Protein structure prediction by pro-Sp3-TASSER

Challenges of Protein-Protein Docking of the Membrane Proteins

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

Structural resemblance of the DNAJA-family protein, Tid1, to the DNAJB-family Hsp40

The specific pair of heat shock protein 70 (Hsp70) and Hsp40 constitutes an essential molecular chaperone system involved in numerous cellular processes, including the proper folding/refolding and transport of proteins. Hsp40 family members are characterized by the presence of a conserved J-domain (JD) that functions as a co-chaperone of Hsp70. Tumorous imaginal disc 1 (Tid1) is a tumor suppressor protein belonging to the DNAJA3 subfamily of Hsp40 and functions as a co-chaperone of the mitochondrial Hsp70, mortalin. In this work, we performed nuclear magnetic resonance spectroscopy to determine the solution structure of JD and its interaction with the glycine/phenylalanine-rich region (GF-motif) of human Tid1. Notably, Tid1-JD, whose conformation was consistent with that of the DNAJB1 JD, appeared to stably interact with its subsequent GF-motif region. Collectively with our sequence analysis, the present results demonstrate that the functional and regulatory mode of Tid1 resembles that of the DNAJB1 subfamily members rather than DNAJA1 or DNAJA2 subfamily proteins. Therefore, it is suggested that an allosteric interaction between mortalin and Tid1 is involved in the mitochondrial Hsp70/Hsp40 chaperone system.

Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble

Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings show that co-evolutionary analysis coupled with machine-learning techniques improves the precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from Multi Sequence Analysis reveals the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on energy scores, RMSD clustering, and the centroids selected as the lowest energy structure per cluster. These models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechain orientations. Our pipeline allows to retrieve the experimental structural dynamics experimentally represented by different X-ray conformations for the same sequence as well the conformational space observed with the MD simulations. We show the potential correlation between the experimental structure dynamics and the predicted model ensemble demonstrating the susceptibility of the current state-of-the-art methods in protein folding and dynamics prediction and pointing out the areas of improvement.

EViS: An Enhanced Virtual Screening Approach Based on Pocket-Ligand Similarity

Virtual screening (VS) is a popular technology in drug discovery to identify a new scaffold of actives for a specific drug target, which can be classified into ligand-based and structure-based approaches. As the number of protein-ligand complex structures available in public databases increases, it would be possible to develop a template searching-based VS approach that utilizes such information. In this work, we proposed an enhanced VS approach, which is termed EViS, to integrate ligand docking, protein pocket template searching, and ligand template shape similarity calculation. A novel and simple PL-score to characterize local pocket-ligand template similarity was used to evaluate the screening compounds. Benchmark tests were performed on three datasets including DUDE, LIT-PCBA, and DEKOIS. EViS achieved the average enrichment factors (EFs) of 27.8 and 23.4 at a 1% cutoff for experimental and predicted structures on the widely used DUDE dataset, respectively. Detailed data analysis shows that EViS benefits from obtaining favorable ligand poses from docking and using such ligand geometric information to perform three-dimensional (3D) ligand similarity calculations, and the PL-score is efficient to screen compounds based on template searching in the protein-ligand structure database.

SP3 is associated with migration, invasion, and Akt/PKB signalling in MDA-MB-231 breast cancer cells

Specificity proteins (SPs) have pro-oncogenic functions in cancer cells, ranging from cancer cell proliferation, migration, invasion, and angiogenesis. There is strong evidence that several antineoplastic drugs target depletion of SP proteins via different pathways. However, the mode of action of SP3 and the underlying consequences of its depletion are not well understood. Here, we demonstrate that SP3 is overexpressed in invasive breast cancer cells vs normal counterparts. The gene expression analysis from The Cancer Genome Atlas datasets indicated that SP3 is strongly correlated with Akt signalling-related proteins, G protein subunit alpha 13, and RAB33B (RAB33B, member RAS oncogene family). RNA interference of SP3 decreased active phosphorylation of Akt at serine and threonine sites. These findings indicate that SP3 exhibits a pro-oncogenic function, which clearly fits the description of an nononcogene addiction gene. Future analyses are prompted to uncover the SP3 gene regulation function and to reveal downstream targets of SP3 in breast cancer.

Structural Models for the Dynamic Effects of Loss-of-Function Variants in the Human SIM1 Protein Transcriptional Activation Domain

Single-minded homologue 1 (SIM1) is a transcription factor with numerous different physiological and developmental functions. SIM1 is a member of the class I basic helix-loop-helix-PER-ARNT-SIM (bHLH-PAS) transcription factor family, that includes several other conserved proteins, including the hypoxia-inducible factors, aryl hydrocarbon receptor, neuronal PAS proteins, and the CLOCK circadian regulator. Recent studies of HIF-a-ARNT and CLOCK-BMAL1 protein complexes have revealed the organization of their bHLH, PASA, and PASB domains and provided insight into how these heterodimeric protein complexes form; however, experimental structures for SIM1 have been lacking. Here, we describe the first full-length atomic structural model for human SIM1 with its binding partner ARNT in a heterodimeric complex and analyze several pathogenic variants utilizing state-of-the-art simulations and algorithms. Using local and global positional deviation metrics, deductions to the structural basis for the individual mutants are addressed in terms of the deleterious structural reorganizations that could alter protein function. We propose new experiments to probe these hypotheses and examine an interesting SIM1 dynamic behavior. The conformational dynamics demonstrates conformational changes on local and global regions that represent a mechanism for dysfunction in variants presented. In addition, we used our ab initio hybrid model for further prediction of variant hotspots that can be engineered to test for counter variant (restoration of wild-type function) or basic research probe.

Efficient rational modification of non-ribosomal peptides by adenylation domain substitution

Non-ribosomal peptide synthetase (NRPS) enzymes form modular assembly-lines, wherein each module governs the incorporation of a specific monomer into a short peptide product. Modules are comprised of one or more key domains, including adenylation (A) domains, which recognise and activate the monomer substrate; condensation (C) domains, which catalyse amide bond formation; and thiolation (T) domains, which shuttle reaction intermediates between catalytic domains. This arrangement offers prospects for rational peptide modification via substitution of substrate-specifying domains. For over 20 years, it has been considered that C domains play key roles in proof-reading the substrate; a presumption that has greatly complicated rational NRPS redesign. Here we present evidence from both directed and natural evolution studies that any substrate-specifying role for C domains is likely to be the exception rather than the rule, and that novel non-ribosomal peptides can be generated by substitution of A domains alone. We identify permissive A domain recombination boundaries and show that these allow us to efficiently generate modified pyoverdine peptides at high yields. We further demonstrate the transferability of our approach in the PheATE-ProCAT model system originally used to infer C domain substrate specificity, generating modified dipeptide products at yields that are inconsistent with the prevailing dogma.

Comprehensive in silico modeling of the rice plant PRR Xa21 and its interaction with RaxX21-sY and OsSERK2

The first layer of defense that plants deploy to ward off a microbial invasion comes in the form of pattern-triggered immunity (PTI), which is initiated when the pattern-recognition receptors (PRRs) bind with the pathogen-associated molecular patterns (PAMPs) and co-receptor proteins, and transmit a defense signal. Although several plant PRRs have been discovered, very few of them have been fully characterized, and their functional parameters assessed. In this study, the 3D-model prediction of an entire plant PRR protein, Xa21, was done by implementing multiple in silico modeling techniques. Subsequently, the PAMP RaxX21-sY (sulphated RaxX21) and leucine-rich repeat (LRR) domain of the co-receptor OsSERK2 were docked with the LRR domain of Xa21. The docked complex of these three proteins formed a heterodimer that closely resembles the other crystallographic PTI complexes available. Molecular dynamics simulations and MM/PBSA calculations were applied for an in-depth analysis of the interactions between Xa21 LRR, RaxX21-sY, and OsSERK2 LRR. Arg230 and Arg185 from Xa21 LRR, Val2 and Lys15 from RaxX21-sY and Lys164 from OsSERK2 LRR were found to be the prominent residues which might contribute significantly in the formation of a heterodimer during the PTI process mediated by Xa21. Additionally, RaxX21-sY interacted much more favorably with Xa21 LRR in the presence of OsSERK2 LRR in the complex, which substantiates the necessity of the co-receptor in Xa21 mediated PTI to recognize the PAMP RaxX21-sY. However, the free energy binding calculation reveals the favorability of a heterodimer formation of PRR Xa21 and co-receptor OsSERK2 without the presence of PAMP RaxX21-sY, which validate the previous lab result.

LRFragLib: an effective algorithm to identify fragments for de novo protein structure prediction

Motivation

The quality of fragment library determines the efficiency of fragment assembly, an approach that is widely used in most de novo protein-structure prediction algorithms. Conventional fragment libraries are constructed mainly based on the identities of amino acids, sometimes facilitated by predicted information including dihedral angles and secondary structures. However, it remains challenging to identify near-native fragment structures with low sequence homology.

Results

We introduce a novel fragment-library-construction algorithm, LRFragLib, to improve the detection of near-native low-homology fragments of 7-10 residues, using a multi-stage, flexible selection protocol. Based on logistic regression scoring models, LRFragLib outperforms existing techniques by achieving a significantly higher precision and a comparable coverage on recent CASP protein sets in sampling near-native structures. The method also has a comparable computational efficiency to the fastest existing techniques with substantially reduced memory usage.

Availability and Implementation

The source code is available for download at http://166.111.152.91/Downloads.html.

Contact

hgong@tsinghua.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Molecular details of secretory phospholipase A2 from flax (Linum usitatissimum L.) provide insight into its structure and function

Secretory phospholipase A₂ (sPLA₂) are low molecular weight proteins (12-18 kDa) involved in a suite of plant cellular processes imparting growth and development. With myriad roles in physiological and biochemical processes in plants, detailed analysis of sPLA₂ in flax/linseed is meagre. The present work, first in flax, embodies cloning, expression, purification and molecular characterisation of two distinct sPLA₂s (I and II) from flax. PLA₂ activity of the cloned sPLA₂s were biochemically assayed authenticating them as bona fide phospholipase A₂. Physiochemical properties of both the sPLA₂s revealed they are thermostable proteins requiring di-valent cations for optimum activity.While, structural analysis of both the proteins revealed deviations in the amino acid sequence at C- & N-terminal regions; hydropathic study revealed LusPLA₂I as a hydrophobic protein and LusPLA₂II as a hydrophilic protein. Structural analysis of flax sPLA₂s revealed that secondary structure of both the proteins are dominated by α-helix followed by random coils. Modular superimposition of LusPLA₂ isoforms with rice sPLA₂ confirmed monomeric structural preservation among plant phospholipase A₂ and provided insight into structure of folded flax sPLA₂s.

Molecular details of secretory phospholipase A2 from flax (Linum usitatissimum L.) provide insight into its structure and function

Secretory phospholipase A2 (sPLA2) are low molecular weight proteins (12-18 kDa) involved in a suite of plant cellular processes imparting growth and development. With myriad roles in physiological and biochemical processes in plants, detailed analysis of sPLA2 in flax/linseed is meagre. The present work, first in flax, embodies cloning, expression, purification and molecular characterisation of two distinct sPLA2s (I and II) from flax. PLA2 activity of the cloned sPLA2s were biochemically assayed authenticating them as bona fide phospholipase A2. Physiochemical properties of both the sPLA2s revealed they are thermostable proteins requiring di-valent cations for optimum activity.While, structural analysis of both the proteins revealed deviations in the amino acid sequence at C- & N-terminal regions; hydropathic study revealed LusPLA2I as a hydrophobic protein and LusPLA2II as a hydrophilic protein. Structural analysis of flax sPLA2s revealed that secondary structure of both the proteins are dominated by α-helix followed by random coils. Modular superimposition of LusPLA2 isoforms with rice sPLA2 confirmed monomeric structural preservation among plant phospholipase A2 and provided insight into structure of folded flax sPLA2s.

The PINK1 p.I368N mutation affects protein stability and ubiquitin kinase activity

Background

Mutations in PINK1 and PARKIN are the most common causes of recessive early-onset Parkinson’s disease (EOPD). Together, the mitochondrial ubiquitin (Ub) kinase PINK1 and the cytosolic E3 Ub ligase PARKIN direct a complex regulated, sequential mitochondrial quality control. Thereby, damaged mitochondria are identified and targeted to degradation in order to prevent their accumulation and eventually cell death. Homozygous or compound heterozygous loss of either gene function disrupts this protective pathway, though at different steps and by distinct mechanisms. While structure and function of PARKIN variants have been well studied, PINK1 mutations remain poorly characterized, in particular under endogenous conditions. A better understanding of the exact molecular pathogenic mechanisms underlying the pathogenicity is crucial for rational drug design in the future.

Methods

Here, we characterized the pathogenicity of the PINK1 p.I368N mutation on the clinical and genetic as well as on the structural and functional level in patients’ fibroblasts and in cell-based, biochemical assays.

Results

Under endogenous conditions, PINK1 p.I368N is expressed, imported, and N-terminally processed in healthy mitochondria similar to PINK1 wild type (WT). Upon mitochondrial damage, however, full-length PINK1 p.I368N is not sufficiently stabilized on the outer mitochondrial membrane (OMM) resulting in loss of mitochondrial quality control. We found that binding of PINK1 p.I368N to the co-chaperone complex HSP90/CDC37 is reduced and stress-induced interaction with TOM40 of the mitochondrial protein import machinery is abolished. Analysis of a structural PINK1 p.I368N model additionally suggested impairments of Ub kinase activity as the ATP-binding pocket was found deformed and the substrate Ub was slightly misaligned within the active site of the kinase. Functional assays confirmed the lack of Ub kinase activity.

Conclusions

Here we demonstrated that mutant PINK1 p.I368N can not be stabilized on the OMM upon mitochondrial stress and due to conformational changes in the active site does not exert kinase activity towards Ub. In patients’ fibroblasts, biochemical assays and by structural analyses, we unraveled two pathomechanisms that lead to loss of function upon mutation of p.I368N and highlight potential strategies for future drug development.

Electronic supplementary material

The online version of this article (doi:10.1186/s13024-017-0174-z) contains supplementary material, which is available to authorized users.

Computational Approaches for Revealing the Structure of Membrane Transporters: Case Study on Bilitranslocase

The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and this present a huge experimental and computational challenge. Nowadays, thanks to X-ray crystallography or NMR spectroscopy over 3000 structures of membrane proteins have been solved, among them only a few hundred unique ones. Due to the vast biological and pharmaceutical interest in the elucidation of the structure and the functional mechanisms of transmembrane proteins, several computational methods have been developed to overcome the experimental gap. If combined with experimental data the computational information enables rapid, low cost and successful predictions of the molecular structure of unsolved proteins. The reliability of the predictions depends on the availability and accuracy of experimental data associated with structural information. In this review, the following methods are proposed for in silico structure elucidation: sequence-dependent predictions of transmembrane regions, predictions of transmembrane helix–helix interactions, helix arrangements in membrane models, and testing their stability with molecular dynamics simulations. We also demonstrate the usage of the computational methods listed above by proposing a model for the molecular structure of the transmembrane protein bilitranslocase. Bilitranslocase is bilirubin membrane transporter, which shares similar tissue distribution and functional properties with some of the members of the Organic Anion Transporter family and is the only member classified in the Bilirubin Transporter Family. Regarding its unique properties, bilitranslocase is a potentially interesting drug target.

Structural dynamics of Casein Kinase I (CKI) from malarial parasite Plasmodium falciparum (Isolate 3D7): Insights from theoretical modelling and molecular simulations

The protein kinases (PKs), belonging to serine/threonine kinase (STKs), are important drug targets for a wide spectrum of diseases in human. Among protein kinases, the Casein Kinases (CKs) are vastly expanded in various organisms, where, the malarial parasite Plasmodium falciparum possesses a single member i.e., PfCKI, which can phosphorylate various proteins in parasite extracts in vitro condition. But, the structure-function relationship of PfCKI and dynamics of ATP binding is yet to be understood. Henceforth, an attempt was made to study the dynamics, stability, and ATP binding mechanisms of PfCKI through computational modelling, docking, molecular dynamics (MD) simulations, and MM/PBSA binding free energy estimation. Bi-lobed catalytic domain of PfCKI shares a high degree of secondary structure topology with CKI domains of rice, human, and mouse indicating co-evolution of these kinases. Molecular docking study revealed that ATP binds to the active site where the glycine-rich ATP-binding motif (G16-X-G18-X-X-G21) along with few conserved residues plays a crucial role maintaining stability of the complex. Structural superposition of PfCKI with close structural homologs depicted that the location and length of important loops are different, indicating the dynamic properties of these loops among CKIs, which is consistent with principal component analysis (PCA). PCA displayed that the overall global motion of ATP-bound form is comparatively higher than that of apo form. The present study provides insights into the structural features of PfCKI, which could contribute towards further understanding of related protein structures, dynamics of catalysis and phosphorylation mechanism in these important STKs from malarial parasite in near future.

Heterozygous PINK1 p.G411S increases risk of Parkinson's disease via a dominant-negative mechanism

See Gandhi and Plun-Favreau (doi:10.1093/aww320) for a scientific commentary on this article.

Heterozygous mutations in recessive Parkinson’s disease genes have been postulated to increase disease risk. Puschmann et al. report a genetic association between heterozygous PINK1 p.G411S and Parkinson’s disease. They provide structural and functional explanations for a partial dominant-negative effect of the mutant protein, which impairs wild-type PINK1 activity through hetero-dimerization.

See Gandhi and Plun-Favreau (doi:10.1093/aww320) for a scientific commentary on this article.

It has been postulated that heterozygous mutations in recessive Parkinson’s genes may increase the risk of developing the disease. In particular, the PTEN-induced putative kinase 1 (PINK1) p.G411S (c.1231G>A, rs45478900) mutation has been reported in families with dominant inheritance patterns of Parkinson’s disease, suggesting that it might confer a sizeable disease risk when present on only one allele. We examined families with PINK1 p.G411S and conducted a genetic association study with 2560 patients with Parkinson’s disease and 2145 control subjects. Heterozygous PINK1 p.G411S mutations markedly increased Parkinson’s disease risk (odds ratio = 2.92, P = 0.032); significance remained when supplementing with results from previous studies on 4437 additional subjects (odds ratio = 2.89, P = 0.027). We analysed primary human skin fibroblasts and induced neurons from heterozygous PINK1 p.G411S carriers compared to PINK1 p.Q456X heterozygotes and PINK1 wild-type controls under endogenous conditions. While cells from PINK1 p.Q456X heterozygotes showed reduced levels of PINK1 protein and decreased initial kinase activity upon mitochondrial damage, stress-response was largely unaffected over time, as expected for a recessive loss-of-function mutation. By contrast, PINK1 p.G411S heterozygotes showed no decrease of PINK1 protein levels but a sustained, significant reduction in kinase activity. Molecular modelling and dynamics simulations as well as multiple functional assays revealed that the p.G411S mutation interferes with ubiquitin phosphorylation by wild-type PINK1 in a heterodimeric complex. This impairs the protective functions of the PINK1/parkin-mediated mitochondrial quality control. Based on genetic and clinical evaluation as well as functional and structural characterization, we established p.G411S as a rare genetic risk factor with a relatively large effect size conferred by a partial dominant-negative function phenotype.

Sampling Enrichment toward Target Structures Using Hybrid Molecular Dynamics-Monte Carlo Simulations

Sampling enrichment toward a target state, an analogue of the improvement of sampling efficiency (SE), is critical in both the refinement of protein structures and the generation of near-native structure ensembles for the exploration of structure-function relationships. We developed a hybrid molecular dynamics (MD)-Monte Carlo (MC) approach to enrich the sampling toward the target structures. In this approach, the higher SE is achieved by perturbing the conventional MD simulations with a MC structure-acceptance judgment, which is based on the coincidence degree of small angle x-ray scattering (SAXS) intensity profiles between the simulation structures and the target structure. We found that the hybrid simulations could significantly improve SE by making the top-ranked models much closer to the target structures both in the secondary and tertiary structures. Specifically, for the 20 mono-residue peptides, when the initial structures had the root-mean-squared deviation (RMSD) from the target structure smaller than 7 Å, the hybrid MD-MC simulations afforded, on average, 0.83 Å and 1.73 Å in RMSD closer to the target than the parallel MD simulations at 310K and 370K, respectively. Meanwhile, the average SE values are also increased by 13.2% and 15.7%. The enrichment of sampling becomes more significant when the target states are gradually detectable in the MD-MC simulations in comparison with the parallel MD simulations, and provide >200% improvement in SE. We also performed a test of the hybrid MD-MC approach in the real protein system, the results showed that the SE for 3 out of 5 real proteins are improved. Overall, this work presents an efficient way of utilizing solution SAXS to improve protein structure prediction and refinement, as well as the generation of near native structures for function annotation.

The unexpected structure of the designed protein Octarellin V.1 forms a challenge for protein structure prediction tools

Despite impressive successes in protein design, designing a well-folded protein of more 100 amino acids de novo remains a formidable challenge. Exploiting the promising biophysical features of the artificial protein Octarellin V, we improved this protein by directed evolution, thus creating a more stable and soluble protein: Octarellin V.1. Next, we obtained crystals of Octarellin V.1 in complex with crystallization chaperons and determined the tertiary structure. The experimental structure of Octarellin V.1 differs from its in silico design: the (αβα) sandwich architecture bears some resemblance to a Rossman-like fold instead of the intended TIM-barrel fold. This surprising result gave us a unique and attractive opportunity to test the state of the art in protein structure prediction, using this artificial protein free of any natural selection. We tested 13 automated webservers for protein structure prediction and found none of them to predict the actual structure. More than 50% of them predicted a TIM-barrel fold, i.e. the structure we set out to design more than 10years ago. In addition, local software runs that are human operated can sample a structure similar to the experimental one but fail in selecting it, suggesting that the scoring and ranking functions should be improved. We propose that artificial proteins could be used as tools to test the accuracy of protein structure prediction algorithms, because their lack of evolutionary pressure and unique sequences features.

A neural network learning approach for improving the prediction of residue depth based on sequence-derived features

Residue depth is a solvent exposure measure that quantitatively describes the depth of a residue from the protein surface.

Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures

Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.

Each protein folds into a unique three-dimensional structure that enables it to carry out its biological function. Knowledge of the atomic details of protein structures is therefore a key to understanding their function. Advances in high throughput experimental technologies have lead to an exponential increase in the availability of known protein sequences. Although strong progress has been made in experimental protein structure determination, it remains a fact that more than 99% of structural information is provided by computational modeling methods. We describe here a novel structure prediction method, SmotifTF, which uses a unique library of known protein fragments to assemble the three-dimensional structure of a sequence. The fragment library has saturated over time and therefore provides a complete set of building blocks required for model building. The method performs competitively compared to existing methods of structure prediction.

Three-dimensional structure and molecular dynamics studies of prorrenin/renin receptor: description of the active site

The recent finding of a specific receptor for prorrenin/renin (PRR) has brought new insights into the physiology of the renin-angiotensin-aldosterone system. No undoubtable role has been described for this receptor so far. Its role seems to be important in chronic illnesses such as hypertension, possibly participating in the cardiovascular remodeling process, and diabetes where participation in inflammation development has been described. It is not possible, however, to explore the PRR function using classical pharmacological approaches due to the lack of specific agonists or antagonists. Two synthetic peptides have been described to accomplish these roles, but no conclusive data have been provided. There are no X-ray crystallography studies available to describe the structure and potential sites for drug development. So, the aim of this work was to model and theoretically describe the PRR. We describe and characterize the whole receptor protein, its spatial conformation and the potential interactions of PRR with the synthetic peptides available, describing the amino acid residues responsible for these interactions. This information provides the basis for directed development of drugs, seeking to agonize or antagonize PRR activity and study its function in health and ill stages.

Detecting local residue environment similarity for recognizing near-native structure models

We developed a new representation of local amino acid environments in protein structures called the Side-chain Depth Environment (SDE). An SDE defines a local structural environment of a residue considering the coordinates and the depth of amino acids that locate in the vicinity of the side-chain centroid of the residue. SDEs are general enough that similar SDEs are found in protein structures with globally different folds. Using SDEs, we developed a procedure called PRESCO (Protein Residue Environment SCOre) for selecting native or near-native models from a pool of computational models. The procedure searches similar residue environments observed in a query model against a set of representative native protein structures to quantify how native-like SDEs in the model are. When benchmarked on commonly used computational model datasets, our PRESCO compared favorably with the other existing scoring functions in selecting native and near-native models.

Phosphorylation by PINK1 releases the UBL domain and initializes the conformational opening of the E3 ubiquitin ligase Parkin

Loss-of-function mutations in PINK1 or PARKIN are the most common causes of autosomal recessive Parkinson's disease. Both gene products, the Ser/Thr kinase PINK1 and the E3 Ubiquitin ligase Parkin, functionally cooperate in a mitochondrial quality control pathway. Upon stress, PINK1 activates Parkin and enables its translocation to and ubiquitination of damaged mitochondria to facilitate their clearance from the cell. Though PINK1-dependent phosphorylation of Ser65 is an important initial step, the molecular mechanisms underlying the activation of Parkin's enzymatic functions remain unclear. Using molecular modeling, we generated a complete structural model of human Parkin at all atom resolution. At steady state, the Ub ligase is maintained inactive in a closed, auto-inhibited conformation that results from intra-molecular interactions. Evidently, Parkin has to undergo major structural rearrangements in order to unleash its catalytic activity. As a spark, we have modeled PINK1-dependent Ser65 phosphorylation in silico and provide the first molecular dynamics simulation of Parkin conformations along a sequential unfolding pathway that could release its intertwined domains and enable its catalytic activity. We combined free (unbiased) molecular dynamics simulation, Monte Carlo algorithms, and minimal-biasing methods with cell-based high content imaging and biochemical assays. Phosphorylation of Ser65 results in widening of a newly defined cleft and dissociation of the regulatory N-terminal UBL domain. This motion propagates through further opening conformations that allow binding of an Ub-loaded E2 co-enzyme. Subsequent spatial reorientation of the catalytic centers of both enzymes might facilitate the transfer of the Ub moiety to charge Parkin. Our structure-function study provides the basis to elucidate regulatory mechanisms and activity of the neuroprotective Parkin. This may open up new avenues for the development of small molecule Parkin activators through targeted drug design.

Parkinson's disease (PD) is a devastating neurological condition caused by the selective and progressive degeneration of dopaminergic neurons in the brain. Loss-of-function mutations in the PINK1 or PARKIN genes are the most common causes of recessively inherited PD. Together the encoded proteins coordinate a protective cellular quality control pathway that allows elimination of impaired mitochondria in order to prevent further cellular damage and ultimately death. Although it is known that the kinase PINK1 operates upstream and activates the E3 Ubiquitin ligase Parkin, the molecular mechanisms remain elusive. Here, we combined state-of-the art computational and functional biological methods to demonstrate that Parkin is sequentially activated through PINK1-dependent phosphorylation and subsequent structural rearrangement. The induced motions result in release of Parkin's closed, auto-inhibited conformation to liberate its enzymatic functions. We provide for the first time a complete protein structure of Parkin at an all atom resolution and a comprehensive molecular dynamics simulation of its activation and opening conformations. The generated models will allow uncovering the exact mechanisms of regulation and enzymatic activity of Parkin and potentially the development of novel therapeutics through a structure-function-based drug design.

WeFold: a coopetition for protein structure prediction

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.

Evidence supporting the existence of a NUPR1-like family of helix-loop-helix chromatin proteins related to, yet distinct from, AT hook-containing HMG proteins

NUPR1, a small chromatin protein, plays a critical role in cancer development, progression, and resistance to therapy. Here, using a combination of structural bioinformatics and molecular modeling methods, we report several novel findings that enhance our understanding of the biochemical function of this protein. We find that NUPR1 has been conserved throughout evolution, and over time it has undergone duplications and transpositions to form other transcriptional regulators. Using threading, homology-based molecular modeling, molecular mechanics calculations, and molecular dynamics simulations, we generated structural models for four of these proteins: NUPR1a, NUPR1b, NUPR2, and the NUPR-like domain of GTF2-I. Comparative analyses of these models combined with extensive linear motif identification reveal that these four proteins, though similar in their propensities for folding, differ in size, surface changes, and sites amenable for posttranslational modification. Lastly, taking NUPR1a as the paradigm for this family, we built models of a NUPR–DNA complex. Additional structural comparisons revealed that NUPR1 defines a new family of small-groove-binding proteins that share structural features with, yet are distinct from, helix-loop-helix AT-hook-containing HMG proteins. These models and inferences should lead to a better understanding of the function of this group of chromatin proteins, which play a critical role in the development of human malignant diseases.

From local structure to a global framework: recognition of protein folds

Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.

Forcefield_PTM: Ab Initio Charge and AMBER Forcefield Parameters for Frequently Occurring Post-Translational Modifications

In this work, we introduce Forcefield_PTM, a set of AMBER forcefield parameters consistent with ff03 for 32 common post-translational modifications. Partial charges were calculated through ab initio calculations and a two-stage RESP-fitting procedure in an ether-like implicit solvent environment. The charges were found to be generally consistent with others previously reported for phosphorylated amino acids, and trimethyllysine, using different parameterization methods. Pairs of modified and their corresponding unmodified structures were curated from the PDB for both single and multiple modifications. Background structural similarity was assessed in the context of secondary and tertiary structures from the global dataset. Next, the charges derived for Forcefield_PTM were tested on a macroscopic scale using unrestrained all-atom Langevin molecular dynamics simulations in AMBER for 34 (17 pairs of modified/unmodified) systems in implicit solvent. Assessment was performed in the context of secondary structure preservation, stability in energies, and correlations between the modified and unmodified structure trajectories on the aggregate. As an illustration of their utility, the parameters were used to compare the structural stability of the phosphorylated and dephosphorylated forms of OdhI. Microscopic comparisons between quantum and AMBER single point energies along key χ torsions on several PTMs were performed and corrections to improve their agreement in terms of mean squared errors and squared correlation coefficients were parameterized. This forcefield for post-translational modifications in condensed-phase simulations can be applied to a number of biologically relevant and timely applications including protein structure prediction, protein and peptide design, docking, and to study the effect of PTMs on folding and dynamics. We make the derived parameters and an associated interactive webtool capable of performing post-translational modifications on proteins using Forcefield_PTM available at http://selene.princeton.edu/FFPTM.

Importance of the interaction protein-protein of the CaM-PDE1A and CaM-MLCK complexes in the development of new anti-CaM drugs

Protein-protein interactions play central roles in physiological and pathological processes. The bases of the mechanisms of drug action are relevant to the discovery of new therapeutic targets. This work focuses on understanding the interactions in protein-protein-ligands complexes, using proteins calmodulin (CaM), human calcium/calmodulin-dependent 3',5'-cyclic nucleotide phosphodiesterase 1A active human (PDE1A), and myosin light chain kinase (MLCK) and ligands αII-spectrin peptide (αII-spec), and two inhibitors of CaM (chlorpromazine (CPZ) and malbrancheamide (MBC)). The interaction was monitored with a fluorescent biosensor of CaM (hCaM M124C-mBBr). The results showed changes in the affinity of CPZ and MBC depending on the CaM-protein complex under analysis. For the Ca(2+) -CaM, Ca(2+) -CaM-PDE1A, and Ca(2+) -CaM-MLCK complexes, CPZ apparent dissociation constants (Kds ) were 1.11, 0.28, and 0.55 μM, respectively; and for MBC Kds were 1.43, 1.10, and 0.61 μM, respectively. In competition experiments the addition of calmodulin binding peptide 1 (αII-spec) to Ca(2+) -hCaM M124C-mBBr quenched the fluorescence (Kd  = 2.55 ± 1.75 pM) and the later addition of MBC (up to 16 μM) did not affect the fluorescent signal. Instead, the additions of αII-spec to a preformed Ca(2+) -hCaM M124C-mBBr-MBC complex modified the fluorescent signal. However, MBC was able to displace the PDE1A and MLCK from its complex with Ca(2+) -CaM. In addition, docking studies were performed for all complexes with both ligands showing an excellent correlation with experimental data. These experiments may help to explain why in vivo many CaM drugs target prefer only a subset of the Ca(2+) -CaM regulated proteins and adds to the understanding of molecular interactions between protein complexes and small ligands.

Interplay of physics and evolution in the likely origin of protein biochemical function

The intrinsic ability of protein structures to exhibit the geometric and sequence properties required for ligand binding without evolutionary selection is shown by the coincidence of the properties of pockets in native, single domain proteins with those in computationally generated, compact homopolypeptide, artificial (ART) structures. The library of native pockets is covered by a remarkably small number of representative pockets (∼400), with virtually every native pocket having a statistically significant match in the ART library, suggesting that the library is complete. When sequences are selected for ART structures based on fold stability, pocket sequence conservation is coincident to native. The fact that structurally and sequentially similar pockets occur across fold classes combined with the small number of representative pockets in native proteins implies that promiscuous interactions are inherent to proteins. Based on comparison of PDB (real, single domain protein structures found in the Protein Data Bank) and ART structures and pockets, the widespread assumption that the co-occurrence of global structure, pocket similarity, and amino acid conservation demands an evolutionary relationship between proteins is shown to significantly underestimate the random background probability. Indeed, many features of biochemical function arise from the physical properties of proteins that evolution likely fine-tunes to achieve specificity. Finally, our study suggests that a repertoire of thermodynamically (marginally) stable proteins could engage in many of the biochemical reactions needed for living systems without selection for function, a conclusion with significant implications for the origin of life.

Spatial simulations in systems biology: from molecules to cells

Cells are highly organized objects containing millions of molecules. Each biomolecule has a specific shape in order to interact with others in the complex machinery. Spatial dynamics emerge in this system on length and time scales which can not yet be modeled with full atomic detail. This review gives an overview of methods which can be used to simulate the complete cell at least with molecular detail, especially Brownian dynamics simulations. Such simulations require correct implementation of the diffusion-controlled reaction scheme occurring on this level. Implementations and applications of spatial simulations are presented, and finally it is discussed how the atomic level can be included for instance in multi-scale simulation methods.

A position-specific distance-dependent statistical potential for protein structure and functional study

Although studied extensively, designing highly accurate protein energy potential is still challenging. A lot of knowledge-based statistical potentials are derived from the inverse of the Boltzmann law and consist of two major components: observed atomic interacting probability and reference state. These potentials mainly distinguish themselves in the reference state and use a similar simple counting method to estimate the observed probability, which is usually assumed to correlate with only atom types. This article takes a rather different view on the observed probability and parameterizes it by the protein sequence profile context of the atoms and the radius of the gyration, in addition to atom types. Experiments confirm that our position-specific statistical potential outperforms currently the popular ones in several decoy discrimination tests. Our results imply that, in addition to reference state, the observed probability also makes energy potentials different and evolutionary information greatly boost performance of energy potentials.

Template-based protein structure modeling using TASSER(VMT.)

Template-based protein structure modeling is commonly used for protein structure prediction. Based on the observation that multiple template-based methods often perform better than single template-based methods, we further explore the use of a variable number of multiple templates for a given target in the latest variant of TASSER, TASSER(VMT) . We first develop an algorithm that improves the target-template alignment for a given template. The improved alignment, called the SP(3) alternative alignment, is generated by a parametric alignment method coupled with short TASSER refinement on models selected using knowledge-based scores. The refined top model is then structurally aligned to the template to produce the SP(3) alternative alignment. Templates identified using SP(3) threading are combined with the SP(3) alternative and HHEARCH alignments to provide target alignments to each template. These template models are then grouped into sets containing a variable number of template/alignment combinations. For each set, we run short TASSER simulations to build full-length models. Then, the models from all sets of templates are pooled, and the top 20-50 models selected using FTCOM ranking method. These models are then subjected to a single longer TASSER refinement run for final prediction. We benchmarked our method by comparison with our previously developed approach, pro-sp(3) -TASSER, on a set with 874 easy and 318 hard targets. The average GDT-TS score improvements for the first model are 3.5 and 4.3% for easy and hard targets, respectively. When tested on the 112 CASP9 targets, our method improves the average GDT-TS scores as compared to pro-sp3-TASSER by 8.2 and 9.3% for the 80 easy and 32 hard targets, respectively. It also shows slightly better results than the top ranked CASP9 Zhang-Server, QUARK and HHpredA methods. The program is available for download at http://cssb.biology.gatech.edu/.

Statistical mechanics-based method to extract atomic distance-dependent potentials from protein structures

In this study, we have developed a statistical mechanics-based iterative method to extract statistical atomic interaction potentials from known, nonredundant protein structures. Our method circumvents the long-standing reference state problem in deriving traditional knowledge-based scoring functions, by using rapid iterations through a physical, global convergence function. The rapid convergence of this physics-based method, unlike other parameter optimization methods, warrants the feasibility of deriving distance-dependent, all-atom statistical potentials to keep the scoring accuracy. The derived potentials, referred to as ITScore/Pro, have been validated using three diverse benchmarks: the high-resolution decoy set, the AMBER benchmark decoy set, and the CASP8 decoy set. Significant improvement in performance has been achieved. Finally, comparisons between the potentials of our model and potentials of a knowledge-based scoring function with a randomized reference state have revealed the reason for the better performance of our scoring function, which could provide useful insight into the development of other physical scoring functions. The potentials developed in this study are generally applicable for structural selection in protein structure prediction.

A versatile method for systematic conformational searches: application to CheY

A novel molecular structure prediction method, the Z Method, is described. It provides a versatile platform for the development and use of systematic, grid-based conformational search protocols, in which statistical information (i.e., rotamers) can also be included. The Z Method generates trial structures by applying many changes of the same type to a single starting structure, thereby sampling the conformation space in an unbiased way. The method, implemented in the CHARMM program as the Z Module, is applied here to an illustrative model problem in which rigid, systematic searches are performed in a 36-dimensional conformational space that describes the relative positions of the 10 secondary structural elements of the protein CheY. A polar hydrogen representation with an implicit solvation term (EEF1) is used to evaluate successively larger fragments of the protein generated in a hierarchical build-up procedure. After a final refinement stage, and a total computational time of about two-and-a-half CPU days on AMD Opteron processors, the prediction is within 1.56 Å of the native structure. The errors in the predicted backbone dihedral angles are found to approximately cancel. Monte Carlo and simulated annealing trials on the same or smaller versions of the problem, using the same atomic model and energy terms, are shown to result in less accurate predictions. Although the problem solved here is a limited one, the findings illustrate the utility of systematic searches with atom-based models for macromolecular structure prediction and the importance of unbiased sampling in structure prediction methods.

A multiple-template approach to protein threading

Most threading methods predict the structure of a protein using only a single template. Due to the increasing number of solved structures, a protein without solved structure is very likely to have more than one similar template structures. Therefore, a natural question to ask is if we can improve modeling accuracy using multiple templates. This article describes a new multiple-template threading method to answer this question. At the heart of this multiple-template threading method is a novel probabilistic-consistency algorithm that can accurately align a single protein sequence simultaneously to multiple templates. Experimental results indicate that our multiple-template method can improve pairwise sequence-template alignment accuracy and generate models with better quality than single-template models even if they are built from the best single templates (P-value <10(-6)) while many popular multiple sequence/structure alignment tools fail to do so. The underlying reason is that our probabilistic-consistency algorithm can generate accurate multiple sequence/template alignments. In another word, without an accurate multiple sequence/template alignment, the modeling accuracy cannot be improved by simply using multiple templates to increase alignment coverage. Blindly tested on the CASP9 targets with more than one good template structures, our method outperforms all other CASP9 servers except two (Zhang-Server and QUARK of the same group). Our probabilistic-consistency algorithm can possibly be extended to align multiple protein/RNA sequences and structures.

TASSER_low-zsc: an approach to improve structure prediction using low z-score-ranked templates

In a variety of threading methods, often poorly ranked (low z-score) templates have good alignments. Here, a new method, TASSER_low-zsc that identifies these low z-score-ranked templates to improve protein structure prediction accuracy, is described. The approach consists of clustering of threading templates by affinity propagation on the basis of structural similarity (thread_cluster) followed by TASSER modeling, with final models selected by using a TASSER_QA variant. To establish the generality of the approach, templates provided by two threading methods, SP(3) and SPARKS(2), are examined. The SP(3) and SPARKS(2) benchmark datasets consist of 351 and 357 medium/hard proteins (those with moderate to poor quality templates and/or alignments) of length < or =250 residues, respectively. For SP(3) medium and hard targets, using thread_cluster, the TM-scores of the best template improve by approximately 4 and 9% over the original set (without low z-score templates) respectively; after TASSER modeling/refinement and ranking, the best model improves by approximately 7 and 9% over the best model generated with the original template set. Moreover, TASSER_low-zsc generates 22% (43%) more foldable medium (hard) targets. Similar improvements are observed with low-ranked templates from SPARKS(2). The template clustering approach could be applied to other modeling methods that utilize multiple templates to improve structure prediction.

TASSER_WT: a protein structure prediction algorithm with accurate predicted contact restraints for difficult protein targets

To improve the prediction accuracy in the regime where template alignment quality is poor, an updated version of TASSER_2.0, namely TASSER_WT, was developed. TASSER_WT incorporates more accurate contact restraints from a new method, COMBCON. COMBCON uses confidence-weighted contacts from PROSPECTOR_3.5, the latest version, PROSPECTOR_4, and a new local structural fragment-based threading algorithm, STITCH, implemented in two variants depending on expected fragment prediction accuracy. TASSER_WT is tested on 622 Hard proteins, the most difficult targets (incorrect alignments and/or templates and incorrect side-chain contact restraints) in a comprehensive benchmark of 2591 nonhomologous, single domain proteins ≤ 200 residues that cover the PDB at 35% pairwise sequence identity. For 454 of 622 Hard targets, COMBCON provides contact restraints with higher accuracy and number of contacts per residue. As contact coverage with confidence weight ≥ 3 (F(wt ≥ 3)(cov)) increases, the more improved are TASSER_WT models. When F(wt ≥ 3)(cov) > 1.0 and > 0.4, the average root mean-square deviation of TASSER_WT (TASSER_2.0) models is 4.11 Å (6.72 Å) and 5.03 Å (6.40 Å), respectively. Regarding a structure prediction as successful when a model has a TM-score to the native structure ≥ 0.4, when F(wt ≥ 3)(cov) > 1.0 and > 0.4, the success rate of TASSER_WT (TASSER_2.0) is 98.8% (76.2%) and 93.7% (81.1%), respectively.

Effect of using suboptimal alignments in template-based protein structure prediction

Computational protein structure prediction remains a challenging task in protein bioinformatics. In the recent years, the importance of template-based structure prediction is increasing because of the growing number of protein structures solved by the structural genomics projects. To capitalize the significant efforts and investments paid on the structural genomics projects, it is urgent to establish effective ways to use the solved structures as templates by developing methods for exploiting remotely related proteins that cannot be simply identified by homology. In this work, we examine the effect of using suboptimal alignments in template-based protein structure prediction. We showed that suboptimal alignments are often more accurate than the optimal one, and such accurate suboptimal alignments can occur even at a very low rank of the alignment score. Suboptimal alignments contain a significant number of correct amino acid residue contacts. Moreover, suboptimal alignments can improve template-based models when used as input to Modeller. Finally, we use suboptimal alignments for handling a contact potential in a probabilistic way in a threading program, SUPRB. The probabilistic contacts strategy outperforms the partly thawed approach, which only uses the optimal alignment in defining residue contacts, and also the re-ranking strategy, which uses the contact potential in re-ranking alignments. The comparison with existing methods in the template-recognition test shows that SUPRB is very competitive and outperforms existing methods.

Methods for sequence-structure alignment

Homology modeling is based on the observation that related protein sequences adopt similar three-dimensional structures. Hence, a homology model of a protein can be derived using related protein structure(s) as modeling template(s). A key step in this approach is the establishment of correspondence between residues of the protein to be modeled and those of modeling template(s). This step, often referred to as sequence-structure alignment, is one of the major determinants of the accuracy of a homology model. This chapter gives an overview of methods for deriving sequence-structure alignments and discusses recent methodological developments leading to improved performance. However, no method is perfect. How to find alignment regions that may have errors and how to make improvements? This is another focus of this chapter. Finally, the chapter provides a practical guidance of how to get the most of the available tools in maximizing the accuracy of sequence-structure alignments.

Improving threading algorithms for remote homology modeling by combining fragment and template comparisons

In this work, we develop a method called fragment comparison and the template comparison (FTCOM) for assessing the global quality of protein structural models for targets of medium and hard difficulty (remote homology) produced by structure prediction approaches such as threading or ab initio structure prediction. FTCOM requires the C(alpha) coordinates of full length models and assesses model quality based on fragment comparison and a score derived from comparison of the model to top threading templates. On a set of 361 medium/hard targets, FTCOM was applied to and assessed for its ability to improve on the results from the SP(3), SPARKS, PROSPECTOR_3, and PRO-SP(3)-TASSER threading algorithms. The average TM-score improves by 5-10% for the first selected model by the new method over models obtained by the original selection procedure in the respective threading methods. Moreover, the number of foldable targets (TM-score >or= 0.4) increases from least 7.6% for SP(3) to 54% for SPARKS. Thus, FTCOM is a promising approach to template selection. Proteins 2010. (c) 2010 Wiley-Liss, Inc.

Multiple interests in structural models of DARC transmembrane protein

Duffy Antigen Receptor for Chemokines (DARC) is an unusual transmembrane chemokine receptor which (i) binds the two main chemokine families and (ii) does not transduct any signal as it lacks the DRY consensus sequence. It is considered as silent chemokine receptor, a tank useful for chemiotactism. DARC had been particularly studied as a major actor of malaria infection by Plasmodium vivax. It is also implicated in multiple chemokine inflammation, inflammatory diseases, in cancer and might play a role in HIV infection and AIDS. In this review, we focus on the interest to build structural model of DARC to understand more precisely its abilities to bind its physiological ligand CXCL8 and its malaria ligand. We also present innovative development on VHHs able to bind DARC protein. We underline difficulties and limitations of such bioinformatics approaches and highlight the crucial importance of biological data to conduct these kinds of researches.

Protein structure prediction enhanced with evolutionary diversity: SPEED

For naturally occurring proteins, similar sequence implies similar structure. Consequently, multiple sequence alignments (MSAs) often are used in template-based modeling of protein structure and have been incorporated into fragment-based assembly methods. Our previous homology-free structure prediction study introduced an algorithm that mimics the folding pathway by coupling the formation of secondary and tertiary structure. Moves in the Monte Carlo procedure involve only a change in a single pair of phi,psi backbone dihedral angles that are obtained from a Protein Data Bank-based distribution appropriate for each amino acid, conditional on the type and conformation of the flanking residues. We improve this method by using MSAs to enrich the sampling distribution, but in a manner that does not require structural knowledge of any protein sequence (i.e., not homologous fragment insertion). In combination with other tools, including clustering and refinement, the accuracies of the predicted secondary and tertiary structures are substantially improved and a global and position-resolved measure of confidence is introduced for the accuracy of the predictions. Performance of the method in the Critical Assessment of Structure Prediction (CASP8) is discussed.

Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.

Fast and accurate automatic structure prediction with HHpred

Automated protein structure prediction is becoming a mainstream tool for biological research. This has been fueled by steady improvements of publicly available automated servers over the last decade, in particular their ability to build good homology models for an increasing number of targets by reliably detecting and aligning more and more remotely homologous templates. Here, we describe the three fully automated versions of the HHpred server that participated in the community-wide blind protein structure prediction competition CASP8. What makes HHpred unique is the combination of usability, short response times (typically under 15 min) and a model accuracy that is competitive with those of the best servers in CASP8.

Performance of the Pro-sp3-TASSER server in CASP8

The performance of the protein structure prediction server pro-sp3-TASSER in CASP8 is described. Compared to CASP7, the major improvement in prediction is in the quality of input models to TASSER. These improvements are due to the PRO-SP(3) threading method, the improved quality of contact predictions provided by TASSER_2.0, multiple short TASSER simulations for building the full-length model, and the accuracy of model selection using the TASSER-QA quality assessment method. Finally, we analyze the overall performance and highlight some successful predictions of the pro-sp3-TASSER server.

Discrete-continuous duality of protein structure space

Recently, the nature of protein structure space has been widely discussed in the literature. The traditional discrete view of protein universe as a set of separate folds has been criticized in the light of growing evidence that almost any arrangement of secondary structures is possible and the whole protein space can be traversed through a path of similar structures. Here we argue that the discrete and continuous descriptions are not mutually exclusive, but complementary: the space is largely discrete in evolutionary sense, but continuous geometrically when purely structural similarities are quantified. Evolutionary connections are mainly confined to separate structural prototypes corresponding to folds as islands of structural stability, with few remaining traceable links between the islands. However, for a geometric similarity measure, it is usually possible to find a reasonable cutoff that yields paths connecting any two structures through intermediates.