discover accurate molecular markers. For the past five years, the author has been using mass spectrometry systems for proteomics and metabolomics and mentoring students in experimental design, sample preparation, data analysis, and publishing findings. Current collaborative projects include investigating glycoproteomes, the effects of honey on aging, proteomes of salivary glands in birds, and proteomes and metabolomes of Alzheimer's mouse brains.
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.
Image result for homology modeling
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").
Structural Bioinformatics - Homology modeling & its ScopeNixon Mendez
Homology modeling also known as comparative modeling uses homologous sequences with known 3D structures for the modelling and prediction of the structure of a target sequence
Homology modeling is one of the most best performing prediction methods that gives “accurate” predicted models.
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.
Image result for homology modeling
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").
Structural Bioinformatics - Homology modeling & its ScopeNixon Mendez
Homology modeling also known as comparative modeling uses homologous sequences with known 3D structures for the modelling and prediction of the structure of a target sequence
Homology modeling is one of the most best performing prediction methods that gives “accurate” predicted models.
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.
Molecular modelling for in silico drug discoveryLee Larcombe
A slide set based on the small molecule section of "Introduction to in silico drug discovery" with more detail on molecular modelling and simulation aspects. Including a bit more on protein structure prediction
Classification of Enzymes Using Machine Learning Based Approaches: A Review mlaij
Enzymes play an important role in metabolism that helps in catalyzing bio-chemical reactions. A
computational method is required to predict the function of enzymes. Many feature selection technique
have been used in this paper by examining many previous research paper. This paper presents supervised
machine learning approach to predict the functional classes and subclass of enzymes based on set of 857
sequence derived features. It uses seven sequence derived properties including amino acid composition,
dipeptide composition, correlation feature, composition, transition, distribution and pseudo amino acid
composition .Support vector machine recursive Feature elimination (SVRRFE) is used to select the optimal
number of features. The Random Forest has been used to construct a three level model with optimal
number of features selected by SVMRFE, where top level distinguish a query protein as an enzyme or nonenzyme,
second level predicts the enzyme functional class and the third layer predict the sub functional
class. The proposed model reported overall accuracy of 100%, precision of 100% and MCC value of 1.00
for the first level, whereas accuracy of 90.1%,precision of 90.5% and MCC value of 0.88 for second level
and accuracy of 88.0%, precision of 88.7% and MCC value of 0.87 for the third level.
Mascot is a software package from Matrix Science that interprets mass spectral data into protein identities.
In this presentation we will study about MASCOT and also on how to use it.
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.
Molecular modelling for in silico drug discoveryLee Larcombe
A slide set based on the small molecule section of "Introduction to in silico drug discovery" with more detail on molecular modelling and simulation aspects. Including a bit more on protein structure prediction
Classification of Enzymes Using Machine Learning Based Approaches: A Review mlaij
Enzymes play an important role in metabolism that helps in catalyzing bio-chemical reactions. A
computational method is required to predict the function of enzymes. Many feature selection technique
have been used in this paper by examining many previous research paper. This paper presents supervised
machine learning approach to predict the functional classes and subclass of enzymes based on set of 857
sequence derived features. It uses seven sequence derived properties including amino acid composition,
dipeptide composition, correlation feature, composition, transition, distribution and pseudo amino acid
composition .Support vector machine recursive Feature elimination (SVRRFE) is used to select the optimal
number of features. The Random Forest has been used to construct a three level model with optimal
number of features selected by SVMRFE, where top level distinguish a query protein as an enzyme or nonenzyme,
second level predicts the enzyme functional class and the third layer predict the sub functional
class. The proposed model reported overall accuracy of 100%, precision of 100% and MCC value of 1.00
for the first level, whereas accuracy of 90.1%,precision of 90.5% and MCC value of 0.88 for second level
and accuracy of 88.0%, precision of 88.7% and MCC value of 0.87 for the third level.
Mascot is a software package from Matrix Science that interprets mass spectral data into protein identities.
In this presentation we will study about MASCOT and also on how to use it.
Are you interested in research like Lord Cranbrook? Are you going to contribute to Swiflet Industry?For more information please logon to www,yongkangbirdnest.blogspot.com for detail.
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Edible Bird’s Nest Attenuates Procoagulation Effects of High-Fat Diet in RatsElabscience
Edible bird’s nest (EBN) is used traditionally in many parts of Asia to improve wellbeing, but there are limited studies on its
efficacy. We explored the potential use of EBN for prevention of high fat diet- (HFD-) induced insulin resistance in rats.
Have you ever wondered what is Bird' s Nest? Whether you purchased it in Singapore, or Malaysia, are there any difference? Paying $39 and $390, is the latter better? While this slide tries its best to inform you about Bird's Nest, for a good understanding, we will like to invite you to one of our seminars, with Dr Leh, an expert that has been studying them for years. Look forward to seeing you.
Microorganisms such as bacteria, actinomycetes, and fungi are ubiquitous on our planet. They are widely distributed in soil, water, the human body and other environments. Microorganisms and their activities are of great importance to biogeochemical cycles and to all biological systems. Creative Proteomics provides a one-stop proteomics service from sample collection, protein separation, to protein quantification and bioinformatics analysis. We offer both relative quantification (including iTRAQ, TMT and SILAC) and absolute quantification (such as SRM/MRM and PRM) approaches to help you discover, detect and quantify proteins in a broad array of samples. https://www.creative-proteomics.com/services/proteomics-service.htm
Microorganisms such as bacteria, actinomycetes, and fungi are ubiquitous on our planet. They are widely distributed in soil, water, the human body and other environments. Microorganisms and their activities are of great importance to biogeochemical cycles and to all biological systems. Creative Proteomics provides a one-stop proteomics service from sample collection, protein separation, to protein quantification and bioinformatics analysis. We offer both relative quantification (including iTRAQ, TMT and SILAC) and absolute quantification (such as SRM/MRM and PRM) approaches to help you discover, detect and quantify proteins in a broad array of samples. https://www.creative-proteomics.com/services/proteomics-service.htm
Proteomics is a discipline that analyzes the dynamics of protein components, including expression levels and modification states from a holistic perspective, understands the interactions and connections between proteins, reveals the function of proteins and the laws of cell life, and studies all proteins in cells and their behaviours. Creative Proteomics can provide a comprehensive range of proteomics services to help you better conduct research in the drug discovery process, which include: protein gel and imaging analysis, protein identification, protein quantification, top-down proteomics, peptidomics, post-translational modification analysis, and protein-protein interaction. https://www.creative-proteomics.com/services/protein-gel-and-imaging-analysis.htm
Proteomics is a discipline that analyzes the dynamics of protein components, including expression levels and modification states from a holistic perspective, understands the interactions and connections between proteins, reveals the function of proteins and the laws of cell life, and studies all proteins in cells and their behaviours. Creative Proteomics can provide a comprehensive range of proteomics services to help you better conduct research in the drug discovery process, which includes: protein gel and imaging analysis, protein identification, protein quantification, top-down proteomics, peptidomics, post-translational modification analysis, and protein-protein interaction. https://www.creative-proteomics.com/services/protein-gel-and-imaging-analysis.htm
Proteomics is a discipline that analyzes the dynamics of protein components, including expression levels and modification states from a holistic perspective, understands the interactions and connections between proteins, reveals the function of proteins and the laws of cell life, and studies all proteins in cells and their behaviours. Creative Proteomics can provide a comprehensive range of proteomics services to help you better conduct research in the drug discovery process, which includes: protein gel and imaging analysis, protein identification, protein quantification, top-down proteomics, peptidomics, post-translational modification analysis, and protein-protein interaction. https://www.creative-proteomics.com/services/protein-gel-and-imaging-analysis.htm
1. Research Statement
Discovery of molecular markers is my favorite. I do believe that the integration of
OMICs approaches can shed more light on the biological facts which help us to find more
accurate molecular markers. Since five years ago, I have been dedicated myself to the integration
of Proteomics and Metabolomics approaches and I would like to continue this journey
considering other OMICs as well. Sine four years ago, I have been using Mass Spectrometry
systems for shotgun Proteomics and Metabolomics and I love the challenges I have been facing.
I would like to contribute in advancing Mass Spectrometry techniques in Multi-OMICs analysis.
In my current position as Specialist in Mass Spectrometry in Medical Biotechnology
Laboratory, I have developed a complete work flow of mass spectrometry-based proteomics and
metabolomics, from sample preparation until publication of the final findings. I am in charge of
Agilent LC/ MS Q-TOF 6550. I am currently collaborating with four professor's PhD and
Master's students. I am mentoring them for proteomics and metabolomics experimental design
and sample preparation. I am in charge of mass spectrometry data acquisition, statistical data
analysis, identification of proteins and metabolites, relative quantitation, gene ontology analysis,
protein-protein network analysis and pathway analysis. I am also contributing in the publication
of the final findings.
I have organized several Mass specrometry workshops with the collaboration of Agilent
Company to train our staff.
The focus of my PhD research was proteomics and metabolomics investigation of cattle
sperm for fertility and heat tolerance selective molecular marker discovery. I am currently
working on the protein molecular markers for sperm sexing.
Few of my current collaborative projects are as follow:
Glycoproteome comparison of whey and milk fat globule membrane components of
human, caprine and bovine milk.
The effect of Glam honey on aging. A proteome investigation of rat heart
mitochondria. (Collaboration with National University of Malaysia)
Proteome comparison of salivary glands from Malaysian Swiftlet breeds.
(Collaboration with University Putra Malayisa, Malaysia)
Proteome and metabolome investigation of the brain from Alzheimer mice model.
(Collaboration with Shiga University, Japan).