| 1. |
- Laine, Elodie, et al.
(author)
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Protein sequence-to-structure learning : Is this the end(-to-end revolution)?
- 2021
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In: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 89:12, s. 1770-1786
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Research review (peer-reviewed)abstract
- The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. In CASP14, deep learning has boosted the field to unanticipated levels reaching near-experimental accuracy. This success comes from advances transferred from other machine learning areas, as well as methods specifically designed to deal with protein sequences and structures, and their abstractions. Novel emerging approaches include (i) geometric learning, that is, learning on representations such as graphs, three-dimensional (3D) Voronoi tessellations, and point clouds; (ii) pretrained protein language models leveraging attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; and (vi) finally truly end-to-end architectures, that is, differentiable models starting from a sequence and returning a 3D structure. Here, we provide an overview and our opinion of the novel deep learning approaches developed in the last 2 years and widely used in CASP14.
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| 2. |
- Menéndez Hurtado, David, 1990-
(author)
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Structured Learning for Structural Bioinformatics : Applications of Deep Learning to Protein Structure Prediction
- 2019
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Doctoral thesis (other academic/artistic)abstract
- Proteins are the basic molecular machines of the cell, performing a broad range of tasks, from structural support to catalysisof chemical reactions. Their function is determined by their 3D structure, which in turn is dictated by the order of their components, the amino acids.This thesis is dedicated to applications of machine learning to the problems of contact prediction, ab-initio, and model quality assessment. In particular, my research has been focused on developing methods that are both effective, and easy to use.In the first paper, we improved the already state-of-the-art model quality assessment (MQA) program ProQ3 replacing the underlying machine learning algorithm from svm to Deep Learning, baptised ProQ3D. The correlation between predicted and true scores was improved from 0.85 to 0.90, using the same training data and features.The second paper joined several programs into a single pipeline for ab-initio structure prediction: contact prediction,folding, and model selection. We attempted to predict the structures of all 6379 PFAM families with unknown structure, ofwhich 558 we believe to be accurate. Of these, 415 had not been reported before.The third paper uses advances in machine learning to build a contact predictor, PconsC4, that is fast and easy to deployin large-scale studies, since it requires a single Multiple Sequence Alignment (MSA), and no external dependencies. The predictions are state-of-the-art, yielding a 12% improvement in precision over PconsC3, and 244 times faster.With ProQ4, in the fourth paper, we introduce a novel way of training deep networks for MQA in a way that minimises the bias of the training data, and emphasises model ranking, and demonstrate its viability with a minimal description ofthe protein. The ranking correlation was improved with respect to ProQ3D from 0.82 to 0.90.Lastly, in the fifth paper, weshow the results of ProQ3D and ProQ4 in a completely blind test: CASP13.
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| 3. |
- Moretti, Rocco, et al.
(author)
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Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions
- 2013
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In: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 81:11, s. 1980-1987
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Journal article (peer-reviewed)abstract
- Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies. Proteins 2013; 81:1980-1987.
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