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A computer model that employs techniques used to analyze social networks could aid the development of new cancer treatments, according to researchers.
The model analyzes the unique behaviors of cancer-causing proteins, spotting what makes them different from normal proteins and mapping out molecular targets for drugs that could potentially be developed to treat cancers.
The researchers described this model in PLOS Computational Biology.
Bissan Al-Lazikani, PhD, of The Institute of Cancer Research in London, UK, and her colleagues compared proteins to members of an enormous social network, mapping the ways they interact. This allowed the team to predict which proteins might be most effectively targeted with drugs.
Cancer-causing proteins that have already been successfully targeted tended to have particular “social” characteristics that differed from non-cancer proteins. “Hub-like” proteins that were shown to “communicate” with lots of other proteins were more likely to cause cancers.
The researchers said this suggests that previously unexplored cancer proteins with similar characteristics could make good drug targets.
“Our study is the first to identify the rules of social behavior of cancer proteins and use it to predict new targets for potential cancer drugs,” Dr Al-Lazikani said.
“It shows that cancer drug targets behave very differently from normal proteins and often have a complex web of social interactions. Finding new targets is one of the most important steps in drug discovery, but it can be a lengthy, expensive process.”
“The map that we’ve made will help researchers design better new drugs, more quickly, saving time and money. It also sheds light on how resistance to treatments may occur and, in just a few years, could help doctors choose the best drug combinations to suit individual patients.”
All of the researchers’ target predictions are available on the canSAR website. The underlying data and tools are also available on the site. 
Photo by Darren Baker
A computer model that employs techniques used to analyze social networks could aid the development of new cancer treatments, according to researchers.
The model analyzes the unique behaviors of cancer-causing proteins, spotting what makes them different from normal proteins and mapping out molecular targets for drugs that could potentially be developed to treat cancers.
The researchers described this model in PLOS Computational Biology.
Bissan Al-Lazikani, PhD, of The Institute of Cancer Research in London, UK, and her colleagues compared proteins to members of an enormous social network, mapping the ways they interact. This allowed the team to predict which proteins might be most effectively targeted with drugs.
Cancer-causing proteins that have already been successfully targeted tended to have particular “social” characteristics that differed from non-cancer proteins. “Hub-like” proteins that were shown to “communicate” with lots of other proteins were more likely to cause cancers.
The researchers said this suggests that previously unexplored cancer proteins with similar characteristics could make good drug targets.
“Our study is the first to identify the rules of social behavior of cancer proteins and use it to predict new targets for potential cancer drugs,” Dr Al-Lazikani said.
“It shows that cancer drug targets behave very differently from normal proteins and often have a complex web of social interactions. Finding new targets is one of the most important steps in drug discovery, but it can be a lengthy, expensive process.”
“The map that we’ve made will help researchers design better new drugs, more quickly, saving time and money. It also sheds light on how resistance to treatments may occur and, in just a few years, could help doctors choose the best drug combinations to suit individual patients.”
All of the researchers’ target predictions are available on the canSAR website. The underlying data and tools are also available on the site.
Photo by Darren Baker
A computer model that employs techniques used to analyze social networks could aid the development of new cancer treatments, according to researchers.
The model analyzes the unique behaviors of cancer-causing proteins, spotting what makes them different from normal proteins and mapping out molecular targets for drugs that could potentially be developed to treat cancers.
The researchers described this model in PLOS Computational Biology.
Bissan Al-Lazikani, PhD, of The Institute of Cancer Research in London, UK, and her colleagues compared proteins to members of an enormous social network, mapping the ways they interact. This allowed the team to predict which proteins might be most effectively targeted with drugs.
Cancer-causing proteins that have already been successfully targeted tended to have particular “social” characteristics that differed from non-cancer proteins. “Hub-like” proteins that were shown to “communicate” with lots of other proteins were more likely to cause cancers.
The researchers said this suggests that previously unexplored cancer proteins with similar characteristics could make good drug targets.
“Our study is the first to identify the rules of social behavior of cancer proteins and use it to predict new targets for potential cancer drugs,” Dr Al-Lazikani said.
“It shows that cancer drug targets behave very differently from normal proteins and often have a complex web of social interactions. Finding new targets is one of the most important steps in drug discovery, but it can be a lengthy, expensive process.”
“The map that we’ve made will help researchers design better new drugs, more quickly, saving time and money. It also sheds light on how resistance to treatments may occur and, in just a few years, could help doctors choose the best drug combinations to suit individual patients.”
All of the researchers’ target predictions are available on the canSAR website. The underlying data and tools are also available on the site.