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Human SNF5 and BAF155 constitute the core subunit of multi-protein SWI/SNF chromatin-remodeling complexes that are required for ATP-dependent nucleosome mobility and transcriptional control. Human SNF5 (hSNF5) utilizes its repeat 1 (RPT1) domain to associate with the SWIRM domain of BAF155. Here, we employed X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, and various biophysical methods in order to investigate the detailed binding mechanism between hSNF5 and BAF155. Multi-angle light scattering data clearly indicate that hSNF5 171-258 and BAF155 SWIRM are both monomeric in solution and they form a heterodimer. NMR data and crystal structure of the hSNF5 171-258 /BAF155 SWIRM complex further reveal a unique binding interface, which involves a coil-to-helix transition upon protein binding. The newly formed α N helix of hSNF5 171-258 interacts with the β2-α1 loop of hSNF5 via hydrogen bonds and it also displays a hydrophobic interaction with BAF155 SWIRM . Therefore, the N -terminal region of hSNF5 171-258 plays an important role in tumorigenesis and our data will provide a structural clue for the pathogenesis of Rhabdoid tumors and malignant melanomas that originate from mutations in the N -terminal loop region of hSNF5.

Corneal dystrophies (CDs) are a diverse group of inherited disorders with a heterogeneous genetic background. Here, we report the identification of a novel ZNF143 heterozygous missense mutation in three individuals of the same family with clinical and pathological features that are consistent with endothelial CD. Ophthalmologic examination revealed diffuse corneal clouding and edema with decreased endothelial cell density. Pathological findings showed increased corneal thickness due to edema of basal epithelial cells and stroma, and abnormal metaplastic endothelium with stratified epithelium-like changes. Patients' metaplastic corneal endothelial cells expressed predominantly cytokerain 7, cytokeratin 19, and E-cadherin. Although Sanger sequencing did not detect any mutation associated with endothelial CDs, whole exome sequencing identified the ZNF143 c.937G>C p.(Asp313His) mutation as a candidate gene for our patients' endothelial CD. In-vitro functional studies demonstrated that mutant ZNF143 promoted the mesenchymal-to-epithelial transition; it upregulated the expression of genes associated with epithelialization in human corneal endothelial cells. Additionally, proinflammatory cytokine responsive genes were significantly enriched after mutant ZNF143  transfection, which may contribute to the severe phenotype of the three patients. These findings link a mutation in ZNF143 with endothelial CD for the first time., Competing Interests: The authors declare no conflict of interests.

The computational atomistic description of the folding reactions of the B1 domains, GB1 and LB1, of protein G and protein L, respectively, is an important challenge in current protein folding studies. Although the two proteins have overall very similar backbone structures (β-hairpin-α-helix-β-hairpin), their apparent folding behaviors observed experimentally were remarkably different. LB1 folds in a two-state manner with the single-exponential kinetics, whereas GB1 folds in a more complex manner with an early stage intermediate that may exist on the folding pathway. Here, we used a new method of all-atom molecular dynamics simulations to investigate the folding mechanisms of GB1 and LB1. With the Lorentzian energy term derived from the native structure, we successfully observed frequent folding and unfolding events in the simulations at a high temperature (414 K for GB1 or 393 K for LB1) for both the proteins. Three and two transition-state structures were predicted for the GB1 and LB1 folding, respectively, at the high temperature. Two of the three transition-state structures of GB1 have a better formed second β-hairpin. One of the LB1 transition states has a better formed first hairpin, and the other has both hairpins equally formed. The structural features of these transition states are in good agreement with experimental transition-state analysis. At 300 K, more complex folding processes were observed in the simulations for both the proteins. Several intermediate structures were predicted for the two proteins, which led to the conclusion that both the proteins folded through similar mechanisms. However, the intermediate state accumulated in a sufficient amount only in the GB1 folding, which led to the double-exponential feature of its folding kinetics. On the other hand, the LB1 folding kinetics were well fitted by a single-exponential function. These results are fully consistent with those previously observed experimentally.

Protein structure alignment is an important tool for studying evolutionary biology and protein modeling. A tool which intensively searches for the globally optimal non-sequential alignments is rarely found. We propose ALIGN-CSA which shows improvement in scores, such as DALI-score, SP-score, SO-score and TM-score over the benchmark set including 286 cases. We performed benchmarking of existing popular alignment scoring functions, where the dependence of the search algorithm was effectively eliminated by using ALIGN-CSA. For the benchmarking, we set the minimum block size to 4 to prevent much fragmented alignments where the biological relevance of small alignment blocks is hard to interpret. With this condition, globally optimal alignments were searched by ALIGN-CSA using the four scoring functions listed above, and TM-score is found to be the most effective in generating alignments with longer match lengths and smaller RMSD values. However, DALI-score is the most effective in generating alignments similar to the manually curated reference alignments, which implies that DALI-score is more biologically relevant score. Due to the high demand on computational resources of ALIGN-CSA, we also propose a relatively fast local refinement method, which can control the minimum block size and whether to allow the reverse alignment. ALIGN-CSA can be used to obtain much improved alignment at the cost of relatively more extensive computation. For faster alignment, we propose a refinement protocol that improves the score of a given alignment obtained by various external tools. All programs are available from http://lee.kias.re.kr., Competing Interests: The authors have declared that no competing interests exist.

Methods to reliably estimate the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this article, the most successful groups in CASP12 describe their latest methods for estimates of model accuracy (EMA). We show that pure single model accuracy estimation methods have shown clear progress since CASP11; the 3 top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). Although the pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain, and Wallner) in model selection, they are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact-based model quality measures (CAD, lDDT) the single model quality methods perform relatively better., (© 2017 Wiley Periodicals, Inc.)

For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain re-modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue-residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template-based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded., (© 2017 Wiley Periodicals, Inc.)

In CASP12, 2 types of data-assisted protein structure modeling were experimented. Either SAXS experimental data or cross-linking experimental data was provided for a selected number of CASP12 targets that the CASP12 predictor could utilize for better protein structure modeling. We devised 2 separate energy terms for SAXS data and cross-linking data to drive the model structures into more native-like structures that satisfied the given experimental data as much as possible. In CASP11, we successfully performed protein structure modeling using simulated sparse and ambiguously assigned NOE data and/or correct residue-residue contact information, where the only energy term that folded the protein into its native structure was the term which was originated from the given experimental data. However, the 2 types of experimental data provided in CASP12 were far from being sufficient enough to fold the target protein into its native structure because SAXS data provides only the overall shape of the molecule and the cross-linking contact information provides only very low-resolution distance information. For this reason, we combined the SAXS or cross-linking energy term with our regular modeling energy function that includes both the template energy term and the de novo energy terms. By optimizing the newly formulated energy function, we obtained protein models that fit better with provided SAXS data than the X-ray structure of the target. However, the improvement of the model relative to the 1 modeled without the SAXS data, was not significant. Consistent structural improvement was achieved by incorporating cross-linking data into the protein structure modeling., (© 2018 Wiley Periodicals, Inc.)

We have analyzed the crystal structure of the dimeric form of d-glycero-d-manno-heptose-1,7-bisphosphate phosphatase from Burkholderia thailandensis (BtGmhB), catalyzing the removal of the phosphate at the 7 position of d-glycero-d-manno-heptose-1,7-bisphosphate. The crystal structure of BtGmhB revealed a dimeric form caused by a disruption of a short zinc-binding loop. The dimeric BtGmhB structure was induced by triggering the loss of Zn 2+ via the protonation of cysteine residues at pH 4.8 of the crystallization condition. Similarly, the addition of EDTA also causes the dimerization of BtGmhB. It appears there are two dimeric forms in solution with and without the disulfide bridge mediated by Cys95. The disulfide-free dimer produced by the loss of Zn 2+ in the short zinc-binding loop is further converted to a stable disulfide-bonded dimer in vitro. Though the two dimeric forms are reversible, both of them are inactive due to a deformation of the active site. Single and triple mutant experiments confirmed the presence of two dimeric forms in vitro. Phosphatase assay results showed that only a zinc-bound monomeric form contains catalytic activity in contrast to the inactive zinc-free dimeric forms. The monomer-to-dimer transition caused by the loss of Zn 2+ observed in this study is an example of reversal phenomenon caused by artificial proteins containing protein engineered zinc-finger motifs where the monomer-to-dimer transitions occurred in the presence of Zn 2+ . Therefore, this unusual dimerization process may be applicable to designing proteins possessing a short zinc-binding loop with a novel regulatory role., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Molecular simulations restrained to single or multiple templates are commonly used in protein-structure modeling. However, the restraints introduce additional barriers, thus impairing the ergodicity of simulations, which can affect the quality of the resulting models. In this work, the effect of restraint types and simulation schemes on ergodicity and model quality was investigated by performing template-restrained canonical molecular dynamics (MD), multiplexed replica-exchange molecular dynamics, and Hamiltonian replica exchange molecular dynamics (HREMD) simulations with the coarse-grained UNRES force field on nine selected proteins, with pseudo-harmonic log-Gaussian (unbounded) or Lorentzian (bounded) restraint functions. The best ergodicity was exhibited by HREMD. It has been found that non-ergodicity does not affect model quality if good templates are used to generate restraints. However, when poor-quality restraints not covering the entire protein are used, the improved ergodicity of HREMD can lead to significantly improved protein models. © 2017 Wiley Periodicals, Inc., (© 2017 Wiley Periodicals, Inc.)

Improving the quality of a given protein structure can serve as the ultimate solution for accurate protein structure prediction, and seeking such a method is currently a challenge in computational structural biology. In order to promote and encourage much needed such efforts, CASP (Critical Assessment of Structure Prediction) has been providing an ideal computational experimental platform, where it was reported only recently (since CASP10) that systematic protein structure refinement is possible by carrying out extensive (approximately millisecond) MD simulations with proper restraints generated from the given structure. Using an explicit solvent model and much reduced positional and distance restraints than previously exercised, we propose a refinement protocol that combines a series of short (5 ns) MD simulations with energy minimization procedures. Testing and benchmarking on 54 CASP8-10 refinement targets and 34 CASP11 refinement targets shows quite promising results. Using only a small fraction of MD simulation steps (nanosecond versus millisecond), systematic protein structure refinement was demonstrated in this work, indicating that refinement of a given model can be achieved using a few hours of desktop computing.

Finding overlapping communities of complex networks remains a challenge in network science. To address this challenge, one of the widely used approaches is finding the communities of links by optimizing the objective function, partition density. In this study, we show that partition density suffers from inverse resolution limit; it has a strong preference to triangles. This resolution limit makes partition density an improper objective function for global optimization. The conditions where partition density prefers triangles to larger link community structures are analytically derived and confirmed with global optimization calculations using synthetic and real-world networks. To overcome this limitation of partition density, we suggest an alternative measure, Link Surprise, to find link communities, which is suitable for global optimization. Benchmark studies demonstrate that global optimization of Link Surprise yields meaningful and more accurate link community structures than partition density optimization.

Motivation: The accurate ranking of predicted structural models and selecting the best model from a given candidate pool remain as open problems in the field of structural bioinformatics. The quality assessment (QA) methods used to address these problems can be grouped into two categories: consensus methods and single-model methods. Consensus methods in general perform better and attain higher correlation between predicted and true quality measures. However, these methods frequently fail to generate proper quality scores for native-like structures which are distinct from the rest of the pool. Conversely, single-model methods do not suffer from this drawback and are better suited for real-life applications where many models from various sources may not be readily available., Results: In this study, we developed a support-vector-machine-based single-model global quality assessment (SVMQA) method. For a given protein model, the SVMQA method predicts TM-score and GDT_TS score based on a feature vector containing statistical potential energy terms and consistency-based terms between the actual structural features (extracted from the three-dimensional coordinates) and predicted values (from primary sequence). We trained SVMQA using CASP8, CASP9 and CASP10 targets and determined the machine parameters by 10-fold cross-validation. We evaluated the performance of our SVMQA method on various benchmarking datasets. Results show that SVMQA outperformed the existing best single-model QA methods both in ranking provided protein models and in selecting the best model from the pool. According to the CASP12 assessment, SVMQA was the best method in selecting good-quality models from decoys in terms of GDTloss., Availability and Implementation: SVMQA method can be freely downloaded from http://lee.kias.re.kr/SVMQA/SVMQA_eval.tar.gz., Contact: jlee@kias.re.kr., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

Global searching for reaction pathways is a long-standing challenge in computational chemistry and biology. Most existing approaches perform only local searches due to computational complexity. Here we present a computational approach, Action-CSA, to find multiple diverse reaction pathways connecting fixed initial and final states through global optimization of the Onsager-Machlup action using the conformational space annealing (CSA) method. Action-CSA successfully overcomes large energy barriers via crossovers and mutations of pathways and finds all possible pathways of small systems without initial guesses on pathways. The rank order and the transition time distribution of multiple pathways are in good agreement with those of long Langevin dynamics simulations. The lowest action folding pathway of FSD-1 is consistent with recent experiments. The results show that Action-CSA is an efficient and robust computational approach to study the multiple pathways of complex reactions and large-scale conformational changes.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

For the template-based modeling (TBM) of CASP11 targets, we have developed three new protein modeling protocols (nns for server prediction and LEE and LEER for human prediction) by improving upon our previous CASP protocols (CASP7 through CASP10). We applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For more successful fold recognition, a new alignment method called CRFalign was developed. It can incorporate sensitive positional and environmental dependence in alignment scores as well as strong nonlinear correlations among various features. Modifications and adjustments were made to the form of the energy function and weight parameters pertaining to the chain building procedure. For the side-chain remodeling step, residue-type dependence was introduced to the cutoff value that determines the entry of a rotamer to the side-chain modeling library. The improved performance of the nns server method is attributed to successful fold recognition achieved by combining several methods including CRFalign and to the current modeling formulation that can incorporate native-like structural aspects present in multiple templates. The LEE protocol is identical to the nns one except that CASP11-released server models are used as templates. The success of LEE in utilizing CASP11 server models indicates that proper template screening and template clustering assisted by appropriate cluster ranking promises a new direction to enhance protein 3D modeling. Proteins 2016; 84(Suppl 1):221-232. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)

For the template-free modeling of human targets of CASP11, we utilized two of our modeling protocols, LEE and LEER. The LEE protocol took CASP11-released server models as the input and used some of them as templates for 3D (three-dimensional) modeling. The template selection procedure was based on the clustering of the server models aided by a community detection method of a server-model network. Restraining energy terms generated from the selected templates together with physical and statistical energy terms were used to build 3D models. Side-chains of the 3D models were rebuilt using target-specific consensus side-chain library along with the SCWRL4 rotamer library, which completed the LEE protocol. The first success factor of the LEE protocol was due to efficient server model screening. The average backbone accuracy of selected server models was similar to that of top 30% server models. The second factor was that a proper energy function along with our optimization method guided us, so that we successfully generated better quality models than the input template models. In 10 out of 24 cases, better backbone structures than the best of input template structures were generated. LEE models were further refined by performing restrained molecular dynamics simulations to generate LEER models. CASP11 results indicate that LEE models were better than the average template models in terms of both backbone structures and side-chain orientations. LEER models were of improved physical realism and stereo-chemistry compared to LEE models, and they were comparable to LEE models in the backbone accuracy. Proteins 2016; 84(Suppl 1):118-130. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)

We have applied the conformational space annealing method to the contact-assisted protein structure modeling in CASP11. For Tp targets, where predicted residue-residue contact information was provided, the contact energy term in the form of the Lorentzian function was implemented together with the physical energy terms used in our template-free modeling of proteins. Although we observed some structural improvement of Tp models over the models predicted without the Tp information, the improvement was not substantial on average. This is partly due to the inaccuracy of the provided contact information, where only about 18% of it was correct. For Ts targets, where the information of ambiguous NOE (Nuclear Overhauser Effect) restraints was provided, we formulated the modeling in terms of the two-tier optimization problem, which covers: (1) the assignment of NOE peaks and (2) the three-dimensional (3D) model generation based on the assigned NOEs. Although solving the problem in a direct manner appears to be intractable at first glance, we demonstrate through CASP11 that remarkably accurate protein 3D modeling is possible by brute force optimization of a relevant energy function. For 19 Ts targets of the average size of 224 residues, generated protein models were of about 3.6 Å Cα atom accuracy. Even greater structural improvement was observed when additional Tc contact information was provided. For 20 out of the total 24 Tc targets, we were able to generate protein structures which were better than the best model from the rest of the CASP11 groups in terms of GDT-TS. Proteins 2016; 84(Suppl 1):189-199. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)

Silicon is the most popular material used in electronic devices. However, its poor optical properties owing to its indirect band gap nature limit its usage in optoelectronic devices. Here we present the discovery of super-stable pure-silicon superlattice structures that can serve as promising materials for solar cell applications and can lead to the realization of pure Si-based optoelectronic devices. The structures are almost identical to that of bulk Si except that defective layers are intercalated in the diamond lattice. The superlattices exhibit dipole-allowed direct band gaps as well as indirect band gaps, providing ideal conditions for the investigation of a direct-to-indirect band gap transition. The fact that almost all structural portions of the superlattices originate from bulk Si warrants their stability and good lattice matching with bulk Si. Through first-principles molecular dynamics simulations, we confirmed their thermal stability and propose a possible method to synthesize the defective layer through wafer bonding.

We have carried out numerical experiments to investigate the applicability of the global optimization method of conformational space annealing (CSA) to the enhanced NMR protein structure determination over existing PDB structures. The NMR protein structure determination is driven by the optimization of collective multiple restraints arising from experimental data and the basic stereochemical properties of a protein-like molecule. By rigorous and straightforward application of CSA to the identical NMR experimental data used to generate existing PDB structures, we redetermined 56 recent PDB protein structures starting from fully randomized structures. The quality of CSA-generated structures and existing PDB structures were assessed by multiobjective functions in terms of their consistencies with experimental data and the requirements of protein-like stereochemistry. In 54 out of 56 cases, CSA-generated structures were better than existing PDB structures in the Pareto-dominant manner, while in the remaining two cases, it was a tie with mixed results. As a whole, all structural features tested improved in a statistically meaningful manner. The most improved feature was the Ramachandran favored portion of backbone torsion angles with about 8.6% improvement from 88.9% to 97.5% (P-value <10(-17)). We show that by straightforward application of CSA to the efficient global optimization of an energy function, NMR structures will be of better quality than existing PDB structures., (© 2015 Wiley Periodicals, Inc.)

Motivation: Glycans play critical roles in many biological processes, and their structural diversity is key for specific protein-glycan recognition. Comparative structural studies of biological molecules provide useful insight into their biological relationships. However, most computational tools are designed for protein structure, and despite their importance, there is no currently available tool for comparing glycan structures in a sequence order- and size-independent manner., Results: A novel method, GS-align, is developed for glycan structure alignment and similarity measurement. GS-align generates possible alignments between two glycan structures through iterative maximum clique search and fragment superposition. The optimal alignment is then determined by the maximum structural similarity score, GS-score, which is size-independent. Benchmark tests against the Protein Data Bank (PDB) N-linked glycan library and PDB homologous/non-homologous N-glycoprotein sets indicate that GS-align is a robust computational tool to align glycan structures and quantify their structural similarity. GS-align is also applied to template-based glycan structure prediction and monosaccharide substitution matrix generation to illustrate its utility., Availability and Implementation: http://www.glycanstructure.org/gsalign., Contact: wonpil@ku.edu, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

We present a new all-atom structure-based method to study protein conformational transitions using Lorentzian attractive interactions based on native structures. The variability of each native contact is estimated based on evolutionary information using a machine learning method. To test the validity of this approach, we have investigated the conformational transition of adenylate kinase (ADK). The intrinsic boundedness of the Lorentzian attractive interactions facilitated frequent conformational transitions, and consequently we were able to observe more than 1000 structural interconversions between the open and closed states of ADK out of a total of 6 μs MD simulations. ADK has three domains: the nucleoside monophosphate (NMP) binding domain, the LID-domain, and the CORE domain, which catalyze the interconversion between ATP and ADP. We identified two transition states: a more frequent LID-closed-NMP-open (TS1) state and a less frequent LID-open-NMP-closed (TS2) state. The transition was found to be symmetric in both directions via TS1. We also obtained an off-pathway metastable state that was previously observed with physics-based all-atom simulations but not with coarse-grained models. In the metastable state, the LID domain was slightly twisted and formed contacts with the NMP domain. Our model correctly identified a total of 14 out of the top 16 residues with highest fluctuation by NMR experiment, thus showing excellent agreement with experimental NMR relaxation data and overwhelmingly better results than existing models.

Background: In template-based modeling when using a single template, inter-atomic distances of an unknown protein structure are assumed to be distributed by Gaussian probability density functions, whose center peaks are located at the distances between corresponding atoms in the template structure. The width of the Gaussian distribution, the variability of a spatial restraint, is closely related to the reliability of the restraint information extracted from a template, and it should be accurately estimated for successful template-based protein structure modeling., Results: To predict the variability of the spatial restraints in template-based modeling, we have devised a prediction model, Sigma-RF, by using the random forest (RF) algorithm. The benchmark results on 22 CASP9 targets show that the variability values from Sigma-RF are of higher correlations with the true distance deviation than those from Modeller. We assessed the effect of new sigma values by performing the single-domain homology modeling of 22 CASP9 targets and 24 CASP10 targets. For most of the targets tested, we could obtain more accurate 3D models from the identical alignments by using the Sigma-RF results than by using Modeller ones., Conclusions: We find that the average alignment quality of residues located between and at two aligned residues, quasi-local information, is the most contributing factor, by investigating the importance of input features used in the RF machine learning. This average alignment quality is shown to be more important than the previously identified quantity of a local information: the product of alignment qualities at two aligned residues.

Acrodysostosis without hormone resistance is a rare skeletal disorder characterized by brachydactyly, nasal hypoplasia, mental retardation and occasionally developmental delay. Recently, loss-of-function mutations in the gene encoding cAMP-hydrolyzing phosphodiesterase-4D (PDE4D) have been reported to cause this rare condition but the pathomechanism has not been fully elucidated. To understand the pathogenetic mechanism of PDE4D mutations, we conducted 3D modeling studies to predict changes in the binding efficacy of cAMP to the catalytic pocket in PDE4D mutants. Our results indicated diminished enzyme activity in the two mutants we analyzed (Gly673Asp and Ile678Thr; based on PDE4D4 residue numbering). Ectopic expression of PDE4D mutants in HEK293 cells demonstrated this reduction in activity, which was identified by increased cAMP levels. However, the cells from an acrodysostosis patient showed low cAMP accumulation, which resulted in a decrease in the phosphorylated cAMP Response Element-Binding Protein (pCREB)/CREB ratio. The reason for this discrepancy was due to a compensatory increase in expression levels of PDE4A and PDE4B isoforms, which accounted for the paradoxical decrease in cAMP levels in the patient cells expressing mutant isoforms with a lowered PDE4D activity. Skeletal radiographs of 10-week-old knockout (KO) rats showed that the distal part of the forelimb was shorter than in wild-type (WT) rats and that all the metacarpals and phalanges were also shorter in KO, as the name acrodysostosis implies. Like the G-protein α-stimulatory subunit and PRKAR1A, PDE4D critically regulates the cAMP signal transduction pathway and influences bone formation in a way that activity-compromising PDE4D mutations can result in skeletal dysplasia. We propose that specific inhibitory PDE4D mutations can lead to the molecular pathology of acrodysostosis without hormone resistance but that the pathological phenotype may well be dependent on an over-compensatory induction of other PDE4 isoforms that can be expected to be targeted to different signaling complexes and exert distinct effects on compartmentalized cAMP signaling., (Copyright © 2014. Published by Elsevier Inc.)

Recently, predicting proteins three-dimensional (3D) structure from its sequence information has made a significant progress due to the advances in computational techniques and the growth of experimental structures. However, selecting good models from a structural model pool is an important and challenging task in protein structure prediction. In this study, we present the first application of random forest based model quality assessment (RFMQA) to rank protein models using its structural features and knowledge-based potential energy terms. The method predicts a relative score of a model by using its secondary structure, solvent accessibility and knowledge-based potential energy terms. We trained and tested the RFMQA method on CASP8 and CASP9 targets using 5-fold cross-validation. The correlation coefficient between the TM-score of the model selected by RFMQA (TMRF) and the best server model (TMbest) is 0.945. We benchmarked our method on recent CASP10 targets by using CASP8 and 9 server models as a training set. The correlation coefficient and average difference between TMRF and TMbest over 95 CASP10 targets are 0.984 and 0.0385, respectively. The test results show that our method works better in selecting top models when compared with other top performing methods. RFMQA is available for download from http://lee.kias.re.kr/RFMQA/RFMQA&#95;eval.tar.gz.

Lectins play major roles in biological processes such as immune recognition and regulation, inflammatory responses, cytokine signaling, and cell adhesion. Recently, glycan microarrays have shown to play key roles in understanding glycobiology, allowing us to study the relationship between the specificities of glycan binding proteins and their natural ligands at the omics scale. However, one of the drawbacks in utilizing glycan microarray data is the lack of systematic analysis tools to extract information. In this work, we attempt to group various lectins and their interacting carbohydrates by using community-based analysis of a lectin-carbohydrate network. The network consists of 1119 nodes and 16769 edges and we have identified 3 lectins having large degrees of connectivity playing the roles of hubs. The community based network analysis provides an easy way to obtain a general picture of the lectin-glycan interaction and many statistically significant functional groups.

In the template-based modeling (TBM) category of CASP10 experiment, we introduced a new protocol called protein modeling system (PMS) to generate accurate protein structures in terms of side-chains as well as backbone trace. In the new protocol, a global optimization algorithm, called conformational space annealing (CSA), is applied to the three layers of TBM procedure: multiple sequence-structure alignment, 3D chain building, and side-chain re-modeling. For 3D chain building, we developed a new energy function which includes new distance restraint terms of Lorentzian type (derived from multiple templates), and new energy terms that combine (physical) energy terms such as dynamic fragment assembly (DFA) energy, DFIRE statistical potential energy, hydrogen bonding term, etc. These physical energy terms are expected to guide the structure modeling especially for loop regions where no template structures are available. In addition, we developed a new quality assessment method based on random forest machine learning algorithm to screen templates, multiple alignments, and final models. For TBM targets of CASP10, we find that, due to the combination of three stages of CSA global optimizations and quality assessment, the modeling accuracy of PMS improves at each additional stage of the protocol. It is especially noteworthy that the side-chains of the final PMS models are far more accurate than the models in the intermediate steps., (Copyright © 2013 Wiley Periodicals, Inc.)

We present a method to predict the solvent accessibility of proteins which is based on a nearest neighbor method applied to the sequence profiles. Using the method, continuous real-value prediction as well as two-state and three-state discrete predictions can be obtained. The method utilizes the z-score value of the distance measure in the feature vector space to estimate the relative contribution among the k-nearest neighbors for prediction of the discrete and continuous solvent accessibility. The Solvent accessibility database is constructed from 5717 proteins extracted from PISCES culling server with the cutoff of 25% sequence identities. Using optimal parameters, the prediction accuracies (for discrete predictions) of 78.38% (two-state prediction with the threshold of 25%), 65.1% (three-state prediction with the thresholds of 9 and 36%), and the Pearson correlation coefficient (between the predicted and true RSA's for continuous prediction) of 0.676 are achieved An independent benchmark test was performed with the CASP8 targets where we find that the proposed method outperforms existing methods. The prediction accuracies are 80.89% (for two state prediction with the threshold of 25%), 67.58% (three-state prediction), and the Pearson correlation coefficient of 0.727 (for continuous prediction) with mean absolute error of 0.148. We have also investigated the effect of increasing database sizes on the prediction accuracy, where additional improvement in the accuracy is observed as the database size increases. The SANN web server is available at http://lee.kias.re.kr/~newton/sann/., (Copyright © 2012 Wiley Periodicals, Inc.)

High accuracy protein modeling from its sequence information is an important step toward revealing the sequence-structure-function relationship of proteins and nowadays it becomes increasingly more useful for practical purposes such as in drug discovery and in protein design. We have developed a protocol for protein structure prediction that can generate highly accurate protein models in terms of backbone structure, side-chain orientation, hydrogen bonding, and binding sites of ligands. To obtain accurate protein models, we have combined a powerful global optimization method with traditional homology modeling procedures such as multiple sequence alignment, chain building, and side-chain remodeling. We have built a series of specific score functions for these steps, and optimized them by utilizing conformational space annealing, which is one of the most successful combinatorial optimization algorithms currently available.