ClusPro in rounds 38 to 45 of CAPRI: Toward combining template-based methods with free docking

Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template-based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models. The available templates also provided distance restraints that were directly used as input to the server. We show here that such an approach has some advantages. Free docking with template-based restraints using ClusPro reproduced some interfaces suggested by weak or ambiguous templates while not reproducing others, resulting in correct server predicted models. More recently we developed the fully automated ClusPro TBM server that performs template-based modeling and thus can use sequences rather than structures of component proteins as input. The performance of the server, freely available for noncommercial use at https://tbm.cluspro.org , is demonstrated by predicting the protein-protein targets of rounds 38 to 45 of CAPRI.     

Integrative modeling of protein-protein interactions with pyDock for the new docking challenges

The seventh CAPRI edition imposed new challenges to the modeling of protein-protein complexes, such as multimeric oligomerization, protein-peptide, and protein-oligosaccharide interactions. Many of the proposed targets needed the efficient integration of rigid-body docking, template-based modeling, flexible optimization, multiparametric scoring, and experimental restraints. This was especially relevant for the multimolecular assemblies proposed in the CASP12-CAPRI37 and CASP13-CAPRI46 joint rounds, which were described and evaluated elsewhere. Focusing on the purely CAPRI targets of this edition (rounds 38-45), we have participated in all 17 assessed targets (considering heteromeric and homomeric interfaces in T125 as two separate targets) both as predictors and as scorers, by using integrative modeling based on our docking and scoring approaches: pyDock, IRaPPA, and LightDock. In the protein-protein and protein-peptide targets, we have also participated with our webserver (pyDockWeb). On these 17 CAPRI targets, we submitted acceptable models (or better) within our top 10 models for 10 targets as predictors, 13 targets as scorers, and 4 targets as servers. In summary, our participation in this CAPRI edition confirmed the capabilities of pyDock for the scoring of docking models, increasingly used within the context of integrative modeling of protein interactions and multimeric assemblies.     

An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.     

Template-based modeling and ab-initio docking using CoDock in CAPRI

Integration of template-based modeling, global sampling and precise scoring is crucial for the development of molecular docking programs with improved accuracy. We combined template-based modeling and ab-initio docking protocol as hybrid docking strategy called CoDock for the docking and scoring experiments of the seventh CAPRI edition. For CAPRI rounds 38-45, we obtained acceptable or better models in the top 10 submissions for eight out of the 16 evaluated targets as predictors, nine out of the 16 targets as scorers. Especially, we submitted acceptable models for all of the evaluated protein-oligosaccharide targets. For the CASP13-CAPRI experiment (round 46), we obtained acceptable or better models in the top 5 submissions for 10 out of the 20 evaluated targets as predictors, 11 out of the 20 targets as scorers. The failed cases for our group were mainly the difficult targets and the protein-peptide systems in CAPRI and CASP13-CAPRI experiments. In summary, this CAPRI edition showed that our hybrid docking strategy can be efficiently adapted to the increasing variety of challenges in the field of molecular interactions.     

Structural modeling of protein complexes: Current capabilities and challenges

Proteins frequently interact with each other, and the knowledge of structures of the corresponding protein complexes is necessary to understand how they function. Computational methods are increasingly used to provide structural models of protein complexes. Not surprisingly, community-wide Critical Assessment of protein Structure Prediction (CASP) experiments have recently started monitoring the progress in this research area. We participated in CASP13 with the aim to evaluate our current capabilities in modeling of protein complexes and to gain a better understanding of factors that exert the largest impact on these capabilities. To model protein complexes in CASP13, we applied template-based modeling, free docking and hybrid techniques that enabled us to generate models of the topmost quality for 27 of 42 multimers. If templates for protein complexes could be identified, we modeled the structures with reasonable accuracy by straightforward homology modeling. If only partial templates were available, it was nevertheless possible to predict the interaction interfaces correctly or to generate acceptable models for protein complexes by combining template-based modeling with docking. If no templates were available, we used rigid-body docking with limited success. However, in some free docking models, despite the incorrect subunit orientation and missed interface contacts, the approximate location of protein binding sites was identified correctly. Apparently, our overall performance in docking was limited by the quality of monomer models and by the imperfection of scoring methods. The impact of human intervention on our results in modeling of protein complexes was significant indicating the need for improvements of automatic methods.     

Docking proteins and peptides under evolutionary constraints in Critical Assessment of PRediction of Interactions rounds 38 to 45

Computational structural prediction of macromolecular interactions is a fundamental tool toward the global understanding of cellular processes. The Critical Assessment of PRediction of Interactions (CAPRI) community-wide experiment provides excellent opportunities for blind testing computational docking methods and includes original targets, thus widening the range of docking applications. Our participation in CAPRI rounds 38 to 45 enabled us to expand the way we include evolutionary information in structural predictions beyond our standard free docking InterEvDock pipeline. InterEvDock integrates a coarse-grained potential that accounts for interface coevolution based on joint multiple sequence alignments of two protein partners (co-alignments). However, even though such co-alignments could be built for none of the CAPRI targets in rounds 38 to 45, including host-pathogen and protein-oligosaccharide complexes and a redesigned interface, we identified multiple strategies that can be used to incorporate evolutionary constraints, which helped us to identify the most likely macromolecular binding modes. These strategies include template-based modeling where only local adjustments should be applied when query-template sequence identity is above 30% and larger perturbations are needed below this threshold; covariation-based structure prediction for individual protein partners; and the identification of evolutionarily conserved and structurally recurrent anchoring interface motifs. Overall, we submitted correct predictions among the top 5 models for 12 out of 19 interface challenges, including four High- and five Medium-quality predictions. Our top 20 models included correct predictions for three out of the five targets we missed in the top 5, including two targets for which misleading biological data led us to downgrade correct free docking models.     

Structure prediction of biological assemblies using GALAXY in CAPRI rounds 38-45

We participated in CARPI rounds 38-45 both as a server predictor and a human predictor. These CAPRI rounds provided excellent opportunities for testing prediction methods for three classes of protein interactions, that is, protein-protein, protein-peptide, and protein-oligosaccharide interactions. Both template-based methods (GalaxyTBM for monomer protein, GalaxyHomomer for homo-oligomer protein, GalaxyPepDock for protein-peptide complex) and ab initio docking methods (GalaxyTongDock and GalaxyPPDock for protein oligomer, GalaxyPepDock-ab-initio for protein-peptide complex, GalaxyDock2 and Galaxy7TM for protein-oligosaccharide complex) have been tested. Template-based methods depend heavily on the availability of proper templates and template-target similarity, and template-target difference is responsible for inaccuracy of template-based models. Inaccurate template-based models could be improved by our structure refinement and loop modeling methods based on physics-based energy optimization (GalaxyRefineComplex and GalaxyLoop) for several CAPRI targets. Current ab initio docking methods require accurate protein structures as input. Small conformational changes from input structure could be accounted for by our docking methods, producing one of the best models for several CAPRI targets. However, predicting large conformational changes involving protein backbone is still challenging, and full exploration of physics-based methods for such problems is still to come.     

Score_set: A CAPRI benchmark for scoring protein complexes

Critical Assessment of PRedicted Interactions (CAPRI) has proven to be a catalyst for the development of docking algorithms. An essential step in docking is the scoring of predicted binding modes in order to identify stable complexes. In 2005, CAPRI introduced the scoring experiment, where upon completion of a prediction round, a larger set of models predicted by different groups and comprising both correct and incorrect binding modes, is made available to all participants for testing new scoring functions independently from docking calculations. Here we present an expanded benchmark data set for testing scoring functions, which comprises the consolidated ensemble of predicted complexes made available in the CAPRI scoring experiment since its inception. This consolidated scoring benchmark contains predicted complexes for 15 published CAPRI targets. These targets were subjected to 23 CAPRI assessments, due to existence of multiple binding modes for some targets. The benchmark contains more than 19,000 protein complexes. About 10% of the complexes represent docking predictions of acceptable quality or better, the remainder represent incorrect solutions (decoys). The benchmark set contains models predicted by 47 different predictor groups including web servers, which use different docking and scoring procedures, and is arguably as diverse as one may expect, representing the state of the art in protein docking. The data set is publicly available at the following URL: http://cb.iri.univ‐lille1.fr/Users/lensink/Score_set . Proteins 2014; 82:3163–3169. © 2014 Wiley Periodicals, Inc.     

Template-based modeling by ClusPro in CASP13 and the potential for using co-evolutionary information in docking

As a participant in the joint CASP13-CAPRI46 assessment, the ClusPro server debuted its new template-based modeling functionality. The addition of this feature, called ClusPro TBM, was motivated by the previous CASP-CAPRI assessments and by the proven ability of template-based methods to produce higher-quality models, provided templates are available. In prior assessments, ClusPro submissions consisted of models that were produced via free docking of pre-generated homology models. This method was successful in terms of the number of acceptable predictions across targets; however, analysis of results showed that purely template-based methods produced a substantially higher number of medium-quality models for targets for which there were good templates available. The addition of template-based modeling has expanded ClusPro's ability to produce higher accuracy predictions, primarily for homomeric but also for some heteromeric targets. Here we review the newest additions to the ClusPro web server and discuss examples of CASP-CAPRI targets that continue to drive further development. We also describe ongoing work not yet implemented in the server. This includes the development of methods to improve template-based models and the use of co-evolutionary information for data-assisted free docking.     

Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional (3D) structure is reassessed by evaluating blind predictions, performed during 2003-2004 as part of Rounds 3-5 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Ten newly determined structures of protein-protein complexes were used as targets for these rounds. They comprised 2 enzyme-inhibitor complexes, 2 antigen-antibody complexes, 2 complexes involved in cellular signaling, 2 homo-oligomers, and a complex between 2 components of the bacterial cellulosome. For most targets, the predictors were given the experimental structures of 1 unbound and 1 bound component, with the latter in a random orientation. For some, the structure of the free component was derived from that of a related protein, requiring the use of homology modeling. In some of the targets, significant differences in conformation were displayed between the bound and unbound components, representing a major challenge for the docking procedures. For 1 target, predictions could not go to completion. In total, 1866 predictions submitted by 30 groups were evaluated. Over one-third of these groups applied completely novel docking algorithms and scoring functions, with several of them specifically addressing the challenge of dealing with side-chain and backbone flexibility. The quality of the predicted interactions was evaluated by comparison to the experimental structures of the targets, made available for the evaluation, using the well-agreed-upon criteria used previously. Twenty-four groups, which for the first time included an automatic Web server, produced predictions ranking from acceptable to highly accurate for all targets, including those where the structures of the bound and unbound forms differed substantially. These results and a brief survey of the methods used by participants of CAPRI Rounds 3-5 suggest that genuine progress in the performance of docking methods is being achieved, with CAPRI acting as the catalyst.     

Docking and scoring protein complexes: CAPRI 3rd Edition

The performance of methods for predicting protein-protein interactions at the atomic scale is assessed by evaluating blind predictions performed during 2005-2007 as part of Rounds 6-12 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). These Rounds also included a new scoring experiment, where a larger set of models contributed by the predictors was made available to groups developing scoring functions. These groups scored the uploaded set and submitted their own best models for assessment. The structures of nine protein complexes including one homodimer were used as targets. These targets represent biologically relevant interactions involved in gene expression, signal transduction, RNA, or protein processing and membrane maintenance. For all the targets except one, predictions started from the experimentally determined structures of the free (unbound) components or from models derived by homology, making it mandatory for docking methods to model the conformational changes that often accompany association. In total, 63 groups and eight automatic servers, a substantial increase from previous years, submitted docking predictions, of which 1994 were evaluated here. Fifteen groups submitted 305 models for five targets in the scoring experiment. Assessment of the predictions reveals that 31 different groups produced models of acceptable and medium accuracy-but only one high accuracy submission-for all the targets, except the homodimer. In the latter, none of the docking procedures reproduced the large conformational adjustment required for correct assembly, underscoring yet again that handling protein flexibility remains a major challenge. In the scoring experiment, a large fraction of the groups attained the set goal of singling out the correct association modes from incorrect solutions in the limited ensembles of contributed models. But in general they seemed unable to identify the best models, indicating that current scoring methods are probably not sensitive enough. With the increased focus on protein assemblies, in particular by structural genomics efforts, the growing community of CAPRI predictors is engaged more actively than ever in the development of better scoring functions and means of modeling conformational flexibility, which hold promise for much progress in the future.     

Scoring a diverse set of high-quality docked conformations: a metascore based on electrostatic and desolvation interactions

Predicting protein-protein interactions involves sampling and scoring docked conformations. Barring some large structural rearrangement, rapidly sampling the space of docked conformations is now a real possibility, and the limiting step for the successful prediction of protein interactions is the scoring function used to reduce the space of conformations from billions to a few, and eventually one high affinity complex. An atomic level free-energy scoring function that estimates in units of kcal/mol both electrostatic and desolvation interactions (plus van der Waals if appropriate) of protein-protein docked conformations is used to rerank the blind predictions (860 in total) submitted for six targets to the community-wide Critical Assessment of PRediction of Interactions (CAPRI; http://capri.ebi.ac.uk). We found that native-like models often have varying intermolecular contacts and atom clashes, making unlikely that one can construct a universal function that would rank all these models as native-like. Nevertheless, our scoring function is able to consistently identify the native-like complexes as those with the lowest free energy for the individual models of 16 (out of 17) human predictors for five of the targets, while at the same time the modelers failed to do so in more than half of the cases. The scoring of high-quality models developed by a wide variety of methods and force fields confirms that electrostatic and desolvation forces are the dominant interactions determining the bound structure. The CAPRI experiment has shown that modelers can predict valuable models of protein-protein complexes, and improvements in scoring functions should soon solve the docking problem for complexes whose backbones do not change much upon binding. A scoring server and programs are available at http://structure.pitt.edu.     

Homology-based modeling of 3D structures of protein-protein complexes using alignments of modified sequence profiles

Customary practice in predicting 3D structures of protein-protein complexes is employment of various docking methods when the structures of separate monomers are known a priori. The alternative approach, i.e. the template-based prediction with pure sequence information as a starting point, is still considered as being inferior mostly due to presumption that the pool of available structures of protein-protein complexes, which can serve as putative templates, is not sufficiently large. Recently, however, several labs have developed databases containing thousands of 3D structures of protein-protein complexes, which enable statistically reliable testing of homology-based algorithms. In this paper we report the results on homology-based modeling of 3D structures of protein complexes using alignments of modified sequence profiles. The method, called HOMology-BAsed COmplex Prediction (HOMBACOP), has two distinctive features: (I) extra weight on aligning interfacial residues in the dynamical programming algorithm, and (II) increased gap penalties for the interfacial segments. The method was tested against our recently developed ProtCom database and against the Boston University protein-protein BENCHMARK. In both cases, models generated were compared to the models built on basis of customarily protein structure initiative (PSI)-BLAST sequence alignments. It was found that existence of homologous (by the means of PSI-BLAST) templates (44% of cases) enables both methods to produce models of good quality, with the profiles method outperforming the PSI-BLAST models (with respect to the percentage of correctly predicted residues on the complex interface and fraction of native interfacial contacts). The models were evaluated according to the CAPRI assessment criteria and about two thirds of the models were found to fall into acceptable and medium-quality categories. The same comparison of a larger set of 463 protein complexes showed again that profiles generate better models. We further demonstrate, using our ProtCom database, the suitability of the profile alignment algorithm in detecting remote homologues between query and template sequences, where the PSI-BLAST method fails.     

The use of soluble protein structures in modeling helical proteins in a layered membrane

Major advances have been made in the prediction of soluble protein structures, led by the knowledge-based modeling methods that extract useful structural trends from known protein structures and incorporate them into scoring functions. The same cannot be reported for the class of transmembrane proteins, primarily due to the lack of high-resolution structural data for transmembrane proteins, which render many of the knowledge-based method unreliable or invalid. We have developed a method that harnesses the vast structural knowledge available in soluble protein data for use in the modeling of transmembrane proteins. At the core of the method, a set of transmembrane protein decoy sets that allow us to filter and train features recognized from soluble proteins for transmembrane protein modeling into a set of scoring functions. We have demonstrated that structures of soluble proteins can provide significant insight into transmembrane protein structures. A complementary novel two-stage modeling/selection process that mimics the two-stage helical membrane protein folding was developed. Combined with the scoring function, the method was successfully applied to model 5 transmembrane proteins. The root mean square deviations of the predicted models ranged from 5.0 to 8.8?Å to the native structures.     

Native fold and docking pose discrimination by the same residue‐based scoring function

Structure prediction and quality assessment are crucial steps in modeling native protein conformations. Statistical potentials are widely used in related algorithms, with different parametrizations typically developed for different contexts such as folding protein monomers or docking protein complexes. Here, we describe BACH‐SixthSense, a single residue‐based statistical potential that can be successfully employed in both contexts. BACH‐SixthSense shares the same approach as BACH, a knowledge‐based potential originally developed to score monomeric protein structures. A term that penalizes steric clashes as well as the distinction between polar and apolar sidechain‐sidechain contacts are crucial novel features of BACH‐SixthSense. The performance of BACH‐SixthSense in discriminating correctly the native structure among a competing set of decoys is significantly higher than other state‐of‐the‐art scoring functions, that were specifically trained for a single context, for both monomeric proteins (QMEAN, Rosetta, RF_CB_SRS_OD, benchmarked on CASP targets) and protein dimers (IRAD, Rosetta, PIE*PISA, HADDOCK, FireDock, benchmarked on 14 CAPRI targets). The performance of BACH‐SixthSense in recognizing near‐native docking poses within CAPRI decoy sets is good as well. Proteins 2015; 83:621–630. © 2015 Wiley Periodicals, Inc.     

Prediction and scoring of docking poses with pyDock

The two previous CAPRI experiments showed the success of our rigid-body and refinement approach. For this third edition of CAPRI, we have used a new faster protocol called pyDock, which uses electrostatics and desolvation energy to score docking poses generated with FFT-based algorithms. In target T24 (unbound/model), our best prediction had the highest value of fraction of native contacts (40%) among all participants, although it was not considered as acceptable by the CAPRI criteria. In target T25 (unbound/bound), we submitted a model with medium quality. In target T26 (unbound/unbound), we did not submit any acceptable model (but we would have submitted acceptable predictions if we had included available mutational information about the binding site). For targets T27 (unbound/unbound) and T28 (homo-dimer using model), nobody (including us) submitted any acceptable model. Intriguingly, the crystal structure of target T27 shows an alternative interface that correlates with available biological data (we would have submitted acceptable predictions if we had included this). We also participated in all targets of the SCORERS experiment, with at least acceptable accuracy in all valid cases. We submitted two medium and four acceptable scoring models of T25. Using additional distance restraints (from mutational data), we had two medium and two acceptable scoring models of T26. For target T27, we submitted two acceptable scoring models of the alternative interface in the crystal structure. In summary, CAPRI showed the excellent capabilities of pyDock in identifying near-native docking poses.     

Refinement of unreliable local regions in template-based protein models

Contemporary template-based modeling techniques allow applications of modeling methods to vast biological problems. However, they tend to fail to provide accurate structures for less-conserved local regions in sequence even when the overall structure can be modeled reliably. We call these regions unreliable local regions (ULRs). Accurate modeling of ULRs is of enormous value because they are frequently involved in functional specificity. In this article, we introduce a new method for modeling ULRs in template-based models by employing a sophisticated loop modeling technique. Combined with our previous study on protein termini, the method is applicable to refinement of both loop and terminus ULRs. A large-scale test carried out in a blind fashion in CASP9 (the 9th Critical Assessment of techniques for protein structure prediction) shows that ULR structures are improved over initial template-based models by refinement in more than 70% of the successfully detected ULRs. It is also notable that successful modeling of several long ULRs over 12 residues is achieved. Overall, the current results show that a careful application of loop and terminus modeling can be a promising tool for model refinement in template-based modeling.     

Refinement of protein termini in template-based modeling using conformational space annealing

The rapid increase in the number of experimentally determined protein structures in recent years enables us to obtain more reliable protein tertiary structure models than ever by template-based modeling. However, refinement of template-based models beyond the limit available from the best templates is still needed for understanding protein function in atomic detail. In this work, we develop a new method for protein terminus modeling that can be applied to refinement of models with unreliable terminus structures. The energy function for terminus modeling consists of both physics-based and knowledge-based potential terms with carefully optimized relative weights. Effective sampling of both the framework and terminus is performed using the conformational space annealing technique. This method has been tested on a set of termini derived from a nonredundant structure database and two sets of termini from the CASP8 targets. The performance of the terminus modeling method is significantly improved over our previous method that does not employ terminus refinement. It is also comparable or superior to the best server methods tested in CASP8. The success of the current approach suggests that similar strategy may be applied to other types of refinement problems such as loop modeling or secondary structure rearrangement.     

Novel sampling strategies and a coarse-grained score function for docking homomers,flexible heteromers,and oligosaccharides using Rosetta in CAPRI rounds 37–45

Critical Assessment of PRediction of Interactions (CAPRI) rounds 37 through 45 introduced larger complexes, new macromolecules, and multistage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined near-native models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pregenerated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in round 41. Although the results were broadly encouraging, they also highlighted the pressing need to invest in (a) flexible docking algorithms with the ability to model loop and linker motions and in (b) new sampling and scoring methods for oligosaccharide-protein interactions.     

How different from random are docking predictions when ranked by scoring functions?

Docking algorithms predict the structure of protein–protein interactions. They sample the orientation of two unbound proteins to produce various predictions about their interactions, followed by a scoring step to rank the predictions. We present a statistical assessment of scoring functions used to rank near‐native orientations, applying our statistical analysis to a benchmark dataset of decoys of protein–protein complexes and assessing the statistical significance of the outcome in the Critical Assessment of PRedicted Interactions (CAPRI) scoring experiment. A P value was assigned that depended on the number of near‐native structures in the sampling. We studied the effect of filtering out redundant structures and tested the use of pair‐potentials derived using ZDock and ZRank. Our results show that for many targets, it is not possible to determine when a successful reranking performed by scoring functions results merely from random choice. This analysis reveals that changes should be made in the design of the CAPRI scoring experiment. We propose including the statistical assessment in this experiment either at the preprocessing or the evaluation step. Proteins 2010. © 2010 Wiley‐Liss, Inc.