Google DeepMind reveals the shape of life
1 December 2020 | Written by Alberto Laratro
Google's artificial intelligence opens the door to a revolution in the biomedical field, determining the three-dimensional shape of proteins with unprecedented precision
“Medicine will change, research will change, bioengineering will change, everything will change”. The words of Andrei Lupas, evolutionary biologist of the Max Planck Institute reveal the extent of this achievement. The artificial intelligence developed by Google, DeepMind, through its AlphaFold program, has been able to solve one of the most important questions of modern biology: determining the three-dimensional shape of a protein from its amino acid sequence.
This is a challenge that has been going on for decades and that could have enormous repercussions in the medical field, and in any sector that concerns biology. The ability to predict the structure of a protein will allow accelerating, for example, the design of specific drugs, or to reveal the role of thousands of proteins within the human genome whose shape and function are still unknown today. opening the door to the understanding of those genes that can cause genetic diseases.
CASP, a competition to unveil the shape of life. The announcement of the results obtained by AlphaFold took place in the context of CASP, Critical Assessment of Structure Prediction, an international competition that since 1994 has seen research groups from all over the world compete to try to improve computational techniques to predict the structure of proteins. Google’s AI had already participated in 2018, placing itself well but without obtaining particular results. On November 30 this year, however, the announcement is moving research laboratories around the world. If on average the prediction methods and techniques are about 70% accurate, AlphaFold has reached 90%.
To date, X-ray crystallography is used to verify that the predicted structure corresponds to the real one, and AlphaFold will not replace these expensive and time-consuming methods but will provide researchers with new ways to approach research theoretically. before verifying the predictions experimentally, and will allow doing so at unparalleled speed.
The Lupas research team has worked for about ten years on the definition of the structure of a protein, also using crystallography, which, however, is not always able to reveal the form sought in a clear way. AlphaFold was able to rebuild it in half an hour.
The importance of proteins. Proteins are the building blocks of life, and their functions are determined by their shape. Guided by the atomic forces and by the “interlocking” between the different molecules, the amino acids, the proteins bend, deform and wrap themselves in a myriad of different shapes. Biologically speaking, life is a game of interlocking proteins.
Since the 1950s, when the first structure of a protein was defined thanks to X-ray crystallography, researchers have worked to understand how different combinations of amino acids can create such an enormous variety of shapes and therefore functions.
Developing a drug is a bit like creating a puzzle, but without knowing which pieces you have available to fill a certain empty piece. Accelerating the ability to predict the structure of a protein means accelerating the development of a drug, allowing us to understand more quickly which shape must have a piece to complete the puzzle.
Research becomes more and more “artificially intelligent”. The one used by the Google DeepMind team is not the only approach to making use of artificial intelligence. Half of the research teams that participated in this year’s CASP used Deep Learning, AlphaFold simply managed to do it more efficiently. Deep Learning, simplifying it, is the technique that allows you to train an artificial intelligence by providing it with a large number of examples to understand how a certain phenomenon works, in this case, the folding of proteins.
The use of these techniques is increasingly widespread and will play a fundamental role in biomedical research in the coming years. Demis Hassabis, co-founder and CEO of DeepMind said the company intends to make AlphaFold available and accessible. His previous version, in fact, was made public, allowing others to replicate his approach.
How laboratories will change. In early 2020 AlphaFold was able to predict the structure of some SARS-CoV-2 proteins before they were experimentally determined. Despite the competitive advantage of this technique, however, we will hardly find ourselves in front of a transformation in a short time of research laboratories: artificial intelligence techniques are already part of the tools used to study the structure of proteins. AlphaFold and other methods that exploit similar algorithms will complement and improve the techniques already in use, providing scientists with more and more methods
Google DeepMind inaonyesha muundo wa maisha - Alberto Larratro - Google's Artificial Intelligence opens the door to a revolution in the biomedical field, determining the three-dimensional shape of proteins with unprecedented precision - Google's AI opens the door to a revolution in the biomedical field, determining the three-dimensional shape of proteins with unprecedented precision - Google's AI opens the door to a revolution in the biomedical field. Andrei Lupin, mwanabiolojia wa mageuzi wa Taasisi ya Max Planck, anaelezea jinsi hii inavyoweza kuwa na athari kubwa. Google imeanzisha DeepMind, ambayo imepata mafanikio makubwa katika kuunda programu ya AlphaFold, ambayo ina uwezo wa kuamua muundo wa protini ya 3D kutoka kwa mfululizo wa amino asidi. Hii ni changamoto ambayo imekuwa ikiendelea kwa miongo kadhaa na ambayo inaweza kuwa na athari kubwa katika uwanja wa matibabu, na katika sekta yoyote inayohusu biolojia. Uwezo wa kutabiri muundo wa protini utaruhusu kuharakisha, kwa mfano, kubuni dawa maalum, au kufunua jukumu la maelfu ya protini ndani ya genome ya binadamu ambayo muundo na kazi bado haijulikani leo. Hii ni njia ya kufungua milango kwa uelewa wa jeni ambazo zinaweza kusababisha magonjwa ya urithi. CASP ni mashindano ya kufunua umbo la uhai. Tathmini ya AlphaFold ya Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu wa Utaratibu. Google AI tayari ilishiriki katika 2018, ikijishughulisha vizuri lakini bila kupata matokeo maalum. Hata hivyo, tarehe 30 Novemba mwaka huu, tangazo hilo linafanya maabara ya utafiti duniani kote ziendelee kufanya kazi. Kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano, kwa mfano. Kwa sasa, X-ray crystallography hutumiwa kuthibitisha kwamba muundo uliotarajiwa unalingana na ule halisi, na AlphaFold haitabadilisha mbinu hizi za gharama kubwa na zinazotumia muda lakini itatoa watafiti njia mpya za kukaribia utafiti kwa nadharia. kabla ya kuthibitisha utabiri kwa majaribio, na kuruhusu kufanya hivyo kwa kasi unparalleled. Lupas amekuwa akifanya kazi kwa miaka kumi kuamua muundo wa protini, akitumia pia crystallography, ambayo, hata hivyo, haifanyi kazi kila wakati kufunua muundo unaotafutwa kwa njia ya wazi. AlphaFold iliweza kuijenga upya kwa nusu saa. Umuhimu wa protini. Protini ni vipande vya msingi vya uhai, na kazi zao huamuliwa na umbo lao. Zikiwa zinaongozwa na nguvu za atomu na kwa "kuunganishwa" kati ya molekuli tofauti, asidi za amino, protini huinama, kugeuka na kujifunga katika maumbo mengi tofauti. Kwa maoni ya kibiolojia, uhai ni mchezo wa protini zilizounganishwa. Tangu miaka ya 1950, wakati muundo wa kwanza wa protini ulipotambuliwa kwa shukrani kwa X-ray crystallography, watafiti wamefanya kazi kuelewa jinsi mchanganyiko tofauti wa amino asidi unaweza kuunda aina kubwa ya maumbo na hivyo kazi. "Kujenga dawa ni kama kuunda ""puzzle"" bila kujua vipande vinavyoweza kujaza kipande fulani." Kuongeza uwezo wa kutabiri muundo wa protini inamaanisha kuharakisha maendeleo ya dawa, ikituwezesha kuelewa haraka zaidi ni umbo gani lazima liwe na kipande ili kukamilisha fumbo. Utafiti unakuwa zaidi na zaidi "kiakili bandia". "Timu ya Google ya DeepMind imesema kuwa ""haijawahi kuwa na uwezo wa kugundua jinsi ya kutumia akili bandia.""" Katika utafiti wa mwaka huu, timu za utafiti zilizoshiriki katika CASP zilitumia kujifunza kwa kina, na AlphaFold ilifaulu kufanya hivyo kwa ufanisi zaidi. Deep Learning ni mbinu ya mafunzo ya akili bandia kwa kutoa mfano wa jinsi ya folding protini. Matumizi ya mbinu hizi ni kuongezeka kwa wingi na itakuwa na jukumu la msingi katika utafiti wa biomedical katika miaka ijayo. Demis Hassabis, mwanzilishi mwenza na Mkurugenzi Mtendaji wa DeepMind, alisema kampuni hiyo inakusudia kufanya AlphaFold inapatikana na kupatikana. Kwa kweli, toleo lake la awali lilifanywa kuwa la umma, likiruhusu wengine waige mbinu yake. Jinsi maabara yatakavyobadilika. Katika mwanzo wa 2020, AlphaFold iliweza kutabiri muundo wa baadhi ya protini za SARS-CoV-2 kabla ya kuamua kwa majaribio. Hata hivyo, licha ya faida ya ushindani ya mbinu hii, hatuwezi kujikuta mbele ya mabadiliko katika muda mfupi wa maabara ya utafiti: mbinu za akili bandia tayari ni sehemu ya zana zinazotumiwa kujifunza muundo wa protini. AlphaFold na mbinu nyingine zinazotumia algorithms sawa zitakamilisha na kuboresha mbinu zilizotumika tayari, kutoa wanasayansi na mbinu zaidi na zaidi.