Help Cure Muscular Dystrophy, Phase 2, Помоги вылечить мускульную дистрофию, 2 фаза Налаштування

[Rilian]

Обновление статистики - посчитано 56.83%

с этой инфой, Sekerob опубликовал рассчет времени кранчинга проекта

My abacus makes it late Q2-2012, Spring Break and Summer Holiday effects inclusive. This indicator has not moved for quite a few months, hard entered May 31, and computed May 29, subject to change without notice of course )

http://www.worldcommunitygrid.org/forums/w...ad_thread,30815

Примерно проект завершится в конце мая 2012

[corsar83]

Обновление статистики за 18 февраля - 59.78%

[Rilian]

Statistics updated on 2011 March 4th : 62.43%

> Statistics updated on 2011 February 18th : 59.78%

[corsar83]

Statistics updated on 2011 June 27th : 76.67%

[Rilian]

July 31, 2010

Hi to all! Thought to try to explain what we are doing right now before you take some vacation, like the scientists here. Hope it will help to feel that things are improving and that the project is very active from this side!! Actually, someone new will join the group on september, Anne Lopes. Anne is assistant professor in structural bioinformatics and has a background in physical-chemistry. She is very interested in working on the protein partnership problem with the numerical approach we developed and on the data analysis of the huge amount of information you are producing!

The state of the art here is the following.

In the paper [S. Sacquin-Mora, A. Carbone and R. Lavery (2008), Identification of protein interaction partners and protein-protein interaction sites, J. Mol. Biol. 382, p1276-1289] we developed a numerical method to detect protein partners. The method was presented and tested on a small quantity of known protein complexes. As you can imagine, as soon as the data from HCMD Phase 1 arrived (THANKS TO YOUR CONTRIBUTION!!) we retested the approach to verify whether we could confirm the results on a larger dataset. This is indeed the case, the method works, and we can distinguish protein partners within the about 150 proteins tested. We observed that the signal is much less sharp when we work with 150 proteins than with 12 proteins (like in the paper) though and that some extra work should be done to improve the numerical method. Remember that for HCMD Phase 2 we shall search for partners among about 2200 proteins.

At the moment, we have improved the formula introduced in the paper and we are developing an "intelligent" approach to arrive fast and surely to identify a small number of potential partners for any protein.

Let me give you an insight on the complexity underlying the problem. It has something to do with the understanding of protein population. This is an important point to assimilate, if you like to understand a bit more of our analysis. When we consider a protein, we do not just study one protein (that is, its geometry and its physico-chemical properties: this is already taken into account in the docking algorithm running on your computers and into JET, the program that allowed us to predict protein binding sites) but we rather study its behaviour with the population of proteins that are around it (in the cell; for the HCMD phase 2, population means the 2200 proteins analyzed in your computers). In other words, when we look at a protein we hope to get a signal on its partnership by looking at her way to interact with all other proteins in the population. This means that we hope to learn from bad interactions as well as from good interactions. The information that YOU are giving us provides to us some insight on what is bad and what is good! but this is not enough and we shall use also some extra observation on the interaction of the protein within a population.

Some proteins are slippery, meaning that they do not seem to glue to any partner. Some others are gluing, meaning that they do glue to essentially everybody. Then there are many other proteins (about a half) that seem to stick on the right place with some specificity. They are the easiest to study. When we use, in our calculations, contributions coming from the entire population, one should think that these contributions come, in principle, from slippery proteins, gluing proteins and many other proteins whose behaviour is less sharply characterizable. "Noise" might enter into the calculation and we wish to reduce it. Learning from the whole set of interactions of a protein, means to learn to which group the protein belongs to. Once this is determined, the numerical criteria that we developed could be adjusted to accurately predict a partner or a small set of potential partners, whenever possible. The understanding of the whole set of behaviours that we need to take into account to know how to correctly evaluate the data coming from WCG is our goal today.

There are a few other concerns that are present in our analysis, and they have something to do with :

1. the algorithmic aspects concerning the handling of large amount of information to be combined for the "learning" approach I mentioned above.

2. the fact that on HCMD phase 2 data analysis, we use JET predictions of protein interaction in our numerical criteria instead of actual real interfaces as done in the paper cited above. This implies a loss of precision that we should consider in our numerical evaluations of the interactions.

These informations should give you some insight on the complexity of the question we face today. Hope that everyone will be feeling that we are advancing, together, for a project that runs alive and hopefully will reserve exciting surprises to all. We expect it.

Have a good summer! Alessandra

[Rilian]

UPDATES for the project

December 21, 2011

Hi to all, with Sophie and Richard we have written up an account of the docking analysis of the 168 protein complexes of the Mintseris dataset tested in phase 1. The paper is under review right now and will give you the link as soon as it is published.

The analysis of the dataset of 168 protein complexes is not finished yet! In fact, we try to improve the signals for the detection of partnership. There are two main points that one needs to keep in mind. In phase 2 we do not know the real partners and we had to use predictions of interaction sites to run MaXDO. This was because the search space on a protein surface could not be exhaustively explored, even with the help of WCG. it would be far too big! This means that we need to understant on a pool of proteins that we know (that is the 168 protein complexes) how the predictions of protein interaction sites will impact partners predictions. This is what we carefully investigate right now. It takes time! There are a number of intermediate results that you might like to know about:

1. the analysis realized in [Sacquin-Mora et al. 2008] on 12 complexes, has been scaled to 168 complexes, and it highlighted a predictive protein-protein interaction power of AUC=0.84 (see Figure A below) when using knowledge on real interaction surfaces and when exploring the whole protein surface. It is important to stress that this successful scaling of the analysis in [Sacquin-Mora et al. 2008] to 168 proteins was not an obvious guess! Why successful? The AUC is a probability measure used to evaluate the accuracy of the test. Values vary from 0 to 1, where 1 represents a perfect test and 0.5 represents a worthless test. Roughly speaking, one can think of the following ranking:
.90-1 = excellent, .80-.90 = good, .70-.80 = fair, .60-.70 = poor, .50-.60 = fail.

2. We also observed that amongst the 168 protein complexes several had the tendency to bind to nearly all other proteins and others showed very few strong interactions. Both these families of proteins negatively contribute to partnership prediction, and, when eliminated, enable the predictive power to be increased to an AUC=0.98 (see Figure B).

3. When experimental information on interaction surfaces is replaced with data from JET [Engelen 2009] (the tool for conservation analysis developed within our consortium) the predictive power only decreases slightly, with an AUC=0.82. This suggests that coupling protein interface predictions with docking is a very promising approach.

4. Nevertheless improvements are still required, since when JET predictions are used to delimit the docking area, as well as to compute the numerical index that discriminates partners, the predictive power falls at an AUC=0.59. This implies that better interaction patch detection has to be developed. However, we note that a subgroup of 20 complexes was identified where JET predictions already yielded very good predictions (AUC=0.97; Figure C below), suggesting that generating subgroups by categorizing protein interaction proclivities could improve performance.

5. Lastly, we systematically analyzed complexes in terms of the functional classes of the interacting proteins. The complexes could be grouped into: Enzyme-Inhibitors (46 proteins), Antigen-Antibody (20), Antigen-Antibody Bound (24), Others (78), and also as, Rigid Body (126), Medium (26) and Difficult (16). Interactions within certain classes, such as Enzyme-Inhibitors, were clearly easier to predict suggesting that such classifications should be considered in partnership prediction.

Figure. Matrices of pairwise interaction indexes for different subsets of proteins. High interaction scores (between 0.7 and 1, blue and black in the color scale) indicate a high probability of interaction. Proteins are ordered in the matrix such that true interacting partners lie on the diagonal. A: full dataset of 168 protein complexes. Interaction scores were computed using knowledge of the experimental interfaces (AUC=0.84). B: subset of 44 protein complexes leading to an AUC=0.98. Interaction scores were computed using knowledge on experimental interfaces. C: subset of 20 proteine complexes leading to an AUC=0.97. Interaction scores were computed using interfaces predicted by JET.

At the moment we work on coevolution between protein interfaces and on improving JET interaction predictions. With both advancements we count improving identification of new partners, and increase the AUC above. We have done a lot of work already on this. A new approach to coevolution analysis, treating especially conserved sites like protein interfaces, has been recently developed at the lab. More on this soon.

Merry Christmas and a Happy New Year to all!

Alessandra

[KING100N]

До конца проекта осталось 18 дней:

Hello all,

This is the official statement saying there is an estimated 18 days left of new work to be sent out. In addition, we will be changing the way work units are distributed for this project. Previously priority was given to work units that were children/grandchilden of the original parent work units. We will be changing this to give priority to parent work units (so these will be sent prior to children/grandchild work units). This will allow for a more consistent supply of hcmd2 work units until the end of the project.

Thank you for all your help and support with this project!

Seippel

https://secure.worldcommunitygrid.org/forum...ad,32499#361380

[corsar83]

Отличная новость. Жаль не досчитаю до сапфира, 3 месяца расчётов еще нужно

UPD. Или ще работ подвалят чото не понял

[KING100N]

Или ще работ подвалят чото не понял

Да вроде нет, хотя, может и насыпят охапку, как в HFCC

[Rilian]

У меня сапфир есть - любезно кранчу другие проекты, чтобы предоставить шанс охотникам за бейджи

[Rilian]

Спешите докранчить до бейджика (минимум 14 процессорных дней)! Больше такой возможности в жизни не будет!

[corsar83]

Уже поздно . Я не успею, эхх..

[Rilian]

corsar83, Еще время есть. Подтягивайся

[corsar83]

Мне нужно еще 80 дней расчетов, вроде и не много, но.... Круглосуточно считать не могу, так может и успел бы

[KING100N]

corsar83,
Давай слабый ключ от учетки. Со вторника, надеюсь, я снова в строю, помогу 8ю ядрами