The document discusses computational protein design techniques. It covers topics like sequence-based and structure-based computational protein design, molecular force fields, knowledge-based potentials, and predicting protein dynamics. The author aims to provide an overview of different computational protein design approaches and challenges in the field.
This document provides an overview of tissue engineering of bone. It discusses the objectives of understanding bone formation/repair and the components of bone tissue engineering. The key components are scaffolds, growth factors, and cells. Various materials are described for use as scaffolds, including metals, ceramics, and polymers. Growth factors can stimulate bone formation and fracture healing. In vitro models are used to test and screen growth factors and their effects on bone marrow stem cells and cell lines prior to in vivo studies. Bone's macroscopic structure and the processes of intramembranous and endochondral bone formation are also summarized.
Tissue engineering uses scaffolds, cells, and signaling molecules to regenerate tissues and organs. Scaffolds provide a structure for cell attachment, growth, and tissue formation. Natural polymers like collagen and hyaluronic acid, and synthetic polymers like poly-lactic-co-glycolic acid are commonly used as scaffold materials. Scaffolds can be fabricated using various methods including freeze drying, electrospinning, 3D printing, and textile technologies to produce scaffolds with desirable properties like porosity and pore size for tissue growth. Scaffolds seeded with stem cells or tissue-specific cells aim to repair and regenerate tissues for applications in skin, bone, cartilage, and other organs.
Bones have important mechanical, synthetic, and metabolic functions in the body. Tissue engineering aims to induce new functional bone tissue through the use of scaffolds, growth factors, and cells. Strategies for bone tissue engineering generally involve a carrier scaffold and biologically active factors like cells and proteins. Materials used can include metals, ceramics, and natural or synthetic polymers. The goal is for the scaffold to deliver osteoinductive molecules and cells to fill bone defects and facilitate healing through new bone formation.
Introduction
Anatomy and Physiology of bone
Bone Tissue Engineering
Recent studies related to bone tissue engineering
Commercialized products and ongoing clinical trials
Biomedical start-ups
Concluding remarks
Introduction
Anatomy and Physiology of bone
Bone Tissue Engineering
Recent studies related to bone tissue engineering
Commercialized products and ongoing clinical trials
Biomedical start-ups
Concluding remarks
Introduction
Anatomy and Physiology of bone
Bone Tissue Engineering
Recent studies related to bone tissue engineering
Commercialized products and ongoing clinical trials
Biomedical start-ups
Concluding remarks
Biomaterials for tissue engineering slideshareBukar Abdullahi
An overview of Tissue Engineering with some basics in Biomaterials and Synthetic Polymers. Further references should be considered as I presented this a specific target audience.
This document discusses regenerative medicine and tissue engineering. It outlines examples of regeneration in nature and clinical needs where regeneration could help such as heart disease and bone fractures. Stem cells are described as a potential cell source along with factors like growth factors and scaffold materials. Challenges in tissue engineering like optimal cell delivery and scaffold design are covered. Cardiovascular applications are discussed in depth as a promising target for regenerative approaches.
Cancer is caused by changes in gene expression or mutations that lead to abnormal cell growth. The presentation discusses the types and properties of cancer cells, causes like carcinogenic agents and viruses, tumor suppressor genes like p53 and oncogenes. Proteomics is the study of the complete set of proteins in a cell or organism and techniques used include biomarkers, 2D gel electrophoresis, and mass spectrometry like MALDI-TOF and SELDI-TOF. Improvements in multidimensional separations and nanotechnology may help identify more biomarkers and develop novel cancer diagnostics and therapeutics.
This document provides an overview of tissue engineering of bone. It discusses the objectives of understanding bone formation/repair and the components of bone tissue engineering. The key components are scaffolds, growth factors, and cells. Various materials are described for use as scaffolds, including metals, ceramics, and polymers. Growth factors can stimulate bone formation and fracture healing. In vitro models are used to test and screen growth factors and their effects on bone marrow stem cells and cell lines prior to in vivo studies. Bone's macroscopic structure and the processes of intramembranous and endochondral bone formation are also summarized.
Tissue engineering uses scaffolds, cells, and signaling molecules to regenerate tissues and organs. Scaffolds provide a structure for cell attachment, growth, and tissue formation. Natural polymers like collagen and hyaluronic acid, and synthetic polymers like poly-lactic-co-glycolic acid are commonly used as scaffold materials. Scaffolds can be fabricated using various methods including freeze drying, electrospinning, 3D printing, and textile technologies to produce scaffolds with desirable properties like porosity and pore size for tissue growth. Scaffolds seeded with stem cells or tissue-specific cells aim to repair and regenerate tissues for applications in skin, bone, cartilage, and other organs.
Bones have important mechanical, synthetic, and metabolic functions in the body. Tissue engineering aims to induce new functional bone tissue through the use of scaffolds, growth factors, and cells. Strategies for bone tissue engineering generally involve a carrier scaffold and biologically active factors like cells and proteins. Materials used can include metals, ceramics, and natural or synthetic polymers. The goal is for the scaffold to deliver osteoinductive molecules and cells to fill bone defects and facilitate healing through new bone formation.
Introduction
Anatomy and Physiology of bone
Bone Tissue Engineering
Recent studies related to bone tissue engineering
Commercialized products and ongoing clinical trials
Biomedical start-ups
Concluding remarks
Introduction
Anatomy and Physiology of bone
Bone Tissue Engineering
Recent studies related to bone tissue engineering
Commercialized products and ongoing clinical trials
Biomedical start-ups
Concluding remarks
Introduction
Anatomy and Physiology of bone
Bone Tissue Engineering
Recent studies related to bone tissue engineering
Commercialized products and ongoing clinical trials
Biomedical start-ups
Concluding remarks
Biomaterials for tissue engineering slideshareBukar Abdullahi
An overview of Tissue Engineering with some basics in Biomaterials and Synthetic Polymers. Further references should be considered as I presented this a specific target audience.
This document discusses regenerative medicine and tissue engineering. It outlines examples of regeneration in nature and clinical needs where regeneration could help such as heart disease and bone fractures. Stem cells are described as a potential cell source along with factors like growth factors and scaffold materials. Challenges in tissue engineering like optimal cell delivery and scaffold design are covered. Cardiovascular applications are discussed in depth as a promising target for regenerative approaches.
Cancer is caused by changes in gene expression or mutations that lead to abnormal cell growth. The presentation discusses the types and properties of cancer cells, causes like carcinogenic agents and viruses, tumor suppressor genes like p53 and oncogenes. Proteomics is the study of the complete set of proteins in a cell or organism and techniques used include biomarkers, 2D gel electrophoresis, and mass spectrometry like MALDI-TOF and SELDI-TOF. Improvements in multidimensional separations and nanotechnology may help identify more biomarkers and develop novel cancer diagnostics and therapeutics.
Tissue engineering aims to regenerate tissues by combining cells, scaffolds, and signaling molecules. There are two main strategies - in vitro construction of tissues in the lab prior to implantation, and in vivo regeneration of tissues at the implantation site. Successful tissue engineering requires the right cells, scaffolding for cell attachment and growth, and signaling to guide tissue development. Stem cells are promising cell sources due to their ability to differentiate into many cell types.
Proteomics is the study of the structure and function of proteins. It involves identifying and quantifying the proteins expressed by a genome or cell type. Key aspects of proteomics include protein separation techniques like gel electrophoresis, mass spectrometry to identify proteins, and analyzing protein interactions and post-translational modifications. While genomes provide the blueprint, proteomics helps understand the diversity of proteins expressed and how they function together to direct cellular activities. It is a promising tool for disease diagnosis by identifying protein biomarkers.
Introduction:
Protein
Protein motif.
2. History:
3. A brief overview of protein structure.
4. The Structural Classification of Protein(SCOP):
All α.
All β
α/β
α+β
5.The super secondary structure.
6. Rules for formation of Protein Motifs.
7. Structural motifs.
8. Some Common Protein Motifs:
β-hairpin.
β-meander.
Alpha-alpha corner.
Helix-turn-helix motif.
β-α-β motif.
β-sandwich.
β-barrel.
Greek key.
The Jellyroll topology.
Omega loop.
Zinc finger motif.
9. Conclusion.
10. References.
This document discusses protein engineering techniques for modifying proteins, including rational protein design using site-directed mutagenesis and directed evolution using random mutagenesis. Site-directed mutagenesis involves introducing point mutations in a particular known area to modify a specific protein function, while directed evolution generates genetic diversity through random mutagenesis and screens variants to identify successful mutations without requiring structural information. Common random mutagenesis methods discussed are error-prone PCR and DNA shuffling, which can be used to engineer properties like protein folding, stability, binding, and catalysis.
This document discusses protein threading modeling methods. Protein threading, also called fold recognition, is used to model proteins that have the same fold as proteins with known structures but no homologous sequences. It differs from homology modeling which is used for proteins that have homologous sequences. Protein threading works by using statistical knowledge of relationships between structures in the Protein Data Bank and the sequence of the protein being modeled. It is based on observations that there are a limited number of folds in nature and most new structures have similar folds to ones already in the PDB. The document then describes the general steps of the protein threading method.
DNA Nanotechnology: Concept and its Applications
DNA Nanotechnology # Various 2 and 3 dimensional shapes of DNA nanotechnology # DNA Origami # with their application and Future scope
This document summarizes different computational methods for protein structure prediction, including homology modeling, fold recognition, threading, and ab initio modeling. Homology modeling relies on identifying proteins with similar sequences and known structures. Fold recognition and threading can be used when there are no homologs, to identify proteins with the same overall fold but different sequences. Ab initio modeling uses physics-based modeling and protein fragments to predict structure from sequence alone, and has challenges due to the vast number of possible conformations.
HERE IN THIS PRESENTATION HY HOMOLOGY MODELING IS EXPLAIN , WITH EXAMPLES OF PROTEIN PRIMARY AND SECONDARY, SHOWING THE IMAGES FORM WHICH MAKES EASY TO UNDERSTAND
Databases pathways of genomics and proteomics Sachin Kumar
The document discusses several databases related to human metabolism and pharmacology. It describes the contents and purpose of each database, including the Human Metabolome Database (HMDB), KEGG, MetaCyc, PubChem, ChEBI, DrugBank, the Therapeutic Target Database (TTD), PharmGKB, and Chemical Entities of Biological Interest (ChEBI). These databases contain chemical, clinical, molecular biology, pathway, and genomic data on human metabolites, drugs, and targets.
The document discusses tissue engineering approaches for the nervous system. It begins with an introduction to the anatomy and limited regenerative capacity of the central and peripheral nervous systems. For peripheral nerve injuries, the current gold standard treatment is autologous nerve grafts, but these have limitations. Alternative approaches discussed include the use of nerve guides containing matrices and scaffolds to bridge gaps and guide axon regeneration. Factors like scaffold composition and geometry, inclusion of cells and growth factors, and degradation properties can influence how well scaffolds support regeneration across critical gaps in nerves. The document reviews considerations for scaffold and matrix design and various strategies for incorporating growth-promoting components in peripheral nerve engineering.
1) The document discusses various methods for determining the 3D structure of proteins, including x-ray crystallography, NMR spectroscopy, and cryo-electron microscopy.
2) X-ray crystallography involves purifying the protein, crystallizing it, collecting diffraction data from x-rays hitting the crystal, using this data to determine phases and calculate an electron density map, and building an atomic model through refinement.
3) NMR spectroscopy involves dissolving the purified protein and using nuclear magnetic resonance to measure distances between atomic nuclei, allowing the structure to be calculated.
The document discusses scaffolds for tissue engineering. It defines scaffolds as temporary or permanent artificial extracellular matrices that accommodate cells and support 3D tissue regeneration. Scaffolds aim to mimic the natural extracellular matrix and promote cell response to engineer replacement tissues. The key requirements for scaffolds are that they be porous, biocompatible, and have properties matching the target tissue. Various fabrication techniques can be used to control the scaffold architecture, composition, and other properties. Common scaffold materials discussed include natural polymers like collagen and synthetic polymers.
This document discusses bacteriophages and their use in phage display. Specifically, it notes that bacteriophages infect bacterial cells and use them to replicate viruses. It then explains that phage display involves fusing foreign genes or proteins to the surface of phages, creating libraries of phages that each display a single protein. These libraries can be exposed to targets, and phages that interact are selected and amplified through multiple rounds. The document outlines several applications of proteins isolated through phage display, such as epitope mapping, drug discovery, and developing new vaccines or treatments that have a specific interaction with a target antigen, protein, or disease.
Abstract: The focus in this session will be put on the differences between standard DNA mapping and RNAseq-specific transcript mapping: identifying splice variants and isoforms. The issue of transcript quantification and genomic variants that can be identified from RNAseq data will be discussed.
The document summarizes the work done at the Liu Nanobionics Lab, which focuses on biomaterials, tissue engineering, and nanotechnology. The lab studies how biomaterials interact with biological systems, develops tissue engineering approaches using scaffolds and growth factors, and modifies material surfaces at the nano-scale to enhance biocompatibility. It also explores techniques like 3D printing and electrospinning to control scaffold architecture for tissue regeneration applications.
Proteins : is made of chain of amino acids ( amino acid= monomers) therefor the protein is polymers .
The proteins are made up of carbon, hydrogen, oxygen, and nitrogen.
Amino acid :
it will help you to understand how the protein microarrays are made, what are the different types and what all purposes they are used for. its very useful ppt
Computational Protein Design. 4. A Practical ExercisePablo Carbonell
This document outlines the 5 steps of the computational protein design cycle: 1) analyzing protein binding regions, 2) building structural models and predicting hotspots, 3) performing in silico mutagenesis to select best variants, 4) validating predictions against experimental data, and 5) implementing a protein design strategy. It directs the reader to a wiki page for a practical exercise in computational protein design.
Computational Protein Design. 3. Applications in Systems and Synthetic BiologyPablo Carbonell
The document discusses applications of computational protein design (CPD) in systems and synthetic biology. It describes using CPD to model antibody-antigen interactions and enhance the binding affinity of antibodies for tumor necrosis factor-alpha (TNF-α). The modeling process involves building homology models, docking complexes, predicting hotspots, generating mutant libraries, and screening variants. CPD can also inform protein modular design by decomposing proteins into independently folding domains and submodules within domains. Binding sites often correspond to highly cooperative submodules. Considering protein modularity provides insights into determining binding affinity, specificity, and engineering new functions.
Tissue engineering aims to regenerate tissues by combining cells, scaffolds, and signaling molecules. There are two main strategies - in vitro construction of tissues in the lab prior to implantation, and in vivo regeneration of tissues at the implantation site. Successful tissue engineering requires the right cells, scaffolding for cell attachment and growth, and signaling to guide tissue development. Stem cells are promising cell sources due to their ability to differentiate into many cell types.
Proteomics is the study of the structure and function of proteins. It involves identifying and quantifying the proteins expressed by a genome or cell type. Key aspects of proteomics include protein separation techniques like gel electrophoresis, mass spectrometry to identify proteins, and analyzing protein interactions and post-translational modifications. While genomes provide the blueprint, proteomics helps understand the diversity of proteins expressed and how they function together to direct cellular activities. It is a promising tool for disease diagnosis by identifying protein biomarkers.
Introduction:
Protein
Protein motif.
2. History:
3. A brief overview of protein structure.
4. The Structural Classification of Protein(SCOP):
All α.
All β
α/β
α+β
5.The super secondary structure.
6. Rules for formation of Protein Motifs.
7. Structural motifs.
8. Some Common Protein Motifs:
β-hairpin.
β-meander.
Alpha-alpha corner.
Helix-turn-helix motif.
β-α-β motif.
β-sandwich.
β-barrel.
Greek key.
The Jellyroll topology.
Omega loop.
Zinc finger motif.
9. Conclusion.
10. References.
This document discusses protein engineering techniques for modifying proteins, including rational protein design using site-directed mutagenesis and directed evolution using random mutagenesis. Site-directed mutagenesis involves introducing point mutations in a particular known area to modify a specific protein function, while directed evolution generates genetic diversity through random mutagenesis and screens variants to identify successful mutations without requiring structural information. Common random mutagenesis methods discussed are error-prone PCR and DNA shuffling, which can be used to engineer properties like protein folding, stability, binding, and catalysis.
This document discusses protein threading modeling methods. Protein threading, also called fold recognition, is used to model proteins that have the same fold as proteins with known structures but no homologous sequences. It differs from homology modeling which is used for proteins that have homologous sequences. Protein threading works by using statistical knowledge of relationships between structures in the Protein Data Bank and the sequence of the protein being modeled. It is based on observations that there are a limited number of folds in nature and most new structures have similar folds to ones already in the PDB. The document then describes the general steps of the protein threading method.
DNA Nanotechnology: Concept and its Applications
DNA Nanotechnology # Various 2 and 3 dimensional shapes of DNA nanotechnology # DNA Origami # with their application and Future scope
This document summarizes different computational methods for protein structure prediction, including homology modeling, fold recognition, threading, and ab initio modeling. Homology modeling relies on identifying proteins with similar sequences and known structures. Fold recognition and threading can be used when there are no homologs, to identify proteins with the same overall fold but different sequences. Ab initio modeling uses physics-based modeling and protein fragments to predict structure from sequence alone, and has challenges due to the vast number of possible conformations.
HERE IN THIS PRESENTATION HY HOMOLOGY MODELING IS EXPLAIN , WITH EXAMPLES OF PROTEIN PRIMARY AND SECONDARY, SHOWING THE IMAGES FORM WHICH MAKES EASY TO UNDERSTAND
Databases pathways of genomics and proteomics Sachin Kumar
The document discusses several databases related to human metabolism and pharmacology. It describes the contents and purpose of each database, including the Human Metabolome Database (HMDB), KEGG, MetaCyc, PubChem, ChEBI, DrugBank, the Therapeutic Target Database (TTD), PharmGKB, and Chemical Entities of Biological Interest (ChEBI). These databases contain chemical, clinical, molecular biology, pathway, and genomic data on human metabolites, drugs, and targets.
The document discusses tissue engineering approaches for the nervous system. It begins with an introduction to the anatomy and limited regenerative capacity of the central and peripheral nervous systems. For peripheral nerve injuries, the current gold standard treatment is autologous nerve grafts, but these have limitations. Alternative approaches discussed include the use of nerve guides containing matrices and scaffolds to bridge gaps and guide axon regeneration. Factors like scaffold composition and geometry, inclusion of cells and growth factors, and degradation properties can influence how well scaffolds support regeneration across critical gaps in nerves. The document reviews considerations for scaffold and matrix design and various strategies for incorporating growth-promoting components in peripheral nerve engineering.
1) The document discusses various methods for determining the 3D structure of proteins, including x-ray crystallography, NMR spectroscopy, and cryo-electron microscopy.
2) X-ray crystallography involves purifying the protein, crystallizing it, collecting diffraction data from x-rays hitting the crystal, using this data to determine phases and calculate an electron density map, and building an atomic model through refinement.
3) NMR spectroscopy involves dissolving the purified protein and using nuclear magnetic resonance to measure distances between atomic nuclei, allowing the structure to be calculated.
The document discusses scaffolds for tissue engineering. It defines scaffolds as temporary or permanent artificial extracellular matrices that accommodate cells and support 3D tissue regeneration. Scaffolds aim to mimic the natural extracellular matrix and promote cell response to engineer replacement tissues. The key requirements for scaffolds are that they be porous, biocompatible, and have properties matching the target tissue. Various fabrication techniques can be used to control the scaffold architecture, composition, and other properties. Common scaffold materials discussed include natural polymers like collagen and synthetic polymers.
This document discusses bacteriophages and their use in phage display. Specifically, it notes that bacteriophages infect bacterial cells and use them to replicate viruses. It then explains that phage display involves fusing foreign genes or proteins to the surface of phages, creating libraries of phages that each display a single protein. These libraries can be exposed to targets, and phages that interact are selected and amplified through multiple rounds. The document outlines several applications of proteins isolated through phage display, such as epitope mapping, drug discovery, and developing new vaccines or treatments that have a specific interaction with a target antigen, protein, or disease.
Abstract: The focus in this session will be put on the differences between standard DNA mapping and RNAseq-specific transcript mapping: identifying splice variants and isoforms. The issue of transcript quantification and genomic variants that can be identified from RNAseq data will be discussed.
The document summarizes the work done at the Liu Nanobionics Lab, which focuses on biomaterials, tissue engineering, and nanotechnology. The lab studies how biomaterials interact with biological systems, develops tissue engineering approaches using scaffolds and growth factors, and modifies material surfaces at the nano-scale to enhance biocompatibility. It also explores techniques like 3D printing and electrospinning to control scaffold architecture for tissue regeneration applications.
Proteins : is made of chain of amino acids ( amino acid= monomers) therefor the protein is polymers .
The proteins are made up of carbon, hydrogen, oxygen, and nitrogen.
Amino acid :
it will help you to understand how the protein microarrays are made, what are the different types and what all purposes they are used for. its very useful ppt
Computational Protein Design. 4. A Practical ExercisePablo Carbonell
This document outlines the 5 steps of the computational protein design cycle: 1) analyzing protein binding regions, 2) building structural models and predicting hotspots, 3) performing in silico mutagenesis to select best variants, 4) validating predictions against experimental data, and 5) implementing a protein design strategy. It directs the reader to a wiki page for a practical exercise in computational protein design.
Computational Protein Design. 3. Applications in Systems and Synthetic BiologyPablo Carbonell
The document discusses applications of computational protein design (CPD) in systems and synthetic biology. It describes using CPD to model antibody-antigen interactions and enhance the binding affinity of antibodies for tumor necrosis factor-alpha (TNF-α). The modeling process involves building homology models, docking complexes, predicting hotspots, generating mutant libraries, and screening variants. CPD can also inform protein modular design by decomposing proteins into independently folding domains and submodules within domains. Binding sites often correspond to highly cooperative submodules. Considering protein modularity provides insights into determining binding affinity, specificity, and engineering new functions.
Protein networks as a scaffold for structuring other dataLars Juhl Jensen
This document discusses using protein interaction networks as a scaffold to integrate other biological data sources. It describes several high-throughput studies that mapped protein interaction networks in yeast, worm, fruit fly and human. While the protein interaction networks are incomplete and have high false positive rates, topology-based scoring and other filtering methods can be used to identify high-confidence interactions. The document goes on to discuss integrating protein interaction networks with gene expression time series data from yeast to build temporal networks and identify periodically expressed genes and modules involved in cell cycle regulation.
Cryotherapy for Pathogen Free Planting Material in Ornamental Crops Abhay Kumar Gaurav
Cryotherapy of shoot tips is a novel technique used for pathogen eradication from plants, where sample is exposed to a low temperature (-196°C) followed by shoot tip culture. Cryotherapy of shoot tips can result in virus-free plants at a high frequency. Cryotherapy facilitates treatment of large numbers of samples because pathogen eradication by this method is independent of the size of shoot tips used.
This document discusses several articles about proteins and their roles. It first provides background on proteins and their functions as enzymes, antibodies, channels and receptors. It then summarizes two research articles. The first article finds that the RAB35 protein, which normally regulates protein transport, can also drive cancer formation when mutated. The second article details research that used x-ray crystallography to determine the atomic structure of the HIV capsid protein, which helps HIV replicate and could inform new antiviral drugs. The document concludes by discussing the medical applications of studying protein structures.
Antigen processing and presentation involves two pathways: 1) Exogenous antigens are internalized, processed in the endosome, and presented on MHC class II to CD4+ T cells. 2) Endogenous antigens are processed by the proteasome in the cytosol, transported to the ER by TAP, loaded onto MHC class I, and presented to CD8+ T cells. For an immune response, antigen must be degraded into peptides and bound to MHC molecules on antigen presenting cells to activate T cells through TCR recognition and co-stimulation.
This document provides an overview of computational protein design. It discusses challenges in protein engineering like the protein design cycle and screening methods. It also describes computational protein design methods including protein descriptors, sequence-based methods, structure-based methods, and search algorithms. Finally, it outlines applications in systems and synthetic biology such as protein affinity enhancement and modular design.
This document discusses site-directed mutagenesis and protein engineering. It provides an introduction to mutagenesis and defines site-directed mutagenesis. Various methods for site-directed mutagenesis are described, including using M13 bacteriophage, plasmid DNA, and PCR. Examples are given of using site-directed mutagenesis to modify lysozyme, xylanase, human pancreatic ribonuclease, and subtilisin proteins to improve properties like thermal stability and metal binding.
Computational Protein Design. 1. Challenges in Protein EngineeringPablo Carbonell
This document discusses computational protein design and outlines several key challenges in protein engineering. It begins with an overview of the protein design cycle and discusses locating amino acid substitutions, types of protein interactions, and engineering protein activity and binding affinity. The goal of protein engineering is to alter protein structures to improve properties, with the main challenge being developing accurate models to predict substitutions that enhance desired properties. The document provides details on computational approaches for increasing thermostability, catalytic activity, and binding affinity/specificity.
Developing an Efficient Infrastruture, Standards and Data-Flow for MetabolomicsChristoph Steinbeck
The document discusses the development of efficient infrastructure, standards, and data flow for metabolomics. It describes the European Bioinformatics Institute (EBI) and its role in archiving, classifying, analyzing, and sharing metabolomics data through databases like MetaboLights. MetaboLights has experienced rapid data growth and is now the recommended repository for several journals. Efforts are underway to establish global standards and facilitate data exchange through initiatives like COSMOS and MetabolomeXchange. The document outlines plans to build out reference metabolomes and enable large-scale computing with medical metabolomics data.
The types of tissue culture can be grouped by the structures formed in culture.
Plantlets
Seedlings
Callus
Somatic EmbryogenesisPlantlet formationThis is the most common form of micropropagation. Uses a portion of the stem with one to several nodes
1. Axillary shoot formation Meristem culture Shoot culture
2. Adventitious shoot formation Diploid plant regenerationPseudocorms
Pseudocorms are the structures initiated after seed germination in orchids Haploid and triploid regeneration
Plant tissue culture is the process of maintaining or growing plant cells, tissues or organs under sterile conditions on a nutrient culture medium of known composition. It involves techniques like cell culture, organ culture or meristem culture to produce clones of a plant through micropropagation. The key steps are selection of explant tissue from a donor plant, sterilization, establishment of the explant on a culture medium, multiplication through cell division and shoot formation, rooting of shoots, and acclimatization of plantlets in soil. Micropropagation allows for rapid mass multiplication of plant materials while maintaining genetic uniformity.
The document discusses natural and artificial regeneration of forests. Natural regeneration refers to the natural process by which plants replace or re-establish themselves through seed dispersal or vegetative reproduction like coppicing. It depends on several factors like seed production, germination conditions, seedling establishment and survival. Artificial regeneration involves human intervention through methods like sowing, planting or other means to renew forest crops. The choice of species, site selection, nursery practices and planting methods are important considerations for artificial regeneration.
Protein engineering involves modifying protein structure using recombinant DNA technology or chemical treatment to improve function for use in medicine, industry, and agriculture. The objectives of protein engineering are to create superior enzymes for specific chemical production, produce enzymes in large quantities, and produce superior biological compounds. Protein engineering aims to alter properties like kinetic properties, thermostability, stability in nonaqueous solvents, substrate specificity, and cofactor requirements to meet industrial needs. Common methods for protein engineering include mutagenesis, selection, and recombinant DNA technology.
SARS first emerged in China in November 2002 and killed nearly 800 people worldwide by July 2003. It originated in Guangdong province and was spread internationally by air travelers starting in February 2003. A timeline details its spread from China to Hong Kong, Vietnam, and beyond. Symptoms are flu-like with fever, cough and breathing difficulties. There is no vaccine and treatment focuses on isolation. It is spread through respiratory droplets from sneezing or coughing. Health authorities worked to control its spread and lifted travel advisories by July once new cases stopped being reported.
EUGM15 - Michael J. Bodkin (Evotec): Algorithms, Evolution and Network-Based ...ChemAxon
Drug research generates huge quantities of data around targets, compounds and their effects. Network modelling can be used to describe such relationships with the aim to couple our understanding of disease networks with the changes in small molecule properties. This talk will build off of the data that is routinely captured in drug discovery and describe the methods and tools that we have developed for compound design using predictive modelling, evolutionary algorithms and network-based mining.
Oral presentation at Protein Folding Consortium Workshop in Berkeley (2017)Temple University
This document describes the Bayesian Inference of Conformational Populations (BICePs) method for reconciling simulated protein conformational ensembles with experimental data. BICePs uses Bayesian inference and Markov Chain Monte Carlo sampling to estimate conformational populations from a force field that are consistent with experimental measurements like NMR data. The document discusses how BICePs has been used to validate protein force fields and identify the most accurate combinations of force fields and solvent models for all-atom simulations against NMR data. While coarse-graining limits its accuracy, BICePs is shown to be robust to experimental noise and has potential for force field parameterization.
We propose a regularized method for multivariate linear regression when the number of predictors may exceed the sample size. This method is designed to strengthen the estimation and the selection of the relevant input features with three ingredients: it takes advantage of the dependency pattern between the responses by estimating the residual covariance; it performs selection on direct links between predictors and responses; and selection is driven by prior structural information. To this end, we build on a recent reformulation of the multivariate linear regression model to a conditional Gaussian graphical model and propose a new regularization scheme accompanied with an efficient optimization procedure. On top of showing very competitive performance on artificial and real data sets, our method demonstrates capabilities for fine interpretation of its parameters, as illustrated in applications to genetics, genomics and spectroscopy.
ENHANCED POPULATION BASED ANT COLONY FOR THE 3D HYDROPHOBIC POLAR PROTEIN STR...ijbbjournal
Population-based Ant Colony algorithm is stochastic local search algorithm that mimics the behavior of
real ants, simulating pheromone trails to search for solutions to combinatorial optimization problems. This
paper introduces population-based Ant Colony algorithm to solve 3D Hydrophobic Polar Protein structure
Prediction Problem then introduces a new enhanced approach of population-based Ant Colony algorithm
called Enhanced Population-based Ant Colony algorithm (EP-ACO) to avoid stagnation problem in
population-based Ant Colony algorithm and increase exploration in the search space escaping from local
optima, The experiments show that our approach appears more efficient results than state of art method.
This paper reports about progress in two areas towards quantum computing architectures with elements inspired from biological controls, as proposed in an earlier paper. The first area is about exploiting mathematical results in coloured algebras, which, combined with the colouring of particle flows, would reduce the decoherence and enhance the decidability in the quantum processing elements; definitions are being recalled, with the required assumptions and results. The second area is to provide experimental results, and a patented biological feedback process in synapse , about light and acoustic excitations in a live animal species to enhance reactivity; the experimental set-up is characterized , the measurement results provided, and the implications are explicated for quantum processing elements approximating a synapse. A paragraph on open issues explains how the results in the two areas will be combined and will help in the design a very early compiler version.
COLOURED ALGEBRAS AND BIOLOGICAL RESPONSE IN QUANTUM BIOLOGICAL COMPUTING ARC...ijcsit
This paper reports about progress in two areas towards quantum computing architectures with elements inspired from biological controls, as proposed in an earlier paper. The first area is about exploiting mathematical results in coloured algebras, which, combined with the colouring of particle flows, would reduce the decoherence and enhance the decidability in the quantum processing elements; definitions are being recalled, with the required assumptions and results. The second area is to provide experimental results, and a patented biological feedback process in synapse , about light and acoustic excitations in a live animal species to enhance reactivity; the experimental set-up is characterized , the measurement results provided, and the implications are explicated for quantum processing elements approximating a synapse. A paragraph on open issues explains how the results in the two areas will be combined and will help in the design a very early compiler version.
Principal component analysis (PCA) was used to analyze the conformational diversity of Ras proteins based on X-ray crystal structures. PCA separated the structures into two main clusters corresponding to the GTP-bound and GDP-bound conformations, capturing over 57.4% of the variance in the first two principal components. PCA loading plots identified displacements of switch regions as dominant features describing the conformational differences.
PCA was also used to analyze interactions between ligands and protein structures of CYP3A4 based on molecular interaction fields calculated using grid probes. Consensus PCA separated the structures based on differences in interactions with hydrophobic probes. PCA score plots distinguished the homology model from crystal structures based on interactions with Phe304, Thr309 and
A Non Parametric Estimation Based Underwater Target ClassifierCSCJournals
Underwater noise sources constitute a prominent class of input signal in most underwater signal processing systems. The problem of identification of noise sources in the ocean is of great importance because of its numerous practical applications. In this paper, a methodology is presented for the detection and identification of underwater targets and noise sources based on non parametric indicators. The proposed system utilizes Cepstral coefficient analysis and the Kruskal-Wallis H statistic along with other statistical indicators like F-test statistic for the effective detection and classification of noise sources in the ocean. Simulation results for typical underwater noise data and the set of identified underwater targets are also presented in this paper.
Increasingly Accurate Representation of Biochemistry (v2)Michel Dumontier
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Computational Protein Design. 2. Computational Protein Design Techniques
1. Computational Protein Design
2. Computational Protein Design Techniques
Pablo Carbonell
pablo.carbonell@issb.genopole.fr
iSSB, Institute of Systems and Synthetic Biology
Genopole, University d’Évry-Val d’Essonne, France
mSSB: December 2010
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 1 / 45
2. Outline
1 Introduction
2 Computational Protein Descriptors
3 Sequence-based CPD
4 Structure-based CPD
5 Search Algorithms in CPD
6 De Novo Design
7 Challenges in Sequence and Structure-Based CPD
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 2 / 45
3. Outline
1 Introduction
2 Computational Protein Descriptors
3 Sequence-based CPD
4 Structure-based CPD
5 Search Algorithms in CPD
6 De Novo Design
7 Challenges in Sequence and Structure-Based CPD
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 3 / 45
5. A Blueprint of CPD Approaches
∗ RS : research studies
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 5 / 45
6. Outline
1 Introduction
2 Computational Protein Descriptors
3 Sequence-based CPD
4 Structure-based CPD
5 Search Algorithms in CPD
6 De Novo Design
7 Challenges in Sequence and Structure-Based CPD
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 6 / 45
7. Molecular Signature Descriptors
A 2D representation of the molecular graphs Atomic signature :
as an undirected colored graphs G(V , E, C),
Xh
with V : atoms, E : bonds, C : atom type h
σ(G) = σ(x) (1)
The signature descriptor of height h of atom x x∈V
in the molecular graph G, or h σ(x), is a
The signature is a systematic
canonical representation of the subgraph of
codification of the molecular
G containing all atoms that are at distance h
graph [Faulon et al., 2004]
from x
σ(methylcyclopropane) =
1 [C]([H][C]([H][H][C,0])[C,0]([H][H])[C]([H][H][H]))
2 [C]([H][H][C]([H][C,0][C]([H][H][H]))[C,0]([H][H]))
1 [C]([H][H][H][C]([H][C]([H][H][C,0])[C,0]([H][H])))
1 [H]([C]([C]([H][H][C,0])[C,0]([H][H])[C]([H][H][H])))
4 [H]([C]([H][C]([H][C,0][C]([H][H][H]))[C,0]([H][H])))
3 [H]([C]([H][H][C]([H][C]([H][H][C,0])[C,0]([H][H]))))
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 7 / 45
8. Molecular Signature of Reactions and Proteins
Signature of a reaction. The signature of reaction R
S1 + S2 + . . . + Sn → P1 + P2 + . . . + Pn (2)
that transforms n substrates into m products is given by the difference between the
signature of the products and the signature of the substrates:
h
Xh Xh
σ(R) = σ(p) − σ(s) (3)
p∈P s∈S
Signature of protein sequences. The protein P is represented by the linear
chain given by its collapsed graph at residue level, a reduced molecular graph
representation G(V , E, C) known as string signature where V : residues a ∈ A,
E : contiguous in sequence, C : amino acid type
h
Xh
σ(P) = σ(a) (4)
a∈A
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 8 / 45
9. Protein Contact Maps
The protein contact map is a graph
representation of the 3D interactions
at residue level G(V , E, C) where V :
residues, E : contacts, C : amino acid
type
Two residues are considered to
interact when atoms between both
residues are at a distance lower than a
predetermined threshold (tipically
4.5 ∼ 5 Å)
Contact maps can account for
long-range interactions and
conformational states
Song et al. [2010]
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 9 / 45
10. Outline
1 Introduction
2 Computational Protein Descriptors
3 Sequence-based CPD
4 Structure-based CPD
5 Search Algorithms in CPD
6 De Novo Design
7 Challenges in Sequence and Structure-Based CPD
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 10 / 45
11. Sequence and Structure-Based CPD
Sequence-based CPD methods are in some cases a good trade-off between
complexity of the model and accuracy of the predictions
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 11 / 45
12. Sequence-based Knowledge-based potentials
The simplest way to score a protein and to identify active regions is through amino
acid scales or indexes
AAindex is a database of
544 amino acid indexes
94 Amino Acid Matrices
47 amino acid pair-wise contact potentials
Examples: hydrophobicity,
accessibility, van der Waals volume,
secondary structure propensity,
flexibility
This approach is widely used when
analyzing conserved motifs and
correlated mutations in protein fold
families through multiple alignments
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 12 / 45
13. Quantitative Structure-Activity Relationship (QSAR) Techniques
The goal is to model causal relationships
QSAR is a statistical method used
between
extensively by the chemical and
pharmaceutical industries in structures of interacting molecules
small-molecules and peptide measurables properties of scientific
optimization or commercial interest such as
ADME/Tox (absorption, distribution,
metabolism, excretion, and toxicity) of
drugs
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 13 / 45
14. QSAR Model Evaluation
Model predictability is generally evaluated through the leave-one-out (LOO)
cross-validation correlation coefficient q 2
Partial least-squares (PLS) regression is commonly used
Additional nonlinear terms can be added through the use of nonlinear regression
or machine learning techniques (kernel methods, random forests, etc)
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 14 / 45
15. QSAR Modeling Workflow
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 15 / 45
18. The ProSAR Algorithm
An extension of SAR-based approaches to CPD
It formalizes the decision-making processes about which mutations to include in
combinatorial libraries
N
XX
y = cij xij (5)
i=1 j∈A
y : the predicted function (activity) of the protein sequence
cij : the regression coefficients corresponding to the mutational effect of having residue
j among the 20 amino acids A at postion i
xij : binary variable indicating the presence or absence of residue j at position i
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 18 / 45
19. Improving Catalytic Function by ProSAR-driven Enzyme Evolution
Statistical analysis of protein sequence
activity relationships
Bacterial biocatalysis of
Atorvastatin (Lipitor)
(cholesterol-lowering drug)
Codexis Inc.
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 19 / 45
20. Outline
1 Introduction
2 Computational Protein Descriptors
3 Sequence-based CPD
4 Structure-based CPD
5 Search Algorithms in CPD
6 De Novo Design
7 Challenges in Sequence and Structure-Based CPD
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 20 / 45
21. Structure-based CPD
Energy functions and molecular force fields
Local conformational restrictions
Predicting entropic factors
Protein topological properties
From Narasimhan et al. [2010]
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 21 / 45
22. Energy Functions and Molecular Force Fields
In structure-based CPD, folds are usually
represented by the spatial coordinates of the
backbone atoms or design scaffold
Protein design is done by amino acid side
chains along the scaffold
Side chains are only permitted to assume a
discrete set of statistically preferred
conformations: rotamers
Rotamer/backbone and rotamer/rotamer
interaction energies are tabulated
These potential energies can then be
approximated by using any of the standard
force fields : CHARMM, AMBER, GROMOS
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 22 / 45
23. Molecular Force Fields
AMBER: a classical force field for energy and MD calculations:
X 1 X 1 X 1
V (r N ) = kb (l − l0 )2 + ka (θ − θ0 )2 + Vn [1 + cos(nω − γ)]
2 2 2
bonds angles torsions
N−1 X
( "„ « „ «6 # )
X N r0ij
12
r0ij qi qj
+ i,j −2 + (6)
rij rij 4π 0 rij
j=1 i=j+1
P
1 (·): energy between covalently bonded atoms.
Pbonds
angles (·): energy due to the geometry of electron orbitals involved in covalent
2
bonding.
P
torsions (·): energy for twisting a bond due to bond order (e.g. double bonds) and
3
neighboring bonds or lone pairs of electrons.
PN−1 PN
i=j+1 (·): non-bonded energy between all atom pairs:
4
j=1
1 van der Waals energies
2 Electrostatic energies
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 23 / 45
24. Structure-based Knowledge-based Potentials
They are built by performing a large-scale statistical study of structural databases
such as PDB (Protein Data Bank)
Rotamer libraries (∼ 150 rotameric states)
Binary patterning: only some type of amino acids are allowed based on the
hydrophobic environment
An implicit solvation model
Secondary structure propensity
Frequency of small segments in the PDB
Pairwise potentials
van der Waals interactions
Hydrogen bonding
Electrostatics
Entropy-based penalties for flexible side-chains
From Boas and Harbury [2007]
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 24 / 45
25. Energy Functions
Design along the backbone or scaffold
Rotamer/backbone and rotamer/rotamer interact. energies tabulated
Precomputed from molecular force fields : CHARMM, AMBER, GROMOS
Total energy of the protein
X X
ETOT = Ek (rk ) + Ekl (rk , rl ) (7)
k k =l
N : length of the protein
rk : the rotamer of the kth side chain
Ek (rk ) : the self-energy of a particular rotamer rk
Ekl (rk , rl ) : the pair energy of rotamers rk , rj
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 25 / 45
26. The Role of Dynamics
Besides protein structure, protein dynamics can play a direct role in molecular
recognition
Flexible proteins recognize their targets through induced fit or conformational
selection, likely showing promiscuity
Binding is commonly enthalpy-driven, but in some cases entropy is important, for
instance:
Proteins with multiple binding sites
Small hydrophobic molecules
Two types of source of protein motions:
Protein flexibility: intraconformational dynamics (fast time scale motions)
Conformational heterogeneity: interconformational dynamics
Gibbs free energy:
∆G = ∆H − T ∆S (8)
∆S = ∆Ssolv + ∆Sconf + ∆Srt (9)
∆Sconf : conformational entropy of protein and ligand
∆Srtf : rotational and translational degree of freedoms
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 26 / 45
27. Predicting Side-chain Dynamics from Structural Descriptors
The Lipari-Szabo model free approach approach allows to quantify motions from
NMR experiments by computing the generalized order parameter S 2
Protein backbone dynamics : 15 NH and 13 Cα H NMR relaxation methods
Protein side chain methyl dynamics : 13 Cα H NMR relaxation methods (side-chain
motions in the picosecond-to-nanosecond time regime)
From the BMRB we compiled S 2 data for 18 proteins, including 10 proteins in 2 or
more different states : calmodulin, barnase, pdz, mup, dfhr, staphylococcal
nuclease, pin1, sh3 domain, MSG
This technique provides only measurements for the Cα of methyl groups in side
chains : ALA, LEU, ILE, MET, THR, VAL
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 27 / 45
28. Structural Descriptors of Methyl Dynamics
We consider the following parameters influencing side-chain dynamics :
Packing density at the methyl site i and its neighboring residues j within a sphere of
r =5Å
0 1
X X B X
Pi = Cj e−rij = e−rjk A e−rij (10)
C
@
rij <5Å rij <5Å rjk <5Å
Side chain stiffness : number of dihedral angles separating the backbone from the
methyl carbon. weighted by the side-chain packing
Rotameric state : angular distance ∆χ = χ − χ0 to the closest rotameric state χ0 in
the library
Elongation : distance from the methyl site to the Cα
Pairwise contact potential : a knowledge-based potential of frequence of contacts
between residues at several distances computed from the PDB
Solvation effect : DSSP accessibility and residue hydrophobicity
Van der Waals contacts
Hydrogen bonds (in the case of Threonine)
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 28 / 45
29. Predicting Methyl Side-chain Dynamics
Algorithm : neural network
Cross-validation : r = 0.71 ± 0.029 Example : experimental and predicted
(p-value = 4.6 × 10−87 ) changes in ∆S 2 of barnase after binding
barstar
Protein MD method r (MD) r (nnet)
ubiquitin AMBER99SB 0.81 0.81
TNfn3 CHARMM 22 0.62 0.79 ∆S 2 > 0 ∆S 2 < 0
FNfn10 CHARMM 22 0.51 0.64 rigidification flexibilization
barnase OPLS-AA/L 0.55 0.64
calmodulin FDPB 0.60 0.72
[Carbonell and del Sol, 2009]
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 29 / 45
30. Outline
1 Introduction
2 Computational Protein Descriptors
3 Sequence-based CPD
4 Structure-based CPD
5 Search Algorithms in CPD
6 De Novo Design
7 Challenges in Sequence and Structure-Based CPD
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 30 / 45
31. Search Algorithms in CPD
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 31 / 45
32. Search Algorithms
Objective: finding the best design within the space of all possible amino
acid/rotameric states
A vast search space: 20N or pN
N: number of positions to mutate
p: number of rotameric states
Strategies
Deterministic algorithms
Dead-end elimination (DEE) algorithm: a pruning method.
Some accelerations of the DEE algorithm: upper-bound estimation; the “magic bullet” metric;
conformational splitting; background optimization
Stochastic algorithms
Monte Carlo
Simulated annealing
Genetic algorithms
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 32 / 45
33. The DEE Algorithm
It assumes that the energy of the protein can be written as
X X
ETOT = Ek (rk ) + Ekl (rk , rl ) (11)
k k =l
N : length of the protein
rk : the rotamer of the kth side chain
Ek (rk ):" the self-energy of a particular rotamer rk
Ekl (rk , rl ): the pair energy of the rotamers rk , rj
Complexity:
Single search scales quadratically with total number of rotamers O((p × N)2 )
Pair search scales cubically O((p × N)3 )
Brute force enumeration : O(pN )
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 33 / 45
34. The DEE Algorithm
Single rotamers and rotamer pairs are eliminated during the computational cycles
Single elimination : eliminate rotamer if some other rotamer in the side chain gives
better energy
N
X N
X
A
Ek (rk ) + min Ekl (rk , rlX )
A
> B
Ek (rk ) + max Ekl (rk , rlX )
B
(12)
X X
l=1 l=1
Pairs elimination : eliminate pair of rotamers in two positions if there exists another
pair that gives better energy
def
Ukl = Ek (rk ) + El (rlB ) + Ekl (rk , rlB )
AB A A
(13)
N
X “ ”
AB
Ukl + min Eki (rk , riX ) + Elj (rlB , rjX ) >
A
X
i=1
N
X “ ”
CD
Ukl + max Eki (rk , riX ) + Elj (rlD , rjX )
C
(14)
X
i=1
Values are precomputed and stored in energy matrices
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 34 / 45
35. Stochastic Algorithms
Search in the space of feasible designs by making a series of combinations of
random and directed moves
Monte Carlo Metropolis: a move consists of exchanging one rotamer for another
at a randomly chosen position, a modification is accepted if it lowers the energy
Simulated Annealing allows to explore nearby solutions at the initial cycles of the
search
Genetic Algorithms: a population of models is propagated (evolved) throughout
the course of the run and genetic operators, such as recombination, are used to
create new models from existing parents
They are fast, can be scaled up to problems of large complexity
They are not guaranteed to converge to the optimal solution
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 35 / 45
36. The SCHEMA Algorithm
Equivalent to an in silico directed evolution
Consists of scoring libraries of hybrid protein
sequences against the parental sequence
Scoring:
Calculate the number of interactions between residues
(contacts within 4.5 Å) that are disrupted in the creation
of hybrid proteins
Hybrids are scored for stability by counting the number of
disruptions
Protein is partitioned into blocks that should not
From [Meyer et al., 2006]
interrupted by crossovers (analog to genetic algorithms)
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 36 / 45
37. The OPTCOM and IPRO Algorithms for Library Design
The OPTCOM algorithm: The IPRO algorithm:
Balances size and Identify point mutations in the parent sequences
quality of the library using energy-based scoring fuctions
Residue and rotamer choices are driven by a
mixed-integer linear programming formulation
(MILP)
From [Saraf et al., 2006]
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 37 / 45
38. Some Web Resources
IPRO: Iterative Protein Redesign and Optimization.
http://maranas.che.psu.edu/IPRO.htm
EGAD: A Genetic Algorithm for protein Design.
http://egad.ucsd.edu/software.php
RosettaDesign: A software package.
http://rosettadesign.med.unc.edu/
SCHEMA A pair-wise energy function for scoring protein chimeras made from
homologous proteins. http://www.che.caltech.edu/groups/fha/
schema-tools/schema-overview.html
SHARPEN: Systematic Hierarchical Algorithms for Rotamers and Proteins on
an Extended Network.
http://koko.che.caltech.edu/sharpenabout.html
WHAT IF: Software for protein modelling, design, validation, and
visualisation. http://swift.cmbi.ru.nl/whatif/
FoldX: A force field for energy calculations and protein design.
http://foldx.crg.es/
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 38 / 45
39. Outline
1 Introduction
2 Computational Protein Descriptors
3 Sequence-based CPD
4 Structure-based CPD
5 Search Algorithms in CPD
6 De Novo Design
7 Challenges in Sequence and Structure-Based CPD
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 39 / 45
40. De Novo-Designed Proteins
In de novo designs, some assumptions are needed in order to make the search
space tractable
Usually we start from some basic motifs or domains as scaffolds for the design
Examples:
βαβ motif resembling a zinc finger
3 and 4 helix bundles
Helical coiled-coils
Helix bundle motifs can be parametrized using a few global variables that
describe the global structure
Applications:
New metal-binding sites
Nonbiological cofactors for novel biomaterials and electromechanical devices
Novel enzymatic activities
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 40 / 45
41. Example: De Novo Design of a Metalloprotein
Computational de novo design of a four-helix (108 residues) bundle containing the
non-biological cofactor iron diphenyl porphyrin (DPP-Fe) [Bender et al., 2007]
The initial helix bundle was selected as low-energy structure computed with MCSA
STITCH: a program to select loops connecting helices from PDB Select
CHARMM and PROCHECK for removing overlaps
4 His and the 4 Thr residues to support the 6-point coordination of the Fe(III) cations
SCADS: provides side-dependent amino acid probabilities in each round
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 41 / 45
42. Outline
1 Introduction
2 Computational Protein Descriptors
3 Sequence-based CPD
4 Structure-based CPD
5 Search Algorithms in CPD
6 De Novo Design
7 Challenges in Sequence and Structure-Based CPD
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 42 / 45
43. Challenges in Sequence and Structure-Based CPD
Modeling
Greater availability of 3D protein structural information
More accurate energy functions
Improvement of rigid and flexible docking
Design
Improvement in search algorithms
Parametrization for non-natural amino acids
Prediction
Beyond additive models: using machine-learning algorithms
More complete environment descriptors
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 43 / 45
44. Computational Protein Design
2. Computational Protein Design Techniques
Pablo Carbonell
pablo.carbonell@issb.genopole.fr
iSSB, Institute of Systems and Synthetic Biology
Genopole, University d’Évry-Val d’Essonne, France
mSSB: December 2010
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 44 / 45
45. Bibliography I
Gretchen M. Bender, Andreas Lehmann, Hongling Zou, Hong Cheng, H. Christopher Fry, Don Engel, Michael J. Therien, J. Kent Blasie, Heinrich Roder,
Jeffrey G. Saven, and William F. DeGrado. De Novo Design of a Single-Chain Diphenylporphyrin Metalloprotein. Journal of the American Chemical
Society, 129(35):10732–10740, September 2007. ISSN 0002-7863. doi: 10.1021/ja071199j. URL http://dx.doi.org/10.1021/ja071199j.
F. Edward Boas and Pehr B. Harbury. Potential energy functions for protein design. Current opinion in structural biology, 17(2):199–204, April 2007. ISSN
0959-440X. doi: 10.1016/j.sbi.2007.03.006. URL http://dx.doi.org/10.1016/j.sbi.2007.03.006.
Pablo Carbonell and Antonio del Sol. Methyl side-chain dynamics prediction based on protein structure. Bioinformatics, pages btp463+, July 2009. doi:
10.1093/bioinformatics/btp463. URL http://dx.doi.org/10.1093/bioinformatics/btp463.
Jean-Loup L. Faulon, Michael J. Collins, and Robert D. Carr. The signature molecular descriptor. 4. Canonizing molecules using extended valence
sequences. Journal of chemical information and computer sciences, 44(2):427–436, 2004. ISSN 0095-2338. doi: 10.1021/ci0341823. URL
http://dx.doi.org/10.1021/ci0341823.
Michelle M. Meyer, Lisa Hochrein, and Frances H. Arnold. Structure-guided SCHEMA recombination of distantly related β-lactamases. Protein Engineering
Design and Selection, 19(12):563–570, December 2006. ISSN 1741-0126. doi: 10.1093/protein/gzl045. URL
http://dx.doi.org/10.1093/protein/gzl045.
Diwahar Narasimhan, Mark R. Nance, Daquan Gao, Mei-Chuan Ko, Joanne Macdonald, Patricia Tamburi, Dan Yoon, Donald M. Landry, James H. Woods,
Chang-Guo Zhan, John J. G. Tesmer, and Roger K. Sunahara. Structural analysis of thermostabilizing mutations of cocaine esterase. Protein
Engineering Design and Selection, 23(7):537–547, July 2010. doi: 10.1093/protein/gzq025. URL http://dx.doi.org/10.1093/protein/gzq025.
Manish C. Saraf, Gregory L. Moore, Nina M. Goodey, Vania Y. Cao, Stephen J. Benkovic, and Costas D. Maranas. IPRO: an iterative computational protein
library redesign and optimization procedure. Biophysical journal, 90(11):4167–4180, June 2006. ISSN 0006-3495. doi: 10.1529/biophysj.105.079277. URL
http://dx.doi.org/10.1529/biophysj.105.079277.
Jiangning Song, Kazuhiro Takemoto, Hongbin Shen, Hao Tan, Michael M. Gromiha, and Tatsuya Akutsu. Prediction of Protein Folding Rates from Structural
Topology and Complex Network Properties. IPSJ Transactions on Bioinformatics, 3:40–53, 2010. doi: 10.2197/ipsjtbio.3.40. URL
http://dx.doi.org/10.2197/ipsjtbio.3.40.
Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 45 / 45