Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search

AlphaFold can predict the structure of single- and multiple-chain proteins with very high accuracy. However, the accuracy decreases with the number of chains, and the available GPU memory limits the size of protein complexes which can be predicted. Here we show that one can predict the structure of large complexes starting from predictions of subcomponents. We assemble 91 out of 175 complexes with 10–30 chains from predicted subcomponents using Monte Carlo tree search, with a median TM-score of 0.51. There are 30 highly accurate complexes (TM-score ≥0.8, 33% of complete assemblies). We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. We find that complexes containing symmetry are accurately assembled, while asymmetrical complexes remain challenging. The method is freely available and accesible as a Colab notebook https://colab.research.google.com/github/patrickbryant1/MoLPC/blob/master/MoLPC.ipynb

A structural biology community assessment of AlphaFold2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research

Deep learning solutions to protein quaternary structure

Interactions between proteins are directly involved in most biological processes and are essential for the correct functioning of every form of life. The nature of protein-protein interactions allows functional assemblies of hundreds of protein chains. Given the enormous complexity and the pivotal role of protein interactions in life’s mechanics, the necessity to obtain a complete comprehension of such mechanisms is just as big as the challenge to achieve such knowledge. In the last few decades, experimental procedures constantly improved, dramatically increasing the available structural data for protein interactions. Unfortunately, experimental methods require a lot of time and resources and cannot always be applied with the same degree of success. Several computational methods have been developed in parallel with experimental procedures to overcome such limitations. Therefore, this thesis focused on screening existing computational methods and adopting them to improve the overall accuracy in solving structures of protein-complexes. In the first paper, I propose a simple rigid-body docking framework to test several interface predictors and their ability to drive a protein-protein docking procedure. Next, in the second paper, I display a method to adapt the trRosetta deep neural network to predict inter-residues distances and dihedral angle constraints for full protein complexes. The same concept is then improved in the third paper with FoldDock, an adaptation of Alphafold2 to work on multiple protein sequences and produce the corresponding complex. Finally, in the fourth paper, the FoldDock pipeline is applied to a large dataset of protein pairwise interactions derived from the hu.MAP and HuRI datasets, resulting in the characterization of more than 3000 high-confidence structural models

Deep learning solutions to protein quaternary structure

Interactions between proteins are directly involved in most biological processes and are essential for the correct functioning of every form of life. The nature of protein-protein interactions allows functional assemblies of hundreds of protein chains. Given the enormous complexity and the pivotal role of protein interactions in life’s mechanics, the necessity to obtain a complete comprehension of such mechanisms is just as big as the challenge to achieve such knowledge. In the last few decades, experimental procedures constantly improved, dramatically increasing the available structural data for protein interactions. Unfortunately, experimental methods require a lot of time and resources and cannot always be applied with the same degree of success. Several computational methods have been developed in parallel with experimental procedures to overcome such limitations. Therefore, this thesis focused on screening existing computational methods and adopting them to improve the overall accuracy in solving structures of protein-complexes. In the first paper, I propose a simple rigid-body docking framework to test several interface predictors and their ability to drive a protein-protein docking procedure. Next, in the second paper, I display a method to adapt the trRosetta deep neural network to predict inter-residues distances and dihedral angle constraints for full protein complexes. The same concept is then improved in the third paper with FoldDock, an adaptation of Alphafold2 to work on multiple protein sequences and produce the corresponding complex. Finally, in the fourth paper, the FoldDock pipeline is applied to a large dataset of protein pairwise interactions derived from the hu.MAP and HuRI datasets, resulting in the characterization of more than 3000 high-confidence structural models

Towards a structurally resolved human protein interaction network

Cellular functions are governed by molecular machines that assemble through protein-protein interactions. Their atomic details are critical to studying their molecular mechanisms. However, fewer than 5% of hundreds of thousands of human protein interactions have been structurally characterized. Here we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure. We identify interface residues harboring disease mutations, suggesting potential mechanisms for pathogenic variants. Groups of interface phosphorylation sites show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple protein interactions as signaling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies helping to expand our understanding of human cell biology.ISSN:1545-9993ISSN:1545-998

Towards a structurally resolved human protein interaction network

All cellular functions are governed by complex molecular machines that assemble through protein-protein interactions. Their atomic details are critical to the study of their molecular mechanisms but fewer than 5% of hundreds of thousands of human interactions have been structurally characterized. Here, we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human interactions. We show that higher confidence models are enriched in interactions supported by affinity or structure based methods and can be orthogonally confirmed by spatial constraints defined by cross-link data. We identify 3,137 high confidence models, of which 1,371 have no homology to a known structure, from which we identify interface residues harbouring disease mutations, suggesting potential mechanisms for pathogenic variants. We find groups of interface phosphorylation sites that show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple interactions as signalling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies. Accurate prediction of protein complexes promises to greatly expand our understanding of the atomic details of human cell biology in health and disease

A structural biology community assessment of AlphaFold2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.ISSN:1545-9993ISSN:1545-998

A structural biology community assessment of AlphaFold2 applications.

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research

We are against something that doesn't exist. Conversation with Giovanna Costanza Meli

What does the art system mean if you think of the analysis we just formulated? In that world defined by the media, why should we care about an art system? It is the same kind of propagandistic model. The real country, the real situation is different. So the work of the intellectuals I think is to reconnect the "discourse" to the reality. In the non profit field, curators, artists and intellectuals are used to speaking about a way of being out of the system, or against the system, and I also thought to be against something huge. But then I started to consider everything form a different point of view, looking at the reality. So I say there is no art system in Italy, and if we are against this art system, we are against something that doesn't exist. I would say it is sincere to care about the reality

The small Museum of Migrations in Lampedusa

If we consider the museum as an institution, we spontaneously relate it to concepts such as tradition, collection, scientific committee, administration, more or less codified cultural representations, more or less marked cultural policies, dialogue with its context and search for a public. If we refer to the internal debate within contemporary museums, we immediately think of integration and cultural mediation, alongside the debate concerning their public and social function and the educational departments's research. All this plays a big role in the mental image shared by art historians, anthropologists and curators researching hybridization and crossbreeding, and what these notions could mean within the various discourses on identity, on the one end, and self-representation on the other hand. But what is happening in Lampedusa? What have this island and its population to do with cultural and scientific museological debate?The aim of this journey was not to expose ourselves to the famous cultural legacy of classical antiquity and the Italian Renaissance, but to explore – as artists and non-academic researchers – Italy’s contemporary artistic, social and political scene through active witnesses. We started in Rome and ended in Palermo, passing through Florence, Bologna, Lugo, Milan, Viganella, Turin, Rivoli, Lecce, Matera, Bari, Santa Maria di Leuca, Naples and Gibellina