A promiscuous ancestral enzyme´s structure unveils protein variable regions of the highly diverse metallo-β-lactamase family

The metallo-β-lactamase fold is an ancient protein structure present in numerous enzyme families responsible for diverse biological processes. The crystal structure of the hyperthermostable crenarchaeal enzyme Igni18 from Ignicoccus hospitalis was solved at 2.3 Å and could resemble a possible first archetype of a multifunctional metallo-β-lactamase. Ancestral enzymes at the evolutionary origin are believed to be promiscuous all-rounders. Consistently, Igni18´s activity can be cofactor-dependently directed from β-lactamase to lactonase, lipase, phosphodiesterase, phosphotriesterase or phospholipase. Its core-domain is highly conserved within metallo-β-lactamases from Bacteria, Archaea and Eukarya and gives insights into evolution and function of enzymes from this superfamily. Structural alignments with diverse metallo-β-lactamase-fold-containing enzymes allowed the identification of Protein Variable Regions accounting for modulation of activity, specificity and oligomerization patterns. Docking of different substrates within the active sites revealed the basis for the crucial cofactor dependency of this enzyme superfamily.

A novel sequence alignment algorithm based on deep learning of the protein folding code

Motivation From evolutionary interference, function annotation to structural prediction, protein sequence comparison has provided crucial biological insights. While many sequence alignment algorithms have been developed, existing approaches often cannot detect hidden structural relationships in the ‘twilight zone’ of low sequence identity. To address this critical problem, we introduce a computational algorithm that performs protein Sequence Alignments from deep-Learning of Structural Alignments (SAdLSA, silent ‘d’). The key idea is to implicitly learn the protein folding code from many thousands of structural alignments using experimentally determined protein structures. Results To demonstrate that the folding code was learned, we first show that SAdLSA trained on pure α-helical proteins successfully recognizes pairs of structurally related pure β-sheet protein domains. Subsequent training and benchmarking on larger, highly challenging datasets show significant improvement over established approaches. For challenging cases, SAdLSA is ∼150% better than HHsearch for generating pairwise alignments and ∼50% better for identifying the proteins with the best alignments in a sequence library. The time complexity of SAdLSA is O(N) thanks to GPU acceleration. Availability and implementation Datasets and source codes of SAdLSA are available free of charge for academic users at http://sites.gatech.edu/cssb/sadlsa/. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Consistent Consideration of RNA Structural Alignments Improves Prediction Accuracy of RNA Secondary Structures

ABSTRACTThe probabilistic consideration of the global pairwise sequence alignment of two RNAs tied with their global single secondary structures, or global pairwise structural alignment, is known to predict more accurately global single secondary structures of unaligned homologs by discriminating between conserved local single secondary structures and those not conserved. However, conducting rigorously this consideration is computationally impractical and thus has been done to decompose global pairwise structural alignments into their independent components, i.e. global pairwise sequence alignments and single secondary structures, by conventional methods. ConsHomfold and ConsAlifold, which predict the global single and consensus secondary structures of unaligned and aligned homologs considering consistently preferable (or sparse) global pairwise structural alignments on probability respectively, were developed and implemented in this study. These methods demonstrate the best trade-off of prediction accuracy while exhibiting comparable running time compared to conventional methods. ConsHomfold and ConsAlifold optionally report novel types of loop accessibility, which are useful for the analysis of sequences and secondary structures. These accessibilities are average on sparse global pairwise structural alignment and can be computed to extend the novel inside-outside algorithm proposed in this study that computes pair alignment probabilities on this alignment.

TopMatch-web: pairwise matching of large assemblies of protein and nucleic acid chains in 3D

Frequently, the complete functional units of biological molecules are assemblies of protein and nucleic acid chains. Stunning examples are the complex structures of ribosomes. Here, we present TopMatch-web, a computational tool for the study of the three-dimensional structure, function and evolution of such molecules. The unique feature of TopMatch is its ability to match the protein as well as nucleic acid chains of complete molecular assemblies simultaneously. The resulting structural alignments are visualized instantly using the high-performance molecular viewer NGL. We use the mitochondrial ribosomes of human and yeast as an example to demonstrate the capabilities of TopMatch-web. The service responds immediately, enabling the interactive study of many pairwise alignments of large molecular assemblies in a single session. TopMatch-web is freely accessible at https://topmatch.services.came.sbg.ac.at.

PhyloFold: Precise and Swift Prediction of RNA Secondary Structures to Incorporate Phylogeny among Homologs

MotivationThe simultaneous consideration of sequence alignment and RNA secondary structure, or structural alignment, is known to help predict more accurate secondary structures of homologs. However, the consideration is heavy and can be done only roughly to decompose structural alignments.ResultsThe PhyloFold method, which predicts secondary structures of homologs considering likely pairwise structural alignments, was developed in this study. The method shows the best prediction accuracy while demanding comparable running time compared to conventional methods.AvailabilityThe source code of the programs implemented in this study is available on “https://github.com/heartsh/phylofold” and “https://github.com/heartsh/phyloalifold“.Contact“[email protected]”.Supplementary informationSupplementary data are available.

InterLig: improved ligand-based virtual screening using topologically independent structural alignments

Motivation In the past few years, drug discovery processes have been relying more and more on computational methods to sift out the most promising molecules before time and resources are spent to test them in experimental settings. Whenever the protein target of a given disease is not known, it becomes fundamental to have accurate methods for ligand-based virtual screening, which compares known active molecules against vast libraries of candidate compounds. Recently, 3D-based similarity methods have been developed that are capable of scaffold hopping and to superimpose matching molecules. Results Here, we present InterLig, a new method for the comparison and superposition of small molecules using topologically independent alignments of atoms. We test InterLig on a standard benchmark and show that it compares favorably to the best currently available 3D methods. Availability and implementation The program is available from http://wallnerlab.org/InterLig. Supplementary information Supplementary data are available at Bioinformatics online.

Modular origins of biological electron transfer chains

Oxidoreductases catalyze electron transfer reactions that ultimately provide the energy for life. A limited set of ancestral protein-metal modules are presumably the building blocks that evolved into this diverse protein family. However, the identity of these modules and their path to modern oxidoreductases is unknown. Using a comparative structural analysis approach, we identify a set of fundamental electron transfer modules that have evolved to form the extant oxidoreductases. Using transition metal-containing cofactors as fiducial markers, it is possible to cluster cofactor microenvironments into as few as four major modules: bacterial ferredoxin, cytochrome c, symerythrin, and plastocyanin-type folds. From structural alignments, it is challenging to ascertain whether modules evolved from a single common ancestor (homology) or arose by independent convergence on a limited set of structural forms (analogy). Additional insight into common origins is contained in the spatial adjacency network (SPAN), which is based on proximity of modules in oxidoreductases containing multiple cofactor electron transfer chains. Electron transfer chains within complex modern oxidoreductases likely evolved through repeated duplication and diversification of ancient modular units that arose in the Archean eon.