Queried with a                    protein sequence it returns: multiple sequence alignments, predicted aspects of                    structure  and strands, coiled-coil regions, disulfide bonds and disordered                    regions) and function. The service incorporates analysis methods for the                    identification of functional regions (ConSurf), homology-based inference of Gene                    Ontology terms (metastudent), comprehensive subcellular localization prediction                    (LocTree3), protein–protein binding sites (ISIS2),                    protein–polynucleotide binding sites (SomeNA) and predictions of the                    effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our                    goal has always been to develop a system optimized to meet the demands of                    experimentalists not highly experienced in bioinformatics. To this end, the                    PredictProtein results are presented as both text and a series of intuitive,                    interactive and visually appealing figures. The web server and sources are                    available at                              Molecular biology is moving into the high-throughput mode as the number of                experiments needed to support a single hypothesis is rapidly growing. The line                between experimental result and computational analysis is blurring; this also shifts                what constitutes a reliable annotation. On top, the vast amount of life science data                outpaces computer power. For example, less than 1% of the over 51 million sequences                in UniProt February 2014) (014 (2).             Sustained quality with performance                                estimates. The performance of many tools is not                            sufficiently assessed and/or their performance does not sustain over                            time. Two decades of Critical Assessment of protein Structure Prediction                            (CASP)-like experiments  and 22 Ease of                                use. From the beginning we have aspired to make the use of                            our tools intuitive for all users. Unfortunately, the growth in size and                            scope continues to challenge the realization of this guiding principle.                            In 1992, the service provided alignments and secondary structure                            prediction; in 2014, it includes over 30 complex tools. Creating a                            unified, natural interface for these tools is challenging. Furthermore,                            we need to invest more resources to sustain the increasing usage as the                            data flood surges on. For example, most of our CPU goes into running                            PSI-BLAST (SI-BLAST . Since 2The PredictProtein (PP) server is an automatic service that searches up-to-date                public sequence databases, creates alignments, and predicts aspects of protein                structure and function. In 1992, PredictProtein went online as one of the first                Internet servers in molecular biology at the EMBL . From 1999                to 2009, the server operated from Columbia University  and in 2009 it                moved to the TUM . PredictProtein was one of the first services                realizing state-of-the-art protein sequence analysis, and the prediction of                structural and functional features in a single server. While many outstanding                services  have exp                            Database searches: sequences similar to the                    query are identified by standard, pairwise BLAST  predicts                    alpha-helical transmembrane proteins, the position of transmembrane helices, and                    membrane topology. The method uses a novel segment-based neural network to                    refine the final prediction. TMSEG was developed and evaluated on 166                    transmembrane proteins extracted from PDBTM  predict                                        SNAP2 predicts the effect of single amino acid substitutions on protein function                        . It impr                                            LocTree3 predicts subcellular localization for proteins in all domains of life                        . The met                                        The method metastudent  predicts                                        Intrinsically disordered or unstructured regions in proteins do not fold into                    well-defined three-dimensional (3D) structures when in isolation, but may become                    structured upon binding to a substrate. Because of the heterogeneity of                    disordered regions, we have developed several methods predicting different types                    of disorders. UCON  combines                                        Residues that can bind other proteins are now predicted by ISIS2 instead of ISIS                        . ISIS sp                                        Protein–polynucleotide binding underlies important processes such as                    replication and transcription. SomeNA  predicts                                        ConSurf ,27 estim                                                        The dashboard page of PredictProtein results uses the BioJS  FeatureV                The website features a Help section that includes interactive and instructive                    presentations. Each result section also provides a Help tab with specific                    explanations. All result pages feature an interactive Export menu for the                    download of selected raw data, as well as of the compiled archive with all data                    generated by the server. Additionally, we provide machine-readable output in XML                    and JSON. Output formatted for web presentations is available (HTML link at top                    right corner of main result page). The HTML view—most familiar to                    long-time users—aggregates results from most of the integrated methods                    in one page. This page also contains information that has not been integrated                    into the graphical view—yet—including results generated by some                    component methods and prediction confidence values. While we are working on the                    integration of all results into the graphical view, we highly encourage users to                    inspect this ‘raw’ HTML view. Finally, output is also available                    in text format .                                        One of the most beneficial recent resources from PredictProtein is the                    PPcache—a database that currently holds pre-calculated results for 11.7                    million unique proteins—including all proteins of model organisms. If                    pre-calculated results are available for a PredictProtein query in PPcache,                    these are immediately returned. For results older than three months, users are                    given the option to re-run the query, thereby updating the PPcache. If no result                    exists in the PPcache, the job is processed, and users are notified upon job                    completion. PPcache currently requires roughly 100TB of disk space. We plan to                    open this repository for public access through a specialized API.                                        For full proteome analysis we make the full PredictProtein software suite                    available for download to be run either by installing the software packages on                    local machines or by deploying a virtual machine image in the cloud. Most                    methods from the PredictProtein pipeline are now available as open-source                    packages and are freely distributed through Debian  and Ubun                Users with access to cloud computing can download the PredictProtein Machine                    Image or PPMI , a disk                                                         We demonstrate the usability and properties of PredictProtein through a simple                example, the human endoplasmatic reticulum (ER) membrane protein complex subunit 4                . On the other hand, it also suggests additional insights that might                trigger new experiments, e.g. the importance of the disordered N-terminus, and the                importance of the beta-strand that breaks it. May be this will provide more detail                on the suggested involvement in protein folding and in apoptosis Figure ).                            Over its 22 year existence, the PredictProtein server has substantially expanded.                What started as a service to annotate some aspects of protein structure  has evolved into a                comprehensive suite of methods important for the prediction of protein structural                and functional features. It provides a single-point access to many original                important results. Our focus on making reliable methods available and our technical                focus on keeping our server useful to the community have sustained many challenges                in an environment of low funding, growing use and increasing data deluge. Yet we                continue finding ways to present our results efficiently and without overloading                users from a wide variety of backgrounds and needs. The results pages aspire to give                visually intuitive, unified presentations for most of the structural and functional                annotations. The PredictProtein web server can help when little is known about the                protein in question. For medium-to-high throughput analyses, users will find the                publicly available, downloadable software packages and the PPMI a suitable option.                For approximately every second query, our PPcache repository provides results                immediately.                            Supplementary Data are available at NAR Online.


Protein complexes are basic cellular entities that carry out the functions of their components. It can be found that in databases of protein complexes of yeast like CYC2008, the major type of known protein complexes is heterodimeric complexes. Although a number of methods for trying to predict sets of proteins that form arbitrary types of protein complexes simultaneously have been proposed, it can be found that they often fail to predict heterodimeric complexes.In this paper, we have designed several features characterizing heterodimeric protein complexes based on genomic data sets, and proposed a supervised-learning method for the prediction of heterodimeric protein complexes. This method learns the parameters of the features, which are embedded in the naïve Bayes classifier. The log-likelihood ratio derived from the naïve Bayes classifier with the parameter values obtained by maximum likelihood estimation gives the score of a given pair of proteins to predict whether the pair is a heterodimeric complex or not. A five-fold cross-validation shows good performance on yeast. The trained classifiers also show higher predictability than various existing algorithms on yeast data sets with approximate and exact matching criteria.http://imi.kyushu-u.ac.jp/~om/.Heterodimeric protein complex prediction is a rather harder problem than heteromeric protein complex prediction because heterodimeric protein complex is topologically simpler. However, it turns out that by designing features specialized for heterodimeric protein complexes, predictability of them can be improved. Thus, the design of more sophisticate features for heterodimeric protein complexes as well as the accumulation of more accurate and useful genome-wide data sets will lead to higher predictability of heterodimeric protein complexes. Our tool can be downloaded from  Protein complexes are basic cellular entities that carry out the functions of their components. Those functions are performed in various biological processes in a cell, including transcription, signal transduction, and development. Therefore, it is useful to identify protein complexes in order to elucidate various complicated mechanisms in a cell.There exist a few databases of protein complexes of yeast. One of them is MIPS protein complex catalog, a comprThe major type of protein complexes in these databases is heterodimeric complexes, that is, protein complexes composed of two different proteins. MIPS catalog and CYC2008 have 64 (29%) and 172 (42%) heterodimeric complexes, respectively.A number of unsupervised learning methods for predicting arbitrary types of protein complexes simultaneously have been proposed, for example, MCL, RRW7]7], NWE, PPSamplA fundamental reason of the drawback is that a score function for a predicted complex is often designed based on inter-connectivity between proteins within a predicted complex, like density measure of a subgraph of a protein-protein interaction (PPI) network. Under such a scoring scheme, the number of proteins within a predicted cluster should be high to some degree, at least three or four, in order that such a score function works effectively. As a result, heterodimeric complexes are left out of consideration.This weakness of existing tools toward heterodimeric protein complexes implies the need to develop another approach for predicting heterodimeric complexes. In this paper, we present a method for learning naïve Bayes classifiers for heterodimeric complexes. Those classifiers exploit the features for heterodimeric protein complexes which are designed with genome-wide data sets, including PPI data, gene ontology annotations, and protein localization data. Those features are trained by positive examples, a part of known heterodimeric protein complexes, and negative examples, which will be pairs of two proteins that are not known to form heterodimeric protein complexes. Such a feature can be considered to be essentially a pair of conditional probability distributions of possible values of features. One is a distribution of feature values specialized for positive examples, and the other is specialized for negative examples. The log-likelihood ratio by the naïve Bayes classifier with those trained features can be used to score a given pair of two proteins and predict whether the pair forms a heterodimeric protein complexes or not.It should be noted here that the problem of identification of two components of heterodimeric protein complexes is different from that of identification of PPIs. In the latter problem, it does not matter whether an interaction forms a protein complex with interactions neighboring with that interaction. On the other hand, in the former problem, it is required to determine whether an interaction solely forms a protein complex or not.We have carried out a five-fold cross-validation over the positive and negative examples derived from known heterodimeric complexes in CYC2008 and known PPIs in WI-PHI, which is a database of PPIs of yeast. This computational experiment shows acceptable performance and gives us interesting insights into heterodimeric protein complexes. Furthermore, the trained classifiers are evaluated by predicting whether each of the PPIs in WI-PHI is heterodimeric or not. It then turns out that those classifiers show higher predictability than various existing algorithms on yeast data sets with the exact matching criterion. Similar results are also obtained with an approximate matching criterion.In this section, we describe our method for predicting heterodimeric protein complexes by naïve Bayes classifiers. For this task, we have designed several features for heterodimeric protein complexes based on a weighted PPI network, semantic similarities for molecular function and biological process aspects of the Gene Ontology, and protein localization information. Here a feature means a  function mapping from a pair of proteins to a real number or an integer, which is called a score.Given a set of features, a five-fold cross-validation is carried out, in which classifiers are trained with training sets of positive and negative examples and those trained classifiers are evaluated with test sets of positive and negative examples  PPI weights of WI-PHIunsuperviols, MCL, PPSamplols, MCL, RNSC[1ols, MCL, MCODE[ols, MCL, DPClusols, MCL, CMC[13ols, MCL. Howeverols, MCL is such Usually, all PPIs of a database are taken as input to unsupervised learning algorithms. Here, all WI-PHI PPIs are given to the above unsupervised learning algorithms. In order to compare our classifiers with those algorithms as fair as possible, the five trained classifiers are imposed on the prediction of the class of the pair of the proteins of each of the PPIs.approximately evaluated whether they are matched with some known complexes. A typical matching threshold isprecision and recall are calculated withC is given by our classifiers, we have C|2=C, i.e.,recall is equal to recall. On the other hand, in general, it does not hold for unsupervised learning methods. Thus, this performance comparison with this approximate matching criterion is advantageous to unsupervised learning methods. However, as shown in FigureIn the literature, heteromeric predicted clusters are example,,11,14,33Npc, and Nkc underNpc and Nkc, respectively. From these numbers, the three performance measures of precision, recall, and F-measure are calculated and the resulting graph is shown in FigureThe number of predicted clusters, η=1. In this case, Npc is equal to Nkc, which is shown in TableNext is the performance comparison with the exact matching criterion of t. Note that |t| is limited to ten in this work because ofs|=2. Notice that those predicted clusters are completely included in the matching known complexes from the definition of the overlap ratio. Secondly, the recall of all the unsupervised learning methods, especially MCL, PPSampler, and RNSC, are also reduced. This fact indicates that some of the known complexes of size two, i.e., heterodimeric protein complexes, are approximately matched with predicted clusters of size ranging from three to ten. These two observations imply the difficulty of predicting heterodimeric protein complex exactly.By comparing FiguresAlthough the trained classifiers outperforms other methods, the performance measures are lower than those in the cross-validation. One of the reasons is that the unbalanced ratio of the number of negative examples to that of positive ones. The ratio is 49448 to 159. These numbers are obtained as follows. CYC2008 contains 172 heterodimeric protein complexes. Among them, 13 heterodimeric complexes do not have the corresponding PPIs in WI-PHI. Thus, 159 positive examples are determined from WI-PHI and CYC2008. Recall that WI-PHI has 49607 non-self interactions. Thus, the resulting negative examples in the WI-PHI database is 49448. In general, to avoid making many false positives, the LLR threshold and the class LLR,x, satisfying the following conditions are extracted: (i) x is known to have an interaction between the proteins of x, (ii) x does not correspond to any heterodimeric protein complexes, and (iii) x is predicted to be positive by all the five classifiers. The total number of those PPIs are 154. Among them, 51 (33%) of them are completely included in known complexes of size three or more. Thus, some of the remaining 103 PPIs are candidates for true heterodimeric complexes. In addition, many of 103 PPIs can be potential subunits of undiscovered protein complexes of size three or more because the fact that they are predicted to be positive by the five classifiers implies that they are functionally and topologically closely related. Thus, these PPIs are good candidates for unknown protein complexes. Raw data of this analysis can be found in the last part of Additional fileWe have conducted further analysis as follows. All pairs of proteins, Currently, there is no high-quality weighted PPI data in human, like WI-PHI in yeast. It is a future work to create such data set and apply our method to human data sets. In addition, it is also an interesting future work to apply classifiers trained by yeast data sets to other organisms. In this case, the requirement is at least input data sets to features embedded into the classifiers.It is also a future work to design more sophisticated features or templates for concrete features using some genome-wide data sets. Especially, a feature based on 3D structure information can be promising.Very recently, an independent work for predicting heterodimeric protein complexes by a support vector machine (SVM) with new features based on protein domain information has been published. AlthougIn this paper, we have proposed a supervised learning method for heterodimeric protein complexes. For this purpose, we have designed templates for features and individual features, which is based on genome-wide data. The naïve Bayes classifiers are evaluated in a five-fold cross-validation, and the trained classifiers are also tested with all known PPIs. Those classifiers are shown to attain much better performance than existing unsupervised learning methods.The article processing charge was funded by the Institute of Mathematics for Industry at Kyushu University. There is no other financial or non-financial competing interests.Main output file. This file is the main output file of our tool, which contains results of the five-fold cross-validation and the evaluation of the five trained classifiers with all PPIs of WI-PHI.Click here for fileParameters of trained naïve Bayes classifiers. This file shows for a feature, the pair of multinomial distributions trained by positive and negative training examples in each fold of the cross-validation.Click here for file


Motivation: The determination of absolute quantities of proteins in biological samples is necessary for multiple types of scientific inquiry. While relative quantification has been commonly used in proteomics, few proteomic datasets measuring absolute protein quantities have been reported to date. Various technologies have been applied using different types of input data, e.g. ion intensities or spectral counts, as well as different absolute normalization strategies. To date, a user-friendly and transparent software supporting large-scale absolute protein quantification has been lacking.Results: We present a bioinformatics tool, termed aLFQ, which supports the commonly used absolute label-free protein abundance estimation methods  for LC-MS/MS proteomics data, together with validation algorithms enabling automated data analysis and error estimation.Availability and implementation:aLFQ is written in R and freely available under the GPLv3 from CRAN (http://www.cran.r-project.org). Instructions and example data are provided in the R-package. The raw data can be obtained from the PeptideAtlas raw data repository (PASS00321).Contact:lars.malmstroem@imsb.biol.ethz.chSupplementary information:Supplementary data are available at Bioinformatics online. A variety of quantitative proteomic methods have been established to measure the relative abundance of proteins across samples. Although relative quantification methods are useful to compare the same proteins between multiple biological samples, they do not provide the possibility to directly compare the data with other datasets or compare different proteins within a dataset with each other and they, by definition, do not provide absolute quantitative data. Further, specific applications, such as differential equation-based modeling of biological systems or determination of subunit stoichiometry of protein complexes depend on absolute protein quantities.aLFQ, an open-source implementation of algorithms supporting the estimation of protein quantities by any of the aforementioned methods, and additionally provide automated workflows for data analysis and error estimation.The current gold standard for LC-MS/MS–based absolute protein quantification is the use of stable isotope-labeled standard (SIS) peptides or proteins in precisely determined concentrations . In addition, an input table with the SIS anchor peptides or anchor proteins and sample-specific absolute abundances or an estimate of the total protein concentration is required.ProteinInference module enables inference of protein quantities from precursor intensities, transition intensities or spectral counts. If the dataset contains targeted proteomics data, the paired precursor and fragment ion signals, the transitions are first summarized to the precursor level using one of multiple algorithms. To summarize precursor intensities or spectral counts to protein intensities, the TopN  as objective function.The aLFQ R-package. The Universal Proteomic Standard 2  consists of 48 proteins spanning a dynamic range of five orders of magnitude in bins of eight proteins. The sample was measured in a complex background in shotgun and targeted MS modes . The example data can be accessed using the following commands:library(aLFQ)data(UPS2MS)An example dataset was produced for this study and is delivered with the ALF(UPS2_SRM)An exemplary integrated workflow termed ALF  , H.L.R. was funded by ETH (ETH-30 11-2), R.A. was funded by PhosphonetX project of SystemsX.ch, the advanced European Research Council grant Proteomics v3.0 (233226) and the Swiss National Science Foundation.Conflict of Interest: none declared.


The hardware accelerators will provide solutions to computationally complex problems in bioinformatics fields. However, the effect of acceleration depends on the nature of the application, thus selection of an appropriate accelerator requires some consideration.In the present study, we compared the effects of acceleration using graphics processing unit (GPU) and many integrated core (MIC) on the speed of fast Fourier transform (FFT)-based protein-protein docking calculation. The GPU implementation performed the protein-protein docking calculations approximately five times faster than the MIC offload mode implementation. The MIC native mode implementation has the advantage in the implementation costs. However, the performance was worse with larger protein pairs because of memory limitations.The results suggest that GPU is more suitable than MIC for accelerating FFT-based protein-protein docking applications. Many recently developed hardware accelerators, such as ClearSpeed, Cell Accelerator Board, and GRAPE, were developed for specific purposes, but graphics processing units (GPUs) have currently become the most popular because of their excellent performance and simple programming environments, such as NVIDIA's CUDA and OpenCL .Many integrated core (MIC) architectures are hardware accelerators developed by Intel, which have been released recently as the Xeon Phi co-processor. Similar to a GPU, MIC includes many tiny computing cores. However, the core can be used in the same ways as a general CPU core. MIC is one of the main architectures used in current supercomputing systems . For exaThus, the actual applications should be considered during evaluations of hardware accelerators.At present, various applications have been mapped onto accelerators. In particular, GPU-based applications have been developed in various research fields, including genome analysis ,5, molecIn the present study, we evaluated the performance of GPU and MIC using protein-protein docking calculations, which is a real-world application in computational biology. Protein-protein docking is a method used to predict protein complex structures based on monomeric protein structures. At present, the most popular docking methods employ rigid-body docking using a voxel-based representation in a three-dimensional (3D) grid space with a discrete convolution-based scoring function, where the fast Fourier transform (FFT) is employed to speed up the calculations -14. FFT-In this study, we used MEGADOCK ,16, whicMEGADOCK is a protein-protein interaction prediction system that uses FFT-based protein-protein docking based on the Katchalski-Katzir algorithm . MEGADOCS) between a receptor protein and a ligand protein is calculated as the convolution of two discrete functions using N3-point forward FFT and inverse FFT (IFFT), as follows:Figure R and L are the discrete score functions of the receptor and ligand proteins, respectively, v is a coordinate in the 3D grid space t is the parallel translation vector of the ligand protein, ∗ is the complex conjugation operator, and N , which is referred to as the FFT size, is double the size of the grid. The discrete score functions R and L are based on shape complementarity  directly, θ ntimes. MEGADOCK uses an θ nvalue of 3,600 as the default setting. Table 1JK9]; receptor: CCS metallochaperone (243 residues), ligand: SOD1 superoxide dismutase (153 residues)). The FFT size N of the docking calculation was 128, which is typical in the current protein structure database. The profile was obtained using an Intel Xeon E5-2670 2.60 GHz, one CPU core. FFT processes (P5 and P7) accounted for most of the processing time (75.9%). However, other calculations, such as voxelization and identifying the best solutions, still accounted for considerable proportions of the total time.where SC model ), electrSC model ) and desSC model ). To ides in eq.  directlys in eq.  directlyTo compare GPU and MIC, the MEGADOCK program should be mapped onto both GPU and MIC. The MIC implementation was newly implemented for this study. For GPU implementation, we used MEGADOCK-GPU developed in the previous study . In thisIn ligand rotation process (P4), the atomic coordinates of a ligand are updated according to a given rotation matrix. The process is independent for each atom and it can be fully parallelized. We mapped the atomic coordinates onto a GPU. In ligand voxelization process (P4), MEGADOCK sets a suitable rPSC score, electrostatic interaction values, and desolvation free energy scores for the ligand voxel model during this process. Ligand voxelization calculates the distance between the coordinates of an atom and each grid, before assigning a value to each grid within the van der Waals radius of the atom. The assignment process can be parallelized for each atom. The rPSC score and the desolvation free energy score of a ligand has only binary states (0 or 1), and the electrostatic interaction value of a grid is calculated as the cumulative sum of the values of all adjacent atoms, thus the calculation order for each atom can be exchanged freely. Therefore, we processed the atoms in parallel and mapped them onto a GPU. Thus, multiple atoms were processed simultaneously on different GPU cores in this process. In FFT processes , single precision complex 3-dimentional FFT is performed using the NVIDIA cuFFT library to map the FFT calculations onto a GPU. In convolution process (P6), the output of FFT of receptor voxel is complex conjugated and multiplied by the output of FFT of ligand voxel. The convolution can be independent for each grid, thus we mapped them onto a GPU. In identifying the best solutions process (P8), the best docking pose was selected according to the docking score. This process was also implemented on a GPU using reduction.In our implementation, the transfer of large volumes of data from a host to a GPU occurred only once. These data comprised the original atom coordinates of a ligand and the Fourier transformed receptor grid information, which were transferred first. Only trivial volumes of data transfer were required  in the loop for each ligand rotation angle.In this process, the atomic coordinates of a ligand are updated according to a given rotation matrix. The process is independent for each atom and it can be fully parallelized. We mapped the atomic coordinates onto a GPU.MEGADOCK sets a suitable rPSC score, electrostatic interaction values, and desolvation free energy scores for the ligand voxel model during this process. Ligand voxelization calculates the distance between the coordinates of an atom and each grid, before assigning a value to each grid within the van der Waals radius of the atom. The assignment process can be parallelized for each atom. The rPSC score and the desolvation free energy score of a ligand has only binary states (0 or 1), and the electrostatic interaction value of a grid is calculated as the cumulative sum of the values of all adjacent atoms, thus the calculation order for each atom can be exchanged freely. Therefore, we processed the atoms in parallel and mapped them onto a GPU. Thus, multiple atoms were processed simultaneously on different GPU cores in this process.We used the NVIDIA cuFFT library  to map tThe convolution can be independent for each grid, thus we mapped them onto a GPU.In this process, the best docking pose was selected according to the docking score. This process was also implemented on a GPU using reduction.In our implementation, the transfer of large volumes of data from a host to a GPU occurred only once. These data comprised the original atom coordinates of a ligand and the Fourier transformed receptor grid information, which were transferred first. Only trivial volumes of data transfer were required  in the loop for each ligand rotation angle.The MIC architecture can be used in two different modes. In the offload mode, only specific sections of the program are executed on the MIC and the user has to add pragmas in the code to organize the data transfer and parallelization. In the native mode, the program is executed on the MIC alone and there is no need to change the source code of existing applications if the program is parallelized using OpenMP.We accelerated protein-protein docking calculations by utilizing MIC in both the offload and native modes. MEGADOCK has already been parallelized by looping the rotational angles of the ligand protein using OpenMP and it can be run in parallel on a multi-core CPU. Thus, the native mode implementation parallelizes the looping of the rotational angles, whereas the offload mode implementation is parallelized during each docking calculation process.In the offload mode implementation, we mapped the most intensive processes, i.e., from (P4) to (P8), onto the MIC architecture in a similar manner to the GPU implementation. Processes such as ligand atom rotation, voxelization, and convolution were parallelized in the same manner as the GPU implementation with OpenMP. The process used to identify the best solution was accelerated with the "reduction" pragma in OpenMP. The Intel Math Kernel Library (MKL) was used to perform the FFT calculations. On MIC, the FFT calculations were parallelized automatically and the number of threads was optimized to the FFT size.In the native mode implementation, we did not change the program, but added the compile option. In contrast to the offload mode and the GPU implementation, the native mode implementation did not require data transfer from the host to the MIC. However, the serial sections in the program may have operated more slowly compared with that when running on a CPU because the clock of the MIC cores was slower than that of the CPU. In addition, a problem occurred in the native mode when the docking target was a large protein pair. This was because the parallelization of the loop used for rotational angles required per-thread memory. Thus, the largest memory requirements were for the input and output of the FFT, which were specific to each thread. The FFT requirements were:N is the number of 3D FFT points. Thus, if all 240 threads were used, 240 × 16N3 bytes of memory were required. However, the Xeon Phi 5110P, which is a product of MIC architecture, has an onboard memory of 8 GB. Therefore, N could be up to 127 because on each thread, where offload"), and docking calculation executed on a MIC using an OpenMP implementation and the MIC native mode ("MICnative"). Table To evaluate the acceleration of protein-protein docking application using accelerators, we measured the MEGADOCK execution performance using CPUs, a GPU, and a MIC. We also compared the performance of the MIC offload and native modes. We used the following five conditions in the comparisons: docking calculation using one CPU core ("1CPU"), docking calculation using an OpenMP implementation and the eight CPU cores included in a CPU socket ("8CPUs"), GPU-accelerated docking calculation using one GPU and one CPU core ("GPU"), docking calculation accelerated by the MIC offload mode implementation using one MIC and one CPU core  , which cFigure To test the relationship between the FFT size and the speed up with the accelerators, we evaluated the performance with three protein pairs of different sizes. Figure native" was 5.0 times faster than "1CPU" and it was faster than "8CPUs," but "MICoffload" was even slower than "1CPU." This was because the memory offload overhead became large in this case and small FFT calculations were inefficient in the MIC.For a small-sized protein pair, the acceleration rates obtained with GPU and MIC were less than those with larger proteins. "GPU" achieved only a 6.6-fold speed up compared with "1CPU" in this case. "MICnative" and "MICoffload" yielded 7.0-fold and 2.4-fold speed ups compared with "1CPU," respectively. One of the reasons why "MICnative" was not effective in accelerating the calculations was that only 171 threads were used because of the memory limitations on the MIC.For a medium-sized protein pair, the GPU calculation achieved a 17.3-fold speed up compared with the one CPU core, which was much greater than that for a small-sized protein. Because the processes such as FFT and convolution, which show better speedup using accelerators as shown in Table native" was only 3.6 times faster than "1CPU" and it was much smaller compared with the small-sized protein pairs. This was because only 38 threads could be used for the large-sized protein pairs because of the MIC memory limitations. On the other hand, "MICoffload" was relatively fast and it achieved a 6.1-fold speed up compared with "1CPU," which was comparable to eight CPU cores.For large-sized protein pairs, the GPU calculations achieved 17.8-fold speed up compared with the one CPU core and the acceleration rate was almost same as that with a medium-sized protein pair. For a large-sized protein pair, "MICOverall, GPU implementation delivered the most efficient performance with various protein pair sizes. MIC native mode implementation delivered better performance with small- and medium-sized protein pairs, but its performance was worse with large-sized proteins due to the MIC memory limitations.Table 1JK9]). Furthermore, "GPU" achieved the best performance in all of the experiments. Thus, even if X5670 had been replaced with E5-2670, the results of the comparison would not have changed.In this study, we used different nodes to compare the performance of the GPU and MIC. To test MIC, we used a Xeon E5-2670, which was faster than Xeon X5670 used for testing the GPU. However, "GPU" performed almost all of the processes on the GPU so the difference in the performance of the CPU was largely irrelevant with respect to the calculation time. Indeed, the calculation time with "GPU" was almost the same even when we used six CPU cores and one GPU card (9.77 seconds for [PDB:In addition to the computational speed, the costs of mapping a program onto the accelerators are also important when evaluating the accelerators. Learning a new language to implement a program on accelerators is a demanding task. GPU computing requires a novel programming technique, such as CUDA, whereas a program is ready-to-use with MIC if it is implemented in C, C++, and OpenMP. Thus, we considered the time and effort required to port a code onto a GPU and a MIC, including the offload and native modes.To map a docking calculation onto a GPU, we had to write several CUDA kernel functions, which describe the processes executed on the GPU, as well as adding statements to facilitate data transfer between the host and the accelerator. We had to add the code with approximately 1,000 lines to the MEGADOCK original code with approximately 7,000 lines. Therefore, the implementation costs were high, although we were familiar with CUDA programming. Furthermore, the source code management costs were increased because the code required many branches and additional source code files for GPU computing.For the MIC offload mode implementation, we also had to add several pragmas to the offload section so we could execute them on a MIC. This was similar to the GPU implementation but the size of the additional statements was approximately 500 lines, which was less than that required for the GPU implementation.By contrast, to implement a MIC in the native mode code, we did not need to write any additional code because MEGADOCK had already been parallelized for looping ligand protein rotational angles using OpenMP. We only added a compile option for constructing the MIC native mode binary. Thus, the implementation cost was lowest for the MIC native mode. To execute the program, however, we needed to copy the binary and libraries files, and execute them remotely on each Xeon Phi. Therefore, the operability was more complex than that with the other systems.In this study, we compared the acceleration obtained by applying GPU and MIC to protein-protein docking calculation, which is a FFT-based real-world application. GPU computing required considerable effort to map the calculations but it achieved the best performance. The MIC offload mode implementation had similar costs to GPU but its performance was far inferior to that obtained with the GPU. With the GPU implementation, the protein-protein docking calculations were completed about five times faster than the MIC offload mode implementation. The MIC native mode implementation had the advantage that the user did not have to write additional code, but this was mainly because the program code had already been parallelized using OpenMP. However, the performance became worse with larger protein pairs because some of the MIC computing cores could not be used due to memory limitations. The overall performance was comparable to eight CPU cores, i.e., a CPU socket. These results suggest that a GPU is now more suitable than a MIC to accelerate FFT-based protein-protein docking calculations.GPU: graphics processing unit; CUDA: compute unified device architecture; MIC: many integrated core; FFT: fast Fourier transform; IFFT: inverse fast Fourier transform; PDB: protein data bank; MKL: math kernel library.The authors declare that they have no competing interests.TS, SS and MO performed the MIC implementation of protein-protein docking. TS performed the computational experiments and validated the results. TI assisted with the implementation. YA supervised and directed the entire study. TS and MO wrote the manuscript. All authors read and approved the final manuscript.


Aspergillus fumigatus), lipopolysaccharide of Pseudomonas aeruginosa, Streptococcus pneumonia and Salmonella typhimurium. Thus, PTX-3 protein helps to clear these pathogens by activating downstream innate immune process. In this study, computational methods were used to analyze various non-synonymous single nucleotide polymorphisms (nsSNPs) in PTX-3 gene. Three different databases were used to retrieve SNP data sets followed by seven different in silico algorithms to screen nsSNPs in PTX-3 gene. Sequence homology based approach was used to identify nsSNPs. Conservation profile of PTX-3 protein amino acid residues were predicted by ConSurf web server. In total, 10 high-risk nsSNPs were identified in pentraxin-domain of PTX-3 gene. Out of these 10 high-risk nsSNPs, 4 were present in the conserved structural and functional residues of the pentraxin-domain, hence, selected for structural analyses. The results showed alteration in the putative structure of pentraxin-domain. Prediction of protein–protein interactions analysis showed association of PTX-3 protein with C1q component of complement pathway. Different functional and structural residues along with various putative phosphorylation sites and evolutionary relationship were also predicted for PTX-3 protein. This is the first extensive computational analyses of pentraxin protein family with nsSNPs and will serve as a valuable resource for future population based studies.Pentraxin-3 (PTX-3) protein is an evolutionary conserved protein that acts as a soluble pattern-recognition receptor for pathogens and plays important role in innate immune response. It recognizes various pathogens by interacting with extracellular moieties such as glactomannan of conidia ( SIFT utilizes the sequence homology of the proteins and by aligning the natural occurring nsSNPs with paralogous and orthologous protein sequences to predict the deleterious effect of nsSNPs. The SIFT score less than 0.05 indicates the harmful effect of nsSNPs on protein function. Another algorithm used for the prediction of functional consequences of nsSNPs was polyphen-2 (http://genetics.bwh.harvard.edu/pph2). Polyphen-2 software uses the protein sequence as well as the amino acid variant position in protein sequence to predict the effect of nsSNP on protein structure and function.nsSNPs were collected from retrieved SNPs and their functional consequences were predicted by using different https://www.broadinstitute.org/mpg/snap/), PhD-SNP (http://snps.biofold.org/phd-snp/phd-snp.html), MAPP (http://www.ngrl.org.uk/Manchester/page/missense-prediction-tools) and PANTHER (http://www.pantherdb.org/). In total, seven different SNP prediction algorithms were used. nsSNPs predicted to be deleterious by at least five different in silico SNP algorithms were categorized as high-risk nsSNPs. Because each algorithm uses different parameters to assess the nsSNPs, hence, nsSNPs with more positive results in SNP algorithms are more likely to be deleterious. 10 nsSNPs predicted to be deleterious by SNP prediction algorithms were subjected for further analyses.If there is a change in amino acid or mutation in protein sequence, it is evaluated as “possibly damaging” (probabilistic score > 0.15), “probably damaging” (probabilistic score > 0.85) and “benign” (remaining mutations). PolyPhen-2 also calculates the PISC score for each amino acid substitution in protein. The PSIC score difference among variants directly indicates the functional consequences of nsSNPs on protein function. Another algorithms used for the prediction of nsSNPs effects on protein function were SNAP (NP_002843.2) and protein sequences for other eight species of Hominidae family such as Pan troglodytes (XP_516838.2), Mesocricetus auratus (XP_005077995.1), Gorilla gorilla gorilla (XP_004037950.1), Cavia porcellus (XP_003476359.1), Bos Taurus (NP_001069727.1), Bison bison bison (XP_010838164.1), and for Muridae family, Mus musculus (CAA58580.1) and Rattus norvegicus (NP_001103006.1) were retrieved from the National Center for Biotechnology Information (NCBI) and subjected for their evolutionary conservation. Protein sequence alignment was performed using ClustalX software version 1.8331  and by using multiple sequence comparison by log-expectation  using the MEGA version 6  by subtracting the mutated protein unfold Gibbs free energy (ΔG) from wild type proteins unfold Gibbs free energy (ΔG). Prediction of energy change can be performed by use of either protein sequence or structure.I-Mutant version 2 also predict the sign of decrease (Dec.) or increase (Inc.) in Gibbs free energy with Reliability Index (RI) for change in amino acid, where RI-0 indicates lowest reliability and RI-10 indicates highest reliability. The value of change in free energy (DDG) below 0 (< 0) indicates decrease in protein stability and value higher than 0 (>0) indicates increase in protein stability. During prediction of energy change, the pH and temperature was set as 7 and 25°C, respectively, for all nsSNP submissions.http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) and InterPro . Homology modeling was done by Web based server Phyre2 (http://www.sbg.bio.ic.ac.uk/phyre2). This server uses Hidden Markov Method to generate alignments of submitted protein sequence against proteins with published protein structure. The resulting alignments are then used to produce homology based models of the query sequence to predict its three-dimensional structure. This server also uses an ab-initio folding simulation called Poing to model region of a query sequence with no detectable similarities to known structures. It combines multiple templates of known structure to produce final model of the query sequence. The 3D-structure model for pentraxin-domain was developed by submitting FASTA sequence of pentraxin-domain region to Phyre2 server under intensive mode. The Phyre2 server has used these templates having PDB ID (% identity); {1sac (24%), 1b09 (24%), 4pbo (23%), and 3flp (22%)} for the modeling of 3D-structure of pentraxin-domain. When the degrees of similarity of these templates were analyzed using BLASTp, human's templates 1sac and 4pbo showed 51% similarity with 70% positives. The structure refinement of the predicted model was carried out by ModRefiner by submitting PDB file of model structure . RAMPAGE is a popular program used to check the stereo-chemical quality of a protein structure. A Phi/Psi Ramachandran plot was obtained from RAMPAGE to validate the structure of pentraxin-domain.Three dimensional (3D) structure analyses were done for pentraxin-domain (wild type) and each of the high-risk nsSNP located within this domain. Homology modeling of pentraxin-domain was carried out to predict 3D-structure. The protein sequence of human PTX-3 protein was retrieved from UniProt data base (UniProtKB ID https://www.pymol.org/). Further, Tm-score, and Root Mean Square Deviation (RMSD) was estimated for each mutated models using TM-Align  and GPS 2.1 (http://gps.biocuckoo.org/). For GPS 2.1 analysis, cut-off values ranging from 0.776-11 were selected. In NetPhos 2.0, threonine, serine, and tyrosine residues with a score of 0.5 were considered phosphorylated  program and SUMOplot (http://www.abgent.com/sumoplot) program. For SUMOplot, only high probability motifs with a score 0.5 were considered sumoylated. Medium level threshold with a 2.64 cut-off value was selected for SUMOsp 2.0 analysis, respectively  programs  was used. This tool provides unique coverage and ease of access to both theoretical and experimental interaction evidence of PTX-3 protein of human. The input options for STRING database include protein sequence, protein name, and multiple sequences. STRING database is presently equipped with 5,214,234 proteins belonging to 1133 organisms. The interaction studies were performed in various modes. The different modes include evidence view, confidence view, interactive mode, and action view to deduce the most appropriate interactions among nodes in PTX-3 protein interaction network   in PTX-3 gene is associated with colonization of Conserved amino acids in proteins are involved in various cellular processes in a biological system including genome stability  and contains three exons. First two exons encode for leader and N-terminus, whereas third exon encodes for C-terminus known as pentraxin-domain. Human pentraxin-domain of PTX-3 protein is highly conserved as compared to N-terminal domain  of PTX-3 is homologous to short-chain pentraxin proteins and are involved in recognition of pathogenic bacteria and fungi to activate the classical complement pathway via C1q, a complement pathway component . Whereas, in action view showed association of PTX-3 protein with following proteins; C1qA , C1qB , C1qC , CFH (complement factor H) and FGF2 (Fibroblast growth factor 2) Figure . FurtherA. fumigatus conidia, P. aeruginosa, S. pneumonia, and S. typhimurium. Due to the presence of these high-risk nsSNPs, it could be possible that the domain may not be functionally active and humans with these nsSNPs in their genome may be susceptible to infection for selected pathogens. Finally, we propose that these nsSNPs should be considered for risk assessment against infectious microbes in a population based study.We conclude that several nsSNPs are present in PTX-3 gene. Most of these nsSNPs are located into pentraxin-domain of PTX-3 protein. Structural analysis of selected high-risk nsSNPs showed that the amino acid residue substitutions in pentraxin-domain had the deleterious impact on the stability of the PTX-3 protein. Amino acid residues which undergo substitution were E313K (rs4478039), N337S (rs76994524), R188C (rs564774580) and R360W (rs138818541). Overall, these nsSNPs showed deleterious effect on structure and subsequently to the function of PTX-3 protein. As, pentraxin-domain of pentraxin protein family previously known to interact with components of complement pathway and activates them and also function as pathogen recognition receptor for these pathogens e.g., Conceived and designed the experiments: RT and JS. Performed the experiments: RT. Analyzed the data: RT and JS. Contributed reagents/materials/analysis tools: JS. Wrote the paper: RT and JS.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


CoDNaS  is a protein conformational diversity database. Conformational diversity describes structural differences between conformers that define the native state of proteins. It is a key concept to understand protein function and biological processes related to protein functions. CoDNaS offers a well curated database that is experimentally driven, thoroughly linked, and annotated. CoDNaS facilitates the extraction of key information on small structural differences based on protein movements. CoDNaS enables users to easily relate the degree of conformational diversity with physical, chemical and biological properties derived from experiments on protein structure and biological characteristics. The new version of CoDNaS includes ∼70% of all available protein structures, and new tools have been added that run sequence searches, display structural flexibility profiles and allow users to browse the database for different structural classes. These tools facilitate the exploration of protein conformational diversity and its role in protein function.Database URL:http://ufq.unq.edu.ar/codnas Conformational diversity is a key concept for understanding protein function . ConformWe created a conformational diversity database of native state proteins called CoDNaS to study different aspects related to conformational diversity . CoDNaS CoDNaS version 2 integrates extensive new upgrades into the original program. A total of 17 714 different protein chains have been included . We also incorporated new tools for sequence searches, flexibility analyses and structural profiles, which enhance the capacity and use of the database to explore conformational diversity in proteins.http://www.bioinf.org.uk/software/profit/). This RMSD per position was used to derive the corresponding Z-score for each residue on a pair of aligned conformers. We also calculated the Z-scores of B-factors as a measure of intrinsic protein flexibility for each position (Different conformers for each protein were identified and extracted for the Protein Data Bank (PDB)  using thposition .Each conformer in CoDNaS is characterized by the experimental conditions of its structure estimation, including pH, temperature, presence of ligands, mutations, oligomeric state, post-translational modifications and presence of disorder. This information was retrieved using different data sources and our own scripts coded in PERL. The temperature, pH and sequence variation occurrence were extracted from the experimental data deposited in the PDB file. We assigned ligands using the heteroatoms in the PDB file and the BioLip database . The oliwww.mysql.com). Some data were retrieved from online resources like UniProt RESTful web services. The web interface was developed in HTML, CSS and JavaScript using the JQuery and Bootstrap frameworks. Server-side scripting was performed by means of PHP 5.3.3 and AJAX.CoDNaS was implemented on a LAMP  server architecture. We stored most of the data in a relational database containing five tables, which was constructed using the MySQL 5.5.43 database server  models as different protein conformations. The new version incorporates 263 014 new conformers belonging to 17 714 different proteins chains. These additional data effectively double the number of previously released entries. The total number of proteins covers ∼70% of PDB protein structures; the remaining 30% represents structures with resolution lower than 4.00 Å and protein clusters without two different PDB structures. There are ∼11 million total pair-wise structural comparisons between conformers for all proteins in the new database. The database can be updated semi-automatically to increase the number of protein chains and conformations with PDB updates.The website was redesigned to generate a dynamic and user-friendly interface. Information is organized and shown in different sections. The user can search for proteins with different combinations of filters, and the retrieved results are shown in intuitive and informative reports. We utilized jQuery and Bootstrap frameworks to style the new design.Extension of conformational diversity of the protein. Sets the lower and upper limits of maximum RMSD between all conformers for a given protein.Causes of conformational diversity. Selects different causes of conformational diversity to retrieve proteins that present pairs of conformers associated with one or more causes.Using structural information. The current version supports searching for proteins that belong to the same structural superfamily using the CATH hierarchy .Using sequence information. The tab function 'by protein sequence' is a new feature that allows users to search by protein/DNA sequence. The server runs a BLAST query against all protein sequences in CoDNaS and retrieves proteins with a BLAST E-value better than 1E-04.To facilitate database use, the CoDNaS webserver offers different search methods. The quickest method is by means of a PDB code, UniProt ID or protein name to search for a specific entry in the database. A second method (included in the Search section) allows the user to select from the following protein characteristics:These results can be further filtered by selecting a specific experimental method (e.g. X-ray or NMR).CoDNaS version 2.0 supports browsing using the CATH hierarchy. The four major CATH levels can be explored by clicking on any of the class names to retrieve proteins with the same structural classification.After performing a protein search, the results page provides a table with the entries of retrieved proteins . The tab. The results page includes a number of boxes that can be collapsed to optimize available workspace. The ‘General information’ box . We also included a link to download a TSV file with all conformer pairs present in the database. Most of the sections in different views of the database allow the download of search results in TSV format or JSON (JavaScript Object Notation).Understanding protein function necessarily involves understanding protein motions. CoDNaS provides a well curated, experimentally driven and thoroughly linked database of protein conformations. The capture of different conformations of the same protein allows key information to be extracted depending on protein movements, and supports the exploration of relationships between protein dynamics, experimental conditions and protein biological properties. CoDNaS includes ∼70% of all available protein structures presently deposited in PDB, and utilizes PDB updates to maintain current information for our database. Backbone and surface structural differences are very well characterized in CoDNaS, and we are planning to incorporate parameters characterizing conformational diversity largely independent of the backbone movements, such as those related to the opening and closing of tunnels and those associated with cavities or pockets. This future update will incorporate important information for these movements, which are significant for protein biological function.This work was supported by the Universidad Nacional de Quilmes (UNQ). G.P. and M.S.F. are researchers of the Argentinean National Research Council (CONICET). A.M.M. and C.O.R. are fellows of the Argentinean National Research Council (CONICET). Funding for open access charge: Universidad Nacional de Quilmes, grant entitled “Simulación de procesos moleculares de relevancia fisicoquímica y biológica.” and PIP-CONICET (112 201101-01002). Conflict of interest. None declared.


Protein complexes are key catalysts and regulators for the majority of cellular processes. Unveiling their assembly and structure is essential to understanding their function and mechanism of action. Although conventional structural techniques such as X-ray crystallography and NMR have solved the structure of important protein complexes, they cannot consistently deal with dynamic and heterogeneous assemblies, limiting their applications to small scale experiments. A novel methodological paradigm, integrative structural biology, aims at overcoming such limitations by combining complementary data sources into a comprehensive structural model. Recent applications have shown that a range of mass spectrometry (MS) techniques are able to generate interaction and spatial restraints (cross-linking MS) information on native complexes or to study the stoichiometry and connectivity of entire assemblies (native MS) rapidly, reliably, and from small amounts of substrate. Although these techniques by themselves do not solve structures, they do provide invaluable structural information and are thus ideally suited to contribute to integrative modeling efforts. The group of Brian Chait has made seminal contributions in the use of mass spectrometric techniques to study protein complexes. In this perspective, we honor the contributions of the Chait group and discuss concepts and milestones of integrative structural biology. We also review recent examples of integration of structural MS techniques with an emphasis on cross-linking MS. We then speculate on future MS applications that would unravel the dynamic nature of protein complexes upon diverse cellular states. Mass spectrometry (MS) is an analytical technique that has found wide application in biological research. The development of instrumentation and experimental workflows targeted at the identification and quantification of ever larger sets of proteins has revolutionized our understanding of the expressed proteome and how it is related to the diverse functional states of the cell . The advMass spectrometry has contributed to our understanding of protein complexes for many years. The versatility and sensitivity of mass spectrometers spurred the development of a variety of techniques. The group of Brian Chait significantly contributed to the inception of these technical developments and realized several pioneering advances. For example, limited proteolysis coupled to MS , 9 and hAmong the structural techniques employing MS, cross-linking mass spectrometry (XL-MS) has probably gained the most interest in the community of structural biologists because of its relative simplicity of implementation, reproducibility, and high information content of the obtained distance restraints. In XL-MS, the chemical linking of amino acid residues within a native protein complex are combined with the direct identification of cross-linked peptides by tandem MS. By identifying cross-links between amino acid residues that are nearby in space, XL-MS can prove the physical proximity of protein complex subunits, determine their topological arrangement, and indicate the precise location where protein domains interface. Furthermore, the length of the cross-linker defines an upper bound of the distance between the cross-linked residues that proves useful for orienting and docking known atomic structures. For a detailed account of the recent innovations in XL-MS, we refer to –19.Structural biology has been traditionally driven by high-resolution techniques such as X-ray crystallography and nuclear magnetic resonance (NMR). For many years, these techniques produced structures of isolated proteins and dimers . Once itRecently, cryo-electron microscopy (cryo-EM) emerged as an alternative method to tackle the structure of protein complexes. In most cases, cryo-EM is performed on isolated protein complexes frozen in a thin layer of amorphous ice and, therefore, does not need sample crystallization. Furthermore, cryo-EM sample preparation requires a fraction of the sample amount needed for X-ray crystallography. Recent developments in direct electron detectors and image processing software have improved the attainable resolution and extended the molecular mass range of the technique , 22. AltIn this perspective article, we describe the principles of integrative structural biology and discuss the evolving contributions of mass spectrometry to this emerging field, with a particular emphasis on XL-MS. We then discuss how innovations in structural mass spectrometry will facilitate and support the structural characterization of protein complexes in a close to native environment and the study of their dynamics in different cellular states. For many years, Brian Chait and his group have been at the forefront of such innovations and have contributed seminal work to virtually every structural technique in mass spectrometry. Here we mention some of his major discoveries in the context of this exciting and rapidly progressing field of research. We also summarize the contributions of his group along with major milestones of structural mass spectrometry in Figure Integrative structural biology is a discipline concerned with the collection of a diverse set of interaction and structural data, and their integration into an organized computational framework with the aim of proposing comprehensive structural models. By combining data from complementary techniques, it can overcome technical limitations of individual techniques and describe systems spanning wide ranges of size and complexity that may be beyond the reach of each technique. The integrative approach is particularly suitable to large, heterogeneous, and flexible complexes and has the potential to increase the throughput of structural studies , 35. IntThe typical integrative modeling workflow consists of multiple steps Figure . First, One of the earliest examples of integrative structural modeling originated from the collaborative effort of the groups of Michael Rout, Brian Chait, contributing essential mass-spectrometry data, and Andrej Sali. The work represents a milestone of modern structural biology: the molecular architecture of the yeast nuclear pore complex (NPC) , 25. TheTo integrate the heterogeneous datasets, the authors first developed an algorithm that inferred proximity and connectivity maps within each subcomplex from AP-MS data. This dataset provided the richest set of restraints for the determination of the protein configurations within the pore. Then, each protein was represented as one or more beads depending on their approximate shape and expected domain folds. A scoring function was devised integrating the positions of each protein and the distance restraints from the other experiments. The computational framework was implemented in the open source modeling suite Integrative Modeling Platform (IMP) . StartinFrom this extensive experimental and computational effort, the authors could assign a localization probability for each protein, suitable to determine their relative position within the pore. In the nuclear pore architecture, the authors could localize proteins with folds typical of coat proteins  close to the nuclear membrane confirming the evolutionary relatedness of membrane bending systems. Furthermore, the arrangement of pore spokes in vertical columns suggested that the underlying principle of assembly is driven by the hierarchical repetition of structural modules originated by gene duplication .Structural techniques in mass spectrometry cover a wide range of applications. Bottom-up workflows, where the analytes are generally peptides, can cope well with sample complexity. In the case of AP-MS, a few to hundreds of interactors can be identified in a single experiment. To date, XL-MS was successfully applied to assemblies with one to more than 80 subunits . XL-MS pIn the following, we summarize some recent developments of XL-MS and selected examples of integration of diverse MS techniques. Many of these examples build on the pioneering work achieved by the Chait group. We apologize to the many important contributions that we can unfortunately not discuss due to space limitations.One of the first reports of XL-MS in the structural field was the study of the interaction between RNA polymerase II (Pol II) and the TFIIF complex . The aut5 models was then clustered by subunit location and then assessed using the XL-MS distance restraints. The model closely matched the cryo-EM reconstruction of the proteasome regulatory particle [Manual docking of proteins can, in some cases, lead to inaccurate models. An attempt to automate protein interface docking and exhaustively sample the orientational space was applied to the interaction of the proteasomal lid with the core proteasome particle . The autparticle . This woparticle . Since tparticle –45.Although lysine residues are the most common target of cross-linking experiments, they only represent about 5% of all residues in proteins. Several groups have, therefore, developed cross-linkers that can react with other amino acids , 19. OneMany structural mass spectrometry techniques provide orthogonal information and can thus be combined to propose integrative structural models . For exaIn summary, structural techniques in mass spectrometry, when combined in an integrative framework, can elucidate the assembly and structural arrangement of macromolecular machines. Furthermore, the sensitivity of MS methods allows application to cases where only a limited amount of sample can be obtained or when the system is structurally heterogeneous. In those cases, MS approaches and integrative modeling can propose architectural and mechanistic models Figure .Figure 3Mainstream structural techniques have traditionally required large sample amounts and high sample purity. To fulfill these requirements, researchers often resorted to the expression of recombinant complex subunits of interest, first in prokaryotic and then in eukaryotic hosts , 57. WhiConversely, mass spectrometric applications have the potential to accurately and sensitively probe protein complexes at the typical sample scales of cell biology experiments. For example, a pioneering study could obtain XL-MS distance restraints from complexes expressed at endogenous levels, purified by affinity-tag from human cell lines . The croThe recent applications of XL-MS technology to natively expressed complexes both from yeast and mammalian cells opens up the possibility to study the structure of protein complexes in the context of their physiological interactors and regulators. Another emerging technique that could be applied to endogenous complexes is cryo-EM. Cryo-EM can obtain high resolution reconstructions from small sample amounts and can tolerate limited structural heterogeneity , 22. TheInsights into the function of many protein complexes would be increased if we were able to understand their changes in different cellular states . Comparing different states using XL-MS requires the ability to accurately quantify the cross-linked peptides. The group of Carol Robinson pioneered this endeavor by establishing comparative XL-MS to dissect the conformational changes of the F-ATPase upon in vitro dephosphorylation . A similWe think that quantitative cross-linking will expand the structural studies of conformational changes induced by protein binding and protein modifications and reveal the detailed mechanisms of large macromolecular complexes.E. coli anticipated the experimental feasibility [A notable limitation of the previously mentioned approaches is the requirement for sample enrichment, most commonly achieved by affinity purification of tagged proteins. Although most proteomic applications of mass spectrometry can consistently explore the complexity of the cell components, XL-MS has, until now, not achieved a similar extensive coverage. Whereas previous applications in sibility , a signisibility  a human sibility . OverallIn summary, we envisage that the combination of recent experimental developments with powerful multiplexing capability will open up the exploration of endogenous protein complexes and the study of their dynamics and interactions upon different cellular states. Such analyses are now also generally supported by the development of a software tool that accurately quantifies the cross-links generated from complexes in different states, thus identifying subtle structural and compositional differences . Further


Despite recent progress in proteomics most protein complexes are still unknown. Identification of these complexes will help us understand cellular regulatory mechanisms and support development of new drugs. Therefore it is really important to establish detailed information about the composition and the abundance of protein complexes but existing algorithms can only give qualitative predictions. Herein, we propose a new approach based on stochastic simulations of protein complex formation that integrates multi-source data—such as protein abundances, domain-domain interactions and functional annotations—to predict alternative forms of protein complexes together with their abundances. This method, called SiComPre (Simulation based Complex Prediction), achieves better qualitative prediction of yeast and human protein complexes than existing methods and is the first to predict protein complex abundances. Furthermore, we show that SiComPre can be used to predict complexome changes upon drug treatment with the example of bortezomib. SiComPre is the first method to produce quantitative predictions on the abundance of molecular complexes while performing the best qualitative predictions. With new data on tissue specific protein complexes becoming available SiComPre will be able to predict qualitative and quantitative differences in the complexome in various tissue types and under various conditions. Most proteins are biologically active only when part of a complex with other proteins of the same or other type. Hence, to unravel biological functions of proteins, it is important to identify the type of complexes they can form. Multiple copies of each protein are present in cells and some of these could be involved in multiple complexes, thus it is a challenging task to identify protein complex compositions and abundances of all possible complexes. In this article we propose an integrative computational approach able to predict protein complexes from existing data sources on protein-protein and domain-domain interactions and protein abundances. By merging this information we built a computational model of all proteins and their dynamic interactions. Using cell-specific data we performed multiple stochastic simulations to predict protein complexes specific to budding yeast and human cells. Our predictions on protein complex compositions are consistent with a manually curated dataset and, for the first time, provide an approximation of their abundances. Our simulations can also predict how perturbations by a drug can influence the composition and abundance of protein complexes. Saccharomyces cerevisiae [Homo sapiens [Mass-spectrometry (MS) techniques solved many fundamental issues in the identification of protein complexes –3 and otrevisiae –9 and on sapiens , 11. Unf sapiens . TherefoS. cerevisiae and H. sapiens. We consider DDIs only between proteins with a corresponding PPI, but the same domain of a given protein can be bound by multiple proteins with matching DDI and PPI leading to competition for binding sites and limiting formation of unrealistically large complexes. This ensures that proteins with high number of possible interactors do not interact with all possible partners at the same time and limits the size of such complexes [Graph theory algorithms to predict clusters that match protein complexes –15 or reomplexes . The metomplexes , 20.Our approach considered protein domains, retrieved with SMART , togetheWe divided the simulation space into square lattices, called sub-volumes (SV), where proteins are diffused randomly between neighbour lattices at discrete time steps. As a further simplification, we used the same diffusion rate for each protein (this could be improved with proteome wide data on diffusion rates becoming available). Proteins are represented as complex objects with binding sites corresponding to domains similarly to the BlenX modelling language . ComplemThe simulation produces a list of complexes together with their structure . Many SCs are constituted of similar set of proteins . To quanrecall gives the fraction of properly predicted complexes [maximal matching ratio (MMR) measures the ratio of one-to-one matching between reference and predicted complexes [accuracy is a function of proper and improper protein associations to complexes [composite score) quantifying the performance of the prediction [f-score) [composite score of SiComPre CL are equal or higher than any previous methods , but with lower composite score (2.2573 instead of 2.3472). The quality check of predicted protein complexes was done by comparing them with experimentally detected complexes from various sources , 9. We uomplexes ; maximalomplexes  and the omplexes    are discMus Musculus, where their quantification is ~30,000, ~60,000 and ~3,000 respectively [4 on average for all ribosomal subunits. In logarithmic growing yeast cells the estimated ribosome number is 187,000 ± 56,000 ribosomes [5 subunits per cell and assumes that all ribosomal rRNAs are involved in ribosome formation, thus our quantitative prediction could be realistic.SiComPre has the best scores available in the literature and it can also predict the abundance of protein complexes by counting the number of SCs overlapping a RC that can then be associated with experimentally identified complexes. ectively . Approxiibosomes , but thiEven after the clustering and optimization steps, we found that multiple RCs that differ in either size or exact structure  are assoAs a control, we investigated whether protein complex abundances can be predicted simply by averaging the abundance of all the constituting subunits. We found that on average there is a 14-fold difference between SiComPre quantitative predictions of protein complexes and the average abundance of their constitutive subunits with low correlation between them . The imWe also tested SiComPre on the human PPI  with pro6 ribosomes, which is in the same order of the number of ribosome identified in HeLa cells (3.3 × 106) [It is hard to find validation for the predicted abundances of protein complexes in the considered osteosarcoma cell line (U2OS). SiComPre predicted 1.3 × 103 × 106) .2 and 2252  in two simulation runs with a perfectly predicted structure match (matching score = 1). Many other complexes also have small abundance variations between the two simulations (2 and 302 copies and the Pho85p/Pcl8p complex was not observed in the first simulation but 64 (8 simulated) complexes appeared in the second run. To get a broader picture for each complex we calculated the coefficient of variation (CV = standard deviation / mean) of its quantitative predictions. Only 20% of complexes show a CV > 0.5) in the case of the human protein complex predictions after three runs . 2983 complexes were predicted by the first simulation with random initial position of each protein (we considered two time points of that single run). The second stochastic simulation with new, random initial settings, shared 1462 complexes with the first run. Similarity increases to almost 2030 complexes  when complex with similar counterpart (overlap > = 0.75) between two simulations are considered. Moreover, only one simulation is enough to identify 90% of reference complexes, while the addition of a second simulation increases the percentage of predicted reference complexes by only 1% . Additioulations  and onlyree runs . In yeasree runs  Tables.2 molecules, which is roughly the abundance of the most abundant protein we consider for human cells. We mapped protein complexes predicted with and without Bortezomib by finding the best matching complexes in the normal case to the complexes found after Bortezomib addition. Abundances of matching protein complexes were analysed by a t-test to find complexes which were perturbed in their abundance by Bortezomib . Protein complexes without a best matching complex were considered qualitatively altered by Bortezomib. We observed that the abundance of the Proteasome, the Anaphase-Promoting Complex, Prefoldin and the Multisynthetase complex were greatly perturbed by Bortezomib. We also observed that the composition of the above discussed snRNA binding LSM complex and several other predicted complexes were modified by drug treatment  as with more possible interactors there is a higher chance of ending up in a complex. Interestingly there is no correlation between the fraction of free subunits and the abundances (Pearson’s correlation 0.03). A high number of proteins are present in high abundance have a few interactors, but fully used up in complexes . For insHere we introduced a simulation based protein complex prediction method (SiComPre) that outperforms existing tools in qualitatively predicting the components of protein complexes and provides for the first time quantitative predictions on protein complex abundances. SiComPre incorporates multi-source information and our results show that the addition of domain information and protein abundances both increase the qualitative prediction of protein complexes.Membrane-bound protein complexes are often difficult to be detected. Identification of membrane protein interactions  will allwww.cosbi.eu/research/prototypes/sicompre). The scripts are available as supplementary All SiComPre applications and datasets are provided to the community on a dedicated website (S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableThis table is in Microsoft Word format.(DOCX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S1 File(RAR)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.