Posted: 06 Oct 2008 04:34 PM CDT
Tomorrow, October 6, 2008
Posted: 06 Oct 2008 01:12 AM CDT
Small Worlds is a new initiative organised by Alan Cann at the University of Leicester (and of the excellent Microbiology Bytes) that aims to encourage scientists to use the immense power of web 2.0 in their professional lives.
Alan points out that although scientists were the pioneers of the internet, we have been slow to latch onto the idea that it’s latest incarnation, web 2.0 (a.k.a. “the read/write web”), can be an extremely useful tool for forming professional collaborations among groups and individuals.
The Small Worlds project hopes to overcome this by providing information on how scientists can use services like Twitter, Seesmic, Delicious, Friendfeed etc. to build their network and, crucially, by encouraging us all to make a concerted effort to link up.
One group who could particularly benefit are early stage research scientists who lack an adequate mentor/peer support structure around them, for example those in small research groups, who could use web 2.0 applications to build a network of fellow researchers whose experience they can draw upon to help with their professional development.Such a network can also provide valuable moral support at an often difficult period career period!
Many of our readers fall into this category, so I’d encourage you to head along to the Small Worlds project site and start getting yourself networked!
If you use web 2.0 in your professional life already, drop us a comment on your experiences.
Posted: 06 Oct 2008 12:53 AM CDT
Image via WikipediaVery often you hear about new methods, often more computationally expensive, that are pegged as improvements to existing commonly used techniques. Many a “Google killer” comes to mind as do methods for predicting protein-ligand interactions, something I have a little more experience with. All such methods face a common challenge - they have to overcome both a mental block to trying out anything new when existing methods work well, as well as a need to demonstrate that a change will be worth the effort.
Take virtual screening for example. While methods for docking and scoring, esp the high throughput variety, are hardly limited to one big dominant player, a la Google in search, the concepts underlying docking and scoring fall into familiar territory for many people. Some years ago, scientists began experimenting with methods like MM-PBSA and LIE, hoping to come up with results that were based on more physical models of molecular recognition and using better sampling methods. This is a complex problem, but the hope was that you could improve upon existing techniques.
I would argue that such higher order methods have seen some level of acceptance, partly due to frustration with the incumbents, but not quite at the rate that many, including myself, would have hoped. Why is that?
I think there are a few reasons. Better underlying engines for molecular mechanics calculations and molecular dynamics simulations would help. But perhaps the biggest reason is the same old one. You need to leapfrog not just the incumbents in performance and accuracy, but you have to leapfrog chemists, who can whip out 50 compounds a week (that was the number someone from a big pharma company once told me we’d have to beat). Plus I think the approach should be different. With our current limitations we are limited to hierarchical approaches, gradually increasing the expense of our methods, till the improvement is not worth it. Hopefully we can come up with methods that allow us to stop thinking about enrichment and more about whether we can truly evaluate molecular recognition and find the best binding molecules.
So what brought this on? A paper by Julien Michel and Jonathan Essex on Hit Identiﬁcation and Binding Mode Predictions by Rigorous Free Energy Simulations. The paper, in addition to being from Jonathan Essex takes a look at the ability to select the best binding modes from conformations generated by a docking program across a set of structurally diverse ligands.
Docking is inexpensive. Scoring is more expensive, especially given the poor quality of results. If you can get a number of good starting points, and then evaluate the best binding modes, you have already done a good job of identifying potential candidates. If you can also differentiate between a set of diverse compounds you are really achieving that next step that methods must take. The paper is pragmatic and realizes the challenges. In my opinion, we are still not there from the performance point of view. I think there is a lot of promise and as part of a hierarchical approach we can definitely start deploying these methods, somewhat carefully. For lead optimization, where time is not that much of an issue, there is significantly more promise.
I am still somewhat concerned that we have not taken that next quantum leap in performance and capabilities. Perhaps we just can’t, but I’d sure like to see something different try and fail.
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Posted: 06 Oct 2008 12:26 AM CDT
Direct-to-consumer genetic testing company, Smart Genetics, has gone out of business. The two-year-old company sold HIV Mirror and Alzheimer’s Mirror, DNA tests for HIV progression to AIDS and Alzheimer’s risk respectively.
I first wrote about HIVmirror in June 2007. Aimed at HIV+ individuals, the test analyzes the CCR5 Delta32 and CCR2-64I genetic variants previously shown to slow the progression of HIV infection to AIDS. Alzheimer’s Mirror examined the APOE gene.
CEO and co-founder Julian Awad first received funding for the company while at the Wharton School of Business. He was later profiled by CBS News for Alzheimer’s Mirror and found that his own personal lifetime risk of Alzheimer’s disease is about 9 to 10 percent compared to 15% for the general population.
Ironically, he was also mentioned in a Wharton article from 2007 - Can Anyone Make Sense — or Money — Out of Personal DNA Testing?
Posted: 05 Oct 2008 11:28 PM CDT
It seems like we're getting closer to understanding the genetic basis of traits like skin, hair and eye pigmentation. Although it is generally assumed that it will be difficult to fully grasp the genetics of complex traits, it could be within reach in the near-term for these types of traits. Understanding the genetic basis of these traits will provide some guiding principles for uncovering the genetic basis of more 'complex' complex traits, as well as about the evolutionary genetic process, population histories, and interactions with environmental factors.
So what do we learn from this paper (see abstract below) on the association between SLC45A2 variants and hair color among Polish subjects?
First, from the intro, with respect to predicting red hair color:
"Nowadays, red hair phenotype is being predicted usingBack to this study, they basically find a rare variant in SLC45A2 that increases a person's odds of having black hair by about seven.
"However, in the present study, 3.6% of red-haired individuals were found to have the L374 allele associated with darker pigmentation. All of them had two MC1R alterations, which are considered as major function mutations strongly affecting receptor performance (data not present). All these individuals had fair skin and blue eyes. This indicates a predominant role of the MC1R gene."Association of the SLC45A2 gene with physiological human hair colour variation
Wojciech Branicki, Urszula Brudnik, Jolanta Draus-Barini, Tomasz Kupiec and Anna Wojas-Pelc
Journal of Human Genetics Early online
Abstract: Pigmentation is a complex physical trait with multiple genes involved. Several genes have already been associated with natural differences in human pigmentation. The SLC45A2 gene encoding a transporter protein involved in melanin synthesis is considered to be one of the most important genes affecting human pigmentation. Here we present results of an association study conducted on a population of European origin, where the relationship between two non-synonymous polymorphisms in the SLC45A2 gene — rs26722 (E272K) and rs16891982 (L374F) — and different pigmentation traits was examined. The study revealed a significant association between both variable sites and normal variation in hair colour. Only L374F remained significantly associated with hair colour when both SNPs were included in a logistic regression model. No association with other pigmentation traits was detected in this population sample. Our results indicate that the rare allele L374 significantly increases the possibility of having black hair colour (OR = 7.05) and thus may be considered as a future marker for black hair colour prediction.
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