Computational simulations were used to investigate possible dimer structures of the neuronal protein alpha-synuclein. Molecular docking and molecular dynamics simulations were performed on dimers formed from alpha-helical and beta-sheet conformations of alpha-synuclein monomers. Binding energies and interactions between monomers in the dimer structures were analyzed. Both hydrophobic and electrostatic interactions contributed significantly to dimer stability, even though alpha-synuclein is highly charged. The central hydrophobic region of alpha-synuclein formed the majority of the interface between monomers in the dimer structures.
This document summarizes a research paper that proposes a new technique called Protein Tertiary Structure Prediction using Genetic Algorithm (PTSPGA) to predict the tertiary structure of proteins based on their primary amino acid sequences. The technique uses a genetic algorithm approach to find protein conformations with the lowest free energy, as evaluated by the Empirical Conformational Energy Program for Peptides (ECEPP/3) force field model. The proposed genetic algorithm was tested on Met-enkephalin and other proteins, and experimental results found it to be reliable and accurate at predicting protein tertiary structures computationally from sequence alone.
INTRODUCTION
STRUCTURAL PROTEOMICS
WHAT IS THE IMPORTANCE OF STUDY OF PROTEIN
METHODS FOR SOLVING PROTEIN STRUCTURE
1. X- RAY CRYSTALLOGRAPHY
INTRODUCTION
PROCEDURE
LIMITATIONS
2.NUCLEAR MAGNETIC RESONANCE
PROTEIN STRUCTURE DETERMINATION
3. MASS SPECTROMETER
MALDI
ESI
STRUCTURE MODELING
APPLICATIONS
CONCLUSION
REFERENCES
1) Molecular dynamics simulations were used to explore the effects of sodium ions and the mechanism of their role as allosteric modulators of dopaminergic G protein coupled receptors.
2) The simulations showed sodium ions binding to the receptor in two steps: first interacting with negatively charged residues on the extracellular surface, then visiting three binding sites between transmembrane regions.
3) Sodium ions preferentially bound to an allosteric site between transmembrane regions 2 and 3, where they induced a conformational change in a "toggle switch" tryptophan residue to lock the receptor in an inactive state.
METHODS TO DETERMINE PROTEIN STRUCTURE Sabahat Ali
This document discusses several methods for determining protein structure: Edman degradation, X-ray crystallography, Western blotting, SDS-PAGE, 2D gel electrophoresis, and isoelectric focusing. Edman degradation involves chemically removing amino acids from the N-terminus of a protein one by one to determine the sequence. X-ray crystallography provides high-resolution 3D structures of proteins. Western blotting identifies specific proteins in a sample using antibodies. SDS-PAGE and 2D gels separate proteins by size and electric charge properties. Isoelectric focusing separates proteins based on their isoelectric points.
Oracle Research Fellowship NeoN poster presentation_Spring 2015Samender Randhawa
- NeoN is a radical S-adenosyl-L-methionine (SAM) enzyme that epimerizes neomycin C to neomycin B through its two [4Fe-4S] clusters, playing a vital role in neomycin B antibiotic biosynthesis.
- The goal is to crystallize NeoN to understand its structure and catalytic activity at high resolution in order to modify it as a biocatalyst to recognize different substrates.
- NeoN will be cloned, expressed, purified, concentrated, and crystallized for X-ray diffraction to determine its structure and identify active site residues involved in substrate recognition and epimerization.
This document summarizes an ab initio study of the denaturation of the Small Ubiquitin-like Modifier (SUMO) protein using molecular dynamics simulations and NMR calculations. The study found that after denaturing, some residues in SUMO still showed propensities to form secondary structure rather than becoming fully random coils. Molecular dynamics simulations of different SUMO topologies under denaturing conditions were performed. NMR properties were then calculated and compared to experimental observations, showing some residues maintained beta sheet or alpha helical propensities when denatured. This suggests denatured proteins can become trapped in local energy minima rather than fully unfolding.
The document discusses the hierarchical structure of proteins from primary to quaternary structure. It explains that a protein's amino acid sequence determines its 3D tertiary structure and function. The document also covers protein folding, common structural motifs, examples of protein function like enzymes and ligand binding proteins, and how chaperones assist in protein folding. It discusses how proteins evolve into families and provides examples of structural similarities between protein homologs like globins.
levels of protein structure , Domains ,motifs & Folds in protein structureAaqib Naseer
Protein structure is hierarchical, with four levels: primary, secondary, tertiary, and quaternary. The primary structure is the amino acid sequence. Secondary structures include alpha helices and beta sheets formed by hydrogen bonding between amino acids in the sequence. Tertiary structure involves folding of the entire chain into a compact 3D structure. Quaternary structure involves the assembly of protein subunits. Other structural features include domains, which are independently folded and functional regions, motifs like loops and barrels formed by secondary structure elements, and folds defined by the arrangement of alpha helices and beta sheets. Understanding protein structure is important for studying protein function and for developing drugs.
This document summarizes a research paper that proposes a new technique called Protein Tertiary Structure Prediction using Genetic Algorithm (PTSPGA) to predict the tertiary structure of proteins based on their primary amino acid sequences. The technique uses a genetic algorithm approach to find protein conformations with the lowest free energy, as evaluated by the Empirical Conformational Energy Program for Peptides (ECEPP/3) force field model. The proposed genetic algorithm was tested on Met-enkephalin and other proteins, and experimental results found it to be reliable and accurate at predicting protein tertiary structures computationally from sequence alone.
INTRODUCTION
STRUCTURAL PROTEOMICS
WHAT IS THE IMPORTANCE OF STUDY OF PROTEIN
METHODS FOR SOLVING PROTEIN STRUCTURE
1. X- RAY CRYSTALLOGRAPHY
INTRODUCTION
PROCEDURE
LIMITATIONS
2.NUCLEAR MAGNETIC RESONANCE
PROTEIN STRUCTURE DETERMINATION
3. MASS SPECTROMETER
MALDI
ESI
STRUCTURE MODELING
APPLICATIONS
CONCLUSION
REFERENCES
1) Molecular dynamics simulations were used to explore the effects of sodium ions and the mechanism of their role as allosteric modulators of dopaminergic G protein coupled receptors.
2) The simulations showed sodium ions binding to the receptor in two steps: first interacting with negatively charged residues on the extracellular surface, then visiting three binding sites between transmembrane regions.
3) Sodium ions preferentially bound to an allosteric site between transmembrane regions 2 and 3, where they induced a conformational change in a "toggle switch" tryptophan residue to lock the receptor in an inactive state.
METHODS TO DETERMINE PROTEIN STRUCTURE Sabahat Ali
This document discusses several methods for determining protein structure: Edman degradation, X-ray crystallography, Western blotting, SDS-PAGE, 2D gel electrophoresis, and isoelectric focusing. Edman degradation involves chemically removing amino acids from the N-terminus of a protein one by one to determine the sequence. X-ray crystallography provides high-resolution 3D structures of proteins. Western blotting identifies specific proteins in a sample using antibodies. SDS-PAGE and 2D gels separate proteins by size and electric charge properties. Isoelectric focusing separates proteins based on their isoelectric points.
Oracle Research Fellowship NeoN poster presentation_Spring 2015Samender Randhawa
- NeoN is a radical S-adenosyl-L-methionine (SAM) enzyme that epimerizes neomycin C to neomycin B through its two [4Fe-4S] clusters, playing a vital role in neomycin B antibiotic biosynthesis.
- The goal is to crystallize NeoN to understand its structure and catalytic activity at high resolution in order to modify it as a biocatalyst to recognize different substrates.
- NeoN will be cloned, expressed, purified, concentrated, and crystallized for X-ray diffraction to determine its structure and identify active site residues involved in substrate recognition and epimerization.
This document summarizes an ab initio study of the denaturation of the Small Ubiquitin-like Modifier (SUMO) protein using molecular dynamics simulations and NMR calculations. The study found that after denaturing, some residues in SUMO still showed propensities to form secondary structure rather than becoming fully random coils. Molecular dynamics simulations of different SUMO topologies under denaturing conditions were performed. NMR properties were then calculated and compared to experimental observations, showing some residues maintained beta sheet or alpha helical propensities when denatured. This suggests denatured proteins can become trapped in local energy minima rather than fully unfolding.
The document discusses the hierarchical structure of proteins from primary to quaternary structure. It explains that a protein's amino acid sequence determines its 3D tertiary structure and function. The document also covers protein folding, common structural motifs, examples of protein function like enzymes and ligand binding proteins, and how chaperones assist in protein folding. It discusses how proteins evolve into families and provides examples of structural similarities between protein homologs like globins.
levels of protein structure , Domains ,motifs & Folds in protein structureAaqib Naseer
Protein structure is hierarchical, with four levels: primary, secondary, tertiary, and quaternary. The primary structure is the amino acid sequence. Secondary structures include alpha helices and beta sheets formed by hydrogen bonding between amino acids in the sequence. Tertiary structure involves folding of the entire chain into a compact 3D structure. Quaternary structure involves the assembly of protein subunits. Other structural features include domains, which are independently folded and functional regions, motifs like loops and barrels formed by secondary structure elements, and folds defined by the arrangement of alpha helices and beta sheets. Understanding protein structure is important for studying protein function and for developing drugs.
This document discusses various techniques for determining the primary, secondary, tertiary, and quaternary structures of proteins. It describes methods such as determining amino acid composition, degradation of proteins into smaller fragments, sequencing techniques like Edman degradation, and use of X-ray crystallography and NMR to analyze secondary and tertiary structures. Chromatography, electrophoresis, and centrifugation techniques are also covered for protein purification and separation.
This document provides an overview of course content for a USMLE preparation course covering biochemistry and molecular biology, cellular biology, genetics and human development, immunology, and microbiology and infectious diseases. The course content is organized into components that delve into topics such as gene expression, metabolic pathways, cell structure and function, embryology, immunology basics and applications, bacterial diseases, virology, and mycology/parasitology. Emphasis is placed on learning objectives relevant to the USMLE, and multimedia resources such as videos, presentations, and notes are provided to support learning within each topic area.
This document discusses assembly-assisted peptide ligation in native conditions. It proposes that peptide fragments can assemble into protein-like structures through non-covalent interactions in native conditions, bringing peptide termini into close proximity. This proximity could allow the termini to ligate without the need for cysteine residues, as seen in native chemical ligation. The document outlines an experiment to test this hypothesis using two fragments of a zinc finger protein, which would assemble due to zinc ion binding and potentially ligate using the coupling reagent PMSF in native conditions.
The document discusses protein structure and its importance in determining protein function. It covers several key points:
1) There are multiple levels of protein structure from primary to quaternary structure. Higher-order structures like tertiary structure bring distant parts of the amino acid sequence into proximity, allowing proteins to perform their functions.
2) Protein structure is determined by the amino acid sequence through the physical properties of residues. The sequence encodes the folding pathway that results in a stable, functional 3D structure.
3) Experimental methods like X-ray crystallography and NMR spectroscopy are used to determine high-resolution protein structures that reveal how structure enables function. Databases like PDB archive and classify protein structures.
Spin labeling is a technique to introduce stable paramagnetic centers into biomolecules like proteins and nucleic acids to study their structure and dynamics using electron paramagnetic resonance (EPR) spectroscopy. The most common method uses methanethiosulfonate spin labels that form disulfide bonds with engineered cysteine residues in the target protein. Alternative methods include spin labels attached via chemical ligation or nonsense suppression techniques. EPR data from spin-labeled sites can provide information about side chain mobility, solvent accessibility, and intra- or intermolecular distances within the biomolecule.
This document provides an overview of content covered in the USMLE Web Maps for Biochemistry and Molecular Biology (BMS) General Principles. It includes summaries of key topics like gene expression, transcription, translation, protein structure and function, energy metabolism, metabolic pathways and associated diseases, cellular biology, genetics, human development, and embryogenesis. The Web Maps provide learning objects and multimedia resources on each topic to support medical student preparation for Steps 1, 2, or 3 of the USMLE.
This document discusses proteins, including their structure and functions. It covers the following key points:
1. Proteins are made of amino acids that are linked together via peptide bonds. There are 20 common amino acids that make up proteins.
2. Proteins have primary, secondary, tertiary, and sometimes quaternary structures that determine their shape and function.
3. Techniques like chromatography, electrophoresis, and sequencing are used to purify and analyze proteins. Protein sequencing methods like Edman degradation can determine the amino acid sequence.
This doctoral dissertation discusses molecular dynamics simulations of polyglutamine and insulin aggregation. Two projects are described. The first uses replica-exchange molecular dynamics to simulate one and two polyglutamine peptides. It finds the peptides form helical or coil structures at long distances but β-sheets at short distances. The second simulates insulin and binding peptides LVEALYL and RGFFYT. It discovers both peptides aggregate into β-sheets and bind strongly to insulin.
This document outlines the goals and key concepts regarding protein structure. It discusses the four levels of protein structure - primary, secondary, tertiary, and quaternary. Methods for determining protein structure are also covered, including protein purification techniques like chromatography, electrophoresis, and centrifugation. Protein sequencing methods such as Edman degradation are also summarized. The document provides an overview of protein structure and analysis.
Proteins play key roles in living systems through catalysis, transport, and information transfer. They have a hierarchical structure including primary, secondary, tertiary, and quaternary levels. The primary structure is the amino acid sequence, and higher levels of organization are determined by the primary structure. Protein folding and interactions between residues determine the final 3D tertiary and quaternary structures, which are critical for protein function. Misfolded proteins can cause diseases.
Peptidomimetics are compounds whose essential elements (pharmacophore) mimic a natural peptide or protein in 3D space and which retain the ability to interact with the biological target and produce the same biological effect.
Peptidomimetics are designed to circumvent some of the problems associated with a natural peptide for example
Stability against proteolysis (duration of activity)
Poor bioavailability.
Receptor selectivity or potency (often can be substantially improved).
Introduction
Classification
Therapeutic values of peptidomimetics
Design of peptidomimetics by manipulation of amino acids
Modification of peptide backbone
Chemistry of prostaglandins, leukotrienes and thromboxanes
Protein Structural Prediction
1. Molecular Structure prediction
2. Sequence
3. Protein Folding
4. The Leventhal Paradox
5. Energy (Minimization )
6. The Hydrophobic Effect
7. Protein Structure Determination ( X-ray,NMR)
8. Ab initio Prediction
9. Lattice String Folding
10. Rosetta (Monte Carlo based method)
11. Homology-based Prediction
Molecular dynamics (MD) simulations allow atoms and molecules to interact over time, representing a virtual experiment. MD was used to give dynamics to SUMO proteins in solution. The SUMO protein was divided into fragments which were given random conformations using CYANA. These conformations were then converted to GROMACS format and molecular dynamics simulations were performed using GROMACS. The simulations involved energy minimization to relieve strain, followed by production runs. Various analysis tools were then used to analyze the results.
This document provides an overview of peptidomimetics. It begins with the evolution of peptidomimetics and how they were developed to overcome limitations of peptides as drugs. It then covers classification of peptidomimetics, design strategies like modifying amino acids and imposing structural constraints, and examples of peptidomimetic drugs that inhibit enzymes like ACE, thrombin, and HIV protease. The document concludes by stating peptidomimetics are an important area of drug design for developing small molecule mimics of peptide functions.
This document discusses motifs and domains in proteins. It defines motifs as short conserved regions related to function, such as binding sites, that are not detectable by sequence searches. There are sequence motifs consisting of nucleotide or amino acid patterns, and structural motifs formed by amino acid spatial arrangements. Domains are stable, independently folding units of proteins that determine structure and function. Both motifs and domains are useful for classifying protein families and have structural and functional roles, though domains are more stable independently. Motifs and domains form through interactions of alpha helices and beta sheets and have similarities, but domains mainly determine unique functions while motifs mainly provide structural roles within families.
Secondary Structure Prediction of proteins Vijay Hemmadi
Secondary structure prediction has been around for almost a quarter of a century. The early methods suffered from a lack of data. Predictions were performed on single sequences rather than families of homologous sequences, and there were relatively few known 3D structures from which to derive parameters. Probably the most famous early methods are those of Chou & Fasman, Garnier, Osguthorbe & Robson (GOR) and Lim. Although the authors originally claimed quite high accuracies (70-80 %), under careful examination, the methods were shown to be only between 56 and 60% accurate (see Kabsch & Sander, 1984 given below). An early problem in secondary structure prediction had been the inclusion of structures used to derive parameters in the set of structures used to assess the accuracy of the method.
Some good references on the subject:
The document discusses protein structure prediction methods such as homology modeling and threading. Homology modeling relies on sequence similarity between the target and template proteins to generate a structural model. It involves aligning the sequences, building the backbone based on the template, and modeling side chains. Threading methods can be used when sequence similarity is low but still detects structural similarity by identifying conserved protein folds from structural databases. Experimental techniques like X-ray crystallography and NMR spectroscopy determine protein structures but have limitations for some proteins.
The document discusses protein structure and stability. It describes the four levels of protein structure: primary, secondary, tertiary, and quaternary. The primary structure is the amino acid sequence. Secondary structures include alpha helices and beta sheets formed by hydrogen bonding. Tertiary structure involves folding influenced by interactions between R groups. Quaternary structure results from interactions between multiple polypeptide chains, as in hemoglobin. The document also discusses factors that stabilize protein structures such as disulfide bonds and noncovalent interactions, and how denaturation and renaturation can alter protein structure.
The document discusses key topics around new product development and diffusion, including challenges in NPD, stages of the NPD process, trajectories of change for companies, and factors that influence the adoption of new products. It provides details on generating new product ideas, screening ideas, and managing the development process. The traditional and an alternate model of industry lifecycles are presented, along with the 5 stages of consumer adoption of new products - awareness, interest, evaluation, trial, and adoption.
This document discusses various techniques for determining the primary, secondary, tertiary, and quaternary structures of proteins. It describes methods such as determining amino acid composition, degradation of proteins into smaller fragments, sequencing techniques like Edman degradation, and use of X-ray crystallography and NMR to analyze secondary and tertiary structures. Chromatography, electrophoresis, and centrifugation techniques are also covered for protein purification and separation.
This document provides an overview of course content for a USMLE preparation course covering biochemistry and molecular biology, cellular biology, genetics and human development, immunology, and microbiology and infectious diseases. The course content is organized into components that delve into topics such as gene expression, metabolic pathways, cell structure and function, embryology, immunology basics and applications, bacterial diseases, virology, and mycology/parasitology. Emphasis is placed on learning objectives relevant to the USMLE, and multimedia resources such as videos, presentations, and notes are provided to support learning within each topic area.
This document discusses assembly-assisted peptide ligation in native conditions. It proposes that peptide fragments can assemble into protein-like structures through non-covalent interactions in native conditions, bringing peptide termini into close proximity. This proximity could allow the termini to ligate without the need for cysteine residues, as seen in native chemical ligation. The document outlines an experiment to test this hypothesis using two fragments of a zinc finger protein, which would assemble due to zinc ion binding and potentially ligate using the coupling reagent PMSF in native conditions.
The document discusses protein structure and its importance in determining protein function. It covers several key points:
1) There are multiple levels of protein structure from primary to quaternary structure. Higher-order structures like tertiary structure bring distant parts of the amino acid sequence into proximity, allowing proteins to perform their functions.
2) Protein structure is determined by the amino acid sequence through the physical properties of residues. The sequence encodes the folding pathway that results in a stable, functional 3D structure.
3) Experimental methods like X-ray crystallography and NMR spectroscopy are used to determine high-resolution protein structures that reveal how structure enables function. Databases like PDB archive and classify protein structures.
Spin labeling is a technique to introduce stable paramagnetic centers into biomolecules like proteins and nucleic acids to study their structure and dynamics using electron paramagnetic resonance (EPR) spectroscopy. The most common method uses methanethiosulfonate spin labels that form disulfide bonds with engineered cysteine residues in the target protein. Alternative methods include spin labels attached via chemical ligation or nonsense suppression techniques. EPR data from spin-labeled sites can provide information about side chain mobility, solvent accessibility, and intra- or intermolecular distances within the biomolecule.
This document provides an overview of content covered in the USMLE Web Maps for Biochemistry and Molecular Biology (BMS) General Principles. It includes summaries of key topics like gene expression, transcription, translation, protein structure and function, energy metabolism, metabolic pathways and associated diseases, cellular biology, genetics, human development, and embryogenesis. The Web Maps provide learning objects and multimedia resources on each topic to support medical student preparation for Steps 1, 2, or 3 of the USMLE.
This document discusses proteins, including their structure and functions. It covers the following key points:
1. Proteins are made of amino acids that are linked together via peptide bonds. There are 20 common amino acids that make up proteins.
2. Proteins have primary, secondary, tertiary, and sometimes quaternary structures that determine their shape and function.
3. Techniques like chromatography, electrophoresis, and sequencing are used to purify and analyze proteins. Protein sequencing methods like Edman degradation can determine the amino acid sequence.
This doctoral dissertation discusses molecular dynamics simulations of polyglutamine and insulin aggregation. Two projects are described. The first uses replica-exchange molecular dynamics to simulate one and two polyglutamine peptides. It finds the peptides form helical or coil structures at long distances but β-sheets at short distances. The second simulates insulin and binding peptides LVEALYL and RGFFYT. It discovers both peptides aggregate into β-sheets and bind strongly to insulin.
This document outlines the goals and key concepts regarding protein structure. It discusses the four levels of protein structure - primary, secondary, tertiary, and quaternary. Methods for determining protein structure are also covered, including protein purification techniques like chromatography, electrophoresis, and centrifugation. Protein sequencing methods such as Edman degradation are also summarized. The document provides an overview of protein structure and analysis.
Proteins play key roles in living systems through catalysis, transport, and information transfer. They have a hierarchical structure including primary, secondary, tertiary, and quaternary levels. The primary structure is the amino acid sequence, and higher levels of organization are determined by the primary structure. Protein folding and interactions between residues determine the final 3D tertiary and quaternary structures, which are critical for protein function. Misfolded proteins can cause diseases.
Peptidomimetics are compounds whose essential elements (pharmacophore) mimic a natural peptide or protein in 3D space and which retain the ability to interact with the biological target and produce the same biological effect.
Peptidomimetics are designed to circumvent some of the problems associated with a natural peptide for example
Stability against proteolysis (duration of activity)
Poor bioavailability.
Receptor selectivity or potency (often can be substantially improved).
Introduction
Classification
Therapeutic values of peptidomimetics
Design of peptidomimetics by manipulation of amino acids
Modification of peptide backbone
Chemistry of prostaglandins, leukotrienes and thromboxanes
Protein Structural Prediction
1. Molecular Structure prediction
2. Sequence
3. Protein Folding
4. The Leventhal Paradox
5. Energy (Minimization )
6. The Hydrophobic Effect
7. Protein Structure Determination ( X-ray,NMR)
8. Ab initio Prediction
9. Lattice String Folding
10. Rosetta (Monte Carlo based method)
11. Homology-based Prediction
Molecular dynamics (MD) simulations allow atoms and molecules to interact over time, representing a virtual experiment. MD was used to give dynamics to SUMO proteins in solution. The SUMO protein was divided into fragments which were given random conformations using CYANA. These conformations were then converted to GROMACS format and molecular dynamics simulations were performed using GROMACS. The simulations involved energy minimization to relieve strain, followed by production runs. Various analysis tools were then used to analyze the results.
This document provides an overview of peptidomimetics. It begins with the evolution of peptidomimetics and how they were developed to overcome limitations of peptides as drugs. It then covers classification of peptidomimetics, design strategies like modifying amino acids and imposing structural constraints, and examples of peptidomimetic drugs that inhibit enzymes like ACE, thrombin, and HIV protease. The document concludes by stating peptidomimetics are an important area of drug design for developing small molecule mimics of peptide functions.
This document discusses motifs and domains in proteins. It defines motifs as short conserved regions related to function, such as binding sites, that are not detectable by sequence searches. There are sequence motifs consisting of nucleotide or amino acid patterns, and structural motifs formed by amino acid spatial arrangements. Domains are stable, independently folding units of proteins that determine structure and function. Both motifs and domains are useful for classifying protein families and have structural and functional roles, though domains are more stable independently. Motifs and domains form through interactions of alpha helices and beta sheets and have similarities, but domains mainly determine unique functions while motifs mainly provide structural roles within families.
Secondary Structure Prediction of proteins Vijay Hemmadi
Secondary structure prediction has been around for almost a quarter of a century. The early methods suffered from a lack of data. Predictions were performed on single sequences rather than families of homologous sequences, and there were relatively few known 3D structures from which to derive parameters. Probably the most famous early methods are those of Chou & Fasman, Garnier, Osguthorbe & Robson (GOR) and Lim. Although the authors originally claimed quite high accuracies (70-80 %), under careful examination, the methods were shown to be only between 56 and 60% accurate (see Kabsch & Sander, 1984 given below). An early problem in secondary structure prediction had been the inclusion of structures used to derive parameters in the set of structures used to assess the accuracy of the method.
Some good references on the subject:
The document discusses protein structure prediction methods such as homology modeling and threading. Homology modeling relies on sequence similarity between the target and template proteins to generate a structural model. It involves aligning the sequences, building the backbone based on the template, and modeling side chains. Threading methods can be used when sequence similarity is low but still detects structural similarity by identifying conserved protein folds from structural databases. Experimental techniques like X-ray crystallography and NMR spectroscopy determine protein structures but have limitations for some proteins.
The document discusses protein structure and stability. It describes the four levels of protein structure: primary, secondary, tertiary, and quaternary. The primary structure is the amino acid sequence. Secondary structures include alpha helices and beta sheets formed by hydrogen bonding. Tertiary structure involves folding influenced by interactions between R groups. Quaternary structure results from interactions between multiple polypeptide chains, as in hemoglobin. The document also discusses factors that stabilize protein structures such as disulfide bonds and noncovalent interactions, and how denaturation and renaturation can alter protein structure.
The document discusses key topics around new product development and diffusion, including challenges in NPD, stages of the NPD process, trajectories of change for companies, and factors that influence the adoption of new products. It provides details on generating new product ideas, screening ideas, and managing the development process. The traditional and an alternate model of industry lifecycles are presented, along with the 5 stages of consumer adoption of new products - awareness, interest, evaluation, trial, and adoption.
This document provides guidance on teaching procedures in the classroom. It explains that procedures are statements of student expectations that allow classroom activities to run efficiently. The document outlines specific procedures like what to do at the bell, with questions, and finished work. It distinguishes that procedures tell how to do something, while routines are done automatically. It recommends teaching procedures by telling students expectations, displaying them visually, demonstrating through modeling, having students practice, and periodically re-teaching or reviewing procedures.
This document provides substitutes for various foods, herbs, and spices in Urdu and their approximate quantities or tastes in English. It lists over 50 specific ingredients and suggests replacement options that are similar in taste or used in similar quantities and amounts. For example, it suggests that almond milk can be substituted with soy/peanut butter/milk, anise seed with fennel, and bell pepper with capsicum/green pepper. The substitutes are meant to provide comparable flavors and textures in cooking.
This document contains testimonials from various individuals praising Eugene Castello, the President of Global Hawk Resources, and his 25+ years of experience in customer service, sales, marketing and debt collection. The clients highlight Gene's extensive knowledge of the industry, creative solutions, strong relationships, responsiveness and ensuring the best outcomes for his clients. They recommend Gene and Global Hawk Resources for anyone needing assistance with debt collection or business partnerships.
This document discusses the four operating systems certified by Oracle that are recommended for running Oracle workloads on Amazon EC2: Red Hat Enterprise Linux, SUSE Linux Enterprise Server, Oracle Linux, and Microsoft Windows Server. It provides details on the features, licensing, and pricing of each operating system. The best choice depends on factors like workload type, instance selection, familiarity, and cost preference.
Cloud computing & Batch processing: potentiels & perspectives Claude Riousset
Présentation effectuée le 21/10 pour le groupe opérations du "Guide Share France"
Thème: Cloud Computing et Batch processing
Historique et rappel des concepts
Data Center & transformation
Synergie Cloud & Batch, exemple.
Perspectives OpenStack et exemples
The document describes the BECKON approach to learning, which structures learning in phases. BECKON evaluates an individual's current capabilities, designs a learning roadmap to achieve outcomes, and measures the results. It is a six-phase methodology that includes learning, evaluation, and measurable outcomes. BECKON learning impacts both individuals' lives and the organizations they work for.
Method for enhancing retention in complete denture bases/dental coursesIndian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and
offering a wide range of dental certified courses in different formats.for more details please visit
www.indiandentalacademy.com
Instructions for Authors of IJRS (International Journal of Remote Sensing) fo...Atiqa khan
This document provides instructions for authors submitting papers to International Journal of Remote Sensing, including guidelines on manuscript preparation, submission process, copyright and authors' rights. Key points covered include formatting manuscripts with sections, references, figures and equations according to journal style; submitting manuscripts online through ScholarOne; copyright being assigned to the journal; and authors retaining rights to freely access and share their published articles.
This document summarizes a presentation about Siemens' strategy for implementing open innovation. It discusses Siemens' profile and the research problem of implementing open innovation principles in a large organization. It then presents findings about Siemens' "open innovation diamond" framework, which includes four key dimensions - principles, capabilities, context, and technologies - that are important for bringing an open innovation strategy from the strategic vision level to organizational reality. The framework provides a holistic model for conceptualizing and balancing the key factors involved in implementing open innovation in a large company.
Designing for Holistic Cross Channel ExperiencesSamantha Starmer
UX Israel Studio 2013 workshop. Much of the structure and content is similar to other workshop presentations I've posted, but there are some new examples and exercises.
Shape memory alloys are metal alloys that can be deformed at one temperature but return to their original shape when heated or cooled. The most common alloys are nickel-titanium (Nitinol), copper-zinc-aluminum, and copper-aluminum-nickel. Nitinol was discovered in the 1960s and is now used widely in applications such as medical devices, aircraft, and household appliances. Shape memory alloys work through a solid state phase change between martensite and austenite phases - deforming occurs in the martensite phase while heating triggers shape recovery in the austenite phase. They provide advantages like biocompatibility and diverse applications but also
PLAN DE TRAVAIL
*- Introduction
*- Définitions de style Néo-mauresque
*- La naissances de Style Néo-mauresque en Algérie
*- Les conditions d'émergence du style néo mauresque en Algérie
*- L’ architecture Néo-mauresque en Algérie
*- Exemple de l’architecture Néo-mauresque :La grande poste
*- L’histoire de la grande poste
*- Etude de plan de la grande poste
*- Etude de la façade de la grande poste
*- Etude intérieure de la grande poste
*- Caractéristiques de l’architecture Néo-mauresque et l’architecture néoclassique dans la grande poste d’Alger
*- Conclusion
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.
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.
QSAR Studies of the Inhibitory Activity of a Series of Substituted Indole and...inventionjournals
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This gene is an individual from the STEAP family and encodes a multipass film protein that confines to the Golgi complex, the plasma layer, and the vesicular cylindrical structures in the cytosol. A very comparative protein in mouse has both ferrireductase and cupric reductase action and invigorates the cell take-up of both iron and copper in vitro. Expanded transcriptional articulation of the human quality is related with prostate malignant growth movement. Substitute transcriptional graft variations, encoding distinctive isoforms, have been described. Therefore, in the present study, we generated a precise three-dimensional (3D) model of metalloreductase STEAP2 protein using MODELLER 9.21 and validated its structure using PROCHECK software. Modeled protein contains more than 94.5% of amino acids in core region. We interpreted the action of natural compounds docking against the modeled metalloreductase STEAP2 protein. Three compounds (ginkgetin, medicagenin, and erybraedin A) showed lower binding affinity values toward metalloreductase STEAP2 protein compared to mitoxantrone, abiraterone acetate, apalutamide, enzalutamide, and flutamide. Ginkgetin exhibited the lowest binding energy of −9.10 kcal/mol with interacting Trp212 and Thr210. All the 17 compounds showed excellent binding energies than standard drugs for the modeled metalloreductase STEAP2 protein. These computational studies can be helpful to discover novel drug candidates.
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This summarizes a document describing research using machine learning to classify protein helix capping motifs. The researchers:
1) Used structural data from protein databases and helix cap classifications to train machine learning models, including bidirectional LSTM and SVC models, to predict helix cap positions in proteins.
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2. have shown that it forms a rich variety of structures of different
sizes on a transient basis [35]. The conformational ensemble of a-
synuclein has also been explored by computational simulations,
finding that it is more compact than a random coil, with long-range
interactions causing partial condensation of a-synuclein [36].
Simulations have also explored such questions as why b-synuclein
resists aggregation despite having high sequence similarity to a-
synuclein, finding that the aggregation-resistance arises from the
shorter distance between N- and C-termini [37].
We approached the question of how a-synuclein oligomerizes
and aggregates from the point of view of the first step, the forma-
tion of a dimer, using computational tools to model possible
structures for dimers. Methods such as docking and molecular
dynamics (MD) simulations can provide useful insights into the
structures and properties of proteineprotein complexes. Free-
energy calculations using the Molecular Mechanics Pois-
soneBoltzmann/GeneralizedeBorn (MMPB/GB) surface area
method [38,39] can provide a quantitative estimate of the strength
of binding in these complexes. A particular advantage of the
MM(PB/GB)SA method for studying a-synuclein is that it can esti-
mate binding energies with all the individual contributions (van
der Waals, electrostatic, polar and non-polar contributions to sol-
vation free energy, etc.). The sum total of the van der Waals and
non-polar contributions provides an estimate of the strength of
hydrophobic interactions; although electrostatic contributions play
an important role in IDPs [40], non-polar interactions should be
very important for the central hydrophobic region in a-synuclein,
and cannot be neglected. Because it is not yet known which
structures are most important in the aggregation process, we
modeled interfaces both between two helical-structured mono-
mers as well as between two b-structured monomers. Knowledge
of these interfaces and type of interactions contributing to affinity
gives us an idea of what features may be important in a pharma-
cophore that is expected to select putative inhibitors of these in-
teractions from small molecule databases. Therefore, breakdown of
binding affinity into its interaction components is necessary in
order to assist in pharmacophore modeling and virtual screening
for small molecules that may be able to disrupt this interaction and
eventually in rational drug design aimed at clinical treatments of a-
synuclein aggregation.
2. Methods
The initial structure of the a-synuclein monomer used for
modeling helical dimers was obtained from the RCSB protein data
bank (1XQ8) [29], whereas the initial structure for modeling b-
structured dimers resulted from Monte-Carlo simulations (Healey
M et al., in preparation) similar to those published previously [41].
The structures were energy-minimized using the molecular
modeling force field of the Molecular Operating Environment
(MOE, chemical computing group) software [42]. Cluspro and
Patchdock proteineprotein docking servers were used to dimerize
the monomer structures [43e50]. Top-scoring poses were taken for
molecular dynamics simulation using Amber12. Dimers 1 (Fig. 1), 2
(Fig. 2) and 5 (Fig. 5) were three of the top scoring poses obtained
by proteineprotein docking using the Patchdock server for helical
a-synuclein, whereas Dimers 3 (Fig. 3), 4 (Fig. 4) and 6 (Fig. 6) were
the top three helical dimers found using Cluspro. Dimers 1e4 were
obtained by docking the 1XQ8 structure onto itself, but in the case
of Dimers 5 and 6, a linear model of 1XQ8 was created by stretching
this pdb in MOE and then docking the result onto itself. Dimer 7
(Fig. 7) was created by docking the b-structured monomer onto
itself using the Patchdock server, whereas Dimer 8 (Fig. 8) was
found using the Cluspro server.
The leap module of Amber [51] was used to add missing
hydrogen atoms and heavy atoms using the Amber force field (ff10)
parameters [52]. To neutralize the charge of the system, we added
an appropriate number of sodium ions. The model was immersed in
a truncated cube-shaped shell of TIP3P water molecules [53]. The
numbers of TIP3P molecules added were as follows: 81181 to
Dimer-1, 82016 to Dimer-2, 28588 to Dimer-3, 52941 to Dimer-4,
86860 to Dimer-5, 50985 to Dimer-6, 18107 to Dimer-7 and
26516 to Dimer-8. A time step of 2 fs and a direct-space, non-
bonded cutoff of 10 Å were used. After the protein preparation, all
systems were minimized to remove steric clashes. The systems
Fig. 1. Dimer-1, first of the two dimers selected from top scoring poses of docking runs
using the patchdock server.
Fig. 2. Dimer-2, second of the two dimers selected from top scoring poses of docking
runs using the patchdock server.
K.K. Sahu et al. / Biochimie 116 (2015) 133e140134
3. were then gradually heated from 0 to 300 K over a period of 50 ps
with constraints on solute, and then maintained in the iso-
thermaleisobaric ensemble (NPT) at a target temperature of 300 K
and pressure of 1 bar using a Langevin thermostat [54] and [55]
Berendsen barostat, with a collision frequency of 2 ps and a pres-
sure relaxation time of 1 ps, respectively. Hydrogen bonds were
constrained using SHAKE [56].
MD simulations were performed using the velocity-
Verlet algorithm (default algorithm for the Amber MD package).
The particle-mesh Ewald (PME) method was used to treat long-
range electrostatic interactions using default parameters [57].
Once our systems reached the target temperature, they were
equilibrated for 500 ps and the production run was continued for
100 ns in the isothermaleisobaric ensemble using the same Lan-
gevin thermostat and Berendsen barostat. Systems were simulated
for a total of 100,600 ps (ps). Out of this simulation time, 50 ps
accounted for heating, 50 ps was density equilibration and 500 ps
was equilibration at NPT, so that the system was simulated for
600 ps in addition to 100 ns of production run. Representative
structures in the trajectories were collected at 10-ps intervals. The
analysis of trajectories was performed with the PTRAJ module of
Amber.
For the binding free energy calculations, we used the MMeGBSA
method [58], via MMPBSA.py python script [59]. Prior to the
MMeGBSA analysis, all water molecules and the sodium ions were
excluded. The dielectric constant value of 1 for solute and 80 for
surrounding water was used. During the analysis of the MMeGBSA
trajectory, 100 snapshots were collected at the interval of 10 ps
from the last 1 ns of the 100 ns trajectory.
The final estimated binding energy was calculated using the
following equation
DGbind ¼ GComplex À GReceptor À GLigand (1)
where G stands for Gibbs free energy. The change in binding free
energy is calculated as the sum of energies from molecular me-
chanics calculations, polar contribution and non-polar contribution
to solvation free energy
DGbind ¼ DEMM þ DGPolar þ DGnonÀPolar (2)
In Equation (2), EMM ¼ Eint þ Eele þ Evdw. DGPolar is the polar
contribution to the solvation free energy and DGnon-Polar is the non
polar contribution to the solvation free energy, the latter defined as
Fig. 3. Dimer-3, first of the two dimers selected from top scoring poses of docking runs
using the cluspro server.
Fig. 4. Dimer-4, second of the two dimers selected from top scoring poses of docking
runs using the cluspro server.
K.K. Sahu et al. / Biochimie 116 (2015) 133e140 135
4. DGnon-Polar ¼ GSASA þ b, where gamma (surface tension constant) is
expressed as G (gamma) ¼ 0.0072 kcal molÀ1
ÅÀ2
and b ¼ 0 for
Amber GBSA calculations, and SASA is the solvent accessible surface
area. The entropic energy TDS is normally subtracted from DGbind in
Equation (2) but it is typically calculated by computationally
expensive normal mode analysis; since other ligands (same
monomers) will bind to the same protein, we neglected entropic
contributions to the binding free energy in our calculations as
relatively insignificant. DEMM is the molecular mechanics contri-
bution to binding in vacuo expressed as the sum of the internal,
electrostatic and van der Waals contributions. Since this is a single
trajectory approach, the internal energy Eint will cancel out, so that
EMM ¼ Eele þ Evdw.
Calculated binding energies have the following components
that appear in Tables 1e8: (a) DEvdw, the van der Waals contribution
from MM; (b)Eele, the electrostatic energy as calculated by the MM
force field; (c), DGPolar, the electrostatic contribution to the solva-
tion free energy calculated by GB; (d)DGnon-Polar, the non-polar
contribution to the solvation free energy calculated by an empir-
ical model; and (e), DGbind, the final estimated binding free energy
calculated from the all the terms (a to d).
3. Results and discussion
The top-scoring structures found from docking a monomer to
another and used as initial structures for MD simulation are shown
in Figs. 1e8.
After the 50-ps heating phase, all dimers had consistently stable
kinetic, potential and total energies, indicating that the initial
dimer structures were minimized correctly and well-equilibrated,
and that the system did not become destabilized. Several com-
mon features can be discerned in these dimer structures. First, the
C-terminal domains end up being well-separated, owing to elec-
trostatic repulsion between these charged domains: they have high
negative charge (net charge of À8 at pH 7.2 for residues 120e140
[60]). The C-termini also stayed in extended conformation, and
were the most mobile part of the structures. In contrast, the central
region, which has only a slightly positive charge (net charge of þ3
for residues 30e100) and is more hydrophobic, formed the majority
of the interface between monomer domains.
Fig. 5. Dimer-5, a dimer obtained from top scoring poses of docking linear monomers
using the patchdock server.
Fig. 6. Dimer-6, a dimer obtained from top scoring poses of docking linear monomers
using the cluspro server.
K.K. Sahu et al. / Biochimie 116 (2015) 133e140136
5. The binding energies and their components for dimers 1e8 are
listed in corresponding Tables 1e8.
Among the helical dimers (Dimers 1e6), Dimer 6 was estimated
to have the highest binding affinity, in large part because it had the
highest electrostatic contribution to the binding free energy DEele.
Fig. 7. Dimer created by beta-sheet containing monomers and selected from top
scoring poses of docking runs using the patchdock server.
Fig. 8. Dimer created by beta-sheet containing monomers and selected from top
scoring poses of docking runs using the cluspro server.
Table 1
Binding free energy and its components for Dimer-1 obtained by MMGBSA methods
(kcal molÀ1
).
Components Contribution (kcal/mol) Std. Dev.
DEvdw À238.82 9.99
DEele À517.21 61.96
DGpolar 682.13 62.14
DGnon-polar À31.94 1.31
DGbind À105.85 7.54
Table 2
Binding free energy and its components for Dimer-2 obtained by MMGBSA methods
(kcal molÀ1
).
Components Contribution (kcal/mol) Std. Dev.
DEvdw À253.63 13.26
DEele À951.00 77.23
DGpolar 1120.70 77.76
DGnon-polar À38.16 1.80
DGbind À122.11 11.76
Table 3
Binding free energy and its components for Dimer-3 obtained by MMGBSA methods
(kcal molÀ1
).
Components Contribution (kcal/mol) Std. Dev.
DEvdw À249.56 8.54
DEele À1888.91 90.03
DGpolar 2018.48 85.96
DGnon-polar À37.61 0.99
DGbind À157.61 9.16
Table 4
Binding free energy and its components for Dimer-4 obtained by MMGBSA methods
(kcal molÀ1
).
Components Contribution (kcal/mol) Std. Dev.
DEvdw À288.03 13.25
DEele À1682.16 119.43
DGpolar 1836.33 124.72
DGnon-polar À39.95 1.96
DGbind À173.81 12.24
Table 5
Binding free energy and its components for Dimer-5 obtained by MMGBSA methods
(kcal molÀ1
).
Components Contribution (kcal/mol) Std. Dev.
DEvdw À332.14 8.9
DEele À1558.46 97.69
DGpolar 1762.13 92.04
DGnon-polar À47.94 1.00
DGbind À176.42 9.29
Table 6
Binding free energy and its components for Dimer-6 obtained by MMGBSA methods
(kcal molÀ1
).
Components Contribution (kcal/mol) Std. Dev.
DEvdw À252.72 10.75
DEele À2314.85 108.01
DGpolar 2419.29 94.74
DGnon-polar À36.08 0.86
DGbind À184.36 11.11
Table 7
Binding free energy and its components for Dimer-7 obtained by MMGBSA methods
(kcal molÀ1
).
Components Contribution (kcal/mol) Std. Dev.
DEvdw À198.29 17.8
DEele À912.03 76.99
DGpolar 1039.62 79.00
DGnon-polar À29.34 2.24
DGbind À100.04 8.9
K.K. Sahu et al. / Biochimie 116 (2015) 133e140 137
6. Hydrophobic interactions (van der Waals and non-polar contribu-
tions) were strongest in the case of Dimer 4 and Dimer 5, with a
free-energy contribution of À327.9 kcal/mol and 380.0 kcal/mol,
respectively, and weakest in the case of Dimer 1, at À270.7 kcal/
mol. Dimer 1 was also the least stable in terms of total binding
energy, with binding energies of À105.85 kcal/mol. The binding
stability of the dimers was generally correlated with the strength of
the hydrophobic interactions with the exception of Dimer 6: in
terms of decreasing stability for the helical dimers, we found:
Dimer 6, 5, 4, 3, 2, and 1, whereas ranking them according to a
decreasing hydrophobic interaction strength we found: Dimer 5, 4,
2, 6, 3, and 1.
Turning to the two dimers containing b-sheets as secondary-
structure motifs (Dimers 7 and 8), Dimer 8 had the higher contri-
bution from hydrophobic interactions (À271.8 kcal/mol as
compared to À227.6 kcal/mol in Dimer 7) and the higher binding
energy, mirroring the correlation between binding strength and
hydrophobic interactions seen for the helical dimers. Dimer 7 had
lower binding energy mostly owing to a lesser contribution from
hydrophobic interactions. The b-structured dimers were, however,
less stable than the helical dimers, with the most stable b-structured
dimer less stable than all helical dimers except Dimers 1 and 2.
In order to detect changes in the compactness of the dimer
structures over time, the radii of gyration, Rg, were calculated
(Fig. 9) from the MD simulations.
Decreases in Rg indicate increased interactions during the sim-
ulations drawing the residues closer. The helical dimers, Dimers
1e6, all had larger radii of gyration as compared to the b-structured
dimers (7 and 8), indicating the b-sheets led to more compact
structure formation. Rg was quite stable over the course of the
simulation for the b-structured dimers, indicating almost optimal
packing of the monomer domains after the initial docking calcu-
lation, whereas all helical dimers demonstrated some degree of
compaction over time during the MD simulation.
It is somewhat surprising how a disordered protein can
assemble itself into an organized structure like a fibril. a-Synuclein
is known to have great conformational plasticity and can have
different configurations depending upon the environment it is in
Ref. [61]. This study of potential dimer structures formed from
either helical or b-structured monomers is a starting point in
exploring the structural possibilities in a-synuclein oligomers. At
least at the stage of modeling dimers, our results suggest that a b-
structured oligomer may be less stable than a helical oligomer.
However, helical dimers are less compact, suggesting that matu-
ration from helical to b-rich structures [13] should be accompanied
by an overall compaction of the oligomer, which will counteract to
some extent the increase in size associated with addition of more
monomer subunits. Further simulations of other potential dimer
structures, as well as higher-order oligomers (in the ~4e30-mer
range matching observations in single-molecule studies [13,35] as
well as purified oligomers [32,62]) will be needed to understand
better the early aggregation intermediates of a-synuclein associ-
ated with neurodegeneration.
We note that the affinities with which monomers bind to form a
dimer represent an important parameter from the point of view of
designing drugs to inhibit oligomer formation. Designing a small
molecule to disrupt the interactions between monomers will pose a
challenge when dealing with monomers that interact with high
affinity. In such cases, it will be desirable to discover a molecule
with very high affinity towards a-synuclein that simultaneously
possesses favorable pharmacological properties such as low mo-
lecular weight, low polar surface area and a limited number of
hydrogen bond donors, which will allow the molecule to cross the
blood brain barrier as is required for therapies involving neurode-
generative diseases. The role of hydrophobic regions of a-synuclein
Table 8
Binding free energy and its components for Dimer-8 obtained by MMGBSA methods
(kcal molÀ1
).
Components Contribution (kcal/mol) Std. Dev.
DEvdw À239.89 9.4
DEele À875.95 121.83
DGpolar 1003.22 122.31
DGnon-polar À31.97 1.06
DGbind À144.60 7.82
Fig. 9. Radii of Gyration over 100,600 ps of simulation for all 8 dimers.
K.K. Sahu et al. / Biochimie 116 (2015) 133e140138
7. monomers in dimer formation cannot be ignored as our binding
energy calculations indicate hydrophobicity plays an important
role. Therefore, an effective small molecule inhibitor of this inter-
action is expected to have hydrophobic moieties as major design
features.
4. Conclusions
The results of our computational simulations of dimers formed
by the intrinsically disordered protein a-synuclein have led to the
conclusion that stable dimers could be formed from helical as well
as b-structured forms of the monomer. The central hydrophobic
region of the a-synuclein monomer was a prominent feature in all
dimer structures obtained, suggesting that hydrophobic in-
teractions play an important role in the binding affinity. The helical
dimer structures were generally more stable than the b-structured
dimers, but dimers containing b-sheets were more compact than
helical dimers, which could produce stronger interactions between
residues of different monomers when b-structured and play a role
in driving the oligomerization process toward fibrillar structures.
Interfaces between the domains in these dimers may be helpful for
future work in creating a pharmacophore for use in hierarchical or
parallel virtual screening to identify compounds from small mole-
cule databases that may disrupt monomeremonomer interactions.
Author contributions
Sahu, KK e Designed research; Performed research; Analyzed
data; Wrote the Manuscript.
Woodside, MT e Helped design research, Contributed to data
interpretation and writing of manuscript.
Tuszynski, JA e Helped design research, Contributed to data
interpretation and writing of manuscript.
Acknowledgments
This research was funded by an Alberta Innovates Health Solu-
tions CRIO (201200841) grant. The authors thank the funding
agency for their valuable resources, which made this research
possible. All computational work was performed on Pharmamatrix
cluster and Westgrid distributed computing network.
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