RESUMEN
The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.
Asunto(s)
Aprendizaje Profundo , Ligandos , Modelos Moleculares , Proteínas , Programas Informáticos , Humanos , Anticuerpos/química , Anticuerpos/metabolismo , Antígenos/metabolismo , Antígenos/química , Aprendizaje Profundo/normas , Iones/química , Iones/metabolismo , Simulación del Acoplamiento Molecular , Ácidos Nucleicos/química , Ácidos Nucleicos/metabolismo , Unión Proteica , Conformación Proteica , Proteínas/química , Proteínas/metabolismo , Reproducibilidad de los Resultados , Programas Informáticos/normasRESUMEN
A wide class of problems in combinatorics, computer science and physics can be described along the following lines. There are a large number of variables ranging over a finite domain that interact through constraints that each bind a few variables and either encourage or discourage certain value combinations. Examples include the k-SAT problem or the Ising model. Such models naturally induce a Gibbs measure on the set of assignments, which is characterised by its partition function. The present paper deals with the partition function of problems where the interactions between variables and constraints are induced by a sparse random (hyper)graph. According to physics predictions, a generic recipe called the "replica symmetric cavity method" yields the correct value of the partition function if the underlying model enjoys certain properties [Krzkala et al., PNAS (2007) 10318-10323]. Guided by this conjecture, we prove general sufficient conditions for the success of the cavity method. The proofs are based on a "regularity lemma" for probability measures on sets of the form Ωn for a finite Ω and a large n that may be of independent interest. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 49, 694-741, 2016.
RESUMEN
We have measured the elasticity of high quality ultrapure 4He single crystals in the low temperature region where supersolidity is supposed to occur. At 20 mK, our results are consistent with elastic coefficients previously measured at 1.2 K. As the temperature increases from 20 to 100 mK, a large softening occurs because dislocations unpin from ³He impurities. In the absence of ³He impurities, dislocations are free to move down to 20 mK; the crystals are soft. The large magnitude of this anomalous softening shows that dislocations form a mobile mosaic structure. It illustrates the remarkable quantum plasticity of 4He crystals.
