Protein Structure Alignment Research Articles

Protein structure alignment by Reseek improves sensitivity to remote homologs.

Recent breakthroughs in protein fold prediction from amino acid sequences have unleashed a deluge of new structures, presenting new opportunities and challenges to bioinformatics. Reseek is a novel protein structure alignment algorithm based on sequence alignment where each residue in the protein backbone is represented by a letter in a "mega-alphabet" of 85899345920 (∼1011) distinct states. Reseek achieves substantially improved sensitivity to remote homologs compared to state-of-the-art methods including DALI, TMalign, and Foldseek, with comparable speed to Foldseek, the fastest previous method. Scaling to large databases of AI-predicted folds is analyzed. Foldseek E-values are shown to be under-estimated by several orders of magnitude, while Reseek E-values are in good agreement with measured error rates. https://github.com/rcedgar/reseek.

GTalign: spatial index-driven protein structure alignment, superposition, and search

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search—an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign’s high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function.

TM-search: An Efficient and Effective Tool for Protein Structure Database Search.

The quickly increasing size of the Protein Data Bank is challenging biologists to develop a more scalable protein structure alignment tool for fast structure database search. Although many protein structure search algorithms and programs have been designed and implemented for this purpose, most require a large amount of computational time. We propose a novel protein structure search approach, TM-search, which is based on the pairwise structure alignment program TM-align and a new iterative clustering algorithm. Benchmark tests demonstrate that TM-search is 27 times faster than a TM-align full database search while still being able to identify ∼90% of all high TM-score hits, which is 2-10 times more than other existing programs such as Foldseek, Dali, and PSI-BLAST.

The Complex GH32 Enzyme Orchestra from Priestia megaterium Holds the Key to Better Discriminate Sucrose-6-phosphate Hydrolases from Other β-Fructofuranosidases in Bacteria.

Inulin is widely used as a prebiotic and emerging as a priming compound to counteract plant diseases. We isolated inulin-degrading strains from the lettuce phyllosphere, identified as Bacillus subtilis and Priestia megaterium, species hosting well-known biocontrol organisms. To better understand their varying inulin degradation strategies, three intracellular β-fructofuranosidases from P. megaterium NBRC15308 were characterized after expression in Escherichia coli: a predicted sucrose-6-phosphate (Suc6P) hydrolase (SacAP1, supported by molecular docking), an exofructanase (SacAP2), and an invertase (SacAP3). Based on protein multiple sequence and structure alignments of bacterial glycoside hydrolase family 32 enzymes, we identified conserved residues predicted to be involved in binding phosphorylated (Suc6P hydrolases) or nonphosphorylated substrates (invertases and fructanases). Suc6P hydrolases feature positively charged residues near the structural catalytic pocket (histidine, arginine, or lysine), whereas other β-fructofuranosidases contain tryptophans. This correlates with our phylogenetic tree, grouping all predicted Suc6P hydrolases in a clan associated with genomic regions coding for transporters involved in substrate phosphorylation. These results will help to discriminate between Suc6P hydrolases and other β-fructofuranosidases in future studies and to better understand the interaction of B. subtilis and P. megaterium endophytes with sucrose and/or fructans, sugars naturally present in plants or exogenously applied in the context of defense priming.

Three-dimensional structural alignment based discovery and molecular basis of AtoB, catalyzing linear tetracyclic formation.

Enzymes from the nuclear transport factor 2-like (NTF2-like) superfamily represent a rare group of biocatalysts with diverse catalytic functions facilitating intriguing skeleton formations. However, most proteins of this family remain enigmatic and await further elucidation. In this study, a combination of protein structural alignment with clustering analysis uncovers a new aldolase, AtoB, belonging to the NTF2-like superfamily. AtoB catalyzes the key intramolecular aldol reaction in linear tetracyclic meroterpenoid biosynthesis. The X-ray crystal structures of AtoB and AtoB-ligand complex are established at 1.9 Å and 1.6 Å resolution, respectively, revealing the rotation of the α4 helix and key residues in the active site for substrate binding. Molecular docking and site-directed mutagenesis demonstrate an acid-base pair involved in the AtoB-catalyzed aldol reaction, of which Arg59 is responsible for stereocontrol of hydroxylated C-10a during condensation. These findings provide valuable information for understanding the catalytic mechanisms of the AtoB-catalyzed aldol reaction. Additionally, a branching biosynthetic pathway of aspertetranones is elucidated during the exploration of the natural substrate of AtoB.

Protein structural alignment using deep learning.

Protein structural alignment using deep learning.

Structural and molecular biology of Sabiá virus.

Brazilian mammarenavirus, or Sabiá virus (SABV), is a New World (NW) arenavirus associated with fulminant hemorrhagic disease in humans and the sole biosafety level 4 microorganism ever isolated in Brazil. Since the isolation of SABV in the 1990s, studies on viral biology have been scarce, with no available countermeasures against SABV infection or disease. Here we provide a comprehensive review of SABV biology, including key aspects of SABV replication, and comparisons with related Old World and NW arenaviruses. SABV is most likely a rodent-borne virus, transmitted to humans, through exposure to urine and feces in peri-urban areas. Using protein structure prediction methods and alignments, we analyzed shared and unique features of SABV proteins (GPC, NP, Z, and L) that could be explored in search of therapeutic strategies, including repurposing intended application against arenaviruses. Highly conserved catalytic activities present in L protein could be targeted for broad-acting antiviral activity among arenaviruses, while protein-protein interactions, such as those between L and the matrix protein Z, have evolved in NW arenaviruses and should be specific to SABV. The nucleoprotein (NP) also shares targetable interaction interfaces with L and Z and exhibits exonuclease activity in the C-terminal domain, which may be involved in multiple aspects of SABV replication. Envelope glycoproteins GP1 and GP2 have been explored in the development of promising cross-reactive neutralizing antibodies and vaccines, some of which could be repurposed for SABV. GP1 remains a challenging target in SABV as evolutive pressures render it the most variable viral protein in terms of both sequence and structure, while antiviral strategies targeting the Z protein remain to be validated. In conclusion, the prediction and analysis of protein structures should revolutionize research on viruses such as SABV by facilitating the rational design of countermeasures while reducing dependence on sophisticated laboratory infrastructure for experimental validation.

SAFoldNet: A Novel Tool for Discovering and Aligning Three-Dimensional Protein Structures Based on a Neural Network.

The development and improvement of methods for comparing and searching for three-dimensional protein structures remain urgent tasks in modern structural biology. To solve this problem, we developed a new tool, SAFoldNet, which allows for searching, aligning, superimposing, and determining the exact coordinates of fragments of protein structures. The proposed search and alignment tool was built using neural networking. Specifically, we implemented the integrative synergy of neural network predictions and the well-known BLAST algorithm for searching and aligning sequences. The proposed method involves multistage processing, comprising a stage for converting the geometry of protein structures into sequences of a structural alphabet using a neural network, a search stage for forming a set of candidate structures, and a refinement stage for calculating the structural alignment and overlap and evaluating the similarity with the starting structure of the search. The effectiveness and practical applicability of the proposed tool were compared with those of several widely used services for searching and aligning protein structures. The results of the comparisons confirmed that the proposed method is effective and competitive relative to the available modern services. Furthermore, using the proposed approach, a service with a user-friendly web interface was developed, which allows for searching, aligning, and superimposing protein structures; determining the location of protein fragments; mapping onto a protein molecule chain; and providing structural similarity metrices (expected value and root mean square deviation).

Protein remote homology detection and structural alignment using deep learning

Exploiting sequence–structure–function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec and DeepBLAST. TM-Vec allows searching for structure–structure similarities in large sequence databases. It is trained to accurately predict TM-scores as a metric of structural similarity directly from sequence pairs without the need for intermediate computation or solution of structures. Once structurally similar proteins have been identified, DeepBLAST can structurally align proteins using only sequence information by identifying structurally homologous regions between proteins. It outperforms traditional sequence alignment methods and performs similarly to structure-based alignment methods. We show the merits of TM-Vec and DeepBLAST on a variety of datasets, including better identification of remotely homologous proteins compared with state-of-the-art sequence alignment and structure prediction methods.

ICARUS: flexible protein structural alignment based on Protein Units.

Alignment of protein structures is a major problem in structural biology. The first approach commonly used is to consider proteins as rigid bodies. However, alignment of protein structures can be very complex due to conformational variability, or complex evolutionary relationships between proteins such as insertions, circular permutations or repetitions. In such cases, introducing flexibility becomes useful for two reasons: (i) it can help compare two protein chains which adopted two different conformational states, such as due to proteins/ligands interaction or post-translational modifications, and (ii) it aids in the identification of conserved regions in proteins that may have distant evolutionary relationships. We propose ICARUS, a new approach for flexible structural alignment based on identification of Protein Units, evolutionarily preserved structural descriptors of intermediate size, between secondary structures and domains. ICARUS significantly outperforms reference methods on a dataset of very difficult structural alignments. Code is freely available online at https://github.com/DSIMB/ICARUS.

Characterization of two novel colistin resistance gene mcr-1 variants originated from Moraxella spp.

This study aimed to characterize two novel mcr-1 variants, mcr-1.35 and mcr-1.36, which originated from Moraxella spp. that were isolated from diseased pigs in China. The Moraxella spp. carrying novel mcr-1 variants were subjected to whole-genome sequencing (WGS) and phylogenetic analysis based on the 16S rRNA gene. The mcr-1 variants mcr-1.35 and mcr-1.36 were characterized using phylogenetic analysis, a comparison of genetic environments, and protein structure prediction. The WGS indicated that two novel mcr-1 variants were located in the chromosomes of three Moraxella spp. with a genetic environment of mcr-1-pap2. In addition to the novel colistin resistance genes mcr-1.35 and mcr-1.36, the three Moraxella spp. contained other antimicrobial resistance genes, including aac(3)-IId, tet(O), sul2, floR, and blaROB-3. A functional cloning assay indicated that either the mcr-1.35 or mcr-1.36 gene could confer resistance to colistin in Escherichia coli DH5α and JM109. The nucleotide sequences of mcr-1.35 and mcr-1.36 presented 95.33 and 95.33% identities, respectively, to mcr-1.1. The phylogenetic analysis showed that mcr-1.35 and mcr-1.36 were derived from Moraxella spp. that belonged to subclades that were different from those of the mcr-1 variants (mcr-1.1 to mcr-1.34 except mcr-1.10) originating from Enterobacteriaceae. The deduced amino acid sequences of MCR-1.35 (MCR-1.36) showed 96.67% (96.49%), 82.59% (82.04%), 84.07% (83.52%), 55.52% (55.17%), 59.75% (59.57%), and 61.88% (61.69%) identity to MCR-1.10, MCR-2.2, MCR-6.1, MCR-LIN, MCR-OSL, and MCR-POR, respectively, that originated from Moraxella sp. Notably, protein structure alignment showed only a few changes in amino acid residues between MCR-1.1 and MCR-1.35, as well as between MCR-1.1 and MCR-1.36. In conclusion, this study identified Moraxella spp. carrying two novel mcr-1 variants, mcr-1.35 and mcr-1.36, conferring resistance to colistin, which were isolated from pig farms in China. In addition, mcr-like variants were observed to be located in the chromosomes of some species of Moraxella isolated from pig samples.

Shifting the pH Optima of (R)-Selective Transaminases by Protein Engineering

Amine transaminases (ATAs) are powerful biocatalysts for the stereoselective synthesis of chiral amines. However, wild-type ATAs usually show pH optima at slightly alkaline values and exhibit low catalytic activity under physiological conditions. For efficient asymmetric synthesis ATAs are commonly used in combination with lactate dehydrogenase (LDH, optimal pH: 7.5) and glucose dehydrogenase (GDH, optimal pH: 7.75) to shift the equilibrium towards the synthesis of the target chiral amine and hence their pH optima should fit to each other. Based on a protein structure alignment, variants of (R)-selective transaminases were rationally designed, produced in E. coli, purified and subjected to biochemical characterization. This resulted in the discovery of the variant E49Q of the ATA from Aspergillus fumigatus, for which the pH optimum was successfully shifted from pH 8.5 to 7.5 and this variant furthermore had a two times higher specific activity than the wild-type protein at pH 7.5. A possible mechanism for this shift of the optimal pH is proposed. Asymmetric synthesis of (R)-1-phenylethylamine from acetophenone in combination with LDH and GDH confirmed that the variant E49Q shows superior performance at pH 7.5 compared to the wild-type enzyme.

A unified approach to sequential and non-sequential structure alignment of proteins, RNAs, and DNAs

SummaryMany distantly related structure pairs exhibit structural similarities that can only be fully captured by a non-sequential alignment program. We present US-align2, a unified protocol for both sequential and non-sequential alignment of proteins and nucleic acids. On manually curated reference alignments for protein structural pairs with non-sequential relations, US-align2 achieves ≥13% higher agreement with reference alignments than existing sequential and non-sequential alignment methods. Non-sequential alignments also enabled US-align2 to have higher sensitivities in detecting RNA pairs from the same family with sequence identities <40%, obtaining ≥9% higher area under the receiver operating characteristic curve than third-party programs. The unique ability of US-align2 to parse both proteins and nucleic acids allows the method to detect protein-RNA and protein-DNA mimicries. Additionally, US-align2 performs full and semi-non-sequential alignments with at least 48% and 14% faster speed than existing programs for the same tasks, making it particularly useful for large-scale structural similarity detection.

Modularity of the hydrophobic core and evolution of functional diversity in fold A glycosyltransferases

Hydrophobic cores are fundamental structural properties of proteins typically associated with protein folding and stability; however, how the hydrophobic core shapes protein evolution and function is poorly understood. Here, we investigated the role of conserved hydrophobic cores in fold-A glycosyltransferases (GT-As), a large superfamily of enzymes that catalyze formation of glycosidic linkages between diverse donor and acceptor substrates through distinct catalytic mechanisms (inverting versus retaining). Using hidden Markov models and protein structural alignments, we identify similarities in the phosphate-binding cassette (PBC) of GT-As and unrelated nucleotide-binding proteins, such as UDP-sugar pyrophosphorylases. We demonstrate that GT-As have diverged from other nucleotide-binding proteins through structural elaboration of the PBC and its unique hydrophobic tethering to the F-helix, which harbors the catalytic base (xED-Asp). While the hydrophobic tethering is conserved across diverse GT-A fold enzymes, some families, such as B3GNT2, display variations in tethering interactions and core packing. We evaluated the structural and functional impact of these core variations through experimental mutational analysis and molecular dynamics simulations and find that some of the core mutations (T336I in B3GNT2) increase catalytic efficiency by modulating the conformational occupancy of the catalytic base between “D-in” and acceptor-accessible “D-out” conformation. Taken together, our studies support a model of evolution in which the GT-A core evolved progressively through elaboration upon an ancient PBC found in diverse nucleotide-binding proteins, and malleability of this core provided the structural framework for evolving new catalytic and substrate-binding functions in extant GT-A fold enzymes.

Characterization of fenugreek and its natural compounds targeting AKT-1 protein in cancer: Pharmacophore, virtual screening, and MD simulation techniques

ObjectiveCancer is the second biggest cause of death in humans. Rapid advancements in clinical bioinformatics and high-throughput proteomics technology are essential tools and methodologies for cancer research. Medicinal plants are considered operative therapeutic agents that are utilized for novel drug discovery and development, using computer-aided drug design (CAAD). The AKT-1 Serine/Threonine Kinase family was chosen as the target protein because it is implicated in the mediation of the cancer process. In this work, phytocompounds that target AKT-1 in cancer were recognized and were used to identify their binding relationship with the nominated receptor. MethodsA dried extraction of Fenugreek (Trigonella foenum-graecum) seeds in solid form was obtained using an evaporation technique and phytochemicals present in it were tested using qualitative tests. Fenugreek and its natural components were evaluated against AKT-1 because chemical agents acquired from natural sources appear to be particularly appealing alternatives to synthetic compounds. Different bioinformatics computational methods were used for protein structural analysis, interactive comparison, alignment, and superposition of AKT-1 protein structures in 3D. By using molecular docking and molecular dynamic (MD) simulation experiments, the binding interaction of the selected compounds with AKT-1 was investigated. ResultsFenugreek seeds yielded by phytochemicals screening and crude extractions of tannins, saponin, steroids, and cardiac glycosides were found to have very high antioxidant activity. After virtual screening hits, the top three phytocompounds (Compound-1 tannins, Compound-2 saponins, and Compound-3 steroids) were selected against AKT-1 on the basis of their strong binding energy (−10.9 to −9.2 Kcal/mol). Compound-1 showed the highest pharmacophore fit score (43.91) among all compounds. In molecular interactions, some crucial residues (Gln-79, Trp-80, Thr-82, Val-270, Tyr-272, Asp-274, and Asp-292) were observed at binding pockets of AKT-1. MD simulations at 100 ns revealed that the ligand was bound to the protein with some major conformational changes in the protein structure. ConclusionsIn-silico studies report new dimensions for novel drug design targeting AKT-1. The three leading hits inhibit the cancer cell's inflammation activity in the AKT-1 pathway. In the future, these three compounds might be tested for the drug discovery of inflammatory disorders.

Large-Size Subunit Catalases Are Chimeric Proteins: A H2O2 Selecting Domain with Catalase Activity Fused to a Hsp31-Derived Domain Conferring Protein Stability and Chaperone Activity.

Bacterial and fungal large-size subunit catalases (LSCs) are like small-size subunit catalases (SSCs) but have an additional C-terminal domain (CT). The catalytic domain is conserved at both primary sequence and structural levels and its amino acid composition is optimized to select H2O2 over water. The CT is structurally conserved, has an amino acid composition similar to very stable proteins, confers high stability to LSCs, and has independent molecular chaperone activity. While heat and denaturing agents increased Neurospora crassa catalase-1 (CAT-1) activity, a CAT-1 version lacking the CT (C63) was no longer activated by these agents. The addition of catalase-3 (CAT-3) CT to the CAT-1 or CAT-3 catalase domains prevented their heat denaturation in vitro. Protein structural alignments indicated CT similarity with members of the DJ-1/PfpI superfamily and the CT dimers present in LSCs constitute a new type of symmetric dimer within this superfamily. However, only the bacterial Hsp31 proteins show sequence similarity to the bacterial and fungal catalase mobile coil (MC) and are phylogenetically related to MC_CT sequences. LSCs might have originated by fusion of SSC and Hsp31 encoding genes during early bacterial diversification, conferring at the same time great stability and molecular chaperone activity to the novel catalases.

Plant Transglutaminases: New Insights in Biochemistry, Genetics, and Physiology.

Transglutaminases (TGases) are calcium-dependent enzymes that catalyse an acyl-transfer reaction between primary amino groups and protein-bound Gln residues. They are widely distributed in nature, being found in vertebrates, invertebrates, microorganisms, and plants. TGases and their functionality have been less studied in plants than humans and animals. TGases are distributed in all plant organs, such as leaves, tubers, roots, flowers, buds, pollen, and various cell compartments, including chloroplasts, the cytoplasm, and the cell wall. Recent molecular, physiological, and biochemical evidence pointing to the role of TGases in plant biology and the mechanisms in which they are involved allows us to consider their role in processes such as photosynthesis, plant fertilisation, responses to biotic and abiotic stresses, and leaf senescence. In the present paper, an in-depth description of the biochemical characteristics and a bioinformatics comparison of plant TGases is provided. We also present the phylogenetic relationship, gene structure, and sequence alignment of TGase proteins in various plant species, not described elsewhere. Currently, our knowledge of these proteins in plants is still insufficient. Further research with the aim of identifying and describing the regulatory components of these enzymes and the processes regulated by them is needed.

Three new clades of putative viral RNA-dependent RNA polymerases with rare or unique catalytic triads discovered in libraries of ORFans from powdery mildews and the yeast of oenological interest Starmerella bacillaris.

High throughput sequencing allowed the discovery of many new viruses and viral organizations increasing our comprehension of virus origin and evolution. Most RNA viruses are currently characterized through similarity searches of annotated virus databases. This approach limits the possibility to detect completely new virus-encoded proteins with no detectable similarities to existing ones, i.e. ORFan proteins. A strong indication of the ORFan viral origin in a metatranscriptome is the lack of DNA corresponding to an assembled RNA sequence in the biological sample. Furthermore, sequence homology among ORFans and evidence of co-occurrence of these ORFans in specific host individuals provides further indication of a viral origin. Here, we use this theoretical framework to report the finding of three conserved clades of protein-coding RNA segments without a corresponding DNA in fungi. Protein sequence and structural alignment suggest these proteins are distantly related to viral RNA-dependent RNA polymerases (RdRP). In these new putative viral RdRP clades, no GDD catalytic triad is present, but the most common putative catalytic triad is NDD and a clade with GDQ, a triad previously unreported at that site. SDD, HDD, and ADD are also represented. For most members of these three clades, we were able to associate a second genomic segment, coding for a protein of unknown function. We provisionally named this new group of viruses ormycovirus. Interestingly, all the members of one of these sub-clades (gammaormycovirus) accumulate more minus sense RNA than plus sense RNA during infection.

Fast protein structure comparison through effective representation learning with contrastive graph neural networks.

Protein structure alignment algorithms are often time-consuming, resulting in challenges for large-scale protein structure similarity-based retrieval. There is an urgent need for more efficient structure comparison approaches as the number of protein structures increases rapidly. In this paper, we propose an effective graph-based protein structure representation learning method, GraSR, for fast and accurate structure comparison. In GraSR, a graph is constructed based on the intra-residue distance derived from the tertiary structure. Then, deep graph neural networks (GNNs) with a short-cut connection learn graph representations of the tertiary structures under a contrastive learning framework. To further improve GraSR, a novel dynamic training data partition strategy and length-scaling cosine distance are introduced. We objectively evaluate our method GraSR on SCOPe v2.07 and a new released independent test set from PDB database with a designed comprehensive performance metric. Compared with other state-of-the-art methods, GraSR achieves about 7%-10% improvement on two benchmark datasets. GraSR is also much faster than alignment-based methods. We dig into the model and observe that the superiority of GraSR is mainly brought by the learned discriminative residue-level and global descriptors. The web-server and source code of GraSR are freely available at www.csbio.sjtu.edu.cn/bioinf/GraSR/ for academic use.

Topological approach for sequence-independent protein structure alignment

Protein structure alignment is a useful tool for comparing proteins with low sequence similarity and improving the quality of downstream analyses for heterogenous sets of protein systems. Many computational methods have been developed to align any two given protein structures using information such as secondary structure and corresponding distance maps. However, while “optimality” can be determined through a combination of heuristics as well as by leveraging previous biological knowledge, there currently lacks a single algorithm that can robustly minimize topological misalignment of tertiary structures, which are ultimately responsible for the biological function of proteins.