A structural explanation for the twilight zone of protein sequence homology

A survey of the Desulfuromonadia "cytochromome" provides a glimpse of the unexplored diversity of multiheme cytochromes in nature

BACKGROUND

Multiheme cytochromes c (MHC) provide prokaryotes with a broad metabolic versatility that contributes to their role in the biogeochemical cycling of the elements and in energy production in bioelectrochemical systems. However, MHC have only been isolated and studied in detail from a limited number of species. Among these, Desulfuromonadia spp. are particularly MHC-rich. To obtain a broad view of the diversity of MHC, we employed bioinformatic tools to study the cytochromome encoded in the genomes of the Desulfuromonadia class.

RESULTS

We found that the distribution of the MHC families follows a different pattern between the two orders of the Desulfuromonadia class and that there is great diversity in the number of heme-binding motifs in MHC. However, the vast majority of MHC have up to 12 heme-binding motifs. MHC predicted to be extracellular are the least conserved and show high diversity, whereas inner membrane MHC are well conserved and show lower diversity. Although the most prevalent MHC have homologues already characterized, nearly half of the MHC families in the Desulforomonadia class have no known characterized homologues. AlphaFold2 was employed to predict their 3D structures. This provides an atlas of novel MHC, including examples with high beta-sheet content and nanowire MHC with unprecedented high numbers of putative heme cofactors per polypeptide.

CONCLUSIONS

This work illuminates for the first time the universe of experimentally uncharacterized cytochromes that are likely to contribute to the metabolic versatility and to the fitness of Desulfuromonadia in diverse environmental conditions and to drive biotechnological applications of these organisms.

Protein function prediction through multi-view multi-label latent tensor reconstruction

BACKGROUND

In last two decades, the use of high-throughput sequencing technologies has accelerated the pace of discovery of proteins. However, due to the time and resource limitations of rigorous experimental functional characterization, the functions of a vast majority of them remain unknown. As a result, computational methods offering accurate, fast and large-scale assignment of functions to new and previously unannotated proteins are sought after. Leveraging the underlying associations between the multiplicity of features that describe proteins could reveal functional insights into the diverse roles of proteins and improve performance on the automatic function prediction task.

RESULTS

We present GO-LTR, a multi-view multi-label prediction model that relies on a high-order tensor approximation of model weights combined with non-linear activation functions. The model is capable of learning high-order relationships between multiple input views representing the proteins and predicting high-dimensional multi-label output consisting of protein functional categories. We demonstrate the competitiveness of our method on various performance measures. Experiments show that GO-LTR learns polynomial combinations between different protein features, resulting in improved performance. Additional investigations establish GO-LTR's practical potential in assigning functions to proteins under diverse challenging scenarios: very low sequence similarity to previously observed sequences, rarely observed and highly specific terms in the gene ontology.

IMPLEMENTATION

The code and data used for training GO-LTR is available at https://github.com/aalto-ics-kepaco/GO-LTR-prediction .

Evolution of a Human-Specific De Novo Open Reading Frame and Its Linked Transcriptional Silencer

In the human genome, two short open reading frames (ORFs) separated by a transcriptional silencer and a small intervening sequence stem from the gene SMIM45. The two ORFs show different translational characteristics, and they also show divergent patterns of evolutionary development. The studies presented here describe the evolution of the components of SMIM45. One ORF consists of an ultra-conserved 68 amino acid (aa) sequence, whose origins can be traced beyond the evolutionary age of divergence of the elephant shark, ~462 MYA. The silencer also has ancient origins, but it has a complex and divergent pattern of evolutionary formation, as it overlaps both at the 68 aa ORF and the intervening sequence. The other ORF consists of 107 aa. It develops during primate evolution but is found to originate de novo from an ancestral non-coding genomic region with root origins within the Afrothere clade of placental mammals, whose evolutionary age of divergence is ~99 MYA. The formation of the complete 107 aa ORF during primate evolution is outlined, whereby sequence development is found to occur through biased mutations, with disruptive random mutations that also occur but lead to a dead-end. The 107 aa ORF is of particular significance, as there is evidence to suggest it is a protein that may function in human brain development. Its evolutionary formation presents a view of a human-specific ORF and its linked silencer that were predetermined in non-primate ancestral species. The genomic position of the silencer offers interesting possibilities for the regulation of transcription of the 107 aa ORF. A hypothesis is presented with respect to possible spatiotemporal expression of the 107 aa ORF in embryonic tissues.

PL-PatchSurfer3: Improved Structure-Based Virtual Screening for Structure Variation Using 3D Zernike Descriptors

Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.

Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning

Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancements in this field. However, some existing structure-based methods treat protein macromolecules and ligand small molecules in the same way and ignore the data heterogeneity, potentially leading to incomplete exploration of the biochemical information of ligands. In this work, we propose LGN, a graph neural network-based fusion model with extra ligand feature extraction to effectively capture local features and global features within the protein-ligand complex, and make use of interaction fingerprints. By combining the ligand-based features and interaction fingerprints, LGN achieves Pearson correlation coefficients of up to 0.842 on the PDBbind 2016 core set, compared to 0.807 when using the features of complex graphs alone. Finally, we verify the rationalization and generalization of our model through comprehensive experiments. We also compare our model with state-of-the-art baseline methods, which validates the superiority of our model. To reduce the impact of data similarity, we increase the robustness of the model by incorporating ensemble learning.

Proteome-wide structural analysis quantifies structural conservation across distant species

Traditional evolutionary biology research mainly relies on sequence information to infer evolutionary relationships between genes or proteins. In contrast, protein structural information has long been overlooked, although structures are more conserved and closely linked to the functions than the sequences. To address this gap, we conducted a proteome-wide structural analysis using experimental and computed protein structures for organisms from the three distinct domains, including Homo sapiens (eukarya), Escherichia coli (bacteria), and Methanocaldococcus jannaschii (archaea). We reveal the distribution of structural similarity and sequence identity at the genomic level and characterize the twilight zone, where signals obtained from sequence alignment are blurred and evolutionary relationships cannot be inferred unambiguously. We find that structurally similar homologous protein pairs in the twilight zone account for ∼0.004%-0.021% of all possible protein pair combinations, which translates to ∼8%-32% of the protein-coding genes, depending on the species under comparison. In addition, by comparing the structural homologs, we show that human proteins involved in the energy supply are more similar to their E. coli homologs, whereas proteins relating to the central dogma are more similar to their M. jannaschii homologs. We also identify a bacterial GPCR homolog in the E. coli proteome that displays distinctive domain architecture. Our results shed light on the characteristics of the twilight zone and the origin of different pathways from a protein structure perspective, highlighting an exciting new frontier in evolutionary biology.

Comparative homology of Pleurotus ostreatus laccase enzyme: Swiss model or Modeller?

Laccases stand out in the industrial context due to their versatile biotechnological applications. Although these enzymes are frequently investigated, currently, Pleurotus ostreatus laccase structural model is unknown. Therefore, this research aims to predict and validate a P. ostreatus laccase theoretical model by means of comparative homology. The laccase target's primary structure (AOM73725.1) was obtained from the NCBI database, the model was predicted from homologous structures obtained from the PDB (PDB-ID: 5A7E, 2HRG, 4JHU, 1GYC) using the Swiss-Model and Modeller, and was refined in GalaxyRefine. The models were validated using PROCHECK, VERIFY 3D, ERRAT, PROVE and QMEAN Z-score servers. Moreover, molecular docking between the laccase model (Lacc4MN) and ABTS was performed on AutoDock Vina. The models that were generated by the Modeller showed superior stereochemical and structural characteristics to those predicted by the Swiss Model. The refinement made it difficult to stabilize the copper atoms which are typical of laccases. The Lacc4MN model showed the interactions between the amino acids in the active site of the laccase and the copper atoms, thereby hinting the stabilization of the metal through electrostatic interactions with histidine and cysteine. The molecular docking between Lacc4MN and ABTS showed negative free energy and the formation of two hydrogen bonds involving the amino acids ASP 208 and GLY 268, and a Pi-sulfur bond between residue HIS 458 and ABTS, which demonstrates the typical catalytic functionality of laccases. Furthermore, the theoretical model Lacc4MN presented stereochemical and structural characteristics that allow its use in silico tests.Communicated by Ramaswamy H. Sarma.

In silico insights into the dimer structure and deiodinase activity of type III iodothyronine deiodinase from bioinformatics, molecular dynamics simulations, and QM/MM calculations

The homodimeric family of iodothyronine deiodinases (Dios) regioselectively remove iodine from thyroid hormones. Currently, structural data has only been reported for the monomer of the mus type III thioredoxin (Trx) fold catalytic domain (Dio3Trx), but the mode of dimerization has not yet been determined. Various groups have proposed dimer structures that are similar to the A-type and B-type dimerization modes of peroxiredoxins. Computational methods are used to compare the sequence of Dio3Trx to related proteins known to form A-type and B-type dimers. Sequence analysis and in silico protein-protein docking methods suggest that Dio3Trx is more consistent with proteins that adopt B-type dimerization. Molecular dynamics (MD) simulations of the refined Dio3Trx dimer constructed using the SymmDock and GalaxyRefineComplex databases indicate stable dimer formation along the β4α3 interface consistent with other Trx fold B-type dimers. Free energy calculations show that the dimer is stabilized by interdimer interactions between the β-sheets and α-helices. A comparison of MD simulations of the apo and thyroxine-bound dimers suggests that the active site binding pocket is not affected by dimerization. Determination of the transition state for deiodination of thyroxine from the monomer structure using QM/MM methods provides an activation barrier consistent with previous small model DFT studies.Communicated by Ramaswamy H. Sarma.

Identification of Immunodominant Proteins of the Leishmania (Viannia) naiffi SubProteome as Pan-Specific Vaccine Targets against Leishmaniasis

Leishmaniasis is a wide-spectrum disease caused by parasites from Leishmania genus. A well-modulated immune response that is established after the long-lasting clinical cure of leishmaniasis can represent a standard requirement for a vaccine. Previous studies demonstrated that Leishmania (Viannia) naiffi causes benign disease and its antigens induce well-modulated immune responses in vitro. In this work we aimed to identify the immunodominant proteins present in the soluble extract of L. naiffi (sLnAg) as candidates for composing a pan-specific anti-leishmaniasis vaccine. After immunoblotting using cured patients of cutaneous leishmaniasis sera and proteomics approaches, we identified a group of antigenic proteins from the sLnAg. In silico analyses allowed us to select mildly similar proteins to the host; in addition, we evaluated the binding potential and degree of promiscuity of the protein epitopes to HLA molecules and to B-cell receptors. We selected 24 immunodominant proteins from a sub-proteome with 328 proteins. Homology analysis allowed the identification of 13 proteins with the most orthologues among seven Leishmania species. This work demonstrated the potential of these proteins as promising vaccine targets capable of inducing humoral and cellular pan-specific immune responses in humans, which may in the future contribute to the control of leishmaniasis.

Bioinformatics characterization of BcsA-like orphan proteins suggest they form a novel family of pseudomonad cyclic-β-glucan synthases

Bacteria produce a variety of polysaccharides with functional roles in cell surface coating, surface and host interactions, and biofilms. We have identified an 'Orphan' bacterial cellulose synthase catalytic subunit (BcsA)-like protein found in four model pseudomonads, P. aeruginosa PA01, P. fluorescens SBW25, P. putida KT2440 and P. syringae pv. tomato DC3000. Pairwise alignments indicated that the Orphan and BcsA proteins shared less than 41% sequence identity suggesting they may not have the same structural folds or function. We identified 112 Orphans among soil and plant-associated pseudomonads as well as in phytopathogenic and human opportunistic pathogenic strains. The wide distribution of these highly conserved proteins suggest they form a novel family of synthases producing a different polysaccharide. In silico analysis, including sequence comparisons, secondary structure and topology predictions, and protein structural modelling, revealed a two-domain transmembrane ovoid-like structure for the Orphan protein with a periplasmic glycosyl hydrolase family GH17 domain linked via a transmembrane region to a cytoplasmic glycosyltransferase family GT2 domain. We suggest the GT2 domain synthesises β-(1,3)-glucan that is transferred to the GH17 domain where it is cleaved and cyclised to produce cyclic-β-(1,3)-glucan (CβG). Our structural models are consistent with enzymatic characterisation and recent molecular simulations of the PaPA01 and PpKT2440 GH17 domains. It also provides a functional explanation linking PaPAK and PaPA14 Orphan (also known as NdvB) transposon mutants with CβG production and biofilm-associated antibiotic resistance. Importantly, cyclic glucans are also involved in osmoregulation, plant infection and induced systemic suppression, and our findings suggest this novel family of CβG synthases may provide similar range of adaptive responses for pseudomonads.

First detection and genome analysis of simple nosed bat polyomaviruses in Central Europe

Polyomaviruses (PyVs) are known to infect a diverse range of vertebrate host species. We report the discovery of PyVs in vesper bats (family Vespertilionidae) from sampling in Central Europe. Seven partial VP1 sequences from different PyVs were detected in samples originating from six distinct vesper bat species. Using a methodology based on conserved segments within the major capsid virus protein 1 (VP1) among known PyVs, the complete genomes of two different novel bat PyVs were determined. The genetic distances of the large T antigen coding sequences from these PyVs compared to previously-described bat PyVs exceeded 15% meriting classification as representatives of two novel PyV species: Alphapolyomavirus epserotinus and Alphapolyomavirus myodaubentonii. Phylogenetic analysis revealed that both belong to the genus Alphapolyomavirus and clustered together with high confidence in clades including other bat alphapolyomaviruses reported from China, South America and Africa. In silico protein modeling of the VP1 subunits and capsid pentamers, and electrostatic surface potential comparison of the pentamers showed significant differences between the reference template (murine polyomavirus) and the novel bat PyVs. An electrostatic potential difference pattern between the two bat VP1 pentamers was also revealed. Disaccharide molecular docking studies showed that the reference template and both bat PyVs possess the typical shallow sialic acid-binding site located between two VP1 subunits, with relevant oligosaccharide-binding affinities. The characterisation of these novel bat PyVs and the reported properties of their capsid proteins will potentially contribute in the elucidation of the conditions creating the host-pathogen restrictions associated with these viruses.

Combining computational and experimental evidence on the activity of antimalarial drugs on papain-like protease of SARS-CoV-2: A repurposing study

The development of inhibitors that target the papain-like protease (PLpro) has the potential to counteract the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the agent causing coronavirus disease 2019 (COVID-19). Based on a consideration of its several downstream effects, interfering with PLpro would both revert immune suppression exerted by the virus and inhibit viral replication. By following a repurposing strategy, the current study evaluates the potential of antimalarial drugs as PLpro inhibitors, and thereby the possibility of their use for treatment of SARS-CoV-2 infection. Computational tools were employed for structural analysis, molecular docking, and molecular dynamics simulations to screen antimalarial drugs against PLpro, and in silico data were validated by in vitro experiments. Virtual screening highlighted amodiaquine and methylene blue as the best candidates, and these findings were complemented by the in vitro results that indicated amodiaquine as a μM PLpro deubiquitinase inhibitor. The results of this study demonstrate that the computational workflow adopted here can correctly identify active compounds. Thus, the highlighted antimalarial drugs represent a starting point for the development of new PLpro inhibitors through structural optimization.

Modeling Protein Complexes and Molecular Assemblies Using Computational Methods

Many biological molecules are assembled into supramolecular complexes that are necessary to perform functions in the cell. Better understanding and characterization of these molecular assemblies are thus essential to further elucidate molecular mechanisms and key protein-protein interactions that could be targeted to modulate the protein binding affinity or develop new binders. Experimental access to structural information on these supramolecular assemblies is often hampered by the size of these systems that make their recombinant production and characterization rather difficult. Computational methods combining both structural data, molecular modeling techniques, and sequence coevolution information can thus offer a good alternative to gain access to the structural organization of protein complexes and assemblies. Herein, we present some computational methods to predict structural models of the protein partners, to search for interacting regions using coevolution information, and to build molecular assemblies. The approach is exemplified using a case study to model the succinate-quinone oxidoreductase heterocomplex.

Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling

Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The threshold value was long assumed to be around 20-30%. Below this level, obtained sequence identity was getting dangerously close to values that can be obtained by chance, after aligning any random, unrelated sequences. In these cases, other approaches, including ab initio folding simulations or fragment assembly, were usually employed. The most recent editions of the CASP and CAMEO community-wide modeling methods assessment have brought some surprising outcomes, proving that much more clues can be inferred from protein sequence analyses than previously thought. In this chapter, we focus on recent advances in the field of difficult protein modeling, pushing the threshold deep into the "twilight zone", with particular attention devoted to improvements in applications of machine learning and model evaluation.

Accurate protein stability predictions from homology models

Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.

Ab initio modelling of an essential mammalian protein: Transcription Termination Factor 1 (TTF1)

Transcription Termination Factor 1 (TTF1) is an essential mammalian protein that regulates transcription, replication fork arrest, DNA damage repair, chromatin remodelling etc. TTF1 interacts with numerous cellular proteins to regulate various cellular phenomena which play a crucial role in maintaining normal cellular physiology, and dysregulation of this protein has been reported to induce oncogenic transformation of the cells. However, despite its key role in many cellular processes, the complete structure of human TTF1 has not been elucidated to date, neither experimentally nor computationally. Therefore, understanding the structure of human TTF1 is crucial for studying its functions and interactions with other cellular factors. The aim of this study was to construct the complete structure of human TTF1 protein, using molecular modelling approaches. Owing to the lack of suitable homologues in the Protein Data Bank (PDB), the complete structure of human TTF1 was constructed by ab initio modelling. The structural stability was determined with molecular dynamics (MD) simulations in explicit solvent, and trajectory analyses. The frequently occurring conformation of human TTF1 was selected by trajectory clustering, and the central residues of this conformation were determined by centrality analyses of the Residue Interaction Network (RIN) of TTF1. Two residue clusters, one in the oligomerization domain and other in the C-terminal domain, were found to be central to the structural stability of human TTF1. To the best of our knowledge, this study is the first to report the complete structure of this essential mammalian protein, and the results obtained herein will provide structural insights for future research including that in cancer biology and related studies.Communicated by Ramaswamy H. Sarma.

EnsembleFam: towards more accurate protein family prediction in the twilight zone

Background

Current protein family modeling methods like profile Hidden Markov Model (pHMM), k-mer based methods, and deep learning-based methods do not provide very accurate protein function prediction for proteins in the twilight zone, due to low sequence similarity to reference proteins with known functions.

Results

We present a novel method EnsembleFam, aiming at better function prediction for proteins in the twilight zone. EnsembleFam extracts the core characteristics of a protein family using similarity and dissimilarity features calculated from sequence homology relations. EnsembleFam trains three separate Support Vector Machine (SVM) classifiers for each family using these features, and an ensemble prediction is made to classify novel proteins into these families. Extensive experiments are conducted using the Clusters of Orthologous Groups (COG) dataset and G Protein-Coupled Receptor (GPCR) dataset. EnsembleFam not only outperforms state-of-the-art methods on the overall dataset but also provides a much more accurate prediction for twilight zone proteins.

Conclusions

EnsembleFam, a machine learning method to model protein families, can be used to better identify members with very low sequence homology. Using EnsembleFam protein functions can be predicted  using just sequence information with better accuracy than state-of-the-art methods.

A Comparative Evaluation of the Structural and Dynamic Properties of Insect Odorant Binding Proteins

Insects devote a major part of their metabolic resources to the production of odorant binding proteins (OBPs). Although initially, these proteins were implicated in the solubilisation, binding and transport of semiochemicals to olfactory receptors, it is now recognised that they may play diverse, as yet uncharacterised, roles in insect physiology. The structures of these OBPs, the majority of which are known as “classical” OBPs, have shed some light on their potential functional roles. However, the dynamic properties of these proteins have received little attention despite their functional importance. Structural dynamics are encoded in the native protein fold and enable the adaptation of proteins to substrate binding. This paper provides a comparative review of the structural and dynamic properties of OBPs, making use of sequence/structure analysis, statistical and theoretical physics-based methods. It provides a new layer of information and additional methodological tools useful in unravelling the relationship between structure, dynamics and function of insect OBPs. The dynamic properties of OBPs, studied by means of elastic network models, reflect the similarities/dissimilarities observed in their respective structures and provides insights regarding protein motions that may have important implications for ligand recognition and binding. Furthermore, it was shown that the OBPs studied in this paper share conserved structural ‘core’ that may be of evolutionary and functional importance.

OUP accepted manuscript

MOTIVATION

Alignments are correspondences between sequences. How reliable are alignments of amino acid sequences of proteins, and what inferences about protein relationships can be drawn? Using techniques not previously applied to these questions, by weighting every possible sequence alignment by its posterior probability we derive a formal mathematical expectation, and develop an efficient algorithm for computation of the distance between alternative alignments allowing quantitative comparisons of sequence-based alignments with corresponding reference structure alignments.

RESULTS

By analyzing the sequences and structures of 1 million protein domain pairs, we report the variation of the expected distance between sequence-based and structure-based alignments, as a function of (Markov time of) sequence divergence. Our results clearly demarcate the 'daylight', 'twilight' and 'midnight' zones for interpreting residue-residue correspondences from sequence information alone.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ZEAL: protein structure alignment based on shape similarity

MOTIVATION

Most protein-structure superimposition tools consider only Cartesian coordinates. Yet, much of biology happens on the surface of proteins, which is why proteins with shared ancestry and similar function often have comparable surface shapes. Superposition of proteins based on surface shape can enable comparison of highly divergent proteins, identify convergent evolution and enable detailed comparison of surface features and binding sites.

RESULTS

We present ZEAL, an interactive tool to superpose global and local protein structures based on their shape resemblance using 3D (Zernike-Canterakis) functions to represent the molecular surface. In a benchmark study of structures with the same fold, we show that ZEAL outperforms two other methods for shape-based superposition. In addition, alignments from ZEAL were of comparable quality to the coordinate-based superpositions provided by TM-align. For comparisons of proteins with limited sequence and backbone-fold similarity, where coordinate-based methods typically fail, ZEAL can often find alignments with substantial surface-shape correspondence. In combination with shape-based matching, ZEAL can be used as a general tool to study relationships between shape and protein function. We identify several categories of protein functions where global shape similarity is significantly more likely than expected by random chance, when comparing proteins with little similarity on the fold level. In particular, we find that global surface shape similarity is particular common among DNA binding proteins.

AVAILABILITY AND IMPLEMENTATION

ZEAL can be used online at https://andrelab.org/zeal or as a standalone program with command line or graphical user interface. Source files and installers are available at https://github.com/Andre-lab/ZEAL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular docking and molecular dynamics simulation identify a novel Radicicol derivative that predicts exclusive binding to Plasmodium falciparum Topoisomerase VIB

Plasmodium falciparum harbors a unique type II topoisomerase, Topoisomerase VIB (PfTopoVIB), expressed specifically at the actively replicating stage of the parasite. An earlier study showed that Radicicol inhibits the decatenation activity of PfTopoVIB and thereby arrests the parasites at the schizont stage. Radicicol targets a unique ATP-binding fold called the Bergerat fold, which is also present in the N-terminal domain of the heat shock protein 90 (PfHsp90). Hence, Radicicol may manifest off-target activity within the parasite. We speculate that the affinity of Radicicol towards PfTopoVIB could be enhanced by modifying its structure so that it shows preferential binding towards PfTopoVIB but not to PfHsp90. Here, we have performed the docking and affinity studies of 97 derivatives (structural analogs) of Radicicol and have identified 3 analogs that show selective binding only to PfTopoVIB and no binding with PfHsp90 at all. Molecular dynamics simulation study was performed for 50 ns in triplicate with those 3 analogs and we find that one of them shows a stable association with Radicicol. This study identifies the structural molecule which could be instrumental in blocking the function of PfTopoVIB and hence can serve as an important inhibitor for malaria pathogenesis. Communicated by Ramaswamy H. Sarma.

Fine tuned exploration of evolutionary relationships within the protein universe

In the regime of domain classifications, the protein universe unveils a discrete set of folds connected by hierarchical relationships. Instead, at sub-domain-size resolution and because of physical constraints not necessarily requiring evolution to shape polypeptide chains, networks of protein motifs depict a continuous view that lies beyond the extent of hierarchical classification schemes. A number of studies, however, suggest that universal sub-sequences could be the descendants of peptides emerged in an ancient pre-biotic world. Should this be the case, evolutionary signals retained by structurally conserved motifs, along with hierarchical features of ancient domains, could sew relationships among folds that diverged beyond the point where homology is discernable. In view of the aforementioned, this paper provides a rationale where a network with hierarchical and continuous levels of the protein space, together with sequence profiles that probe the extent of sequence similarity and contacting residues that capture the transition from pre-biotic to domain world, has been used to explore relationships between ancient folds. Statistics of detected signals have been reported. As a result, an example of an emergent sub-network that makes sense from an evolutionary perspective, where conserved signals retrieved from the assessed protein space have been co-opted, has been discussed.

Open-Boundary Molecular Mechanics/Coarse-Grained Framework for Simulations of Low-Resolution G-Protein-Coupled Receptor-Ligand Complexes

G-protein-coupled receptors (GPCRs) constitute as much as 30% of the overall proteins targeted by FDA-approved drugs. However, paucity of structural experimental information and low sequence identity between members of the family impair the reliability of traditional docking approaches and atomistic molecular dynamics simulations for in silico pharmacological applications. We present here a dual-resolution approach tailored for such low-resolution models. It couples a hybrid molecular mechanics/coarse-grained (MM/CG) scheme, previously developed by us for GPCR-ligand complexes, with a Hamiltonian-based adaptive resolution scheme (H-AdResS) for the solvent. This dual-resolution approach removes potentially inaccurate atomistic details from the model while building a rigorous statistical ensemble-the grand canonical one-in the high-resolution region. We validate the method on a well-studied GPCR-ligand complex, for which the 3D structure is known, against atomistic simulations. This implementation paves the way for future accurate in silico studies of low-resolution ligand/GPCRs models.

Predicting the ligand-binding properties of Borrelia burgdorferi s.s. Bmp proteins in light of the conserved features of related Borrelia proteins

Bacteria of the genus Borrelia cause vector-borne infections like the most important hard tick-borne disease in the northern hemisphere, Lyme borreliosis (LB), and soft tick or louse transmitted relapsing fevers (RF), prevalent in temperate and tropical areas. Borrelia burgdorferi sensu lato (s.l.) includes several genospecies and causes LB in humans. In infected patients, Borrelia burgdorferi sensu stricto (s.s.) expresses the BmpA, BmpB, BmpC and BmpD proteins. The role of these proteins in the pathogenesis of LB remains incompletely characterized, but they are, however, closely related to Treponema pallidum PnrA (Purine nucleoside receptor A), a substrate-binding lipoprotein of the ATP-binding cassette (ABC) transporter family preferentially binding purine nucleosides. Based on 3D homology modeling, the Bmp proteins share the typical fold of the substrate-binding protein family and the ligand-binding properties of BmpA, BmpB and BmpD are highly similar, whereas those of BmpC differ markedly. Nevertheless, these residues are highly conserved within the genus Borrelia and the inferred phylogenetic tree also reveals that the RF Borrelia lack BmpB proteins but has an additional Bmp protein (BmpA2) missing in LB-causing Borrelia burgdorferi s.l. Our results indicate that the Bmp proteins could bind nucleosides, although BmpC might have a different ligand-binding specificity and, therefore, a distinct function. Furthermore, the work provides a means for classifying the Bmp proteins and supports further elucidation of the roles of these proteins.

ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms' proteomes

Motivation

Protein evolution spans time scales and its effects span the length of an organism. A web app named ProteomeVis is developed to provide a comprehensive view of protein evolution in the Saccharomyces cerevisiae and Escherichia coli proteomes. ProteomeVis interactively creates protein chain graphs, where edges between nodes represent structure and sequence similarities within user-defined ranges, to study the long time scale effects of protein structure evolution. The short time scale effects of protein sequence evolution are studied by sequence evolutionary rate (ER) correlation analyses with protein properties that span from the molecular to the organismal level.

Results

We demonstrate the utility and versatility of ProteomeVis by investigating the distribution of edges per node in organismal protein chain universe graphs (oPCUGs) and putative ER determinants. S.cerevisiae and E.coli oPCUGs are scale-free with scaling constants of 1.79 and 1.56, respectively. Both scaling constants can be explained by a previously reported theoretical model describing protein structure evolution. Protein abundance most strongly correlates with ER among properties in ProteomeVis, with Spearman correlations of -0.49 (P-value < 10-10) and -0.46 (P-value < 10-10) for S.cerevisiae and E.coli, respectively. This result is consistent with previous reports that found protein expression to be the most important ER determinant.

Availability and implementation

ProteomeVis is freely accessible at http://proteomevis.chem.harvard.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

In Silico Discovery of a Substituted 6-Methoxy-quinalidine with Leishmanicidal Activity in Leishmania infantum

There is an urgent need for the discovery of new antileishmanial drugs with a new mechanism of action. Type 2 NADH dehydrogenase from Leishmania infantum (LiNDH2) is an enzyme of the parasite’s respiratory system, which catalyzes the electron transfer from NADH to ubiquinone without coupled proton pumping. In previous studies of the related NADH: ubiquinone oxidoreductase crystal structure from Saccharomyces cerevisiae, two ubiquinone-binding sites (UQ_I and UQ_II) were identified and shown to play an important role in the NDH-2-catalyzed oxidoreduction reaction. Based on the available structural data, we developed a three-dimensional structural model of LiNDH2 using homology detection methods and performed an in silico virtual screening campaign to search for potential inhibitors targeting the LiNDH2 ubiquinone-binding site 1–UQ_I. Selected compounds displaying favorable properties in the computational screening experiments were assayed for inhibitory activity in the structurally similar recombinant NDH-2 from S. aureus and leishmanicidal activity was determined in the wild-type axenic amastigotes and promastigotes of L. infantum. The identified compound, a substituted 6-methoxy-quinalidine, showed promising nanomolar leishmanicidal activity on wild-type axenic promastigotes and amastigotes of L. infantum and the potential for further development.

GPCR Homology Model Generation for Lead Optimization

The vast increase of recently solved GPCR X-ray structures forms the basis for GPCR homology modeling to atomistic accuracy. Nowadays, homology models can be employed for GPCR-ligand optimization and have been reported as invaluable tools for drug design in the last few years. Elucidation of the complex GPCR pharmacology and the associated GPCR conformations made clear that different homology models have to be constructed for different activation states of the GPCRs. Therefore, templates have to be chosen accordingly to their sequence homology as well as to their activation state. The subsequent ligand placement is nontrivial, as some recent X-ray structures show very unusual ligand binding sites and solvent involvement, expanding the space of the putative ligand binding site from the generic retinal binding pocket to the whole receptor. In the present study, a workflow is presented starting from the selection of the target sequence, guiding through the GPCR modeling process, and finishing with ligand placement and pose validation.

Exploring the binding properties and structural stability of an opsin in the chytrid Spizellomyces punctatus using comparative and molecular modeling

Background

Opsin proteins are seven transmembrane receptor proteins which detect light. Opsins can be classified into two types and share little sequence identity: type 1, typically found in bacteria, and type 2, primarily characterized in metazoa. The type 2 opsins (Rhodopsins) are a subfamily of G-protein coupled receptors (GPCRs), a large and diverse class of seven transmembrane proteins and are generally restricted to metazoan lineages. Fungi use light receptors including opsins to sense the environment and transduce signals for developmental or metabolic changes. Opsins characterized in the Dikarya (Ascomycetes and Basidiomycetes) are of the type 1 bacteriorhodopsin family but the early diverging fungal lineages have not been as well surveyed. We identified by sequence similarity a rhodopsin-like GPCR in genomes of early diverging chytrids and examined the structural characteristics of this protein to assess its likelihood to be homologous to animal rhodopsins and bind similar chromophores.

Methods

We used template-based structure modeling, automated ligand docking, and molecular modeling to assess the structural and binding properties of an identified opsin-like protein found in Spizellomyces punctatus, a unicellular, flagellated species belonging to Chytridiomycota, one of the earliest diverging fungal lineages. We tested if the sequence and inferred structure were consistent with a solved crystal structure of a type 2 rhodopsin from the squid Todarodes pacificus.

Results

Our results indicate that the Spizellomyces opsin has structural characteristics consistent with functional animal type 2 rhodopsins and is capable of maintaining a stable structure when associated with the retinaldehyde chromophore, specifically the 9-cis-retinal isomer. Together, these results support further the homology of Spizellomyces opsins to animal type 2 rhodopsins.

Discussion

This represents the first test of structure/function relationship of a type 2 rhodopsin identified in early branching fungal lineages, and provides a foundation for future work exploring pathways and components of photoreception in early fungi.

The Role of Evolutionary Selection in the Dynamics of Protein Structure Evolution

Homology modeling is a powerful tool for predicting a protein's structure. This approach is successful because proteins whose sequences are only 30% identical still adopt the same structure, while structure similarity rapidly deteriorates beyond the 30% threshold. By studying the divergence of protein structure as sequence evolves in real proteins and in evolutionary simulations, we show that this nonlinear sequence-structure relationship emerges as a result of selection for protein folding stability in divergent evolution. Fitness constraints prevent the emergence of unstable protein evolutionary intermediates, thereby enforcing evolutionary paths that preserve protein structure despite broad sequence divergence. However, on longer timescales, evolution is punctuated by rare events where the fitness barriers obstructing structure evolution are overcome and discovery of new structures occurs. We outline biophysical and evolutionary rationale for broad variation in protein family sizes, prevalence of compact structures among ancient proteins, and more rapid structure evolution of proteins with lower packing density.

Bioinformatics and Its Therapeutic Applications

Bioinformatics has emerged as a major element in contemporary biomedical and pharmaceutical region. Bioinformatics deals with growth in biological data and has led to development of many databases. Bioinformatics deals with collection of data that is relevant clinically and these days separate term clinical information has come up. Data mimics are another field which is gaining importance. This chapter shall deal with introduction of bioinformatics and its applications in medicine and health care.

Evolution of bopA Gene in Burkholderia: A Case of Convergent Evolution as a Mechanism for Bacterial Autophagy Evasion

Autophagy is an important defense mechanism targeting intracellular bacteria to restrict their survival and growth. On the other hand, several intracellular pathogens have developed an antiautophagy mechanism to facilitate their own replication or intracellular survival. Up to now, no information about the origin or evolution of the antiautophagic genes in bacteria is available. BopA is an effector protein secreted by Burkholderia pseudomallei via the type three secretion system, and it has been shown to play a pivotal role in their escape from autophagy.  The evolutionary origin of bopA was examined in this work. Sequence similarity searches for BopA showed that no homolog of BopA was detected in eukaryotes. However, eukaryotic linear motifs were detected in BopA. The phylogenetic tree of the BopA proteins in our analysis is congruent with the species phylogeny derived from housekeeping genes. Moreover, there was no obvious difference in GC content values of bopA gene and their respective genomes. Integrated information on the taxonomic distribution, phylogenetic relationships, and GC content of the bopA gene of Burkholderia revealed that this gene was acquired via convergent evolution, not from eukaryotic host through horizontal gene transfer (HGT) event. This work has, for the first time, characterized the evolutionary mechanism of bacterial evasion of autophagy. The results of this study clearly demonstrated the role of convergent evolution in the evolution of how bacteria evade autophagy.

Expanding the biotechnology potential of lactobacilli through comparative genomics of 213 strains and associated genera

Lactobacilli are a diverse group of species that occupy diverse nutrient-rich niches associated with humans, animals, plants and food. They are used widely in biotechnology and food preservation, and are being explored as therapeutics. Exploiting lactobacilli has been complicated by metabolic diversity, unclear species identity and uncertain relationships between them and other commercially important lactic acid bacteria. The capacity for biotransformations catalysed by lactobacilli is an untapped biotechnology resource. Here we report the genome sequences of 213 Lactobacillus strains and associated genera, and their encoded genetic catalogue for modifying carbohydrates and proteins. In addition, we describe broad and diverse presence of novel CRISPR-Cas immune systems in lactobacilli that may be exploited for genome editing. We rationalize the phylogenomic distribution of host interaction factors and bacteriocins that affect their natural and industrial environments, and mechanisms to withstand stress during technological processes. We present a robust phylogenomic framework of existing species and for classifying new species.

Lactobacillus is a lactic acid bacteria and has a wide range of application from use in probiotic food production to biotherapeutics. Here, the authors sequence and compare the genomes of 213 different Lactobacillus strains and related genera, and provide new insight into phylogenomic organization and adaptive immunity elements in this bacteria family.

Choosing the Optimal Rigid Receptor for Docking and Scoring in the CSAR 2013/2014 Experiment

The 2013/2014 Community Structure-Activity Resource (CSAR) challenge was designed to prospectively validate advancement in the field of docking and scoring receptor-small molecule interactions. Purely computational methods have been found to be quite limiting. Thus, the challenges assessed methods that combined both experimental data and computational approaches. Here, we describe our contribution to solve three important challenges in rational drug discovery: rank-ordering protein primary sequences based on affinity to a compound, determining close-to-native bound conformations out of a set of decoy poses, and rank-ordering sets of congeneric compounds based on affinity to a given protein. We showed that the most significant contribution to a meaningful enrichment of native-like models was the identification of the best receptor structure for docking and scoring. Depending on the target, the optimal receptor for cross-docking and scoring was identified by a self-consistent docking approach that used the Vina scoring function, by aligning compounds to the closest cocrystal or by selecting the cocrystal receptor with the largest pocket. For tRNA (m1G37) methyltransferase (TRMD), ranking a set of 31 congeneric binding compounds cross-docked to the optimal receptor resulted in a R(2) = 0.67; whereas, using any other of the 13 receptor structures led to almost no enrichment of native-like complex structures. Furthermore, although redocking predicted lower RMSDs relative to the bound structures, the ranking based on multiple receptor structures did not improve the correlation coefficient. Our predictions highlight the role of rational structure-based modeling in maximizing the outcome of virtual screening, as well as limitations scoring multiple receptors.

CATH FunFHMMer web server: protein functional annotations using functional family assignments

The widening function annotation gap in protein databases and the increasing number and diversity of the proteins being sequenced presents new challenges to protein function prediction methods. Multidomain proteins complicate the protein sequence–structure–function relationship further as new combinations of domains can expand the functional repertoire, creating new proteins and functions. Here, we present the FunFHMMer web server, which provides Gene Ontology (GO) annotations for query protein sequences based on the functional classification of the domain-based CATH-Gene3D resource. Our server also provides valuable information for the prediction of functional sites. The predictive power of FunFHMMer has been validated on a set of 95 proteins where FunFHMMer performs better than BLAST, Pfam and CDD. Recent validation by an independent international competition ranks FunFHMMer as one of the top function prediction methods in predicting GO annotations for both the Biological Process and Molecular Function Ontology. The FunFHMMer web server is available at http://www.cathdb.info/search/by_funfhmmer.

Protein models docking benchmark 2

Structural characterization of protein-protein interactions is essential for our ability to understand life processes. However, only a fraction of known proteins have experimentally determined structures. Such structures provide templates for modeling of a large part of the proteome, where individual proteins can be docked by template-free or template-based techniques. Still, the sensitivity of the docking methods to the inherent inaccuracies of protein models, as opposed to the experimentally determined high-resolution structures, remains largely untested, primarily due to the absence of appropriate benchmark set(s). Structures in such a set should have predefined inaccuracy levels and, at the same time, resemble actual protein models in terms of structural motifs/packing. The set should also be large enough to ensure statistical reliability of the benchmarking results. We present a major update of the previously developed benchmark set of protein models. For each interactor, six models were generated with the model-to-native C(α) RMSD in the 1 to 6 Å range. The models in the set were generated by a new approach, which corresponds to the actual modeling of new protein structures in the "real case scenario," as opposed to the previous set, where a significant number of structures were model-like only. In addition, the larger number of complexes (165 vs. 63 in the previous set) increases the statistical reliability of the benchmarking. We estimated the highest accuracy of the predicted complexes (according to CAPRI criteria), which can be attained using the benchmark structures. The set is available at http://dockground.bioinformatics.ku.edu.

A Fractal Dimension and Wavelet Transform Based Method for Protein Sequence Similarity Analysis

One of the key tasks related to proteins is the similarity comparison of protein sequences in the area of bioinformatics and molecular biology, which helps the prediction and classification of protein structure and function. It is a significant and open issue to find similar proteins from a large scale of protein database efficiently. This paper presents a new distance based protein similarity analysis using a new encoding method of protein sequence which is based on fractal dimension. The protein sequences are first represented into the 1-dimensional feature vectors by their biochemical quantities. A series of Hybrid method involving discrete Wavelet transform, Fractal dimension calculation (HWF) with sliding window are then applied to form the feature vector. At last, through the similarity calculation, we can obtain the distance matrix, by which, the phylogenic tree can be constructed. We apply this approach by analyzing the ND5 (NADH dehydrogenase subunit 5) protein cluster data set. The experimental results show that the proposed model is more accurate than the existing ones such as Su's model, Zhang's model, Yao's model and MEGA software, and it is consistent with some known biological facts.

Comparative modeling of proteins

Much of the biochemistry that underlies health, medicine, and numerous biotechnology applications is regulated by proteins, whereby the ability of proteins to effect such processes is dictated by the three-dimensional structural assembly of the proteins. Thus, a detailed understanding of biochemistry requires not only knowledge of the constituent sequence of proteins, but also a detailed understanding of how that sequence folds spatially. Three-dimensional analysis of protein structures is thus proving to be a critical mode of biological and medical discovery in the early twenty-first century, providing fundamental insight into function that produces useful biochemistry and dysfunction that leads to disease. The large number of distinct proteins precludes rigorous laboratory characterization of the complete structural proteome, but fortunately efficient in silico structure prediction is possible for many proteins that have not been experimentally characterized. One technique that continues to provide accurate and efficient protein structure predictions, called comparative modeling, has become a critical tool in many biological disciplines. The discussion herein is an updated version of a previous 2008 treatise focusing on the general philosophy of comparative modeling methods and on specific strategies for successfully achieving reliable and accurate models. The chapter discusses basic aspects of template selection, sequence alignment, spatial alignment, loop and gap modeling, side chain modeling, structural refinement and validation, and provides an important new discussion on automated computational tools for protein structure prediction.

Evolutionary origins of the multienzyme architecture of giant fungal fatty acid synthase

Fungal fatty acid synthase (fFAS) is a key paradigm for the evolution of complex multienzymes. Its 48 functional domains are embedded in a matrix of scaffolding elements, which comprises almost 50% of the total sequence and determines the emergent multienzymes properties of fFAS. Catalytic domains of fFAS are derived from monofunctional bacterial enzymes, but the evolutionary origin of the scaffolding elements remains enigmatic. Here, we identify two bacterial protein families of noncanonical fatty acid biosynthesis starter enzymes and trans-acting polyketide enoyl reductases (ERs) as potential ancestors of scaffolding regions in fFAS. The architectures of both protein families are revealed by representative crystal structures of the starter enzyme FabY and DfnA-ER. In both families, a striking structural conservation of insertions to scaffolding elements in fFAS is observed, despite marginal sequence identity. The combined phylogenetic and structural data provide insights into the evolutionary origins of the complex multienzyme architecture of fFAS.

Computational design of protein-based inhibitors of Plasmodium vivax subtilisin-like 1 protease

BACKGROUND

Malaria remains a major global health concern. The development of novel therapeutic strategies is critical to overcome the selection of multiresistant parasites. The subtilisin-like protease (SUB1) involved in the egress of daughter Plasmodium parasites from infected erythrocytes and in their subsequent invasion into fresh erythrocytes has emerged as an interesting new drug target.

FINDINGS

Using a computational approach based on homology modeling, protein-protein docking and mutation scoring, we designed protein-based inhibitors of Plasmodium vivax SUB1 (PvSUB1) and experimentally evaluated their inhibitory activity. The small peptidic trypsin inhibitor EETI-II was used as scaffold. We mutated residues at specific positions (P4 and P1) and calculated the change in free-energy of binding with PvSUB1. In agreement with our predictions, we identified a mutant of EETI-II (EETI-II-P4LP1W) with a Ki in the medium micromolar range.

CONCLUSIONS

Despite the challenges related to the lack of an experimental structure of PvSUB1, the computational protocol we developed in this study led to the design of protein-based inhibitors of PvSUB1. The approach we describe in this paper, together with other examples, demonstrates the capabilities of computational procedures to accelerate and guide the design of novel proteins with interesting therapeutic applications.

3DSwap: curated knowledgebase of proteins involved in 3D domain swapping

Three-dimensional domain swapping is a unique protein structural phenomenon where two or more protein chains in a protein oligomer share a common structural segment between individual chains. This phenomenon is observed in an array of protein structures in oligomeric conformation. Protein structures in swapped conformations perform diverse functional roles and are also associated with deposition diseases in humans. We have performed in-depth literature curation and structural bioinformatics analyses to develop an integrated knowledgebase of proteins involved in 3D domain swapping. The hallmark of 3D domain swapping is the presence of distinct structural segments such as the hinge and swapped regions. We have curated the literature to delineate the boundaries of these regions. In addition, we have defined several new concepts like ‘secondary major interface’ to represent the interface properties arising as a result of 3D domain swapping, and a new quantitative measure for the ‘extent of swapping’ in structures. The catalog of proteins reported in 3DSwap knowledgebase has been generated using an integrated structural bioinformatics workflow of database searches, literature curation, by structure visualization and sequence–structure–function analyses. The current version of the 3DSwap knowledgebase reports 293 protein structures, the analysis of such a compendium of protein structures will further the understanding molecular factors driving 3D domain swapping.

Database URL:http://caps.ncbs.res.in/3dswap

Substitution matrices of residue triplets derived from protein blocks

In protein sequence alignment, residue similarity is usually evaluated by substitution matrix, which scores all possible exchanges of one amino acid with another. Several matrices are widely used in sequence alignment, including PAM matrices derived from homologous sequence and BLOSUM matrices derived from aligned segments of BLOCKS. However, most matrices have not addressed the high-order residue-residue interactions that are vital to the bio-properties of protein. With consideration for the inherent correlation in residue triplet, we present a new scoring scheme for sequence alignment. Protein sequence is treated as overlapping and successive 3-residue segments. Two edge residues of a triplet are clustered into hydrophobic or polar categories, respectively. Protein sequence is then rewritten into triplet sequence with 2 x 20 x 2 = 80 alphabets. Using a traditional approach, we construct a new scoring scheme named TLESUM(hp) (TripLEt SUbstitution Matrices with hydrophobic and polar information) for pairwise substitution of triplets, which characterizes the similarity of residue triplets. The applications of this matrix led to marked improvements in multiple sequence alignment and in searching structurally alike residue segments. The reason for the occurrence of the "twilight zone," i.e., structure explosion of low identity sequences, is also discussed.

Juvenile hormone synthesis: "esterify then epoxidize" or "epoxidize then esterify"? Insights from the structural characterization of juvenile hormone acid methyltransferase

Juvenile hormones (JHs) play key roles in regulating metamorphosis and reproduction in insects. The last two steps of JH synthesis diverge depending on the insect order. In Lepidoptera, epoxidation by a P450 monooxygenase precedes esterification by a juvenile hormone acid methyltransferase (JHAMT). In Orthoptera, Dictyoptera, Coleoptera and Diptera epoxidation follows methylation. The aim of our study was to gain insight into the structural basis of JHAMT's substrate recognition as a means to understand the divergence of these pathways. Homology modeling was used to build the structure of Aedes aegypti JHAMT. The substrate binding site was identified, as well as the residues that interact with the methyl donor (S-adenosylmethionine) and the carboxylic acid of the substrate methyl acceptors, farnesoic acid (FA) and juvenile hormone acid (JHA). To gain further insight we generated the structures of Anopheles gambiae, Bombyx mori, Drosophila melanogaster and Tribolium castaneum JHAMTs. The modeling results were compared with previous experimental studies using recombinant proteins, whole insects, corpora allata or tissue extracts. The computational study helps explain the selectivity toward the (10R)-JHA isomer and the reduced activity for palmitic and lauric acids. The analysis of our results supports the hypothesis that all insect JHAMTs are able to recognize both FA and JHA as substrates. Therefore, the order of the methylation/epoxidation reactions may be primarily imposed by the epoxidase's substrate specificity. In Lepidoptera, epoxidase might have higher affinity than JHAMT for FA, so epoxidation precedes methylation, while in most other insects there is no epoxidation of FA, but esterification of FA to form MF, followed by epoxidation to JH III.

The combined effects of amino acid substitutions and indels on the evolution of structure within protein families

BACKGROUND

In the process of protein evolution, sequence variations within protein families can cause changes in protein structures and functions. However, structures tend to be more conserved than sequences and functions. This leads to an intriguing question: what is the evolutionary mechanism by which sequence variations produce structural changes? To investigate this question, we focused on the most common types of sequence variations: amino acid substitutions and insertions/deletions (indels). Here their combined effects on protein structure evolution within protein families are studied.

RESULTS

Sequence-structure correlation analysis on 75 homologous structure families (from SCOP) that contain 20 or more non-redundant structures shows that in most of these families there is, statistically, a bilinear correlation between the amount of substitutions and indels versus the degree of structure variations. Bilinear regression of percent sequence non-identity (PNI) and standardized number of gaps (SNG) versus RMSD was performed. The coefficients from the regression analysis could be used to estimate the structure changes caused by each unit of substitution (structural substitution sensitivity, SSS) and by each unit of indel (structural indel sensitivity, SIDS). An analysis on 52 families with high bilinear fitting multiple correlation coefficients and statistically significant regression coefficients showed that SSS is mainly constrained by disulfide bonds, which almost have no effects on SIDS.

CONCLUSIONS

Structural changes in homologous protein families could be rationally explained by a bilinear model combining amino acid substitutions and indels. These results may further improve our understanding of the evolutionary mechanisms of protein structures.

Atomic interaction networks in the core of protein domains and their native folds

Vastly divergent sequences populate a majority of protein folds. In the quest to identify features that are conserved within protein domains belonging to the same fold, we set out to examine the entire protein universe on a fold-by-fold basis. We report that the atomic interaction network in the solvent-unexposed core of protein domains are fold-conserved, extraordinary sequence divergence notwithstanding. Further, we find that this feature, termed protein core atomic interaction network (or PCAIN) is significantly distinguishable across different folds, thus appearing to be “signature” of a domain's native fold. As part of this study, we computed the PCAINs for 8698 representative protein domains from families across the 1018 known protein folds to construct our seed database and an automated framework was developed for PCAIN-based characterization of the protein fold universe. A test set of randomly selected domains that are not in the seed database was classified with over 97% accuracy, independent of sequence divergence. As an application of this novel fold signature, a PCAIN-based scoring scheme was developed for comparative (homology-based) structure prediction, with 1–2 angstroms (mean 1.61A) C_α RMSD generally observed between computed structures and reference crystal structures. Our results are consistent across the full spectrum of test domains including those from recent CASP experiments and most notably in the ‘twilight’ and ‘midnight’ zones wherein <30% and <10% target-template sequence identity prevails (mean twilight RMSD of 1.69A). We further demonstrate the utility of the PCAIN protocol to derive biological insight into protein structure-function relationships, by modeling the structure of the YopM effector novel E3 ligase (NEL) domain from plague-causative bacterium Yersinia Pestis and discussing its implications for host adaptive and innate immune modulation by the pathogen. Considering the several high-throughput, sequence-identity-independent applications demonstrated in this work, we suggest that the PCAIN is a fundamental fold feature that could be a valuable addition to the arsenal of protein modeling and analysis tools.