MISTRAL: a tool for energy-based multiple structural alignment of proteins

Sequence Analysis, Structure Prediction of Receptor Proteins and In Silico Study of Potential Inhibitors for Management of Life Threatening COVID-19

Background:

Treatment of the Covid-19 pandemic caused by the highly contagious and pathogenic SARS-CoV-2 is a global menace. Day by day, this pandemic is getting worse. Doctors, scientists and researchers across the world are urgently scrambling for a cure for novel corona virus and continuously working at break neck speed to develop vaccines or drugs. But to date, there are no specific drugs or vaccines available in the market to cope up with the virus.

Objective:

The present study helps us to elucidate 3D structures of SARS-CoV-2 proteins and also to identify natural compounds as potential inhibitors against COVID-19.

Methods:

The 3D structures of the proteins were constructed using Modeller 9.16 modeling tool. Modelled proteins were validated with PROCHECK by Ramachandran plot analysis. In this study, a small library of natural compounds (fifty compounds) was docked to the hACE2 binding site of the modelled surface glycoprotein of SARS-CoV-2 using AutoDock Vina to repurpose these inhibitors against SARS-CoV-2. Conceptual density functional theory calculations of the best eight compounds had been performed by Gaussian-09. Geometry optimizations for these molecules were done at M06-2X/ def2-TZVP level of theory. ADME parameters, pharmacokinetic properties and drug likeness of the compounds were analyzed using swissADME website.

Results:

In this study, we analysed the sequences of surface glycoprotein, nucleocapsid phosphoprotein and envelope protein obtained from different parts of the globe. We modelled all the different sequences of surface glycoprotein and envelop protein in order to derive 3D structure of a molecular target, which is essential for the development of therapeutics. Different electronic properties of the inhibitors have been calculated using DFT through M06-2X functional with def2-TZVP basis set. Docking result at the hACE2 binding site of all modelled surface glycoproteins of SARSCoV- 2 showed that all the eight inhibitors (actinomycin D, avellanin C, ichangin, kanglemycin A, obacunone, ursolic acid, ansamiotocin P-3 and isomitomycin A) studied here were many folds better compared to hydroxychloroquine which has been found to be effective to treat patients suffering from COVID-19. All the inhibitors meet most of the criteria of drug likeness assessment.

Conclusion:

We expect that eight compounds (actinomycin D, avellanin C, ichangin, kanglemycin A, obacunone, ursolic acid, ansamiotocin P-3 and isomitomycin A) can be used as potential inhibitors against SARS-CoV-2.

DAMA: a method for computing multiple alignments of protein structures using local structure descriptors

MOTIVATION

The well-known fact that protein structures are more conserved than their sequences forms the basis of several areas of computational structural biology. Methods based on the structure analysis provide more complete information on residue conservation in evolutionary processes. This is crucial for the determination of evolutionary relationships between proteins and for the identification of recurrent structural patterns present in biomolecules involved in similar functions. However, algorithmic structural alignment is much more difficult than multiple sequence alignment. This study is devoted to the development and applications of DAMA-a novel effective environment capable to compute and analyze multiple structure alignments.

RESULTS

DAMA is based on local structural similarities, using local 3D structure descriptors and thus accounts for nearest-neighbor molecular environments of aligned residues. It is constrained neither by protein topology nor by its global structure. DAMA is an extension of our previous study (DEDAL) which demonstrated the applicability of local descriptors to pairwise alignment problems. Since the multiple alignment problem is NP-complete, an effective heuristic approach has been developed without imposing any artificial constraints. The alignment algorithm searches for the largest, consistent ensemble of similar descriptors. The new method is capable to capture most of the biologically significant similarities present in canonical test sets and is discriminatory enough to prevent the emergence of larger, but meaningless, solutions. Tests performed on the test sets, including protein kinases, demonstrate DAMA's capability of identifying equivalent residues, which should be very useful in discovering the biological nature of proteins similarity. Performance profiles show the advantage of DAMA over other methods, in particular when using a strict similarity measure QC, which is the ratio of correctly aligned columns, and when applying the methods to more difficult cases.

AVAILABILITY AND IMPLEMENTATION

DAMA is available online at http://dworkowa.imdik.pan.pl/EP/DAMA. Linux binaries of the software are available upon request.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Topology-independent and global protein structure alignment through an FFT-based algorithm

MOTIVATION

Protein structure alignment is one of the fundamental problems in computational structure biology. A variety of algorithms have been developed to address this important issue in the past decade. However, due to their heuristic nature, current structure alignment methods may suffer from suboptimal alignment and/or over-fragmentation and thus lead to a biologically wrong alignment in some cases. To overcome these limitations, we have developed an accurate topology-independent and global structure alignment method through an FFT-based exhaustive search algorithm, which is referred to as FTAlign.

RESULTS

Our FTAlign algorithm was extensively tested on six commonly used datasets and compared with seven state-of-the-art structure alignment approaches, TMalign, DeepAlign, Kpax, 3DCOMB, MICAN, SPalignNS and CLICK. It was shown that FTAlign outperformed the other methods in reproducing manually curated alignments and obtained a high success rate of 96.7 and 90.0% on two gold-standard benchmarks, MALIDUP and MALISAM, respectively. Moreover, FTAlign also achieved the overall best performance in terms of biologically meaningful structure overlap (SO) and TMscore on both the sequential alignment test sets including MALIDUP, MALISAM and 64 difficult cases from HOMSTRAD, and the non-sequential sets including MALIDUP-NS, MALISAM-NS, 199 topology-different cases, where FTAlign especially showed more advantage for non-sequential alignment. Despite its global search feature, FTAlign is also computationally efficient and can normally complete a pairwise alignment within one second.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/ftalign/.

Protein multiple alignments: sequence-based versus structure-based programs

MOTIVATION

Multiple sequence alignment programs have proved to be very useful and have already been evaluated in the literature yet not alignment programs based on structure or both sequence and structure. In the present article we wish to evaluate the added value provided through considering structures.

RESULTS

We compared the multiple alignments resulting from 25 programs either based on sequence, structure or both, to reference alignments deposited in five databases (BALIBASE 2 and 3, HOMSTRAD, OXBENCH and SISYPHUS). On the whole, the structure-based methods compute more reliable alignments than the sequence-based ones, and even than the sequence+structure-based programs whatever the databases. Two programs lead, MAMMOTH and MATRAS, nevertheless the performances of MUSTANG, MATT, 3DCOMB, TCOFFEE+TM_ALIGN and TCOFFEE+SAP are better for some alignments. The advantage of structure-based methods increases at low levels of sequence identity, or for residues in regular secondary structures or buried ones. Concerning gap management, sequence-based programs set less gaps than structure-based programs. Concerning the databases, the alignments of the manually built databases are more challenging for the programs.

AVAILABILITY AND IMPLEMENTATION

All data and results presented in this study are available at: http://wwwabi.snv.jussieu.fr/people/mathilde/download/AliMulComp/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

mTM-align: an algorithm for fast and accurate multiple protein structure alignment

Motivation

As protein structure is more conserved than sequence during evolution, multiple structure alignment can be more informative than multiple sequence alignment, especially for distantly related proteins. With the rapid increase of the number of protein structures in the Protein Data Bank, it becomes urgent to develop efficient algorithms for multiple structure alignment.

Results

A new multiple structure alignment algorithm (mTM-align) was proposed, which is an extension of the highly efficient pairwise structure alignment program TM-align. The algorithm was benchmarked on four widely used datasets, HOMSTRAD, SABmark_sup, SABmark_twi and SISY-multiple, showing that mTM-align consistently outperforms other algorithms. In addition, the comparison with the manually curated alignments in the HOMSTRAD database shows that the automated alignments built by mTM-align are in general more accurate. Therefore, mTM-align may be used as a reliable complement to construct multiple structure alignments for real-world applications.

Availability and implementation

http://yanglab.nankai.edu.cn/mTM-align.

Contact

zhng@umich.edu or yangjy@nankai.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Structural Basis for the Activation of the Deubiquitinase Calypso by the Polycomb Protein ASX

Ubiquitin C-terminal hydrolase deubiquitinase BAP1 is an essential tumor suppressor involved in cell growth control, DNA damage response, and transcriptional regulation. As part of the Polycomb repression machinery, BAP1 is activated by the deubiquitinase adaptor domain of ASXL1 mediating gene repression by cleaving ubiquitin (Ub) from histone H2A in nucleosomes. The molecular mechanism of BAP1 activation by ASXL1 remains elusive, as no structures are available for either BAP1 or ASXL1. Here, we present the crystal structure of the BAP1 ortholog from Drosophila melanogaster, named Calypso, bound to its activator, ASX, homolog of ASXL1. Based on comparative structural and functional analysis, we propose a model for Ub binding by Calypso/ASX, uncover decisive structural elements responsible for ASX-mediated Calypso activation, and characterize the interaction with ubiquitinated nucleosomes. Our results give molecular insight into Calypso function and its regulation by ASX and provide the opportunity for the rational design of mechanism-based therapeutics to treat human BAP1/ASXL1-related tumors.

Cross-kingdom auxiliary subunit modulation of a voltage-gated sodium channel

Voltage-gated, sodium ion-selective channels (NaV) generate electrical signals contributing to the upstroke of the action potential in animals. NaVs are also found in bacteria and are members of a larger family of tetrameric voltage-gated channels that includes CaVs, KVs, and NaVs. Prokaryotic NaVs likely emerged from a homotetrameric Ca2+-selective voltage-gated progenerator, and later developed Na+ selectivity independently. The NaV signaling complex in eukaryotes contains auxiliary proteins, termed beta (β) subunits, which are potent modulators of the expression profiles and voltage-gated properties of the NaV pore, but it is unknown whether they can functionally interact with prokaryotic NaV channels. Herein, we report that the eukaryotic NaVβ1-subunit isoform interacts with and enhances the surface expression as well as the voltage-dependent gating properties of the bacterial NaV, NaChBac in Xenopus oocytes. A phylogenetic analysis of the β-subunit gene family proteins confirms that these proteins appeared roughly 420 million years ago and that they have no clear homologues in bacterial phyla. However, a comparison between eukaryotic and bacterial NaV structures highlighted the presence of a conserved fold, which could support interactions with the β-subunit. Our electrophysiological, biochemical, structural, and bioinformatics results suggests that the prerequisites for β-subunit regulation are an evolutionarily stable and intrinsic property of some voltage-gated channels.

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications.

Calculating and scoring high quality multiple flexible protein structure alignments

MOTIVATION

Calculating multiple protein structure alignments (MSAs) is important for understanding functional and evolutionary relationships between protein families, and for modeling protein structures by homology. While incorporating backbone flexibility promises to circumvent many of the limitations of rigid MSA algorithms, very few flexible MSA algorithms exist today. This article describes several novel improvements to the Kpax algorithm which allow high quality flexible MSAs to be calculated. This article also introduces a new Gaussian-based MSA quality measure called 'M-score', which circumvents the pitfalls of RMSD-based quality measures.

RESULTS

As well as calculating flexible MSAs, the new version of Kpax can also score MSAs from other aligners and from previously aligned reference datasets. Results are presented for a large-scale evaluation of the Homstrad, SABmark and SISY benchmark sets using Kpax and Matt as examples of state-of-the-art flexible aligners and 3DCOMB as an example of a state-of-the-art rigid aligner. These results demonstrate the utility of the M-score as a measure of MSA quality and show that high quality MSAs may be achieved when structural flexibility is properly taken into account.

AVAILABILITY AND IMPLEMENTATION

Kpax 5.0 may be downloaded for academic use at http://kpax.loria.fr/

CONTACT

dave.ritchie@inria.fr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

POSA: a user-driven, interactive multiple protein structure alignment server

POSA (Partial Order Structure Alignment), available at http://posa.godziklab.org, is a server for multiple protein structure alignment introduced in 2005 (Ye,Y. and Godzik,A. (2005) Multiple flexible structure alignment using partial order graphs. Bioinformatics, 21, 2362–2369). It is free and open to all users, and there is no login requirement, albeit there is an option to register and store results in individual, password-protected directories. In the updated POSA server described here, we introduce two significant improvements. First is an interface allowing the user to provide additional information by defining segments that anchor the alignment in one or more input structures. This interface allows users to take advantage of their intuition and biological insights to improve the alignment and guide it toward a biologically relevant solution. The second improvement is an interactive visualization with options that allow the user to view all superposed structures in one window (a typical solution for visualizing results of multiple structure alignments) or view them individually in a series of synchronized windows with extensive, user-controlled visualization options. The user can rotate structure(s) in any of the windows and study similarities or differences between structures clearly visible in individual windows.

Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor

Background

Transient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement.

Results

The presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions.

Conclusion

Current methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.

The role of non-native interactions in the folding of knotted proteins: insights from molecular dynamics simulations

For several decades, the presence of knots in naturally-occurring proteins was largely ruled out a priori for its supposed incompatibility with the efficiency and robustness of folding processes. For this very same reason, the later discovery of several unrelated families of knotted proteins motivated researchers to look into the physico-chemical mechanisms governing the concerted sequence of folding steps leading to the consistent formation of the same knot type in the same protein location. Besides experiments, computational studies are providing considerable insight into these mechanisms. Here, we revisit a number of such recent investigations within a common conceptual and methodological framework. By considering studies employing protein models with different structural resolution (coarse-grained or atomistic) and various force fields (from pure native-centric to realistic atomistic ones), we focus on the role of native and non-native interactions. For various unrelated instances of knotted proteins, non-native interactions are shown to be very important for favoring the emergence of conformations primed for successful self-knotting events.

Salicylic acid activates DNA damage responses to potentiate plant immunity

DNA damage is normally detrimental to living organisms. Here we show that it can also serve as a signal to promote immune responses in plants. We found that the plant immune hormone salicylic acid (SA) can trigger DNA damage in the absence of a genotoxic agent. The DNA damage sensor proteins RAD17 and ATR are required for effective immune responses. These sensor proteins are negatively regulated by a key immune regulator, SNI1 (suppressor of npr1-1, inducible 1), which we found is a subunit of the structural maintenance of chromosome (SMC) 5/6 complex required for controlling DNA damage. Elevated DNA damage caused by the sni1 mutation or treatment with a DNA-damaging agent markedly enhances SA-mediated defense gene expression. Our study suggests that activation of DNA damage responses is an intrinsic component of the plant immune responses.

Structural phylogeny by profile extraction and multiple superimposition using electrostatic congruence as a discriminator

Phylogenetic analysis of proteins using multiple sequence alignment (MSA) assumes an underlying evolutionary relationship in these proteins which occasionally remains undetected due to considerable sequence divergence. Structural alignment programs have been developed to unravel such fuzzy relationships. However, none of these structure based methods have used electrostatic properties to discriminate between spatially equivalent residues. We present a methodology for MSA of a set of related proteins with known structures using electrostatic properties as an additional discriminator (STEEP). STEEP first extracts a profile, then generates a multiple structural superimposition providing a consolidated spatial framework for comparing residues and finally emits the MSA. Residues that are aligned differently by including or excluding electrostatic properties can be targeted by directed evolution experiments to transform the enzymatic properties of one protein into another. We have compared STEEP results to those obtained from a MSA program (ClustalW) and a structural alignment method (MUSTANG) for chymotrypsin serine proteases. Subsequently, we used PhyML to generate phylogenetic trees for the serine and metallo-β-lactamase superfamilies from the STEEP generated MSA, and corroborated the accepted relationships in these superfamilies. We have observed that STEEP acts as a functional classifier when electrostatic congruence is used as a discriminator, and thus identifies potential targets for directed evolution experiments. In summary, STEEP is unique among phylogenetic methods for its ability to use electrostatic congruence to specify mutations that might be the source of the functional divergence in a protein family. Based on our results, we also hypothesize that the active site and its close vicinity contains enough information to infer the correct phylogeny for related proteins.

Comparing proteins by their internal dynamics: exploring structure-function relationships beyond static structural alignments

The growing interest for comparing protein internal dynamics owes much to the realisation that protein function can be accompanied or assisted by structural fluctuations and conformational changes. Analogously to the case of functional structural elements, those aspects of protein flexibility and dynamics that are functionally oriented should be subject to evolutionary conservation. Accordingly, dynamics-based protein comparisons or alignments could be used to detect protein relationships that are more elusive to sequence and structural alignments. Here we provide an account of the progress that has been made in recent years towards developing and applying general methods for comparing proteins in terms of their internal dynamics and advance the understanding of the structure-function relationship.

Virtual interactomics of proteins from biochemical standpoint

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.

The role of non-native interactions in the folding of knotted proteins

Stochastic simulations of coarse-grained protein models are used to investigate the propensity to form knots in early stages of protein folding. The study is carried out comparatively for two homologous carbamoyltransferases, a natively-knotted N-acetylornithine carbamoyltransferase (AOTCase) and an unknotted ornithine carbamoyltransferase (OTCase). In addition, two different sets of pairwise amino acid interactions are considered: one promoting exclusively native interactions, and the other additionally including non-native quasi-chemical and electrostatic interactions. With the former model neither protein shows a propensity to form knots. With the additional non-native interactions, knotting propensity remains negligible for the natively-unknotted OTCase while for AOTCase it is much enhanced. Analysis of the trajectories suggests that the different entanglement of the two transcarbamylases follows from the tendency of the C-terminal to point away from (for OTCase) or approach and eventually thread (for AOTCase) other regions of partly-folded protein. The analysis of the OTCase/AOTCase pair clarifies that natively-knotted proteins can spontaneously knot during early folding stages and that non-native sequence-dependent interactions are important for promoting and disfavouring early knotting events.

Knotted proteins provide an ideal ground for examining how amino acid interactions (which are local) can favor their folding into a native state of non-trivial topology (which is a global property). Some of the mechanisms that can aid knot formation are investigated here by comparing coarse-grained folding simulations of two enzymes that are structurally similar, and yet have natively knotted and unknotted states, respectively. In folding simulations that exclusively promote the formation of native contacts, neither protein forms knots. Strikingly, when sequence-dependent non-native interactions between amino acids are introduced, one observes knotting events but only for the natively-knotted protein. The results support the importance of non-native interactions in favoring or disfavoring knotting events in the early stages of folding.

Evolution, expansion and expression of the Kunitz/BPTI gene family associated with long-term blood feeding in Ixodes Scapularis

Background

Recent studies of the tick saliva transcriptome have revealed the profound role of salivary proteins in blood feeding. Kunitz/BPTI proteins are abundant in the salivary glands of ticks and perform multiple functions in blood feeding, such as inhibiting blood coagulation, regulating host blood supply and disrupting host angiogenesis. However, Kunitz/BPTI proteins in soft and hard ticks have different functions and molecular mechanisms. How these differences emerged and whether they are associated with the evolution of long-term blood feeding in hard ticks remain unknown.

Results

In this study, the evolution, expansion and expression of Kunitz/BPTI family in Ixodes scapularis were investigated. Single- and multi-domain Kunitz/BPTI proteins have similar gene structures. Single-domain proteins were classified into three groups (groups I, II and III) based on their cysteine patterns. Group I represents the ancestral branch of the Kunitz/BPTI family, and members of this group function as serine protease inhibitors. The group I domain was used as a module to create multi-domain proteins in hard ticks after the split between hard and soft ticks. However, groups II and III, which evolved from group I, are only present and expanded in the genus Ixodes. These lineage-specific expanded genes exhibit significantly higher expression during long-term blood feeding in Ixodes scapularis. Interestingly, functional site analysis suggested that group II proteins lost the ability to inhibit serine proteases and evolved a new function of modulating ion channels. Finally, evolutionary analyses revealed that the expansion and diversification of the Kunitz/BPTI family in the genus Ixodes were driven by positive selection.

Conclusions

These results suggest that the differences in the Kunitz/BPTI family between soft and hard ticks may be linked to the evolution of long-term blood feeding in hard ticks. In Ixodes, the lineage-specific expanded genes (Group II and III) lost the ancient function of inhibiting serine proteases and evolved new functions to adapt to long-term blood feeding. Therefore, these genes may play a profound role in the long-term blood feeding of hard ticks. Based our analysis, we propose that the six genes identified in our study may be candidate target genes for tick control.

Smolign: a spatial motifs-based protein multiple structural alignment method

Availability of an effective tool for protein multiple structural alignment (MSTA) is essential for discovery and analysis of biologically significant structural motifs that can help solve functional annotation and drug design problems. Existing MSTA methods collect residue correspondences mostly through pairwise comparison of consecutive fragments, which can lead to suboptimal alignments, especially when the similarity among the proteins is low. We introduce a novel strategy based on: building a contact-window based motif library from the protein structural data, discovery and extension of common alignment seeds from this library, and optimal superimposition of multiple structures according to these alignment seeds by an enhanced partial order curve comparison method. The ability of our strategy to detect multiple correspondences simultaneously, to catch alignments globally, and to support flexible alignments, endorse a sensitive and robust automated algorithm that can expose similarities among protein structures even under low similarity conditions. Our method yields better alignment results compared to other popular MSTA methods, on several protein structure data sets that span various structural folds and represent different protein similarity levels. A web-based alignment tool, a downloadable executable, and detailed alignment results for the data sets used here are available at http://sacan.biomed. drexel.edu/Smolign and http://bio.cse.ohio-state.edu/Smolign.

Knotted vs. unknotted proteins: evidence of knot-promoting loops

Knotted proteins, because of their ability to fold reversibly in the same topologically entangled conformation, are the object of an increasing number of experimental and theoretical studies. The aim of the present investigation is to assess, on the basis of presently available structural data, the extent to which knotted proteins are isolated instances in sequence or structure space, and to use comparative schemes to understand whether specific protein segments can be associated to the occurrence of a knot in the native state. A significant sequence homology is found among a sizeable group of knotted and unknotted proteins. In this family, knotted members occupy a primary sub-branch of the phylogenetic tree and differ from unknotted ones only by additional loop segments. These “knot-promoting” loops, whose virtual bridging eliminates the knot, are found in various types of knotted proteins. Valuable insight into how knots form, or are encoded, in proteins could be obtained by targeting these regions in future computational studies or excision experiments.

Out of the tens of thousands of known protein structures, only a few hundred are knotted. The latter epitomize, better than unknotted proteins, the degree of coordinated motion of the backbone required to fold reversibly in a specific native conformation, which indeed must contain a precise knot in a specific protein region. In the present work we search for salient features associated to protein “knottedness” through a systematic sequence and structure comparison of knotted and unknotted protein chains. A significant sequence relatedness is found within a sizeable group of knotted and unknotted proteins. Their tree of sequence relatedness suggests that the knotted entries all diverged from a specific evolutionary event. The systematic structural comparison further indicates that the knottedness of several different types of proteins is likely ascribable to the presence of short “knot-promoting” loops. These segments, whose bridging eliminates the knot, are natural candidates for future experimental/computational studies aimed at clarifying whether the global knotted state of a protein is influenced by specific regions of the primary sequence.

ALADYN: a web server for aligning proteins by matching their large-scale motion

The ALADYN web server aligns pairs of protein structures by comparing their internal dynamics and detecting regions that sustain similar large-scale movements. The latter often accompany functional conformational changes in proteins and enzymes. The ALADYN dynamics-based alignment can therefore highlight functionally-oriented correspondences that could be more elusive to sequence- or structure-based comparisons. The ALADYN server takes the structure files of the two proteins as input. The optimal relative positioning of the molecules is found by maximizing the similarity of the pattern of structural fluctuations which are calculated via an elastic network model. The resulting alignment is presented via an interactive graphical Java applet and is accompanied by a number of quantitative indicators and downloadable data files. The ALADYN web server is freely accessible at the http://aladyn.escience-lab.org address.