Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold

Distance-based Protein Folding Powered by Deep Learning

Contact-assisted protein folding has made very good progress, but two challenges remain. One is accurate contact prediction for proteins lack of many sequence homologs and the other is that time-consuming folding simulation is often needed to predict good 3D models from predicted contacts. We show that protein distance matrix can be predicted well by deep learning and then directly used to construct 3D models without folding simulation at all. Using distance geometry to construct 3D models from our predicted distance matrices, we successfully folded 21 of the 37 CASP12 hard targets with a median family size of 58 effective sequence homologs within 4 hours on a Linux computer of 20 CPUs. In contrast, contacts predicted by direct coupling analysis (DCA) cannot fold any of them in the absence of folding simulation and the best CASP12 group folded 11 of them by integrating predicted contacts into complex, fragment-based folding simulation. The rigorous experimental validation on 15 CASP13 targets show that among the 3 hardest targets of new fold our distance-based folding servers successfully folded 2 large ones with <150 sequence homologs while the other servers failed on all three, and that our ab initio folding server also predicted the best, high-quality 3D model for a large homology modeling target. Further experimental validation in CAMEO shows that our ab initio folding server predicted correct fold for a membrane protein of new fold with 200 residues and 229 sequence homologs while all the other servers failed. These results imply that deep learning offers an efficient and accurate solution for ab initio folding on a personal computer

Predicting multidomain protein structure and function via co-evolved amino acids and application to polyketide synthases

Proteins are an important building block of life, and they are responsible for many processes in living organisms. Therefore, understanding their functions and working mechanisms has vital importance to answer many questions about diseases and is a basis for the development of novel drugs. Three dimensional (3D) structure of proteins determine their functions; therefore, the determination of the 3D structures of proteins has been studied widely. Although many experimental techniques have been developed to determine the structures of proteins, they have limitations, especially for large protein complexes. Protein structure can help understand protein function, as can looking at conserved residues, but typically time consuming mutagenesis experiments combined with protein function assays are needed. As an alternative to the experimental methods, researchers have been working on developing computational approaches. While it is relatively easy to predict structures when the structure of a homologous protein is known, as it can be used as a template, the prediction of protein structures in the absence of a template is more challenging. For template-free predictions, coevolved amino acid residue pairs, predicted from the alignment of the homologous sequences, provided promising improvements in the field. More recently, successful implementation of the artificial neural networks, fed by the predicted coevolved residue pairs, improved the accuracy of the predicted structures further. Although there are promising developments in the coevolution based approaches, especially for the structure prediction of small/medium-sized proteins, more developments are needed for predicting protein structure, particularly of large protein complexes. Here, we show that the prediction of distances between residue pairs, via deep neural networks fed by predictions of coevolved residue pairs, improves the accuracy of structure prediction in small/medium-sized proteins. The prediction of residue pair distances, using a similar approach, in two interacting domains also allows us to predict how two domains on the same chain interact with each other. Further, we show that prediction of coevolved residue groups, via statistical coupling analysis, allows us to determine functional boundaries of domains and diverged amino acid patterns in the sub-types of the domains in a multi-domain protein complex, a polyketide synthase. We found that using predicted distances, in addition to the predicted residue pairs in contact, allows us to generate structures closer to the experimental structures, and to select them as the final models in a straightforward approach. Additionally, we reveal that the distances of the residue pairs on interacting domain pairs can be predicted accurately leading to the successful prediction of the structural interface between two interacting proteins when the interface surface is large, and the sequence alignment is comprehensive enough. Finally, we found that functional domain boundaries, which are consistent with the experimental studies, can be determined. Also, some coevolved residue groups have distinct amino acid patterns in different domain sub-types including the positions that have already known as the fingerprint motifs of the different sub-types. These approaches can be applied to predict the structures of individual domains and to predict how two domains interact with each other, which can be used to predict the structure of multi-domain proteins. The work on polyketides here demonstrates how these developments might be applied, since identifying domain boundaries and residues important for substrate specificity should aid in the design of novel polyketide synthases and thus of novel polyketides. This in itself is an important development given the commercial and medicinal importance of polyketides, but also opens the way to similar analysis on other multidomain proteins

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications