Protein contacts, inter-residue interactions and side-chain modelling

EphA4 Targeting Peptide-Conjugated Extracellular Vesicles Rejuvenates Adult Neural Stem Cells and Exerts Therapeutic Benefits in Aging Rats

Aging and various neurodegenerative diseases cause significant reduction in adult neurogenesis and simultaneous increase in quiescent neural stem cells (NSCs), which impact the brain's regenerative capabilities. To deal with this challenging issue, current treatments involve stem cell transplants or prevention of neurodegeneration; however, the efficacy or success of this process remains limited. Therefore, extensive and focused investigation is highly demanding to overcome this challenging task. Here, we have designed an efficient peptide-based EphA4 receptor-targeted ligand through an in silico approach. Further, this strategy involves chemical conjugation of the peptide with adipose tissue stem cell-derived EV (Exo-pep-11). Interestingly, our newly designed engineered EV, Exo-pep-11, targets NSC through EphA4 receptors, which offers promising therapeutic advantages by stimulating NSC proliferation and subsequent differentiation. Our result demonstrates that NSC successfully internalized Exo-pep-11 in both in vitro culture conditions as well as in the in vivo aging rats. We found that the uptake of Exo-pep-11 decreased by ∼2.3-fold when NSC was treated with EphA4 antibody before Exo-pep-11 incubation, which confirms the receptor-specific uptake of Exo-pep-11. Exo-pep-11 treatment also increases NSC proliferation by ∼1.9-fold and also shows ∼1.6- and ∼2.4-fold increase in expressions of Nestin and ID1, respectively. Exo-pep-11 also has the potential to increase neurogenesis in aging rats, which is confirmed by ∼1.6- and ∼1.5-fold increases in expressions of TH and Tuj1, respectively, in rat olfactory bulb. Overall, our findings highlight the potential role of Exo-pep-11 for prospective applications in combating age-related declines in NSC activity and neurogenesis.

Machine learning-based prediction of proteins' architecture using sequences of amino acids and structural alphabets

In addition to the growth of protein structures generated through wet laboratory experiments and deposited in the PDB repository, AlphaFold predictions have significantly contributed to the creation of a much larger database of protein structures. Annotating such a vast number of structures has become an increasingly challenging task. CATH is widely recognized as one the most common platforms for addressing this challenge, as it classifies proteins based on their structural and evolutionary relationships, offering the scientific community an invaluable resource for uncovering various properties, including functional annotations. While CATH annotation involves - to some extent - human intervention, keeping up with the classification of the rapidly expanding repositories of protein structures has become exceedingly difficult. Therefore, there is a pressing need for a fully automated approach. On the other hand, the abundance of protein sequences stemming from next generation sequencing technologies, lacking structural annotations, presents an additional challenge to the scientific community. Consequently, 'pre-annotating' protein sequences with structural features, ensuring a high level of precision, could prove highly advantageous. In this paper, after a thorough investigation, we introduce a novel machine-learning model capable of classifying any protein domain, whether it has a known structure or not, into one of the 40 main CATH Architectures. We achieve an F1 Score of 0.92 using only the amino acid sequence and a score of 0.94 using both the sequence of amino acids and the sequence of structural alphabets.Communicated by Ramaswamy H. Sarma.

An end-to-end deep learning method for protein side-chain packing and inverse folding

Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to tackle this problem, but their speed or accuracy is still unsatisfactory. To address this, we present AttnPacker, a deep learning (DL) method for directly predicting protein side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone 3D geometry to simultaneously compute all side-chain coordinates without delegating to a discrete rotamer library or performing expensive conformational search and sampling steps. This enables a significant increase in computational efficiency, decreasing inference time by over 100× compared to the DL-based method DLPacker and physics-based RosettaPacker. Tested on the CASP13 and CASP14 native and nonnative protein backbones, AttnPacker computes physically realistic side-chain conformations, reducing steric clashes and improving both rmsd and dihedral accuracy compared to state-of-the-art methods SCWRL4, FASPR, RosettaPacker, and DLPacker. Different from traditional PSCP approaches, AttnPacker can also codesign sequences and side chains, producing designs with subnative Rosetta energy and high in silico consistency.

Revisiting the Sweet Taste Receptor T1R2-T1R3 through Molecular Dynamics Simulations Coupled with a Noncovalent Interactions Analysis

It is nowadays widely accepted that sweet taste perception is elicited by the activation of the heterodimeric complex T1R2-T1R3, customarily known as sweet taste receptor (STR). However, the interplay between STR and sweeteners has not yet been fully clarified. Here through a methodology coupling molecular dynamics and the independent gradient model (igm) approach we determine the main interacting signatures of the closed (active) conformation of the T1R2 Venus flytrap domain (VFD) toward aspartame. The igm methodology provides a rapid and reliable quantification of noncovalent interactions through a score (Δg^inter) based on the attenuation of the electronic density gradient when two molecular fragments approach each other. Herein, this approach is coupled to a 100 ns molecular dynamics simulation (MD-igm) to explore the ligand-cavity contacts on a per-residue basis as well as a series of key inter-residue interactions that stabilize the closed form of VFD. We also apply an atomic decomposition scheme of noncovalent interactions to quantify the contribution of the ligand segments to the noncovalent interplay. Finally, a series of structural modification on aspartame are conducted in order to obtain guidelines for the rational design of novel sweeteners. Given that innovative methodologies to reliably quantify the extent of ligand-protein coupling are strongly demanded, this approach combining a noncovalent analysis and MD simulations represents a valuable contribution, that can be easily applied to other relevant biomolecular systems.

DROIDS 3.0-Detecting Genetic and Drug Class Variant Impact on Conserved Protein Binding Dynamics

The application of statistical methods to comparatively framed questions about the molecular dynamics (MD) of proteins can potentially enable investigations of biomolecular function beyond the current sequence and structural methods in bioinformatics. However, the chaotic behavior in single MD trajectories requires statistical inference that is derived from large ensembles of simulations representing the comparative functional states of a protein under investigation. Meaningful interpretation of such complex forms of big data poses serious challenges to users of MD. Here, we announce Detecting Relative Outlier Impacts from Molecular Dynamic Simulation (DROIDS) 3.0, a method and software package for comparative protein dynamics that includes maxDemon 1.0, a multimethod machine learning application that trains on large ensemble comparisons of concerted protein motions in opposing functional states generated by DROIDS and deploys learned classifications of these states onto newly generated MD simulations. Local canonical correlations in learning patterns generated from independent, yet identically prepared, MD validation runs are used to identify regions of functionally conserved protein dynamics. The subsequent impacts of genetic and/or drug class variants on conserved dynamics can also be analyzed by deploying the classifiers on variant MD simulations and quantifying how often these altered protein systems display opposing functional states. Here, we present several case studies of complex changes in functional protein dynamics caused by temperature, genetic mutation, and binding interactions with nucleic acids and small molecules. We demonstrate that our machine learning algorithm can properly identify regions of functionally conserved dynamics in ubiquitin and TATA-binding protein (TBP). We quantify the impact of genetic variation in TBP and drug class variation targeting the ATP-binding region of Hsp90 on conserved dynamics. We identify regions of conserved dynamics in Hsp90 that connect the ATP binding pocket to other functional regions. We also demonstrate that dynamic impacts of various Hsp90 inhibitors rank accordingly with how closely they mimic natural ATP binding.

Sequence specificity despite intrinsic disorder: How a disease-associated Val/Met polymorphism rearranges tertiary interactions in a long disordered protein

The role of electrostatic interactions and mutations that change charge states in intrinsically disordered proteins (IDPs) is well-established, but many disease-associated mutations in IDPs are charge-neutral. The Val66Met single nucleotide polymorphism (SNP) in precursor brain-derived neurotrophic factor (BDNF) is one of the earliest SNPs to be associated with neuropsychiatric disorders, and the underlying molecular mechanism is unknown. Here we report on over 250 μs of fully-atomistic, explicit solvent, temperature replica-exchange molecular dynamics (MD) simulations of the 91 residue BDNF prodomain, for both the V66 and M66 sequence. The simulations were able to correctly reproduce the location of both local and non-local secondary structure changes due to the Val66Met mutation, when compared with NMR spectroscopy. We find that the change in local structure is mediated via entropic and sequence specific effects. We developed a hierarchical sequence-based framework for analysis and conceptualization, which first identifies “blobs” of 4-15 residues representing local globular regions or linkers. We use this framework within a novel test for enrichment of higher-order (tertiary) structure in disordered proteins; the size and shape of each blob is extracted from MD simulation of the real protein (RP), and used to parameterize a self-avoiding heterogenous polymer (SAHP). The SAHP version of the BDNF prodomain suggested a protein segmented into three regions, with a central long, highly disordered polyampholyte linker separating two globular regions. This effective segmentation was also observed in full simulations of the RP, but the Val66Met substitution significantly increased interactions across the linker, as well as the number of participating residues. The Val66Met substitution replaces β-bridging between V66 and V94 (on either side of the linker) with specific side-chain interactions between M66 and M95. The protein backbone in the vicinity of M95 is then free to form β-bridges with residues 31-41 near the N-terminus, which condenses the protein. A significant role for Met/Met interactions is consistent with previously-observed non-local effects of the Val66Met SNP, as well as established interactions between the Met66 sequence and a Met-rich receptor that initiates neuronal growth cone retraction.

Intrinsically disordered proteins are proteins that have no well-defined structure in at least one functional form. Mutations in one amino acid may still affect their function significantly, especially in subtle ways with cumulative adverse effects on health. Here we report on molecular dynamics simulations of a protein that is critical for neuronal health throughout adulthood (brain-derived neurotrophic factor). We investigate the effects of a mutation carried by 30% of human population, which has been widely studied for its association with aging-related and stress-related disorders, reduced volume of the hippocampus, and variations in episodic memory. We identify a molecular mechanism in which the mutation may change the global conformations of the protein and its ability to bind to receptors.

DistAA: Database of amino acid distances in proteins and web application for statistical review of distances

Three-dimensional structure of a protein chain is determined by its amino acid interactions. One approach to the analysis of amino acid interactions refers to geometric distances of amino acid pairs in polypeptide chains. For a detailed analysis of the amino acid distances, the database with three types of amino acid distances in a set of chains was created. Web application Distances of Amino Acids has also been developed to enable scientists to explore interactions of amino acids with different properties based on distances stored in the database. Web application calculates and displays descriptive statistics and graphs of amino acid pair distances with selected properties, such as geometric distance threshold, corresponding SCOP class of proteins and secondary structure types. In addition to the analysis of pre-calculated distances stored in the database, the amino acid distances of a single protein with the specified PDB identifier can also be analyzed. The web application is available at http://andromeda.matf.bg.ac.rs/aadis_dynamic/.

Inter-residue interactions in alpha-helical transmembrane proteins

MOTIVATION

The number of available membrane protein structures has markedly increased in the last years and, in parallel, the reliability of the methods to detect transmembrane (TM) segments. In the present report, we characterized inter-residue interactions in α-helical membrane proteins using a dataset of 3462 TM helices from 430 proteins. This is by far the largest analysis published to date.

RESULTS

Our analysis of residue-residue interactions in TM segments of membrane proteins shows that almost all interactions involve aliphatic residues and Phe. There is lack of polar-polar, polar-charged and charged-charged interactions except for those between Thr or Ser sidechains and the backbone carbonyl of aliphatic and Phe residues. The results are discussed in the context of the preferences of amino acids to be in the protein core or exposed to the lipid bilayer and to occupy specific positions along the TM segment. Comparison to datasets of β-barrel membrane proteins and of α-helical globular proteins unveils the specific patterns of interactions and residue composition characteristic of α-helical membrane proteins that are the clue to understanding their structure.

AVAILABILITY AND IMPLEMENTATION

Results data and datasets used are available at http://lmc.uab.cat/TMalphaDB/interactions.php.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discrete analyses of protein dynamics

Protein structures are highly dynamic macromolecules. This dynamics is often analysed through experimental and/or computational methods only for an isolated or a limited number of proteins. Here, we explore large-scale protein dynamics simulation to observe dynamics of local protein conformations using different perspectives. We analysed molecular dynamics to investigate protein flexibility locally, using classical approaches such as RMSf, solvent accessibility, but also innovative approaches such as local entropy. First, we focussed on classical secondary structures and analysed specifically how β-strand, β-turns, and bends evolve during molecular simulations. We underlined interesting specific bias between β-turns and bends, which are considered as the same category, while their dynamics show differences. Second, we used a structural alphabet that is able to approximate every part of the protein structures conformations, namely protein blocks (PBs) to analyse (i) how each initial local protein conformations evolve during dynamics and (ii) if some exchange can exist among these PBs. Interestingly, the results are largely complex than simple regular/rigid and coil/flexible exchange. AbbreviationsN_eqnumber of equivalentPBProtein BlocksPDBProtein DataBankRMSfroot mean square fluctuationsCommunicated by Ramaswamy H. Sarma.

In Silico Approach for Designing Potent Neuroprotective Hexapeptide

Alzheimer's disease (AD) is a constantly recurring neurodegenerative disease that deteriorates over a period of time. In this pathology, connections between neurons become extremely damaged due to the deposition of senile plaques in the membrane region, which results in abnormal signal transduction processes. Also, the intracellular microtubule networks are disrupted in the hyperphosphorylated tau cascade of AD. Therefore, design and development of potent neuroprotective molecules that can instantaneously target multiple facets of AD pathogenesis are greatly needed to tackle this unmet medical need. Here, we have implemented a pharmacophore based in silico analysis of various neuroprotective peptides known for neurotherapeutic application in AD. Fascinatingly, we have identified an active core of these peptides and designed a library of hexapeptides. We observed that peptide "LETVNQ" (LE6) has shown significant protection ability against degeneration of neurons. Experimental evidence suggests that this peptide immensely reduced the aggregation rate of amyloid-β (Aβ) and helped in microtubule polymerization. Intriguingly, this newly designed peptide does not have any cytotoxicity toward differentiated PC12 neurons; rather it helps in neurite outgrowth. Further, LE6 helps to maintain the complex microtubule network in cells by promoting the polymerization rate of intracellular microtubules and mediates excellent protection of neurons even after removal of nerve growth factor (NGF). Finally, we observed that this LE6 peptide has substantial stability under physiological conditions and helps to retain healthy morphology of primary rat cortical neurons. This excellent piece of work identifies a potent hexapeptide, which has exceptional ability to protect neurons as well as microtubule from degeneration and may become potent therapeutics against AD pathogenesis in the future.

Potential Neuroprotective Peptide Emerged from Dual Neurotherapeutic Targets: A Fusion Approach for the Development of Anti-Alzheimer's Lead

Amyloid-beta (Aβ) peptide misfolds into fibrillary aggregates (β-sheet) and is deposited as amyloid plaques in the cellular environment, which severely damages intraneuronal connections leading to Alzheimer's disease (AD) pathogenesis. Furthermore, neurons are rich in tubulin/microtubules, and the intracellular network of microtubules also gets disrupted by the accumulation of Aβ fiber in the brain. Hence, development of new potent molecules, which can simultaneously inhibit Aβ fibrillations and stabilize microtubules, is particularly needed for the efficient therapeutic application in AD. To address these issues, here we introduced an innovative fusion strategy to design and develop next generation anti-AD therapeutic leads. This unexplored fusion strategy entails design and development of a potent nonapeptide by taking into account both the hydrophobic core (17-21) of Aβ peptide and the taxol binding region of β-tubulin. In vitro results suggest that this newly designed peptide interacts at the taxol binding region of β-tubulin with a moderate binding affinity and promotes microtubule polymerization. It has the ability to bind at the hydrophobic core (17-21) of Aβ, responsible for its aggregation, and prevent amyloid fibril as well as plaque formation. In addition, it interacts at the CAS site (catalytic anionic site) of acetylcholinesterase (AChE) and significantly inhibits AChE induced Aβ fibrillation, stimulates neurite branching, and provides stability to intracellular microtubules and extensive protection of neurons against nerve growth factor (NGF) deprived neuron toxicity. Moreover, this newly designed peptide shows good stability in serum obtained from humans and efficiently permeates the blood-brain barrier (BBB) without showing any toxicity toward differentiated PC12 neurons as well as primary rat cortical neurons. This excellent feature of protecting the neurons by stabilizing the microtubules without showing any toxicity toward neurons will make this peptide a potent therapeutic agent of AD in the near future.

Peptide-Based Acetylcholinesterase Inhibitor Crosses the Blood-Brain Barrier and Promotes Neuroprotection

Design and development of acetylcholinesterase (AChE) inhibitor has tremendous implications in the treatment of Alzheimer's disease (AD). Here, we have adopted a computational approach for the design of a peptide based AChE inhibitor from its active site. We identified an octapeptide, which interacts with the catalytic anionic site (CAS) of AChE enzyme and inhibits its activity. Interestingly, this peptide also inhibits amyloid aggregation through its interaction at the 17-21 region of amyloid-beta (Aβ) and stabilizes microtubules by interacting with tubulin as well. Eventually, in the PC12 derived neurons, it shows noncytotoxicity, promotes neurite out-growth, stabilizes intracellular microtubules, and confers significant neuroprotection even upon withdrawal of nerve growth factor (NGF). Further, results reveal that this peptide possesses good serum stability, crosses the blood-brain barrier (BBB), and maintains the healthy architecture of the primary cortical neurons. This work shows discovery of an excellent peptide-based AChE inhibitor with additional potential as a microtubule stabilizer, which will pave the way for the development of potential anti-AD therapeutics in the near future.

Crafting of Neuroprotective Octapeptide from Taxol-Binding Pocket of β-Tubulin

Microtubules play a crucial role in maintaining the shape and function of neurons. During progression of Alzheimer's disease (AD), severe destabilization of microtubules occurs, which leads to the permanent disruption of signal transduction processes and memory loss. Thus, microtubule stabilization is one of the key requirements for the treatment of AD. Taxol, a microtubule stabilizing anticancer drug, has been considered as a potential anti-AD drug but was never tested in AD patients, likely because of its' toxic nature and poor brain exposure. However, other microtubule-targeting agents such as epothilone D (BMS-241027) and TPI-287 (abeotaxane) and NAP peptide (davunetide) have entered in AD clinical programs. Therefore, the taxol binding pocket of tubulin could be a potential site for designing of mild and noncytotoxic microtubule stabilizing molecules. Here, we adopted an innovative strategy for the development of a peptide based microtubule stabilizer, considering the taxol binding pocket of β-tubulin, by using alanine scanning mutagenesis technique. This approach lead us to a potential octapeptide, which strongly binds to the taxol pocket of β-tubulin, serves as an excellent microtubule stabilizer, increases the expression of acetylated tubulin, and acts as an Aβ aggregation inhibitor and neuroprotective agent. Further, results revealed that this peptide is nontoxic against both PC12 derived neurons and primary cortical neurons. We believe that our strategy and discovery of peptide-based microtubule stabilizer will open the door for the development of potential anti-AD therapeutics in near future.

Designed Tetrapeptide Interacts with Tubulin and Microtubule

Microtubules regulate eukaryotic cell functions, which have tremendous implication in tumor progression. Thus, the design of novel approaches for controlling microtubule function is extremely important. In this manuscript, a novel tetrapeptide Ser-Leu-Arg-Pro (SLRP) has been designed and synthesized from a small peptide library consisting of 14 tetrapeptides, which perturbs microtubule function through interaction in the "anchor region". We have studied the role of peptides on microtubule function on a chemically functionalized 2D platform. Interestingly, we have found that SLRP binds with tubulin and inhibits the kinesin-driven microtubule motility on a kinesin-immobilized chemically functionalized 2D platform. Further, this peptide modulator interacts with intracellular tubulin/microtubule and depolymerizes the microtubule networks. These interesting findings of perturbation of microtubule function both on engineered platforms and inside the cell by this small peptide modulator inspired us to study the effect of this tetrapeptide on cancer cell proliferation. We found that the novel tetrapeptide modulator causes moderate cytotoxicity to the human breast cancer cell (MCF-7 cell), induces the apoptotic death of MCF-7 cell, and activates the tumor suppressor proteins p53 and cyclin-dependent kinase inhibitor 1 (p21). To the best of our knowledge, this is the shortest peptide discovered, which perturbs microtubule function both on an engineered 2D platform and inside the cell.

PBxplore: a tool to analyze local protein structure and deformability with Protein Blocks

This paper describes the development and application of a suite of tools, called PBxplore, to analyze the dynamics and deformability of protein structures using Protein Blocks (PBs). Proteins are highly dynamic macromolecules, and a classical way to analyze their inherent flexibility is to perform molecular dynamics simulations. The advantage of using small structural prototypes such as PBs is to give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, PBs allow analysis of local protein deformability which cannot be done with other methods and had been used efficiently in different applications. PBxplore is able to process large amounts of data such as those produced by molecular dynamics simulations. It produces frequencies, entropy and information logo outputs as text and graphics. PBxplore is available at https://github.com/pierrepo/PBxplore and is released under the open-source MIT license.

Threading: A novel insilico indagation method for genetic characterization of some diplostomoid metacercariae (Digenea:Diplostomidae Poirier, 1886)

The protein encoding zone of Mitochondrial DNA region (inherited from single lineage) seems most suitable and effective for taxonomic, systematic, ecological, evolutionary, DNA barcoding, cryptic species and population studies, exploiting nucleotide/amino acid datasets (1D/2D/3D conformational level). Nowadays, expeditious computerized methods are in trend for analyzing genetic material to demonstrate variations at various levels of protein structures. Structural proteomics have implemented here for genetic identification, differentiation and relationship of species from information rich data of mt COI gene of the family Diplostomidae with inclusion of molecular tools. Various aspects have been utilized herein for re-validation and infallible discrimination of Trematode diplostomoid metacercariae (Tetracotyle lucknowensis Pandey, 1971; T. xenentodoni Chakrabarti, 1970; T. fausti Rai and Pande, 1969; T. muscularius Chakrabarti, 1970 and Diplostomulum minutum Pandey, 1968), the infective stage in the life cycle, causing severe damage to fish host, whose adults are found mainly in fish eating birds and mammals.

Global analysis of VHHs framework regions with a structural alphabet

The VHHs are antigen-binding region/domain of camelid heavy chain antibodies (HCAb). They have many interesting biotechnological and biomedical properties due to their small size, high solubility and stability, and high affinity and specificity for their antigens. HCAb and classical IgGs are evolutionary related and share a common fold. VHHs are composed of regions considered as constant, called the frameworks (FRs) connected by Complementarity Determining Regions (CDRs), a highly variable region that provide interaction with the epitope. Actually, no systematic structural analyses had been performed on VHH structures despite a significant number of structures. This work is the first study to analyse the structural diversity of FRs of VHHs. Using a structural alphabet that allows approximating the local conformation, we show that each of the four FRs do not have a unique structure but exhibit many structural variant patterns. Moreover, no direct simple link between the local conformational change and amino acid composition can be detected. These results indicate that long-range interactions affect the local conformation of FRs and impact the building of structural models.

Novel tubulin-targeted cell penetrating antimitotic octapeptide

Here, we have discovered an antimitotic cell penetrating octapeptide containing single Arg amino acid.

Codon-level information improves predictions of inter-residue contacts in proteins by correlated mutation analysis

Methods for analysing correlated mutations in proteins are becoming an increasingly powerful tool for predicting contacts within and between proteins. Nevertheless, limitations remain due to the requirement for large multiple sequence alignments (MSA) and the fact that, in general, only the relatively small number of top-ranking predictions are reliable. To date, methods for analysing correlated mutations have relied exclusively on amino acid MSAs as inputs. Here, we describe a new approach for analysing correlated mutations that is based on combined analysis of amino acid and codon MSAs. We show that a direct contact is more likely to be present when the correlation between the positions is strong at the amino acid level but weak at the codon level. The performance of different methods for analysing correlated mutations in predicting contacts is shown to be enhanced significantly when amino acid and codon data are combined.

Mining the entire Protein DataBank for frequent spatially cohesive amino acid patterns

BACKGROUND

The three-dimensional structure of a protein is an essential aspect of its functionality. Despite the large diversity in protein structures and functionality, it is known that there are common patterns and preferences in the contacts between amino acid residues, or between residues and other biomolecules, such as DNA. The discovery and characterization of these patterns is an important research topic within structural biology as it can give fundamental insight into protein structures and can aid in the prediction of unknown structures.

RESULTS

Here we apply an efficient spatial pattern miner to search for sets of amino acids that occur frequently in close spatial proximity in the protein structures of the Protein DataBank. This allowed us to mine for a new class of amino acid patterns, that we term FreSCOs (Frequent Spatially Cohesive Component sets), which feature synergetic combinations. To demonstrate the relevance of these FreSCOs, they were compared in relation to the thermostability of the protein structure and the interaction preferences of DNA-protein complexes. In both cases, the results matched well with prior investigations using more complex methods on smaller data sets.

CONCLUSIONS

The currently characterized protein structures feature a diverse set of frequent amino acid patterns that can be related to the stability of the protein molecular structure and that are independent from protein function or specific conserved domains.

Characteristics of protein residue-residue contacts and their application in contact prediction

Contact sites between amino acids characterize important structural features of a protein. We investigated characteristics of contact sites in a representative set of proteins and their relations between protein class or topology. For this purpose, we used a non-redundant set of 5872 protein domains, identically categorized by CATH and SCOP databases. The proteins represented alpha, beta, and alpha+beta classes. Contact maps of protein structures were obtained for a selected set of physical distances in the main backbone and separations in protein sequences. For each set a dependency between contact degree and distance parameters was quantified. We indicated residues forming contact sites most frequently and unique amino acid pairs which created contact sites most often within each structural class. Contact characteristics of specific topologies were compared to the characteristics of their protein classes showing protein groups with a distinguished contact characteristic. We showed that our results could be used to improve the performance of recent top contact predictor — direct coupling analysis. Our work provides values of contact site propensities that can be involved in bioinformatic databases.

Intermolecular β-strand networks avoid hub residues and favor low interconnectedness: a potential protection mechanism against chain dissociation upon mutation

Altogether few protein oligomers undergo a conformational transition to a state that impairs their function and leads to diseases. But when it happens, the consequences are not harmless and the so-called conformational diseases pose serious public health problems. Notorious examples are the Alzheimer's disease and some cancers associated with a conformational change of the amyloid precursor protein (APP) and of the p53 tumor suppressor, respectively. The transition is linked with the propensity of β-strands to aggregate into amyloid fibers. Nevertheless, a huge number of protein oligomers associate chains via β-strand interactions (intermolecular β-strand interface) without ever evolving into fibers. We analyzed the layout of 1048 intermolecular β-strand interfaces looking for features that could provide the β-strands resistance to conformational transitions. The interfaces were reconstructed as networks with the residues as the nodes and the interactions between residues as the links. The networks followed an exponential decay degree distribution, implying an absence of hubs and nodes with few links. Such layout provides robustness to changes. Few links per nodes do not restrict the choices of amino acids capable of making an interface and maintain high sequence plasticity. Few links reduce the “bonding” cost of making an interface. Finally, few links moderate the vulnerability to amino acid mutation because it entails limited communication between the nodes. This confines the effects of a mutation to few residues instead of propagating them to many residues via hubs. We propose that intermolecular β-strand interfaces are organized in networks that tolerate amino acid mutation to avoid chain dissociation, the first step towards fiber formation. This is tested by looking at the intermolecular β-strand network of the p53 tetramer.

Reconstructing protein structures by neural network pairwise interaction fields and iterative decoy set construction

Predicting the fold of a protein from its amino acid sequence is one of the grand problems in computational biology. While there has been progress towards a solution, especially when a protein can be modelled based on one or more known structures (templates), in the absence of templates, even the best predictions are generally much less reliable. In this paper, we present an approach for predicting the three-dimensional structure of a protein from the sequence alone, when templates of known structure are not available. This approach relies on a simple reconstruction procedure guided by a novel knowledge-based evaluation function implemented as a class of artificial neural networks that we have designed: Neural Network Pairwise Interaction Fields (NNPIF). This evaluation function takes into account the contextual information for each residue and is trained to identify native-like conformations from non-native-like ones by using large sets of decoys as a training set. The training set is generated and then iteratively expanded during successive folding simulations. As NNPIF are fast at evaluating conformations, thousands of models can be processed in a short amount of time, and clustering techniques can be adopted for model selection. Although the results we present here are very preliminary, we consider them to be promising, with predictions being generated at state-of-the-art levels in some of the cases.

Defining structural and evolutionary modules in proteins: a community detection approach to explore sub-domain architecture

Background

Assessing protein modularity is important to understand protein evolution. Still the question of the existence of a sub-domain modular architecture remains. We propose a graph-theory approach with significance and power testing to identify modules in protein structures. In the first step, clusters are determined by optimizing the partition that maximizes the modularity score. Second, each cluster is tested for significance. Significant clusters are referred to as modules. Evolutionary modules are identified by analyzing homologous structures. Dynamic modules are inferred from sets of snapshots of molecular simulations. We present here a methodology to identify sub-domain architecture robustly, biologically meaningful, and statistically supported.

Results

The robustness of this new method is tested using simulated data with known modularity. Modules are correctly identified even when there is a low correlation between landmarks within a module. We also analyzed the evolutionary modularity of a data set of α-amylase catalytic domain homologs, and the dynamic modularity of the Niemann-Pick C1 (NPC1) protein N-terminal domain.

The α-amylase contains an (α/β)₈ barrel (TIM barrel) with the polysaccharides cleavage site and a calcium-binding domain. In this data set we identified four robust evolutionary modules, one of which forms the minimal functional TIM barrel topology.

The NPC1 protein is involved in the intracellular lipid metabolism coordinating sterol trafficking. NPC1 N-terminus is the first luminal domain which binds to cholesterol and its oxygenated derivatives. Our inferred dynamic modules in the protein NPC1 are also shown to match functional components of the protein related to the NPC1 disease.

Conclusions

A domain compartmentalization can be found and described in correlation space. To our knowledge, there is no other method attempting to identify sub-domain architecture from the correlation among residues. Most attempts made focus on sequence motifs of protein-protein interactions, binding sites, or sequence conservancy. We were able to describe functional/structural sub-domain architecture related to key residues for starch cleavage, calcium, and chloride binding sites in the α-amylase, and sterol opening-defining modules and disease-related residues in the NPC1. We also described the evolutionary sub-domain architecture of the α-amylase catalytic domain, identifying the already reported minimum functional TIM barrel.

VLDP web server: a powerful geometric tool for analysing protein structures in their environment

Protein structures are an ensemble of atoms determined experimentally mostly by X-ray crystallography or Nuclear Magnetic Resonance. Studying 3D protein structures is a key point for better understanding protein function at a molecular level. We propose a set of accurate tools, for analysing protein structures, based on the reliable method of Voronoi–Laguerre tessellations. The Voronoi Laguerre Delaunay Protein web server (VLDPws) computes the Laguerre tessellation on a whole given system first embedded in solvent. Through this fine description, VLDPws gives the following data: (i) Amino acid volumes evaluated with high precision, as confirmed by good correlations with experimental data. (ii) A novel definition of inter-residue contacts within the given protein. (iii) A measure of the residue exposure to solvent that significantly improves the standard notion of accessibility in some cases. At present, no equivalent web server is available. VLDPws provides output in two complementary forms: direct visualization of the Laguerre tessellation, mostly its polygonal molecular surfaces; files of volumes; and areas, contacts and similar data for each residue and each atom. These files are available for download for further analysis. VLDPws can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/vldp.

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.

Modeling and molecular dynamics of HPA-1a and -1b polymorphisms: effects on the structure of the β3 subunit of the αIIbβ3 integrin

Background

The HPA-1 alloimmune system carried by the platelet integrin αIIbβ3 is the primary cause of alloimmune thrombocytopenia in Caucasians and the HPA-1b allele might be a risk factor for thrombosis. HPA-1a and -1b alleles are defined by a leucine and a proline, respectively, at position 33 in the β3 subunit. Although the structure of αIIbβ3 is available, little is known about structural effects of the L33P substitution and its consequences on immune response and integrin functions.

Methodology/Principal Findings

A complete 3D model of the L33-β3 extracellular domain was built and a P33 model was obtained by in silico mutagenesis. We then performed molecular dynamics simulations. Analyses focused on the PSI, I-EGF-1, and I-EGF-2 domains and confirmed higher exposure of residue 33 in the L33 β3 form. These analyses also showed major structural flexibility of all three domains in both forms, but increased flexibility in the P33 β3 form. The L33P substitution does not alter the local structure (residues 33 to 35) of the PSI domain, but modifies the structural equilibrium of the three domains.

Conclusions

These results provide a better understanding of HPA-1 epitopes complexity and alloimmunization prevalence of HPA-1a. P33 gain of structure flexibility in the β3 knee may explain the increased adhesion capacity of HPA-1b platelets and the associated thrombotic risk. Our study provides important new insights into the relationship between HPA-1 variants and β3 structure that suggest possible effects on the alloimmune response and platelet function.

Optimal definition of inter-residual contact in globular proteins based on pairwise interaction energy calculations, its robustness, and applications

Although a contact is an essential measurement for the topology as well as strength of non-covalent interactions in biomolecules and their complexes, there is no general agreement in the definition of this feature. Most of the definitions work with simple geometric criteria which do not fully reflect the energy content or ability of the biomolecular building blocks to arrange their environment. We offer a reasonable solution to this problem by distinguishing between "productive" and "non-productive" contacts based on their interaction energy strength and properties. We have proposed a method which converts the protein topology into a contact map that represents interactions with statistically significant high interaction energies. We do not prove that these contacts are exclusively stabilizing, but they represent a gateway to thermodynamically important rather than geometry-based contacts. The process is based on protein fragmentation and calculation of interaction energies using the OPLS force field and relies on pairwise additivity of amino acid interactions. Our approach integrates the treatment of different types of interactions, avoiding the problems resulting from different contributions to the overall stability and the different effect of the environment. The first applications on a set of homologous proteins have shown the usefulness of this classification for a sound estimate of protein stability.

The shadow map: a general contact definition for capturing the dynamics of biomolecular folding and function

Structure-based models (SBMs) are simplified models of the biomolecular dynamics that arise from funneled energy landscapes. We recently introduced an all-atom SBM that explicitly represents the atomic geometry of a biomolecule. While this initial study showed the robustness of the all-atom SBM Hamiltonian to changes in many of the energetic parameters, an important aspect, which has not been explored previously, is the definition of native interactions. In this study, we propose a general definition for generating atomically grained contact maps called "Shadow". The Shadow algorithm initially considers all atoms within a cutoff distance and then, controlled by a screening parameter, discards the occluded contacts. We show that this choice of contact map is not only well behaved for protein folding, since it produces consistently cooperative folding behavior in SBMs but also desirable for exploring the dynamics of macromolecular assemblies since, it distributes energy similarly between RNAs and proteins despite their disparate internal packing. All-atom structure-based models employing Shadow contact maps provide a general framework for exploring the geometrical features of biomolecules, especially the connections between folding and function.

Assignment of PolyProline II conformation and analysis of sequence--structure relationship

BACKGROUND

Secondary structures are elements of great importance in structural biology, biochemistry and bioinformatics. They are broadly composed of two repetitive structures namely α-helices and β-sheets, apart from turns, and the rest is associated to coil. These repetitive secondary structures have specific and conserved biophysical and geometric properties. PolyProline II (PPII) helix is yet another interesting repetitive structure which is less frequent and not usually associated with stabilizing interactions. Recent studies have shown that PPII frequency is higher than expected, and they could have an important role in protein-protein interactions.

METHODOLOGY/PRINCIPAL FINDINGS

A major factor that limits the study of PPII is that its assignment cannot be carried out with the most commonly used secondary structure assignment methods (SSAMs). The purpose of this work is to propose a PPII assignment methodology that can be defined in the frame of DSSP secondary structure assignment. Considering the ambiguity in PPII assignments by different methods, a consensus assignment strategy was utilized. To define the most consensual rule of PPII assignment, three SSAMs that can assign PPII, were compared and analyzed. The assignment rule was defined to have a maximum coverage of all assignments made by these SSAMs. Not many constraints were added to the assignment and only PPII helices of at least 2 residues length are defined.

CONCLUSIONS/SIGNIFICANCE

The simple rules designed in this study for characterizing PPII conformation, lead to the assignment of 5% of all amino as PPII. Sequence-structure relationships associated with PPII, defined by the different SSAMs, underline few striking differences. A specific study of amino acid preferences in their N and C-cap regions was carried out as their solvent accessibility and contact patterns. Thus the assignment of PPII can be coupled with DSSP and thus opens a simple way for further analysis in this field.

A novel evaluation of residue and protein volumes by means of Laguerre tessellation

Amino acids control the protein folding process and maintain its functional fold. This study underlines the interest of the Laguerre tessellation to determine relevant amino acid volumes in proteins. Previous studies used a limited number of proteins and only buried residues. The present computations improve the method and results on three main points: (i) a large, high-quality updated and refined data bank of proteins is used; (ii) all residues are taken into account, including those at the protein surface, thanks to (iii) the addition of a realistic solvent. The new values of the average and standard deviation of amino acid volumes show significant corrections with respect to previous studies. Another issue of the method is the polyhedral protein/water interface area (PIA) which quantifies the exposure of atoms or residues to the solvent. We propose this PIA as a new, parameter-free, alternative for measuring accessibility. The comparison with NACCESS is satisfactory; however, the methods disagree in pointing out buried residues: where NACCESS evaluates to zero, the exposure given by PIA ranges from 0 to 20%. Variations of average residue volumes have been analyzed under several conditions, e.g., how they depend on protein size and on secondary structure environments. As it is based on strong mathematical grounds and on numerous high-quality protein structures, our work gives a reliable methodology and up-to-date values of amino acid volumes and surface accessibility.

A short survey on protein blocks

Protein structures are classically described in terms of secondary structures. Even if the regular secondary structures have relevant physical meaning, their recognition from atomic coordinates has some important limitations such as uncertainties in the assignment of boundaries of helical and β-strand regions. Further, on an average about 50% of all residues are assigned to an irregular state, i.e., the coil. Thus different research teams have focused on abstracting conformation of protein backbone in the localized short stretches. Using different geometric measures, local stretches in protein structures are clustered in a chosen number of states. A prototype representative of the local structures in each cluster is generally defined. These libraries of local structures prototypes are named as "structural alphabets". We have developed a structural alphabet, named Protein Blocks, not only to approximate the protein structure, but also to predict them from sequence. Since its development, we and other teams have explored numerous new research fields using this structural alphabet. We review here some of the most interesting applications.

Cross-study analysis of genomic data defines the ciliate multigenic epiplasmin family: strategies for functional analysis in Paramecium tetraurelia

Background

The sub-membranous skeleton of the ciliate Paramecium, the epiplasm, is composed of hundreds of epiplasmic scales centered on basal bodies, and presents a complex set of proteins, epiplasmins, which belong to a multigenic family. The repeated duplications observed in the P. tetraurelia genome present an interesting model of the organization and evolution of a multigenic family within a single cell.

Results

To study this multigenic family, we used phylogenetic, structural, and analytical transcriptional approaches. The phylogenetic method defines 5 groups of epiplasmins in the multigenic family. A refined analysis by Hydrophobic Cluster Analysis (HCA) identifies structural characteristics of 51 epiplasmins, defining five separate groups, and three classes. Depending on the sequential arrangement of their structural domains, the epiplasmins are defined as symmetric, asymmetric or atypical. The EST data aid in this classification, in the identification of putative regulating sequences such as TATA or CAAT boxes. When specific RNAi experiments were conducted using sequences from either symmetric or asymmetric classes, phenotypes were drastic. Local effects show either disrupted or ill-shaped epiplasmic scales. In either case, this results in aborted cell division.

Using structural features, we show that 4 epiplasmins are also present in another ciliate, Tetrahymena thermophila. Their affiliation with the distinctive structural groups of Paramecium epiplasmins demonstrates an interspecific multigenic family.

Conclusion

The epiplasmin multigenic family illustrates the history of genomic duplication in Paramecium. This study provides a framework which can guide functional analysis of epiplasmins, the major components of the membrane skeleton in ciliates. We show that this set of proteins handles an important developmental information in Paramecium since maintenance of epiplasm organization is crucial for cell morphogenesis.

Structure and sequence analysis of influenza A virus nucleoprotein

Influenza A virus nucleoprotein (NP) forms homo-oligomers and multiple copies of NP wrap around genomic RNA, along with a trimeric polymerase making up ribonucleoprotein (RNP) complex. Sequence comparison of more than 2500 influenza A NP showed that this protein contains 30.1 % of polymorphic residues. NP is composed of a head and a body domain and a tail loop/ linker region. The head domain is more conserved than the body domain, as revealed from the structure-based sequence alignment. NP oligomerization is mediated by the insertion of the non-polymorphic and structurally conserved tail loop of one NP molecule to a groove of another NP. The different form of NP oligomers is due to the flexibility of the polymorphic linkers that join the tail loop to the rest of the protein. The RNA binding property of NP is known to involve the protruding element and the flexible basic loop between the head and body domains, both having high degree of primary sequence conservation. To bind RNA, NP may first capture the RNA by the flexible basic loop and then the RNA is clamped by the protruding element.

Analysis of protein chameleon sequence characteristics

Conversion of local structural state of a protein from an α-helix to a β-strand is usually associated with a major change in the tertiary structure. Similar changes were observed during the self assembly of amyloidogenic proteins to form fibrils, which are implicated in severe diseases conditions, e.g., Alzheimer disease. Studies have emphasized that certain protein sequence fragments known as chameleon sequences do not have a strong preference for either helical or the extended conformations. Surprisingly, the information on the local sequence neighborhood can be used to predict their secondary at a high accuracy level. Here we report a large scale-analysis of chameleon sequences to estimate their propensities to be associated with different local structural states such as α -helices, β-strands and coils. With the help of the propensity information derived from the amino acid composition, we underline their complexity, as more than one quarter of them prefers coil state over to the regular secondary structures. About half of them show preference for both α-helix and β-sheet conformations and either of these two states is favored by the rest.

Analysis of protein contacts into Protein Units

Three-dimensional structures of proteins are the support of their biological functions. Their folds are maintained by inter-residue interactions which are one of the main focuses to understand the mechanisms of protein folding and stability. Furthermore, protein structures can be composed of single or multiple functional domains that can fold and function independently. Hence, dividing a protein into domains is useful for obtaining an accurate structure and function determination. In previous studies, we enlightened protein contact properties according to different definitions and developed a novel methodology named Protein Peeling. Within protein structures, Protein Peeling characterizes small successive compact units along the sequence called protein units (PUs). The cutting done by Protein Peeling maximizes the number of contacts within the PUs and minimizes the number of contacts between them. This method is so a relevant tool in the context of the protein folding research and particularly regarding the hierarchical model proposed by George Rose. Here, we accurately analyze the PUs at different levels of cutting, using a non-redundant protein databank. Distribution of PU sizes, number of PUs or their accessibility are screened to determine their common and different features. Moreover, we highlight the preferential amino acid interactions inside and between PUs. Our results show that PUs are clearly an intermediate level between secondary structures and protein structural domains.