Search

An increasing number of density maps of biological macromolecules have been determined by cryo-electron microscopy (cryo-EM) and stored in the public database, EMDB. To interpret the structural information contained in EM density maps, alignment of maps is an essential step for structure modeling, comparison of maps, and for database search. Here, we developed VESPER, which captures the similarity of underlying molecular structures embedded in density maps by taking local gradient directions into consideration. Compared to existing methods, VESPER achieved substantially more accurate global and local alignment of maps as well as database retrieval., Here, the authors present VESPER, a program for EM density map search and alignment. Using benchmark datasets, they demonstrate that VESPER performs accurate global and local alignments and comparisons of EM maps.

Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distances using multiple sequence alignments (MSAs). In this work we present AttentiveDist, a novel approach that uses different MSAs generated with different E-values in a single model to increase the co-evolutionary information provided to the model. To determine the importance of each MSA’s feature at the inter-residue level, we added an attention layer to the deep neural network. We show that combining four MSAs of different E-value cutoffs improved the model prediction performance as compared to single E-value MSA features. A further improvement was observed when an attention layer was used and even more when additional prediction tasks of bond angle predictions were added. The improvement of distance predictions were successfully transferred to achieve better protein tertiary structure modeling.

An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) at near atomic resolution. However, tracing the main-chains and building full-atom models from EM maps of ~4–5 Å is still not trivial and remains a time-consuming task. Here, we introduce a fully automated de novo structure modeling method, MAINMAST, which builds three-dimensional models of a protein from a near-atomic resolution EM map. The method directly traces the protein’s main-chain and identifies Cα positions as tree-graph structures in the EM map. MAINMAST performs significantly better than existing software in building global protein structure models on data sets of 40 simulated density maps at 5 Å resolution and 30 experimentally determined maps at 2.6–4.8 Å resolution. In another benchmark of building missing fragments in protein models for EM maps, MAINMAST builds fragments of 11–161 residues long with an average RMSD of 2.68 Å., Main-chain tracing remains a time-consuming task for medium resolution cryo-EM maps. Here the authors describe MAINMAST, a computational approach for building main-chain structure models of proteins from EM maps of 4-5 Å resolution that builds main-chain models of the protein by tracing local dense points in the density distribution.

Background We comprehensively analyzed X-ray cocrystal structures of dipeptidyl peptidase IV (DPP-4) and its inhibitor to clarify whether DPP-4 alters its general or partial structure according to the inhibitor used and whether DPP-4 has a common rule for inhibitor binding. Results All the main and side chains in the inhibitor binding area were minimally altered, except for a few side chains, despite binding to inhibitors of various shapes. Some residues (Arg125, Glu205, Glu206, Tyr662 and Asn710) in the area had binding modes to fix a specific atom of inhibitor to a particular spatial position in DPP-4. We found two specific water molecules that were common to 92 DPP-4 structures. The two water molecules were close to many inhibitors, and seemed to play two roles: maintaining the orientation of the Glu205 and Glu206 side chains through a network via the water molecules, and arranging the inhibitor appropriately at the S2 subsite. Conclusions Our study based on high-quality resources may provide a necessary minimum consensus to help in the discovery of a novel DPP-4 inhibitor that is commercially useful. Electronic supplementary material The online version of this article (doi:10.1186/s12900-016-0062-8) contains supplementary material, which is available to authorized users.