man i just paid 15 bucks to get my electric guitr re-sringed after having it for less then a week. and now i snapped a string trying to tune it and i dont wana pay, what do i do? im new to guitar so dont talkk shit
Hey man first let me tell you you're getting ripped off if your paying anything over 0.00 to re-string a guitar. Some asshole ripped you off and I get mad when people rip off new guitarist like that!
Well look do you have the guitar string that popped?
If you have a new string like that same type..... look at the other strings on your guitar how are they put try that copying as they're put. Then if you have a winder use it to wind up the little tuning peg.
Till its tight enough that it sounds like not loose, but not too tight either. So do that.
Also if your beginning to play guitar and have NO IDEA about notes or pitches dynamics or any of that good stuff don't tune by ear or with a piano, you will need to replace guitar strings so much this way. Use a little tuner which is like 15 bucks and its pretty good and handy and very accurate. Just remember slandered tuning
e
b
g
d
a
e
You'll be good, oh and I suggest you not to wedge yourself of a tuner till you have a good understanding of relative pitch and maybe even perfect pitch
Good luck if you need anymore help or get stuck edit your question and I'll help you out
Let’s face it – in the music world, guitars set the standard for cool. Since the 1950s, many of the greatest performers in rock ‘n’ roll, blues, and country have played the guitar. Playing electric guitar can put you out in front of a band, where you’re free to roam, sing, and make eye contact with your adoring fans...
frumpy to funky's London based Personal Stylist, Karen Grace, comments on a few of the catwalk shows at London Fashion Week Autumn/Winter 2010
PROPHETIK "Southern Shores" catwalk show.Designer JEFF GARNER
Showing at Vauxhall Fashion Scout 19Feb10
Tennessee based Prophetik kicked off the LFW catwalk shows at Vauxhall Fashion Scout, Freemason's Hall, Covent Garden. And what a kick off!!!
At the beginning we are lulled in to thinking of a carefree southern country theme when violinist Anna Cwad starts playing toe tapping music, with ex-Paramore Jason Bynum joining her on guitar. Then the band Man Raze shatters this illusion with their fusion of punk and pop rock. Not surprising as their drummer is ex-Sex Pistols Paul Cook and their vocals/lead guitar is ex-Def Leppard Phil Collen. And let's not forget their bass guitarist Simon Laffy from Brit glam rock act band Girl.
With Man Raze raising the tempo, the show began for ethical designer Jeff Garner's A/W10 collection titled "Southern Shores". His inspiration came from the American Civil War and sees hope and romance alongside strife and uncertainty.
Main colour palette in true Civil War colours were blues, greys with a touch of red.
The menswear saw layering and serious looking ¾ length coats, waistcoats, vintage buttons, riding boots and peace silk neckties.
For the women, the designs ranged from jodhpurs, ripped leggings, military jackets and capes to full on Little House on the Prairie style dresses. In-between were mini dresses and long flowing dresses both teamed with flat heeled riding boots.
At the end of the show, the music mellowed as Jeff Garner appeared looking the part of the Southern gent taking a stroll with his Southern belle in her striking long red velvet bustier dress.
Garner's collection is proof that no fashion look has to be harmed in the making of ethical clothing.
Prophetik is a sustainable men's and womenswear label using environmentally friendly materials such as organic cottons, hemp, flax, Greenspun (recycled bottles) and organic pigment dyes. One of their signature dresses "the Elle" is made from silk and organic cotton that elephants have painted in collaboration with the AEACP (Asian Elephant Art Conservation Project), which helps to raise funds for an elephant sanctuary in Thailand."
presenting at the Portico Rooms, Somerset House 19Feb10
The stage for Orla Kiely's presentation was 2 impressive make shift rooms decorated in the 60's style with brown and beige designed Orla Kiely wallpaper in the living room and dark & light blue in the bedroom. The furniture was 60's vintage and a modern retro looking TV showed the collection on screen. Models sat or stood in above the knee dresses and skirts teamed with shoes and mid calf navy socks.
Patterns for the wallpaper and dresses were inspired by Ivon Hutchins's abstract art and the photography of Erwin Olaf, and captured the essence of falling autumn leaves.
Main colour palette was autumnal – creams instead of whites, various shades of browns, burnt oranges and navy. The obligatory grey and black could be seen on some pieces.
Fabrics used were mohair and silk crepe teamed with traditional checked wool and wool jacquard,
The models wore their hair in a serious bun and makeup was kept simple, providing a good contrast to the cute collars and fun update of the 60's retro style.
LAKOBUKIA "Emotions" catwalk show
Showing at Fashion Mavericks, the Strand Palace Hotel 20Feb10
With her A/W10 collection titled "Emotions" Lako Bukia wanted to show both positive kind and dark bad emotions through fabric, shape and colour.
The main colour palette is black for the darker side and white for the lighter side of emotions. A few cream pieces appear to show that not everything is black and white.
Fabrics used are light soft flowing chiffon, a stiffer cotton silk and wool.
Geometric shapes are seen on collars, and by way of diagonal zips. The zips are also used as a bridge to join together the stiffer fabrics with the softer fabrics. Some pieces are obvious with the contrast between the hard and soft fabrics. On others the contrast can only be seen once the zip has been undone and then only revealing the hidden soft silk fabric underneath. Zips on shoulders and sides allow the wearer the flexibility of changing her silhouette – whenever the mood takes her.
Lako's shoes look striking in black or white soft leather with triangular heels. She explains the triangle is a more gothic shape and gives a harder emotional contrast to the softness of the leather.
Not surprisingly her favourite piece is the long flowing chiffon pleated dress in white which is teamed with a little sharp cotton silk back to front cape. The cape has a high collar with long geometric lapels which represent the bad emotion. Once the cape is removed, the dress is free of restrictions and can float on just the good emotions.
Lako Bukia, beginning fashion designer, was born on October 4, 1987 in the city of Tbilisi, capital of Georgia. After graduating from School No.58 in Tbilisi, she was immediately offered a place at the A. Kutateladze Tbilisi State Academy of Art, where she did her BA in Fashion Design and Textiles.
The young designer for the Sado label is Russian born Carlotta Gherzi (of Italian parents) who brings a modern elegant edge to the label.
The latest collection had great styles which could be worn by real women and not just catwalk models. The main colour palette was black with blue and pillar box red.
Silks dominated the tops, dresses and even leggings which brought out the vividness of both the blue and red.
"Geometrical glamour" is the title of this autumn/winter 2010 collection – this was shown in the silk leggings, tops and dresses which were all pleated in horizontal tiers giving texture and depth.
Black knits wear glamorous with glitter woven in to produce wide sparkly horizontal bands
Silver and red brocade made an appearance for the evening dresses. Particularly loved the strapless silver brocade maxi dress.
SADO's signature is stylish and classical - this season's collection did not disappoint.
DOII PARIS "Walk in the Forest" catwalk show
Showing at Vauxhall Fashion Scout 21Feb10
This was Korean designer Doii Lee's first catwalk show in London and it didn't let her down.
The collection titled "Walk in the Forest" was inspired by her favourite Russian fairytale "Baba Yaga" In this fairytale a wicked stepmother sends her lovely step daughter in to the forest to visit her aunt,Baba Yaga, the witch.
In Doii's walk in the forest story, she passes through an intoxicating rose maze. Copper haired models wore layered chiffon dresses and glittering sequined dresses - all in the designer's exclusive illustrated print of large pinkish red roses. Red patent high heeled shoes with patterned fabric tied around the ankles in large bows matched the vividness of the designs. White faux fur trims and linings added softness to some of the stiffer sequined fabrics. The first outfit on the catwalk immediately caught our attention – long straight chiffon dress with a kick flare hem, draped with a long trailing shawl covered in the sequined rose pattern and lined with white faux fur.
"A dove guides her along the right path" – the digital prints now were of flying white doves on orange/peach light chiffon or heavily sequined fabric. The short belted double breasted coat covered in sequins on large bold doves looked spectacular with its contrasting print of blues and creams on the lapels. The dove print on the long and short chiffon dresses were more stoned down due to their smaller scale.
"A garden full of glorious sunny memories" Main colour palette was cream with a touch of blue, green and pink. The print of potted plants was hazy as if faded by the sun. Here we saw a loose fitting sequined egg-shaped coat with a sexy kick at the bottom and cuff. The contrasting print on the collar of polka dots added to the glamour.
The story and walk becomes darker, she is in turmoil –mid greys or rich browns are added to the mix. The print is busier and again vivid. The patent shoes colour changes from carefree red to serious black, but still with the patterned fabric tied around the ankle. Here we saw a plain short mid grey double breasted wool coat contrasting perfectly with its brightly patterned lapels, tie belt, cuff bands and buttons.
"Her heart sank"- black and grey dominate and the designer's prints are of lace and netting. Black lace features on the sequined evening dresses, black fur on the coats. Another gorgeous coat (you can see a pattern emerging here – I'm loving the coats!) this time in black with a touch of white and grey. Egg-shaped, black fur collar and that sexy kick at the bottom and cuff. Slouchy black and grey zipped fur leg warmers over the black patent shoes made a luxurious touch. The silver grey lace print teamed with billowing black lace bishop sleeves made a simple egg shaped mini dress a knock out cocktail dress. And the elbow length black lace puffed sleeves and tiered lace high yoke and hem of another silver grey print mini dress gave an Edwardian vintage look.
"But she knew she was a protected one" – the colours here were pale gold and black. Here we saw a sequined gold and black patterned trench coat with plain light grey lapels, tie belt and pockets. A short sequined gold and black patterned dress was given the 20's vintage look with a drop waist and a wide band of scalloped gold lace around the hem.
"My skeleton friend is always with me" – again black and grey dominated for the new print of lace skulls. Here we saw a luxurious knee-length fur wrap coat with silver grey fur wraps and a touch of sequined skull prints; and a plain mid grey mini wrap wool coat adorned with the sequined skull print on the pockets, cuffs, belt and partial front with contrasting polka dot collar and trimming.
"Then the goat queen of the forest is on her side" – honey blonde models came out wearing prints of the goat queen in creams, white, soft browns with a touch of light blue. A padded egg-shaped coat, plain but for the print and teamed with silver grey fur leg warmers. Other outfits lined or trimmed with white faux fur gave a Cossack appearance.
"Divine roses blossom along the dark path of the forest "- Proving anything can look glamorous, a duffle style coat was given the luxury make over with a sequined print of roses along the path of the green forest, lining and trims of light grey faux fur and with the hem longer at the back.
Chiffon dresses were long and short, some with a few of the signature sequins, some decorated with small hanging hearts down the back and some with their hems longer at the back. All were colourful and patterned.
Dresses in heavier fabrics were completely embellished in sequins making Doii's print designs even more dazzling.
My favourites were the coats: all statement pieces proving you don't have to wait to take your coat off before making an entrance.
Doii states that the lady who wears Doii Paris becomes a diva. The last model came out in a black sequined coat and large hat with fringes so long they reached her shoulders. Not representing the end of the story but in true diva style, this piece wanted to take centre stage.
CLEMENTS RIBEIRO presentation
HAUTE BOHEMIAN AUTUMN/WINTER 2010
Presenting at the Portico Room, Somerset House 21Feb10
The style may be bohemian for the husband and wife design duo's (Suzanne Clements & Inacio Ribeiro) latest collection, but this is bohemian with decadent glamour. They chose to look back to the 70's around the time when Yves Saint Laurent created his Russian collection
The footwear was to die for – all embellished with sequin patterns. My favourites were the long boots in soft leather slightly slouchy and gathered at the top.
Sequins also adorned dresses, trousers and cardigans along with crystals.
The collection was divided in to definite sections:
A more masculine tailored section – main colour palette charcoal grey, black and dark plum.
Oversized knits were embellished but in a monochrome tone to create understated classical glamour.
The feminine section showed silk dresses, skirts and tops in paisley and marble effect swirl prints.
One dress and trousers in pale gilded jacquard and cardigans embellished with sequin patterns or bejewelled motifs. The colours were more muted in shades of greens, gold, mustard, light browns and taupe.
The more opulent section showed the heavier brocade for jackets, skirts and coats trimmed with sequins. Main colour palette was black and midnight blue. Nice touch with the leather gloves trimmed with sequins.
This collection shows opulent luxury does not have to be just restricted to eveningwear.
Presenting at the Portico Room, Somerset House 21Feb10
Although advertised as a presentation, designer Julie Wilkins presentation of her label's Future Classics A/W 10 collection was more like a mini catwalk show. The benches were set out so the models could walk down in between them – so everyone had a front row view!
The inspiration for this collection was: "Cut and paste; the written word and renaissance craft (wo)manship."
Main colour palette was neutrals and black (what else for classics) with a splash of pinks and oranges to brighten the collection.
Lots of long knits with draped hoods, trousers and leggings. Especially loved the denim print leggings. Plainer leggings in black or grey were given a more interesting look with a row of buttons sewn down the front.
Fur covered just the sleeves and hats giving a more playful look.
Long black satin fingerless gloves reaching above the elbow looked great with the layered chiffon LBDs.
This collection is for the woman who likes soft tailoring and knitwear but wants a little design twist to her classical look.
For help on personal shopping in London and image consultancy visit frumpy to funky's website http://www.frumpytofunky.com or email Karen on contact@frumpytofunky.com
About the Author
frumpy to funky was established by Karen Grace, an affiliate member of the Federation of Image Consultants. Karen has studied Personal Styling at the London College of Fashion and received her professional training in one of the London's leading Image Consultancy training centre. More details can be seen on http://www.frumpytofunky.com
The line “Change is must and change is accelerating” is very important in human life. There are several changes occur in each and every aspects of human civilization from the age of Homo erectus to today informational age. The main component of information age is computer which can stored a lot of information giving birth of a discipline namely Informatics. Informatics is Informatics is the discipline of science which investigates the structure and properties (not specific content) of scientific information, as well as the regularities of scientific information activity, its theory, history, methodology and organization. The science of informatics is applied indifferent field of science giving birth of different discipline namely Bioinformatics, Chemoinformatics, Geoinformatics, Health informatics, Laboratory informatics, Neuroinformatics, Social informatics.
The term "Chemoinformatics" appeared a few years ago and rapidly gained widespread use. Workshops and symposia are organized that are exclusively devoted to chemoinformatics, and many job advertisements can be found in journals. The first mention of chemoinformatics may be attributed to Frank Brown.
The use of information technology and management has become a critical part of the drug discovery process as well as to solve the chemical problems. So, chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and organization.
Whereas we see here chemoinformatics focused on drug design. Greg Paris came up with a much broader definition Chemoinformatics is a generic term that encompasses the design, creation, organization, management, retrieval, analysis, dissemination, visualization, and use of chemical information. Clearly, the transformation of data into information and of information into knowledge is an endeavor needed in any branch of chemistry not only in drug design. The view that chemoinformatics methods are needed in all areas of chemistry and adhere to a much broader definition:
chemoinformatics is the application of informatics methods to solve chemical problems.
Why do we have to use informatics methods in chemistry?
First of all, chemistry has produced an enormous amount of data and this data avalanche is rapidly increasing. More than 45 million chemical compounds are known and this number is increasing by several millions each year. Novel techniques such as combinatorial chemistry and high-throughput screening generate huge amounts of data. All this data and information can only be managed and made accessible by storing them in proper databases. That is only possible through chemoinformatics.
On the other hand, for many problems the necessary information is not available. We know the 3D structure, determined by X ray crystallography for about 300,000 organic compounds. Or, as another point, the largest database of infrared spectra contains about 200,000 spectra. Although these numbers may seem large, they are small in comparison to the number of known compounds: We know from less than 1% of all compounds their 3D structure or have their infrared spectra. The question is then; can we gain enough knowledge from the known data to make predictions for those cases where the required information is not available?
There is another reason why we need informatics methods in chemistry: Many problems in chemistry are too complex to be solved by methods based on first principles through theoretical calculations. This is true, for the relationships between the structure of a compound and its biological activity, or for the influence of reaction conditions on chemical reactivity.
All these problems in chemistry require novel approaches for managing large amounts of chemical structures and data, for knowledge extraction from data, and for modeling complex relationships. This is where chemoinformatics methods can come in.
The representation of the chemoinformatics in graphical form is given below.
Source: authors
Extracting knowledge from chemical information -lots of data (structure, activities, genes, etc) i.e. called as inductive learning. When we extract data from knowledge, it is called as deductive learning.
Is it Cheminformatics or Chemoinformatics?
The name of our favourite field maybe cheminformatics or chemoinformatics chemiinformatics, molecular informatics, chemical informatics, or even chemobioinformatics. All these options have some advantages. By using short cheminformatics you are saving the keyboard of your computer, chemoinformatics sounds nice in sentences like "... our software solution seamlessly integrates chemoinformatics and bioinformatics ...", and the title "Head of chemobioinformatics" on a business card cannot miss the point. Molecular informatics or chemical informatics is less known, but this also means that you are one of the pioneers on the forefront of a new scientific field. But the name of chemoinformatics and cheminformatics are synonymous in use. In the following table frequencies of words cheminformatics and chemoinformatics in web pages are listed, as determined by a popular search engine Google. The ratio characterizes popularity of term cheminformatics over chemoinformatics.
Year Cheminformatics Chemoinformatics Ratio
2000 39 684 0.05
2001 8,010 2,910 2.75
2002 34,000 16,000 2.12
2203 58,143 32,872 1.77
2204 85,435 60,439 1.41
2005 6,58,298 2,72,096 2.41
2006 3,17,000+ 1,63,000+ 1.94
Source: Leach AR. et.al. (2003)
History of Chemoinformatics
The first, and still the core, journal for the subject, the Journal of Chemical Documentation, started in 1961 (the name Changed to the Journal of Chemical Information and computer Science in 1975). Then the first book appeared in 1971 (Lynch, Harrison, Town and Ash, Computer Handling of Chemical Structure Information). The first international conference on the subject was held in 1973 at Noordwijkerhout and every three years since 1987. The term Chemoinformatics was given by Brown in 1998.
With all the problems at hand in chemistry, complex relationships, profusion of data, lack of necessary data, quite early on the need was felt in many areas of chemistry to have resort to informatics methods. These various roots of chemoinformatics often go back more than 40 years into the 1960s.
1. Chemical Structure Representation
In the early sixties, various forms of machine readable chemical structure representations were explored as a basis for building databases of chemical structures and reactions. Eventually, connection tables that represent molecules by lists of the atoms and of the bonds in a molecule gained universal acceptance. Connection tables were also used for the Chemical Abstracts Registry System which appeared in the second half of the sixties.
A connection table stores the same information that is present in a 2D structure diagram, namely the atoms that are present in a molecule and what bonds exist between the atoms. However, it is stored in a table form which is much easier for a computer to work with. Before a connection table is produced, the atoms in the molecule must be numbered, and an atom lookup table produced. This simply stores atom information (usually just the atom type) cross referenced with the atom number. Here is a numbering and atom lookup table for acetaminophen:
Num Atom
Type
1 C
2 C
3 C
4 N
5 C
6 O
7 C
8 C
9 C
10 C
11 O
Source: authors
The atom lookup table describes the atoms present in a molecule, but says nothing about how they are connected.
The connection table describes how atoms are connected by bonds, and has a row and a column for each atom, the row and column number representing the number given to the atom.
Source: authors
For example, if a bond exists between atom 5 and atom 8, then a “1” is placed at the intersection of row 5 and column 8 (and also row 8 and column 5), otherwise a 0 is placed at the intersection. Further, we may use a 2 to represent a double bond, 3 to represent a triple bond, and so on. Here is the connection table for Acetaminophen, along with the diagram showing which numbers correspond to which atoms.
For clarity, the non-zero entries are showing in bold. Note how the table is symmetrical about the diagonal from top left to bottom right. This will always be the case since, for example, if atom 3 is bonded to atom 2, then atom 2 is also by definition bonded to atom 3. Since this connection table effectively stores each piece of information twice, it is called a redundant connection table. Normally, we just store one half of the table in a non-redundant connection table as shown below:
Source: authors
2. Structure Searching
This involves searching a database for an exact match with a specified query structure. For example, if the following is the query.
Then only an exact match to this structure would be returned by a search. The techniques used to perform the search won’t be covered here, but basically they involve treating the 2D connection table as a mathematical graph, where the nodes represent atoms and the edges represent bonds, and then a test for exact match can be done using a graph isomorphism algorithm (a standard computer science technique).
A connection table is essentially a representation of the molecular graph (A graph is a mathematical conceptualization of anything that consists of connected points).Therefore, for storing a unique representation of a molecule and for allowing its retrieval, the graph isomorphism problem had to be solved to define from a set of potential representations of a molecule a single one as the unique one.
The first solution was the Morgan algorithm for numbering the atoms of a molecule in a unique and unambiguous manner. By Morgan algorithm atoms of the same elemental type can be topologically equivalent or not is judged. Let us label the carbons C, CH and CH1H2, and the hydrogens H, H1 and H2. Obviously, only atoms of the same elemental type can be topologically equivalent. Thus, it is immediately clear that the carbon atoms can be separated from the hydrogen atoms.
The algorithm proceeds by analyzing the extended connectivity in the following way. A score is assigned to each atom. Initially, the scores are computed by counting the number of bonds formed by each atom: i.e. C = 1, CH = 3 and CH1H2 = 3. This tells us that C is unique; hence, amongst the carbons, only CH and CH1H2 can possibly be topologically equivalent. All the hydrogens have a score (i.e. sum connectivity) of 1. In the second iteration, the new score of each atom is calculated by summing the first-iteration scores of all the atoms to which it is bonded. CH gets a score of 1 (C) + 1 (H) + 3 (CH1H2) = 5. CH1H2 gets a score of 3 (CH) + 1 (H1) + 1 (H2) = 5. H gets a score of 3. H1 and H2 also get scores of 3. Scores based on summing the atomic numbers of bound atoms are also computed: CH gets a score of 13, CH1H2 gets a score of 8 and the protons all score 6. This means that CH is distinct from CH1H2. In the third cycle of iteration, the scores based on numbers of bonds become 5 for all the protons, but the scores based on atomic numbers become 13 for H, and 8 for H1 and H2. Thus, H is distinct from H1 and H2.The termination criterion for the iterative process is when no further atoms can be assigned as unique by an iteration. At this point, we know which atoms are grouped together: those that had the same score at each iteration are topologically equivalent. In this example, the fourth pass shows that H1 and H2 are equivalent. This provided the basis for full structure searching. Then, methods were developed for substructure searching, for similarity searching, and for 3D structure searching.
Substructure searching
A substructure search involves finding all the structures in a database that contain one or more particular structural fragments. For example, we might want to find all of the structures in a database which contain the nitro group:
Substructure searching requires some method of specifying a query (i.e., we want to find this and that, but not this, etc). One popular example is SMARTS, an extension to SMILES. Mathematically, substructure searching is performed, as with structure searching, using a graph representation, but this time a subgraph isomorphism algorithm finds occurrences of subgraphs (i.e. substructures) in a structure.
Similarity searching
Similarity searching involves looking for all the structures in a database that are highly similar to a given structure. The most common use is to find compounds that could exhibit similar properties (based on the similar property principle that compounds with similar structures are likely to exhibit similar biological behaviors). Note that “similarity” is a subjective thing. As an example, a similarity search might involve looking for structures with a similarity greater than 0.7 to this molecule
Obviously some method is required for measuring similarity. This is usually done using fingerprint representations and similarity coefficients as described below, which are used in various applications that involve measurement of similarity, for example cluster analysis.
Fingerprint representations
A fingerprint characterizes the 2D structure of a molecule, usually through a string of ‘1’s and ‘0’s. There are two basic types of fingerprint: structural keys and hashed fingerprints.
Structural Keys -Structural keys contain a string of bits (‘1’s and ‘0’s) where each bit is set to 1 or 0 depending on the presence or absence of a particular fragment. They usually employ a pre-defined dictionary of fragments.
Hashed fingerprints- In hashed fingerprints, there is no set dictionary or 1:1 relationship between bits and features. All possible fragments in a compound are generated. The number of fragments represented can be huge. Thus rather than assigning one bit position for each fragment, the bits are “hashed” down onto a fixed number of bits. Thus hashed fingerprints are a less precise form, but they carry more information.
Once fingerprint representations are available, similarity coefficients can be used to give a measure of similarity between two fingerprints.
Building on work by Hammett and Taft in the fifties, Hansch and Fujita showed in 1964 that the influence of substituents on biological activity data can be quantified.
In the last 40 years, an enormous amount of work on relating descriptors derived from molecular structures with a variety of physical, chemical, or biological data has appeared. These studies have established Quantitative Structure-Activity Relationships (QSAR) and Quantitative Structure-Property Relationships (QSPR) as fields of their own, with their own journals, societies, and conferences.
Percent Spikelet Sterility (% Ss) of N-acylanilines Tested in Winter 2001-02 at 1500 ppm Spray Concentrations on PBW 343
Source: Gasteiger J. et.al. (2006)
Modern QSAR involves applying artificial intelligence and Statistical techniques to 2D or 3D molecular representations.
SAR Application
Source: R. K. Lindsay et. al. (1980).
At the time of drug design, we have to look after these following points-
• Single therapeutic target
• Drug like chemical
• Some toxicity anticipated
• Multiple unknown targets
• Diverse Structures
• Human and ecosystems
4. Chemometrics
Initially, the quantitative analysis of chemical data relied exclusively on multilinear regression analysis. However, it was soon recognized in the late sixties that the diversity and complexity of chemical data need a wide range of different and more powerful data analysis methods. Pattern recognition methods were introduced in the seventies to analyze chemical data. In the nineties, artificial neural networks gained prominence for analyzing chemical data. The growing of this area led to the establishment of chemometrics as a discipline of its own with its own society, journals, and scientific meetings.
Source: R. K. Lindsay et. al. (1980).
An artificial neural network (ANN) or commonly just neural network (NN) is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation.
5. Molecular Modeling
In the late sixties, R. Langridge and coworkers developed methods for visualizing 3D molecular models on the screens of Cathode Ray Tubes. At the same time, G. Marshall started visualizing protein structure on graphic screens. The progress in hardware and software technology, particularly as concerns graphics screens and graphics cards, has led to highly sophisticated systems for the visualization of complex molecular structures in great detail. Programs for 3D structure generation, for protein modeling, and for molecular dynamics calculations have made molecular modeling a widely used technique. The commonly available softwares for molecular modeling are ArgusLab, Chimera, and Ghemical.
6. Computer-Assisted Structure Elucidation (CASE)
The elucidation of the structure of a chemical compound, be it a reaction product or a compound isolated as a natural product, is one of the fundamental tasks of a chemist. Structure elucidation has to consider a wide variety of different types of information mostly from various spectroscopic methods, and has to consider many structure alternatives. Thus, it is an ambitious and demanding task. It is therefore not surprising that chemists and computer scientists had taken up the challenge and had started in the 1960?fs to develop systems for computer-assisted structure elucidation (CASE) as a field of exercise for artificial intelligence techniques. The DENDRAL project, initiated in 1964 at Stanford University gained widespread interest.
Other approaches to computer-assisted structure elucidation were initiated in the late sixties by Sasaki at Toyohashi University of Technology and by Munk at the University of Arizona.
7. Computer-Assisted Synthesis Design (CASD)
The design of a synthesis for an organic compound needs a lot of knowledge about chemical reactions and on chemical reactivity. Many decisions have to be made between various alternatives as to how to assemble the building blocks of a molecule and which reactions to choose. Therefore, computer-assisted synthesis design (CASD) was seen as a highly interesting challenge and as a field for applying artificial intelligence techniques. In 1969 Corey and Wipke presented their seminal work on the first steps in the development of a synthesis design system. Nearly simultaneously several other groups such as Ugi and coworkers, Hendrickson and Gelernter reported on their work on CASD systems. Later also at Toyohashi work on a CASD system was initiated.
Basics of Chemoinformatics
The various fields outlined in the previous section have grown from humble beginnings 40 years ago to areas of intensive activities. On top of that it has been realized that these areas share a large number of common problems, rely on highly related data, and work with similar methods. Thus, these different areas have merged to a discipline of its own: Chemoinformatics.
Figure 1. The various areas of activities in chemoinformatics
Source: Lipinski, C.A et.al., (1997)
The extent of this field has recently been documented by a "Handbook of Chemoinformatics", covering 73 contributions by 65 scientists on 1850 pages in four volumes. The following gives an overview of chemoinformatics, emphasizing the problems and solutions - common to the various more specialized subfields.
1. Representation of Chemical Compounds
A whole range of methods for the computer representation of chemical compounds and structures has been developed: linear codes, connection tables, matrices. Special methods had to be devised to uniquely represent a chemical structure, to perceive features such as rings and aromaticity, and to treat stereochemistry, 3D structures, or molecular surfaces. Earlier the chemical 2D structure representations are done by software namely Chemdraw, ISIS etc. But now, chemical structures are represented by molecular graph. A graph is an abstract structure that contains nodes connected by edges. Here nodes are represented by atoms and edges by bonds. A graph represents only topology of a molecules i.e. the ways the nodes i.e. atoms are connected.
Aspirin
Source: J. Zupan et.al.,(1999).
The aspirin structure can be represented by Graph theory, where Oxygen atom is represented by filled bullet and carbon atom is represented by vacant bullet and hydrogen atom is not represented here. So, the aspirin structure will be-
For similarities searching we can use the graph isomorphism or by any algorithm.
Linear notations
Structure linear notations convert chemical structure connection tables to a string, a sequence of letters, using a set of rules. The earliest structure linear notation was the Wiswesser Line Notation (WLN). ISI® adopted WLN to be used in some of their products in 1968 and, it is still use today. It was also adopted in the mid 1960s for internal use by many pharmaceutical companies. At that time (mid 60s to 80s), it was considered the best tool to represent, retrieve and print chemical structures. In WLN, letters represents structural fragments and a complete structure is represented as a string. This system efficiently compressed structural data and, was very useful to storing and searching chemical structures in low performance computer systems. However, the WLN is difficult for non- experts to understand. Later, David Weininger suggested a new linear notation designated as SMILESTM. Since SMILESTM is very close to the “natural language” used by organic chemists, SMILESTM is widely accepted and used in many chemical database systems. To successfully represent a structure, a linear notation should be canonicalized. That is, one structure should not correspond to more than one linear notation string, and conversely, one linear notation string should only be interpreted as one structure.
Attempt to condense all of the connectivity information into a single text string. The two most popular formats are SMILES (from Daylight) and SLN (Tripos format inspired by SMILES).
SMILES (Simplified Molecular Input Line Entry Specification)
Acetaminophen
In SMILES, atoms are generally represented by their chemical symbol, with upper-case representing an aliphatic atom (C = aliphatic carbon, N = aliphatic nitrogen, etc) and lower-case representing an aromatic atom (c = aromatic carbon, etc). Hydrogens are not normally represented explicitly. Consecutive characters represent atoms bonded together with a single bond. Therefore, the SMILES for propane would simply be: CCC or 1-propanol would be: CCCO. Double bonds are represented by an “=” sign, e.g. propene would be: C=CC. Parentheses are used to represent branching in the molecule, e.g. the SMILES for Isopropyl alcohol (2-propanol) is: CC(O)C. Atoms other than the major organic ones (C, S, N, O, P, Cl, Br, I, B) or ions must be enclosed in square brackets. Ring enclosures are represented by using numbers to signify attachment points, usually starting at 1. The first occurrence of the number defines the attachment point, and subsequent occurrences indicate that the structure joins back to the attachment point at that position. For example, the SMILES for Benzene is as follows (note the small ‘c’ for aromatic carbon): c1ccccc1. We can also use branching from the ring system, e.g.
c1cc(Br)ccc1 represents bromobenzene. Note that in many cases there can be several SMILES to represent the same structure – for example, we could alternatively represent bromobenzene as: c1cccc(Br)c1. So here is a SMILES representation for acetaminophen, the structure at the top of this document: c1c(O)ccc(NC(=O)C)c1. The great advantage of these methods is brevity – for example an entire SMILES string can be stored in a single spreadsheet cell. However, it is hard to add additional information (coordinates, properties, etc) in these formats in an elegant way.
Canonicalization
If a structure corresponds to a unique WLN or a unique SMILESTM string, then the structure search results in a string match. WLN could meet this requirement in most cases. The SMILESTM approach can do this after canonical processing. Therefore, both WLN and canonical SMILESTM are able to solve structure search problems by string matches. A molecular graph (2D structure) can also be canonicalized into a real number through a mathematical algorithm. The real number is identified as a molecular topologic index. However, two different structures can have the same topologic index. Therefore, topologic indices can only be used as screens for accelerating structure database searching. Actually, the concept of molecular index was originally proposed for QSAR and QSPR studies. Wiener reported the first molecular topological index in 1947 [25]. If a molecule and its specific topologic index had a one-to-one relationship, then structure search could be done by number comparison [25]. However, substructure search still had to use an atom-by-atom matching algorithm, which, as mentioned earlier, could be very time-consuming. In order to further enhance chemical database search performance, efforts have been on the way to seek better structural screening technologies.
Sources of 3d informations and the Representation of molecules in 3D Form.
3D information can be obtained through X-ray crystallography, NMR spectroscopy or by computational means. The basic forms of 3D representation are the coordinate table and the distance matrix.
A coordinate table is simply an extension of the atom lookup table that also contains coordinates for each atom. These coordinates are relative to a consistent origin. Here is a sample coordinate table for Aspirin, along with a 3D structure with the atoms numbered:
Source: Gasteiger, J., (2003)
Distance matrices are similar to connection tables, except that instead of storing connectivity information, they store relative distances (in Angstroms) between all atoms.
Here is a sample distance matrix for the Aspirin molecule above. Many pattern recognition techniques require distance or similarity measurements to quantitatively measure the distance or similarity of two objects (in our case, the objects are small molecules). Euclidean distance, Mahalanobis distance and correlation coefficients are commonly used for distance measurement,
where n is the number of descriptors, D represents the absolute distance between A and B, R represents the angle of vectors A and B in multidimensional space and, is interpreted as the quantity of the linear correlation of A and B. The value range of R is between –1 to +1 that is, from 100% dissimilar to 100% similar. The Euclidian distance assumes that variables are uncorrelated. When variables are correlated, the simple Euclidean distance is not an appropriate measure, however, the Mahalanobis distance (2) will adequately account such correlations. The Tanimoto coefficient is commonly employed for similarity measurements of bit-strings of structural fingerprints (Boolean logic). The simplified form is
where ? is the count of substructures in structure A, ? the count of substructures in structure B, and ? is the count of substructures in both A and B. Many different similarity calculations have been reported. Holliday, Hu and Willett have published a comparison of 22 similarity coefficients for the calculation of inter-molecular similarity and dissimilarity, using 2D fragment bit-strings [51].
Source: Gasteiger, J., (2003)
Distance matrices are useful when comparing molecules with each other, whereas coordinate tables tend to be used for structure visualization.
2. Representation of Chemical Reactions
Chemical reactions are represented by the starting materials and products as well as by the reaction conditions. On top of that, one also has to indicate the reaction site, the bonds broken and made in a chemical reaction. Furthermore, the stereochemistry of reactions has to be handled. Searching databases of reactions is a little different to straight searching, although the kinds of search are the same (structure, substructure, similarity). However, searching may be done on reactants, products, or both, and searches may be performed for entire reactions (as opposed to single structures). Representation of reactions is by the usual means (connection tables, atom lookup tables), but with additional information about which molecules are products and reagents, and which reagent atoms map to which product atoms. A derivative of SMILES, called Reaction SMILES is available for representing reactions, along with a way for defining reaction queries called SMIRKS.
3. Data in Chemistry
Much of our chemical knowledge has been derived from data. Chemistry offers a rich range of data on physical, chemical, and biological properties: binary data for classification, real data for modeling, and spectral data having a high information density. These data have to be brought into a form amenable to easy exchange of information and to data analysis
4. Datasources and Databases
The enormous amount of data in chemistry has led quite early on to the development of databases to store and disseminate these data in electronic form. Databases have been developed for chemical literature, for chemical compounds, for 3D structures, for reactions, for spectra, etc. The internet is increasingly used to distribute data and information in chemistry. The databases of virtual molecules are available now i.e. the molecules which are not present in the nature, but by just virtually we can prepare databases with the help of databases of other molecules. The commonly available softwares for databases are Amicbase, Asinex Gold, Cheminformatics.org, FDA MRTD, NCI, Otava Dataset, PubChem, and ZINC.
5. Structure Search Methods
In order to retrieve data and information from databases, access has to be provided to chemical structure information. Methods have been developed for full structure, for substructure, and for similarity searching. Those are discussed in above.
6. Methods for Calculating Physical and Chemical Data
A variety of physical and chemical data of compounds can directly be calculated by a range of methods. Foremost are quantum mechanical calculations of various degrees of sophistication. However, simple methods such as additive schemes can also be used to estimate a variety of data with reasonable accuracy.
7. Calculation of Structure Descriptors
In most cases, however, physical, chemical, or biological properties cannot be directly calculated from the structure of a compound. In this situation, an indirect approach has to be taken by, first, representing the structure of the compound by structure descriptors, and, then, to establish a relationship between the structure descriptors and the property by analyzing a series of pairs of structure descriptors and associated properties by inductive learning methods. A variety of structure descriptors has been developed encoding 1D, 2D, or 3D structure information or molecular surface properties. The manipulation and analysis of chemical structure information is made through the molecular structure descriptors. These are the numerical values which characterizes propertities of molecules. They may represents the physiochemical properties of a molecule or may b the values derived from the algorithm technique to the chemical structures. For example, the molecular weight does not represent the whole properties of a molecule but it is very quick. In case of quantum molecular based structure descriptors, it tells about the properties of a molecule but it is time consuming.
The commonly used molecular descriptors are logP and molar refractivity. Hydrophobicity is most commonly modeled using the logarithm values of partition coefficient i.e. logP.
8. Data Analysis Methods
A variety of methods for learning from data, of inductive learning methods is being used in chemistry: statistics, pattern recognition methods, artificial neural networks, genetic algorithms. These methods can be classified into unsupervised and supervised learning methods and are used for classification or quantitative modeling. The softwares are using in data analysis & statistics are ChemTK Lite, PowerMV, & GCluto.
Chemistry Based Data Mining and Exploration
For synthesis a molecule, first we have to search data with the help databases available for that molecule, then we have to search the database available for structure analogue. Now the Structure activity relationships are studied and different biological or mechanistic analogue are synthesized. The scheme is given in below……
Applications of Chemoinformatics
a.Fields of Chemistry
The range of applications of chemoinformatics is rich indeed; any field of chemistry can profit from its methods. The following lists different areas of chemistry and indicates some typical applications of chemoinformatics. It has to be emphasized that this list of applications is by far not complete!
1. Chemical Information
o storage and retrieval of chemical structures and associated data to manage the flood of data by the softwares are available for drawing and databases.
o dissemination of data on the internet
o cross-linking of data to information
2. All fields of chemistry
o prediction of the physical, chemical, or biological properties of compounds
3. Analytical Chemistry
o analysis of data from analytical chemistry to make predictions on the quality, origin, and age of the investigated objects
o elucidation of the structure of a compound based on spectroscopic data
4. Organic Chemistry
o prediction of the course and products of organic reactions
o design of organic syntheses
5. Drug Design as well as for bioactive molecules.
o identification of new lead structures
o optimization of lead structures
o establishment of quantitative structure-activity relationships
o comparison of chemical libraries
o definition and analysis of structural diversity
o planning of chemical libraries
o analysis of high-throughput data
o docking of a ligand into a receptor
Finally, small molecules can be used for docking and drug screening/discovery. Small molecules, as well as their synthetic derivatives, can be docked to a protein target and computationally filtered (e.g. by solubility) to produce a ranked list of candidates that can then be tested in the laboratory. Known ligands can also be used in similarity searches, or as scaffold for further molecular engineering. We will present several recent drug discovery efforts that leverage ChemDB and the computational tools described above. In particular, the discovery of several compounds has done that can bind to the Carboxyltransferase domain of Acyl-CoA Carboxylase, AccD5 from Mycobacterium tuberculosis:, a new TB therapeutic target.
o prediction of the metabolism of xenobiotics
o analysis of biochemical pathways
o Modeling of ADME-Tox properties.
Historically, drug absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies in animal models were performed after a lead compound was identified. Now, pharmaceutical companies are employing higher-throughput, in vitro assays to evaluate the ADMET characteristics of potential leads at earlier stages of development. This is done in order to eliminate candidates as early as possible, thus avoiding costs, which would have been expended on chemical synthesis and biological testing. Scientists are developing computational methods to select only compounds with reasonable ADMET properties for screening. Molecules from these computationally screened virtual libraries can then be synthesized for high-throughput biological activity screening. As the predictive ability of ADME/Tox software improves, and as pharmaceutical companies incorporate computational prediction methods into their R&D programs, the drug discovery process will move from a screening based to a knowledge-based paradigm. Under multi-parametric optimization drug discovery strategies, there is no excuse for failing to know the relative solubility and permeability rankings of collections of chemical compounds for lead identification.
a. Absorption. Passive intestinal absorption (PIA) models have been studied by many groups, for years. The fluid mosaic model holds that the structure of a cell membrane is an interrupted phospholipid bilayer capable of both hydrophilic and hydrophobic interactions. Trans cellular passage through the membrane lipid/aqueous environment is the predominant pathway for passive absorption of lipophilic compounds, while low-molecular-weight (300) of molecular descriptors (constitutional, topological, geometrical, electrostatic, quantum-chemical and thermodynamic) calculated using quantum-chemical semi empirical methodology.
Chemo bioinformatics
Biochemoinformatics (or chemobioinformatics) is a new term to describe the research efforts on meeting the emerging needs for the integration of bioinformatics and chemoinformatics. Historically, bioinformatics and chemoinformatics have largely evolved independently from biology and chemistry. Generally speaking, bioinformatics deals with biological information, which although traditionally refers to sequences information on large biological molecules such as DNA, RNA and proteins, also refers to the more recent emergence of micro array data on gene and protein expression.
Chemoinformatics on the other hand mainly deals with chemical information of drug-like small molecules, the molecular weight of these being several hundred Daltons. The elemental data record in bioinformatics is centered on genes and their products (RNA, protein, and so on), whereas the fundamental data type in chemoinformatics is centered on small molecules.
Source: Drews,J.,(2000)
Key challenges
The key challenge for computational methods then is not traveling through chemical space per se, but rather to be able to focus traveling expeditions in a vast chemical space towards interesting regions, and to be able to recognize interesting stars and galaxies when they are encountered. The notion of what is interesting may vary of course with the task (e.g. drug discovery, reaction discovery, polymer discovery). But at the most fundamental level what is needed are tools to predict the physical, chemical, and biological properties of small molecules and reactions in order to focus searches and filter search results. Computational methods in chemistry can be organized along a spectrum ranging from Schrodinger equation, to molecular dynamics, to statistical machine learning methods. Quantum mechanical methods, or even molecular dynamics methods, are computationally intensive and do not scale well to large datasets. These methods are best applied to specific questions on focused small datasets. Statistical and machine learning methods are more likely to yield successful approaches for rapidly sifting through large datasets of chemical information. Because in the absence of large public database and datasets, chemoinformatics is in a state reminiscent of bioinformatics two or three decades ago, it may be productive to adapt the lessons learnt from bioinformatics to chemoinformatics, while maintaining also a perspective on the fundamental differences between these two relatively young interdisciplinary sciences. If this analogy is correct, two key ingredients were essential for unlocking the large-scale development of bioinformatics and the application of modern statistical machine learning methods to biological data, data and similarity measures. In bioinformatics, such as Genbank, Swissprot, and the PDB while alignment algorithms have provided robust similarity measures with their fast BLAST implementation becoming the workhorse of the field. Mutatis mutandis, the same is likely to be true in chemoinformatics.
This new drug discovery strategy, challenges cheminformatics in the following aspects: (1) cheminformatics should be able to extract knowledge from large-scale raw HTS databases in a shorter time periods, (2) cheminformatics should be able to provide efficient in silico tools to predict ADMET properties,
Conclusions
Chemoinformatics has developed over the last 40 years to a mature discipline that has applications in any area of chemistry. Chemoinformatics is the science of determining those important aspects of molecular structures related to desirable properties for some given function. One can contrast the atomic level concerns of drug design where interaction with another molecule is of primary importance with the set of physical attributes related to ADME, for example. In the latter case, interaction with a variety of macromolecules provides a set of molecular filters that can average out specific geometrical details and allows significant models developed by consideration of molecular properties alone. The field has gained so much in importance that the major topics of chemoinformatics have to be integrated into chemistry curricula, a few universities have to offer full chemoinformatics curricula to satisfy the urgent need for chemoinformation specialists. There are still many problems that await a solution and therefore we still will see many new developments in chemoinformatics.
References
Bhat K; Bock C., Howard NJ.(2002) COS and HTS design of high-performance, non-toxic chemicals for textiles, NTC Project: C00-PH01 (formerly C00-P01)
Brown F.K. (1998), Chemoinformatics: What is it and how does it Impact? Drug Discovery Ann. Reports Med. Chem., 33:375-384.
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About the Author
Md. Wasim Aktar is a Senior Research Fellow in Export Testing Laboratory, APEDA, B.C.K.V., Mohanpur,West Bengal, Pin-741252,India. He is expert in pesticide residue analysis using GC-MS and LC-MS from different environmental samples. He is an Agriculture Graduate and obtained his M.Sc. degree in Agricultural Chemicals from B.C.K.V. He is now doing his Ph.D. work in the same university under the deptt. of Agricultural Chemicals.
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Patty Griffin Living With Ghosts Rock Music CD Review
Not sure what’s happening with me on this one, but it seems like the more I listen to it, the better Living With Ghosts gets. Living With Ghosts stated simply is one of Patty Griffin’s best CDs to date.
I wish it weren’t the case but, it’s not everyday that I get a CD from an artist that I can just pop in and comfortably listen to from beginning to end. There is usually a song or two that I just can’t force myself to get through. Not at all the case with Living With Ghosts. Every track is enjoyable and was pretty easy for me to listen to from start to finish.
One of the refreshingly nice things about this CD is the way all of the participating artists seem to be really enjoying themselves. Combine that with the overall presentation and you’ve got one of Patty Griffin’s most impressive releases ever.
Rock music fans will recognize some of the well known contributors on the project including Steve Barry and Others plus a few other notables as well.
Overall Living With Ghosts is an outstanding release. What I call must have music. I give it two thumbs up and is most definitely a worthy addition to any Rock collection. Truly an outstanding Rock CD. One of those that is completely void of any wasted time, as each track is simply superb.
While this entire CD is really very good some of my favorites are track 4 - Time Will Do The Talking, track 8 - You Never Get What You Want, and track 10 - Not Alone
My Bonus Pick, and the one that got Sore [...as in "Stuck On REpeat"] is track 1 - Moses. Wow!
Living With Ghosts Release Notes:
Patty Griffin originally released Living With Ghosts on May 21, 1996 on the A&M Records label.
CD Track List Follows:
1. Moses 2. Let Him Fly 3. Every Little Bit 4. Time Will Do The Talking 5. Mad Mission 6. Poor Man's House 7. Forgiveness 8. You Never Get What You Want 9. Sweet Lorraine 10. Not Alone
Personnel: Patty Griffin (vocals, acoustic guitar); Adam Steinberg, Ty Tyler (guitar).
About the Author
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