Mnova 6.0, at last! GSD, Line Fitting, Data Analysis, handling of LC/GC/MS data and much more!

It's been over 6 weeks since my last post on this blog but don’t worry, I haven’t been idle. On the contrary, I have a very good excuse for this lack of posts: We all at Mestrelab have been working very hard trying to get version 6.0 of Mnova finished. Now I’m delighted to announce that we have done it and version 6.0 is finally available for download from our Web site. This is certainly a major upgrade of the software in which we have put a lot of work and passion. It brings a number of enhancements and bug fixes but most significantly are the following new developments:

Mnova MS

Yes, Mnova speaks a new language now, not just NMR. Since its conception, Mnova was Multi-document, Multi-Page, Multi-Platform and designed to become Multi-Technique, which it has now done

GSD (Global Spectral Deconvolution)

I have already blogged about it, but now GSD is finally available so that you all can try it and play with it. We are confident that this new powerful analysis tool will open new avenues in many NMR fields

NMR Line Fitting (Deconvolution)

Even though GSD is a fully automatic spectral deconvolution algorithm, a general purpose line fitting (deconvolution) module is always useful. In an effort to maximize user experience, we have developed a powerful, yet easy to use Graphical User Interface which makes possible both the manual and automatic adjustment of any peaks parameters (i.e. peaks positions, heights, line widths, shapes). I will talk more about it in a new post in a few days


NMR Data Analysis Module

Designed for the analysis of arrayed NMR experiments such as DOSY, Relaxation (T1, T2), kinetics, metabonomics, reaction monitoring, etc. This new module includes, among other features, the capability to apply reliable and fast non linear fitting (including specialized mono-exponential fitting), plotting of the experimental and fitted data, etc

Well, this list is not a fair account of all the number of new things implemented in this version. For a detailed list you could check out the ‘What’s new in 6.0'.

From here I encourage you to try this new version and experiment with the new tools. You can download an evaluation version from our website (at www.mestrelab.com). If for some reason your license has already expired, please do not hesitate to get in touch with us at Mestrelab, we will be delighted to supply a license for the software. In the meantime, I can only add that in the next few days I will be creating new posts where I will be revealing in detail each and every new tool of this brand new version as well as some innovative and interesting applications

Agilent Technologies to Acquire Varian

This morning I got up with this shocking news:
Agilent Technologies to Acquire Varian, Inc. for $1.5 Billion
http://www.agilent.com/about/newsroom/presrel/2009/27jul-gp09016.html

Note:
Just to clarify, the word shocking was used in the sense of surprising, and with no negative connotations meant. Not being privy to the detail of the deal or to Agilent's plans, I can of course not foresee how this may affect Varian's position in the NMR marketplace or how it may affect the NMR community, although having a big company with a big interest in R&D like Agilent in our market could well be very positive

Fighting against peak overlap – Introducing Global Spectral Deconvolution (GSD)

1H NMR is for sure the most powerful technique for structure elucidation, especially for small organic molecules. Typically, an organic chemist uses the chemical shift, coupling constants and integration information contained in an 1H-NMR spectrum to either verify or elucidate an unknown compound. Of course, it’s quite common that a simple 1H-NMR spectrum is not enough to unambiguously confirm a structure and thus other NMR experiments (e.g. 13C-NMR, HSQC, COSY, etc) are used to get more structural information.

Nevertheless, I have often found that many organic chemists do not always try to get the most out of 1H-NMR spectra (which is the cheapest experiment), in particular when some multiplets are complex to interpret (strong coupling) or when peaks overlap prevents valuable information to be detected in some multiplets. Overlapping peaks and new ways to get around it will be the subject of this post.

As it is well known, there are two principal factors limiting the resolution power in a spectrum. First, we have the natural line width limitation imposed by the T2 (spin-spin relaxation). For example, if T2 is about 1 second, the peak linewidth at half height cannot be less than 0.32 Hz (remember, line width at half height = 1 / (pi * T2) = 1 / 3.1415 = 0.32) no matter how powerful is our NMR instrument or the field homogeneity. On the other hand, there are instrumental shortcomings (e.g. spatial uniformity of the applied magnetic field, etc).
Nonetheless, there is an additional limiting factor, and whose importance is generally underestimated which has to do with the generally large number of transitions in 1H-NMR spectra. In short, the peaks we can observe in a 1H-NMR are just a small fraction of the actual transition resonances which are not observable because of the limited digital resolution. In fact, every peak in an 1H-NMR spectrum is basically an envelope of a large number of transitions and its shape is dominated by the coupling pattern of the spin system. Even in molecules of modest size the number of distinct peaks is tens to thousands times smaller than that of quantum transitions. As a very simple example, consider an A3B2 spin system. Depending on the second order interaction and on the available digital resolution, we might observe the expected triplet / quadruplet multiplet patterns. This is illustrated in the figure below.



However, if we use Mnova capabilities to display all main transitions of any coupled spin system by simply hovering with the mouse over the particle of interest, we can appreciate the additional number of resonances (see below):


Furthermore, I can easily increase the digital resolution of the A3B2 spectrum above by just reducing the line width used in the spin simulation module of Mnova. As a result, it’s now possible to observe more resonances in this particular A3B2 spin system (although not all of them, of course):


Of course, this way of increasing the digital resolution is only possible with synthetic spectra and cannot be applied to experimental data. Obviously there are many resolution enhancement techniques being Resolution Booster one of the most powerful ones. As a nice example of the application of this technique, let me tell you this story:
A couple of weeks ago, a very good friend of mine, a professor of organic chemist, came to me with an interesting structural problem. His research group had carried out a reaction which resulted in one single product whose 1H-NMR spectrum was, in principle, compatible with two potential structures. In order to ambiguously find the right structure, they acquired more NMR spectra (DEPT, HSQC, HMBC, COSY) which allowed them to find the correct molecule However, while discussing the problem having a few beers at a bar in Santiago, we found that just the 1H spectrum was more than enough in order to discard one of the two structures and completely assign the correct one without the necessity to acquire any other NMR experiment. The key was the ability to resolve a long range coupling (homo-allylic) with the assistance of Resolution Booster. Basically, the 1H-NMR showed a clean double doublet which was compatible with both structures (I’m sorry, but I cannot reveal those structures). This multiplet is shown below:


After appling Resolution Booster, we could clearly appreciate a further splitting which we could assign to the expected homo-allylic coupling with a value of 1.76 Hz. This coupling was also found in its corresponding multiplet partner confirming the structure:


At this point, it’s worth mentioning that Resolution Booster is a very powerful method to resolve overlapped peaks, but it cannot be used for integration as the area of the peaks get distorted by this process. The good news is that we have developed a new method which in addition to taking advantage of the power of resolution booster, it yields accurate integrals.

This method has been named as Global Spectral Deconvolution (GSD) and as its name says, it automatically deconvolves all the peaks in a spectrum. In short, this method first recognizes all significant peaks in a spectrum, then assigns a realistic a-priori bounds to all peak parameters (chemical shift, heights, line widths, etc) and finally fits all these parameters in a very reasonable time.
Following with the example above, if we apply GSD, we get a multiplet with all the individual peaks clearly resolved and this time, with accurate integrals.


It’s important to mention that we haven’t just fitted the multiplet above, but we have actually fitted the whole spectrum!



We are confident that GSD will open new avenues in NMR data interpretation and quantitative analysis (qNMR). I will blog about these points in future posts.

Mspin, RDC’s and efficient use of freely rotating groups

In the last ten years, Residual Dipolar Couplings (RDC) have come to occupy a very important place in the structure determination of proteins, nucleic acids and carbohydrates in liquid state. Although RDCs were originally discovered and theoretically explained for small molecules in liquid crystal solvents by A. Saupe in 1968 (Angew. Chem. Int. Ed. Engl. 1968, 7, 97) the spectra were too complex for a practical use in structure determination. The discovering of weak orienting media in water led to an explosion in the application of RDCs for biomolecule structure determination. However, those aligning media used for biomolecules were not applicable to most of the small molecules. Fortunately, recent research results considerably extended the applications of RDCs to small molecules as new alignment media for organic solvents, either liquid crystal type as poly-?-benzyl-L-glutamate (PBLG), or mechanically stretched cross-linked polymer gels such as poly(methyl methacrylate) gel (PMMA) or polydimethylsiloxane (PDMS) are available. If you are interested in RDCs you should certainly check the very didactic introduction in the theory by Kramer et al. Applications and practical considerations are nicely reviewed in the recent reviews by Cristina Thiele ( See this and this) and Burkhard Luy ( see this).

The use of RDCs in small molecule structural determination is typically based on the determination of the alignment tensor, a 3x3 matrix, which contains the information about the probability of the molecule pointing in a particular direction of the space. This matrix can be determined by least squares fitting to the experimental RDCs.
However, there exists a further potential problem on the application of RDC to the structure determination of small molecules: the lack of enough independent RDCs, i.e, those coming from non parallel vectors, since in most cases only 1DCH RDCs are available from F1 ( see this) or F2 coupled (see this ) HSQC type experiments, thus making the fitting problem underdetermined. Armando Navarro et al. have recently proposed an elegant approach to get the most out of the experimental data by incorporating into the calculations two of the most common freely rotating groups, namely the methyl and phenyl groups (using 2-fold and 3-fold jump models).

The authors have automated this averaging of RDCs from freely rotating groups in version 1.03 of our program Mspin which we hope will facilitate the use of RDC among a broader community of users interested in solving structural questions of small molecules

New Mestrelab Blog

I’m happy to announce our new blog on Mestrelab. As Santi wrote, the purpose of this blog is “to report on company progress and ideas, to tell stories about our trips and conferences, and to highlight aspects of our products which we may think our users may be interested in reading, or hearing, about”
A lot of people seemed to be very interested in what we're doing in Mestrelab so we thought that it would be helpful to create this blog so as to keep you all up to date on what’s going on with our commercial initiatives, trips (including photo sets from those trips) etc.
So if you feel curious about Mestrelab activities, please visit our new blog. We look forward to hearing from you.

Mestrelab's blog: http://blog.mestrec.com

NMR Spectroscopy Explained

When I initiated the development of MestReC back in 1995 (15 years ago!), my knowledge of NMR was fairly elementary and limited to basic theoretical rudiments (quantum mechanics description of NMR phenomenon, vector model, etc) and some experience in the practical interpretation of NMR spectra gained primarily whilst working as an organic chemist at Leicester University.

That said, during that first phase of development, I wish I had enjoyed the opportunity to have access to the book ‘NMR Spectroscopy Explained: Simplified Theory, Applications and Examples for Organic Chemistry and Structural Biology' by Neil Jacobson, I’m sure that my productivity would have been boosted very significantly by it. For example, there is an unmissable section devoted to practical NMR aspects and, in particular, NMR data acquisition and processing. It’s clear from this section that the book was written from the perspective of a spectroscopist who works with NMR on a day-to-day basis (Neil Jacobsen is the NMR Facility Manager at the University of Arizona). Concepts such as oversampling and digital filtering are presented in more detail than that found in standard introductory texts. I bought this book about 6 months ago and I have to say that it is a shame that it wasn’t available much earlier when I started my work on NMR.

Nothing is ever perfect and if I had to point out something missing in the book it would be a chapter devoted to DOSY, which I think would make a nice addition.

Overall, I believe that this is a great book which I warmly recommend to all of you who wish to deepen your understanding of NMR both from a practical and theoretical standpoint. Enjoy, and let me have your thoughts!

Mnova reviewed by Tim Claridge at JCIM

High-Resolution NMR Techniques in Organic Chemistry is one of the most popular books on NMR which is now used at many universities as a foundation for graduate-level courses on NMR techniques. It has been written by Tim Claridge who is the Director of NMR Spectroscopy at the Organic Chemistry Department at Oxford University and has now written a very nice review on Mnova in the Journal of Chemistry Information and Modeling (JCIM). I'll just quote one of his conclusions because I'd rather let you read the full article:

Overall I was very impressed with the package, finding it not only very comfortable and intuitive to use so well suited to non-NMR specialist, but also well endowed with more advanced processing features for more experienced users

(Click here for the full article)

I would like to take this opportunity to thank all of you for your support, advice and contributions to our design and development, and also congratulate my team; it seems we are doing well at developing easy to use but powerful NMR software. But don’t worry, we are not going to get complacent because of reviews like this, on the contrary, they are just a spur to work harder and develop the software further

Article bookmark. Tim Claridge University of Oxford J. Chem. Inf. Model., Article ASAP DOI: 10.1021/ci900090d Publication Date (Web): March 30, 2009 http://pubs.acs.org/doi/abs/10.1021/ci900090d Copyright © 2009 American Chemical Society

Pre-ENC User Meeting Video

As you may know, we are going to hold an user meeting prior to the 50th ENC Conference.
There, Mestrelab's team and some guests are going to present some new Mnova features, algorythms and new products.

You can check the meeting program and get registered here.
Whether you are planning to attend or not I also encourage you to watch this 5 minutes video.

Double-click to switch to full screen

Pre-ENC User Video from Dani Fraga on Vimeo.

NMR and the Chemist’s Illusion

Stan has just posted a nice entry in which he uses the aromatic region of Strychnine to discourse about the different effects in the NMR spectrum (in terms of resolution and multiplicity) produced when the magnetic field frequency is changed. In particular, I like his description of the ‘Chemist’s Illusion’ and as a chemist, I would like to illustrate, just with a picture, what this illusion is all about.

In the picture below, I have synthesized the ABCD spin system corresponding to the aromatic region of Strychnine at different fields (we don’t own a 1500 MHz spectrometer and we don’t expect to get one for Mestrelab in the short- or mid-term :-) ). It can be appreciated that as we move to higher fields, the multiplets appear to be more separated (this is an illusion: their chemical shifts, in ppm, are exactly the same!) and get more resolved and more first-order like.

Below I’m showing an expansion of the right most multiplets:


Another interesting and well known example is represented by an AA’BB’ spin system (for example, o-diclhrobenzene) . Again, as we go to higher fields, the apparent multiplets separation looks larger, although the multiplet fine structure remains virtually unchanged. In other words, in these systems, second order effects will always exist regardless of the magnetic field. When the magnetic field is increased, it will be possible to get a larger chemical shift difference between the AA’ and the BB’ groups, but not between A and A’ or B and B’ (it’s always zero), so that the highest simplification one can achieve by increasing the magnetic field is to move from an AA’BB’ group to an AA’XX’ group which is a second order spin system too.

Peak Shapes in NMR Spectroscopy

Routine analysis of NMR data involves peak picking and integration to get chemical shifts (and couplings) and quantitative information (e.g. number of protons). When the peaks are not well resolved, none of these parameters can be accurately estimated and nonlinear least squares fit (curve fitting or deconvolution) is often performed to extract the desired information. However, deconvolution presents, at least two important difficulties:

Problem #1

In general, line fitting is applied to some limited number of lines in a spectrum as a deconvolution of the full spectrum is very difficult to say the least. This implies a manual intervention of the User (choice of multiplet, specification of the number of lines and of their starting parameters).

Problem #2

Curve fitting requires the definition of an analytical model for the line shape and in particular, NMR lineshaphes have typically been assumed to be either Lorentzian, Gaussian, or a combination of both (e.g. Voight Profile). The problem is that Lorentzian deconvolutions are numerically ill defined because all complete sets of Lorentzian-shaped functions are approximately linearly dependent (in other words, a Lorentzian peak can be approximated very well by several Lorentzian lines). This problem is specially important in 1H-NMR spectra where peaks are really complicated envelopes of many unresolved transitions (for example, in a generic 10 spin system there are 5120 distinct main transitions, but one typically resolves less than 100 peaks).

These problems have been the motivation of the development of a brand new peak analysis algorithm, the so-called GSD (Global Spectral Deconvolution) which has been recently presented by Stan Sykora in a talk he gave at MMCE 2009 conference. In fact, GSD is now fully operative within MestReNova .

If you are interested in GSD and planning to visit ENC, we will be pleased to show you every detail at the user meeting we will keep on Sunday 29th March and at our exhibitor and hospitality suite (you do not need to be a MestReNova User to participate).